{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,15]],"date-time":"2026-07-15T17:27:51Z","timestamp":1784136471134,"version":"3.55.0"},"reference-count":326,"publisher":"Emerald","issue":"1-2","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,11,6]]},"abstract":"<jats:p>The 5th generation (5G) of wireless systems is being deployed with the aim to provide many sets of wireless communication services, such as low data rates for a massive amount of devices, broadband, low latency, and industrial wireless access. Such an aim is even more complex in the next generation wireless systems (6G) where wireless connectivity is expected to serve any connected intelligent unit, such as software robots and humans interacting in the metaverse, autonomous vehicles, drones, trains, or smart sensors monitoring cities, buildings, and the environment. Because of the wireless devices will be orders of magnitude denser than in 5G cellular systems, and because of their complex quality of service requirements, the access to the wireless spectrum will have to be appropriately shared to avoid congestion, poor quality of service, or unsatisfactory communication delays. Spectrum sharing methods have been the objective of intense study through model-based approaches, such as optimization or game theories. However, these methods may fail when facing the complexity of the communication environments in 5G, 6G, and beyond. Recently, there has been significant interest in the application and development of data-driven methods, namely machine learning methods, to handle the complex operation of spectrum sharing. In this survey, we provide a complete overview of the state-of-theart of machine learning for spectrum sharing. First, we map the most prominent methods that we encounter in spectrum sharing. Then, we show how these machine learning methods are applied to the numerous dimensions and sub-problems of spectrum sharing, such as spectrum sensing, spectrum allocation, spectrum access, and spectrum handoff. We also highlight several open questions and future trends.<\/jats:p>","DOI":"10.1561\/1300000073","type":"journal-article","created":{"date-parts":[[2024,11,6]],"date-time":"2024-11-06T06:39:42Z","timestamp":1730875182000},"page":"1-159","source":"Crossref","is-referenced-by-count":7,"title":["Machine Learning for Spectrum Sharing: A Survey"],"prefix":"10.1108","volume":"14","author":[{"given":"Francisco R. V.","family":"Guimar\u00e3es","sequence":"first","affiliation":[{"name":"Federal Institute of Education, Science and Technology of Cear\u00e1(IFCE) ,","place":["Brazil"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"suffix":"Jr.","given":"Jos\u00e9 Mairton B.","family":"da Silva","sequence":"additional","affiliation":[{"name":"Uppsala University ,","place":["Sweden"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Charles","family":"Casimiro Cavalcante","sequence":"additional","affiliation":[{"name":"Federal University of Cear\u00e1(UFC) ,","place":["Brazil"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gabor","family":"Fodor","sequence":"additional","affiliation":[{"name":"KTH Royal Institute of Technology ,","place":["Sweden"]},{"name":"Ericsson Research ,","place":["Sweden"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mats","family":"Bengtsson","sequence":"additional","affiliation":[{"name":"KTH Royal Institute of Technology ,","place":["Sweden"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Carlo","family":"Fischione","sequence":"additional","affiliation":[{"name":"KTH Royal Institute of Technology ,","place":["Sweden"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"140","published-online":{"date-parts":[[2024,11,6]]},"reference":[{"key":"2026032900522741900_ref001","article-title":"NR; NR and NG-RAN Overall Description,","author":"3GPP","year":"2018"},{"issue":"12","key":"2026032900522741900_ref002","doi-asserted-by":"publisher","first-page":"8131","DOI":"10.1109\/TWC.2017.2757919","article-title":"Millimeter wave receiver efficiency: A comprehensive comparison of beamforming schemes with low resolution ADCs,","volume":"16","author":"Abbas","year":"2017","journal-title":"IEEE Transactions Wireless Communications"},{"key":"2026032900522741900_ref003","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/VTCSpring.2019.8746557","article-title":"System level analysis of eMBB and grant-free URLLC multiplexing in uplink,","author":"Abreu","year":"2019","journal-title":"Proc. IEEE Vehicular Technology Conference"},{"key":"2026032900522741900_ref004","doi-asserted-by":"publisher","first-page":"16 193","DOI":"10.1109\/ACCESS.2021.3052462","article-title":"A survey on 4G-5G dual connectivity: Road to 5G implementation,","volume":"9","author":"Agiwal","year":"2021","journal-title":"IEEE Access"},{"key":"2026032900522741900_ref005","doi-asserted-by":"publisher","first-page":"101 673","DOI":"10.1016\/j.phycom.2022.101673","article-title":"Spectrum sensing in cognitive radio networks and metacognition for dynamic spectrum sharing between radar and communication system: A review,","volume":"52","author":"Agrawal","year":"2022","journal-title":"Physical Communication"},{"issue":"4","key":"2026032900522741900_ref006","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1109\/MWC.004.2100694","article-title":"Machine learning-based beamforming for unmanned aerial vehicles equipped with reconfigurable intelligent surfaces,","volume":"29","author":"Ahmad","year":"2022","journal-title":"IEEE Wireless Communications"},{"key":"2026032900522741900_ref007","doi-asserted-by":"publisher","first-page":"133 995","DOI":"10.1109\/ACCESS.2020.3010896","article-title":"6g and beyond: The future of wireless communications systems,","volume":"8","author":"Akyildiz","year":"2020","journal-title":"IEEE Access"},{"issue":"2","key":"2026032900522741900_ref008","doi-asserted-by":"publisher","first-page":"1174","DOI":"10.1109\/TCCN.2021.3120996","article-title":"Multi-agent reinforcement learning-based distributed dynamic spectrum access,","volume":"8","author":"Albinsaid","year":"2022","journal-title":"IEEE Transactions on Cognitive Communications and Networking"},{"key":"2026032900522741900_ref009","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/DySPAN.2019.8935814","article-title":"Machine learning aided hybrid beamforming in massive-MIMO millimeter wave systems,","author":"Aljumaily","year":"2019","journal-title":"Proc. IEEE International Symposium on Dynamic Spectrum Access Networks"},{"key":"2026032900522741900_ref010","doi-asserted-by":"publisher","first-page":"37 328","DOI":"10.1109\/ACCESS.2018.2850226","article-title":"Deep learning coordinated beamforming for highly-mobile millimeter wave systems,","volume":"6","author":"Alkhateeb","year":"2018","journal-title":"IEEE Access"},{"key":"2026032900522741900_ref011","first-page":"1055","article-title":"Machine learning for reliable mmwave systems: Blockage prediction and proactive handoff,","author":"Alkhateeb","year":"2018","journal-title":"Proc. IEEE Global Conference on Signal and Information Processing"},{"key":"2026032900522741900_ref012","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/ICC.2016.7511617","article-title":"A cooperative online learning scheme for resource allocation in 5g systems,","author":"AlQerm","year":"2016","journal-title":"Proc. IEEE International Conference on Communications"},{"key":"2026032900522741900_ref013","doi-asserted-by":"publisher","first-page":"126","DOI":"10.3390\/s19010126","article-title":"A comprehensive survey on spectrum sensing in cognitive radio networks: Recent advances, new challenges, and future research directions,","volume":"1","author":"Arjoune","year":"2019","journal-title":"Sensors"},{"key":"2026032900522741900_ref014","doi-asserted-by":"publisher","first-page":"25 377","DOI":"10.1109\/ACCESS.2018.2825603","article-title":"Spatio-temporal spectrum sensing in cognitive radio networks using beamformeraided SVM algorithms,","volume":"6","author":"Awe","year":"2018","journal-title":"IEEE Access"},{"key":"2026032900522741900_ref015","doi-asserted-by":"publisher","first-page":"223","DOI":"10.1109\/TAAI.2013.52","article-title":"Eigenvalue and support vector machine techniques for spectrum sensing in cognitive radio networks,","author":"Awe","year":"2013","journal-title":"Proc. Conference on Technologies and Applications of Artificial Intelligence"},{"key":"2026032900522741900_ref016","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/ICAEECI58247.2023.10370791","article-title":"A novel spectrum handoff technique for long range applications using adaptive beam selection with machine learning algorithms,","author":"Babjan","year":"2023","journal-title":"2023 First International Conference on Advances in Electrical, Electronics and Computational Intelligence (ICAEECI)"},{"issue":"3","key":"2026032900522741900_ref017","doi-asserted-by":"publisher","first-page":"623","DOI":"10.1109\/JPROC.2015.2511661","article-title":"A new era in elemental digital beamforming for spaceborne communications phased arrays,","volume":"104","author":"Bailleul","year":"2016","journal-title":"Proc. of the IEEE"},{"key":"2026032900522741900_ref018","doi-asserted-by":"crossref","first-page":"240","DOI":"10.1109\/INFOCOM48880.2022.9796985","article-title":"ChARM: NextG spectrum sharing through data-driven real-time O-RAN dynamic control,","author":"Baldesi","year":"2022","journal-title":"IEEE INFOCOM 2022-IEEE Conference on Computer Communications, IEEE"},{"issue":"2","key":"2026032900522741900_ref019","doi-asserted-by":"publisher","first-page":"355","DOI":"10.1109\/SURV.2011.032511.00097","article-title":"Security aspects in software defined radio and cognitive radio networks: A survey and a way ahead,","volume":"14","author":"Baldini","year":"2012","journal-title":"IEEE Communications Surveys Tutorials"},{"issue":"2","key":"2026032900522741900_ref020","doi-asserted-by":"crossref","first-page":"1280","DOI":"10.1109\/COMST.2022.3149714","article-title":"A survey of collaborative machine learning using 5g vehicular communications,","volume":"24","author":"Balkus","year":"2022","journal-title":"IEEE Communications Surveys & Tutorials"},{"issue":"1","key":"2026032900522741900_ref021","doi-asserted-by":"publisher","first-page":"120","DOI":"10.1109\/LCOMM.2018.2875749","article-title":"On throughput maximization in cooperative cognitive radio networks with eavesdropping,","volume":"23","author":"Banerjee","year":"2019","journal-title":"IEEE Communications Letters"},{"key":"2026032900522741900_ref022","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/LWC.2020.2990337","article-title":"Secrecy outage performance of ground-to-air communications with multiple aerial eavesdroppers and its deep learning evaluation,","author":"Bao","year":"2020","journal-title":"IEEE Wireless Communications Letters"},{"key":"2026032900522741900_ref023","doi-asserted-by":"publisher","first-page":"98","DOI":"10.1109\/FMEC.2019.8795319","article-title":"Intrusion detection for iot devices based on rf fingerprinting using deep learning,","author":"Bassey","year":"2019","journal-title":"Proc. International Conference on Fog and Mobile Edge Computing"},{"key":"2026032900522741900_ref024","doi-asserted-by":"publisher","first-page":"706","DOI":"10.1109\/GLOCOMW.2010.5700414","article-title":"A Q-learning based approach to interference avoidance in self-organized femtocell networks,","author":"Bennis","year":"2010","journal-title":"Proc. IEEE Globecom Workshops"},{"key":"2026032900522741900_ref025","volume-title":"Introduction to Probability","author":"Bertsekas","year":"2002"},{"issue":"4","key":"2026032900522741900_ref026","doi-asserted-by":"publisher","first-page":"4989","DOI":"10.1109\/TNSM.2022.3186725","article-title":"A deep-Q learning scheme for secure spectrum allocation and resource management in 6G environment,","volume":"19","author":"Bhattacharya","year":"2022","journal-title":"IEEE Transactions on Network and Service Management"},{"key":"2026032900522741900_ref027","volume-title":"Pattern Recognition and Machine Learning","author":"Bishop","year":"2006"},{"issue":"11","key":"2026032900522741900_ref028","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1109\/MCOM.2016.1600191CM","article-title":"Spectrum pooling in mmwave networks: Opportunities, challenges, and enablers,","volume":"54","author":"Boccardi","year":"2016","journal-title":"IEEE Comm. Mag."},{"issue":"7","key":"2026032900522741900_ref029","doi-asserted-by":"publisher","first-page":"4930","DOI":"10.1109\/TWC.2022.3230872","article-title":"Deep reinforcement learning for simultaneous sensing and channel access in cognitive networks,","volume":"22","author":"Bokobza","year":"2023","journal-title":"IEEE Transactions on Wireless Communications"},{"issue":"10","key":"2026032900522741900_ref030","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1109\/MCOM.101.2001120","article-title":"Intelligence and learning in O-RAN for data-driven nextg cellular networks,","volume":"59","author":"Bonati","year":"2021","journal-title":"IEEE Communications Magazine"},{"key":"2026032900522741900_ref031","doi-asserted-by":"publisher","first-page":"173","DOI":"10.1007\/978-3-319-76207-4","article-title":"Multi-armed bandit learning in iot networks: Learning helps even in non-stationary settings,","author":"Bonnefoi","year":"2017","journal-title":"International Conference on Cognitive Radio Oriented Wireless Networks"},{"issue":"2","key":"2026032900522741900_ref032","doi-asserted-by":"crossref","first-page":"223","DOI":"10.1137\/16M1080173","article-title":"Optimization Methods for Large-Scale Machine Learning,","volume":"60","author":"Bottou","year":"2018","journal-title":"SIAM Review"},{"key":"2026032900522741900_ref033","first-page":"183","article-title":"Multi-agent reinforcement learning: An overview,","author":"Bu\u015foniu","year":"2010","journal-title":"Innovations in multi-agent systems and applications-1"},{"key":"2026032900522741900_ref034","first-page":"772","article-title":"Implementation issues in spectrum sensing for cognitive radios,","volume":"1","author":"Cabric","year":"2004","journal-title":"Proc. Asilomar Conference on Signals, Systems and Computers"},{"issue":"10","key":"2026032900522741900_ref035","doi-asserted-by":"publisher","first-page":"1778","DOI":"10.1109\/LWC.2020.3004687","article-title":"Coordination graph-based deep reinforcement learning for cooperative spectrum sensing under correlated fading,","volume":"9","author":"Cai","year":"2020","journal-title":"IEEE Wireless Communications Letters"},{"issue":"9","key":"2026032900522741900_ref036","doi-asserted-by":"publisher","first-page":"14 204","DOI":"10.1109\/TITS.2022.3153815","article-title":"Survey on cooperative perception in an automotive context,","volume":"23","author":"Caillot","year":"2022","journal-title":"IEEE Transactions on Intelligent Transportation Systems"},{"key":"2026032900522741900_ref037","doi-asserted-by":"publisher","first-page":"134 435","DOI":"10.1109\/ACCESS.2021.3115695","article-title":"Coordinated allocation of radio resources to Wi-Fi and cellular technologies in shared unlicensed frequencies,","volume":"9","author":"Candal-Ventureira","year":"2021","journal-title":"IEEE Access"},{"issue":"4","key":"2026032900522741900_ref038","doi-asserted-by":"publisher","first-page":"1146","DOI":"10.3390\/s20041146","article-title":"Spectrum handoff based on DQN predictive decision for hybrid cognitive radio networks,","volume":"20","author":"Cao","year":"2020","journal-title":"Sensors"},{"key":"2026032900522741900_ref039","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/ICC.2019.8762091","article-title":"A reinforcement learning approach to dynamic spectrum access in internet-of-things networks,","author":"Cha","year":"2019","journal-title":"Proc. IEEE International Conference on Communications"},{"issue":"7","key":"2026032900522741900_ref040","doi-asserted-by":"publisher","first-page":"4674","DOI":"10.1109\/TWC.2018.2829773","article-title":"Proactive resource management forLTE in unlicensed spectrum: A deep learning perspective,","volume":"17","author":"Challita","year":"2018","journal-title":"IEEE Transactions on Wireless Communications"},{"issue":"2","key":"2026032900522741900_ref041","doi-asserted-by":"publisher","first-page":"1938","DOI":"10.1109\/JIOT.2018.2872441","article-title":"Distributive dynamic spectrum access through deep reinforcement learning: A reservoir computing-based approach,","volume":"6","author":"Chang","year":"2019","journal-title":"IEEE Internet of Things Journal"},{"issue":"8","key":"2026032900522741900_ref042","doi-asserted-by":"publisher","first-page":"5337","DOI":"10.1109\/TWC.2022.3233436","article-title":"Federated multiagent deep reinforcement learning (Fed-MADRL) for dynamic spectrum access,","volume":"22","author":"Chang","year":"2023","journal-title":"IEEE Transactions on Wireless Communications"},{"issue":"12","key":"2026032900522741900_ref043","doi-asserted-by":"publisher","first-page":"7023","DOI":"10.1109\/TCOMM.2023.3310537","article-title":"A deep learning method: QoS-aware joint AP clustering and beamforming design for cell-free networks,","volume":"71","author":"Chen","year":"2023","journal-title":"IEEE Transactions on Communications"},{"issue":"2","key":"2026032900522741900_ref044","doi-asserted-by":"publisher","first-page":"364","DOI":"10.1109\/TNSE.2021.3117565","article-title":"RDRL: A recurrent deep reinforcement learning scheme for dynamic spectrum access in reconfigurable wireless networks,","volume":"9","author":"Chen","year":"2022","journal-title":"IEEE Transactions on Network Science and Engineering"},{"key":"2026032900522741900_ref045","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/VTCSpring.2018.8417695","article-title":"DQN-based power control for IoT transmission against jamming,","author":"Chen","year":"2018","journal-title":"Proc. IEEE Vehicular Technology Conference"},{"issue":"11","key":"2026032900522741900_ref046","doi-asserted-by":"publisher","first-page":"7785","DOI":"10.1109\/TCOMM.2019.2940013","article-title":"Sensing ofdm signal: A deep learning approach,","volume":"67","author":"Cheng","year":"2019","journal-title":"IEEE Transactions on Communications"},{"key":"2026032900522741900_ref047","doi-asserted-by":"publisher","first-page":"1","DOI":"10.23919\/EUSIPCO.2019.8903028","article-title":"Spectrum sensing by higher-order SVM-based detection,","author":"Coluccia","year":"2019","journal-title":"Proc. European Signal Processing Conference"},{"issue":"4","key":"2026032900522741900_ref048","doi-asserted-by":"publisher","DOI":"10.3390\/fi14040116","article-title":"ML-based 5G network slicing security: A comprehensive survey,","volume":"14","author":"Dangi","year":"2022","journal-title":"Future Internet"},{"issue":"5","key":"2026032900522741900_ref049","doi-asserted-by":"publisher","first-page":"77","DOI":"10.23919\/JCC.ea.2021-0665.202401","article-title":"Resource allocation in multi-user cellular networks: A transformer-based deep reinforcement learning approach,","volume":"21","author":"Di","year":"2024","journal-title":"China Communications"},{"key":"2026032900522741900_ref050","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/MLSP.2019.8918745","article-title":"Optimal mobile relay beam-forming via reinforcement learning,","author":"Diamantaras","year":"2019","journal-title":"Proc. IEEE International Workshop on Machine Learning for Signal Processing"},{"issue":"4","key":"2026032900522741900_ref051","doi-asserted-by":"publisher","first-page":"126","DOI":"10.1109\/MSP.2013.2251071","article-title":"Kernel-based learning for statistical signal processing in cognitive radio networks: Theoretical foundations, example applications, and future directions,","volume":"30","author":"Ding","year":"2013","journal-title":"IEEE Signal Processing Magazine"},{"issue":"7","key":"2026032900522741900_ref052","doi-asserted-by":"publisher","first-page":"6561","DOI":"10.1109\/TVT.2018.2808347","article-title":"Protecting operation-time privacy of primary users in downlink cognitive two-tier networks,","volume":"67","author":"Dong","year":"2018","journal-title":"IEEE Transactions on Vehicular Technology"},{"key":"2026032900522741900_ref053","article-title":"TV white spaces policies to enable efficient spectrum sharing,","author":"Dosch","year":"2011","journal-title":"European Regional ITS Conference"},{"key":"2026032900522741900_ref054","doi-asserted-by":"publisher","first-page":"340","DOI":"10.1109\/IEEECONF53345.2021.9723270","article-title":"Distributed proximal policy optimization for contention-based spectrum access,","author":"Doshi","year":"2021","journal-title":"Asilomar Conference on Signals, Systems, and Computers"},{"key":"2026032900522741900_ref055","doi-asserted-by":"publisher","first-page":"189","DOI":"10.1109\/IEEECONF56349.2022.10051877","article-title":"Combining contention-based spectrum access and adaptive modulation using deep reinforcement learning,","author":"Doshi","year":"2022","journal-title":"Asilomar Conference on Signals, Systems, and Computers"},{"key":"2026032900522741900_ref056","doi-asserted-by":"publisher","first-page":"274","DOI":"10.1109\/ICCCS57501.2023.10150600","article-title":"Time-variant resource allocation in multi-Ap802.11be network: A DDPG-based approach,","author":"Du","year":"2023","journal-title":"International Conference on Computer and Communication Systems"},{"issue":"06","key":"2026032900522741900_ref057","article-title":"\u201cHarmonised technical conditions for mobile\/fixed communications networks (MFCN) in the band 24.25-27.5 GHz, 2018","volume":"18","author":"ECC Decision"},{"key":"2026032900522741900_ref058","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/MLSP.2019.8918866","article-title":"Robust hybrid beamforming with quantized deep neural networks,","author":"Elbir","year":"2019","journal-title":"Proc. IEEE Workshop on Machine Learning for Signal Processing"},{"issue":"2","key":"2026032900522741900_ref059","doi-asserted-by":"publisher","first-page":"1739","DOI":"10.1109\/JSYST.2021.3089536","article-title":"Leveraging machine learning for millimeter wave beamforming in beyond 5G networks,","volume":"16","author":"ElHalawany","year":"2022","journal-title":"IEEE Systems Journal"},{"key":"2026032900522741900_ref060","author":"ETSI TR 103 588 v1.1.1: Feasibility study on temporary spectrum access for local high-quality wireless networks","year":"2018"},{"key":"2026032900522741900_ref061","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1109\/WCNC.2013.6554535","article-title":"Improving reinforcement learning algorithms for dynamic spectrum allocation in cognitive sensor networks,","author":"Faganello","year":"2013","journal-title":"Proc. IEEE Wireless Communications and Networking Conference"},{"issue":"6","key":"2026032900522741900_ref062","doi-asserted-by":"publisher","first-page":"4954","DOI":"10.1109\/TVT.2017.2750801","article-title":"Learning-based spectrum sharing and spatial reuse in mm-wave ultradense networks,","volume":"67","author":"Fan","year":"2018","journal-title":"IEEE Transactions on Vehicular Technology"},{"issue":"5","key":"2026032900522741900_ref063","doi-asserted-by":"publisher","first-page":"55","DOI":"10.1109\/MWC.001.1900054","article-title":"Machine learning for intelligent authentication in 5G and beyond wireless networks,","volume":"26","author":"Fang","year":"2019","journal-title":"IEEE Wireless Communications"},{"key":"2026032900522741900_ref064","author":"FCC","year":"2015","journal-title":"FCC 15-47 report and order and second further notice of proposed rulemaking"},{"key":"2026032900522741900_ref065","first-page":"3061","volume-title":"Proceedings of the 37th International Conference on Machine Learning","author":"Fedus","year":"2020"},{"issue":"18","key":"2026032900522741900_ref066","doi-asserted-by":"crossref","first-page":"7792","DOI":"10.3390\/s23187792","article-title":"Spectrum sensing, clustering algorithms, and energy-harvesting technology for cognitive-radiobased internet-of-things networks,","volume":"23","author":"Fernando","year":"2023","journal-title":"Sensors"},{"key":"2026032900522741900_ref067","doi-asserted-by":"publisher","first-page":"125","DOI":"10.1109\/TRS.2024.3353112","article-title":"Deep reinforcement learning for cognitive radar spectrum sharing: A continuous control approach,","volume":"2","author":"Flandermeyer","year":"2024","journal-title":"IEEE Transactions on Radar Systems"},{"key":"2026032900522741900_ref068","doi-asserted-by":"publisher","first-page":"1146","DOI":"10.5555\/3305381.3305500","article-title":"Stabilising experience replay for deep multi-agent reinforcement learning,","author":"Foerster","year":"2017","journal-title":"Proc. International Conference on Machine Learning, ser. ICML\u201917"},{"issue":"8","key":"2026032900522741900_ref069","doi-asserted-by":"publisher","first-page":"1820","DOI":"10.1109\/JSAC.2019.2927067","article-title":"Iris: Deep reinforcement learning driven shared spectrum access architecture for indoor neutral-host small cells,","volume":"37","author":"Foukas","year":"2019","journal-title":"IEEE Journal on Selected Areas in Communications"},{"key":"2026032900522741900_ref070","first-page":"1020","article-title":"A sticky HDP-HMM with application to speaker diarization,","author":"Fox","year":"2011","journal-title":"The Annals of Applied Statistics"},{"key":"2026032900522741900_ref071","volume-title":"IEEE Vehicular Technology Conference Fall","author":"Frascolla","year":"2016"},{"issue":"1","key":"2026032900522741900_ref072","doi-asserted-by":"publisher","first-page":"72","DOI":"10.1109\/MWC.2015.7306534","article-title":"5G ultra-dense cellular networks,","volume":"23","author":"Ge","year":"2016","journal-title":"IEEE Wireless Communications"},{"issue":"4","key":"2026032900522741900_ref073","doi-asserted-by":"publisher","first-page":"1165","DOI":"10.1109\/LCOMM.2023.3246052","article-title":"Unsupervised learning feature estimation for MISO beamforming by using spiking neural networks,","volume":"27","author":"Ge","year":"2023","journal-title":"IEEE Communications Letters"},{"key":"2026032900522741900_ref074","doi-asserted-by":"crossref","DOI":"10.1109\/ACCESS.2024.3352034","article-title":"DeepAlloc: Deep learning approach to spectrum allocation in shared spectrum systems,","author":"Ghaderibaneh","year":"2024","journal-title":"IEEE Access"},{"key":"2026032900522741900_ref075","doi-asserted-by":"publisher","first-page":"113","DOI":"10.1109\/CSIEC.2016.7482127","article-title":"A pso-based weighting method to enhance machine learning techniques for cooperative spectrum sensing in cr networks,","author":"Ghazizadeh","year":"2016","journal-title":"Proc. Conference on Swarm Intelligence and Evolutionary Computation"},{"issue":"5","key":"2026032900522741900_ref076","doi-asserted-by":"publisher","first-page":"894","DOI":"10.1109\/JPROC.2009.2015717","article-title":"Breaking spectrum gridlock with cognitive radios: An information theoretic perspective,","volume":"97","author":"Goldsmith","year":"2009","journal-title":"Proceedings of the IEEE"},{"key":"2026032900522741900_ref077","volume-title":"Deep Learning","author":"Goodfellow","year":"2016"},{"key":"2026032900522741900_ref078","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/OECC56963.2023.10209850","article-title":"Spectrum adaptive awareness routing and spectrum allocation based on reinforcement learning,","author":"Guan","year":"2023","journal-title":"Proc. Opto-Electronics and Communications Conference"},{"issue":"9","key":"2026032900522741900_ref079","doi-asserted-by":"publisher","first-page":"8440","DOI":"10.1109\/TVT.2018.2848294","article-title":"Deep learning for an effective nonorthogonal multiple access scheme,","volume":"67","author":"Gui","year":"2018","journal-title":"IEEE Transactions on Vehicular Technology"},{"issue":"3","key":"2026032900522741900_ref080","doi-asserted-by":"publisher","first-page":"460","DOI":"10.1109\/JSTSP.2022.3140660","article-title":"Distributed machine learning for multiuser mobile edge computing systems,","volume":"16","author":"Guo","year":"2022","journal-title":"IEEE Journal of Selected Topics in Signal Processing"},{"key":"2026032900522741900_ref081","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10586-017-0798-3","article-title":"Supervised machine learning techniques in cognitive radio networks during cooperative spectrum handovers,","volume":"20","author":"Haldorai","year":"2017","journal-title":"Cluster Computing"},{"key":"2026032900522741900_ref082","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/CISP-BMEI.2017.8302117","article-title":"Spectrum sensing for cognitive radio based on convolution neural network,","author":"Han","year":"2017","journal-title":"Proc. International Congress on Image and Signal Processing, BioMedical Engineering and Informatics"},{"key":"2026032900522741900_ref083","doi-asserted-by":"publisher","first-page":"2087","DOI":"10.1109\/ICASSP.2017.7952524","article-title":"Two-dimensional anti-jamming communication based on deep reinforcement learning,","author":"Han","year":"2017","journal-title":"Proc. IEEE International Conference on Acoustics, Speech and Signal Processing"},{"key":"2026032900522741900_ref084","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/DASC52595.2021.9594301","article-title":"Dynamic spectrum allocation in urban air transportation system via deep reinforcement learning,","author":"Han","year":"2021","journal-title":"Proc. IEEE\/AIAA Digital Avionics Systems Conference"},{"key":"2026032900522741900_ref085","doi-asserted-by":"crossref","DOI":"10.1007\/978-0-387-84858-7","volume-title":"The Elements of Statistical Learning: Data Mining, Inference and Prediction","author":"Hastie","year":"2009","edition":"2nd ed"},{"key":"2026032900522741900_ref086","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/DySPAN.2019.8935871","article-title":"Possibility of dynamic spectrum sharing system by VHF-band radio sensor and machine learning,","author":"Hayashida","year":"2019","journal-title":"Proc. IEEE International Symposium on Dynamic Spectrum Access Networks"},{"issue":"3","key":"2026032900522741900_ref087","doi-asserted-by":"publisher","first-page":"32","DOI":"10.1109\/MWC.2019.1800397","article-title":"Deep learning based energy efficiency optimization for distributed cooperative spectrum sensing,","volume":"26","author":"He","year":"2019","journal-title":"IEEE Wireless Communications"},{"issue":"4","key":"2026032900522741900_ref088","doi-asserted-by":"publisher","first-page":"6596","DOI":"10.1109\/JIOT.2023.3311993","article-title":"Listen-after-collision mechanism for dynamic spectrum access using deep Q-network with an improved thompson sampling algorithm,","volume":"11","author":"He","year":"2024","journal-title":"IEEE Internet of Things Journal"},{"key":"2026032900522741900_ref089","doi-asserted-by":"publisher","first-page":"62","DOI":"10.1109\/ICCET58756.2023.00018","article-title":"Intelligent spectrum allocation based on deep reinforcement learning for power emergency communications,","author":"He","year":"2023","journal-title":"Proc. International Conference on Communication Engineering and Technology"},{"issue":"4","key":"2026032900522741900_ref090","doi-asserted-by":"publisher","first-page":"290","DOI":"10.1561\/2000000114","article-title":"Wireless for machine learning: A survey,","volume":"15","author":"Hellstr\u00f6m","year":"2022","journal-title":"Foundations and Trends\u00ae in Signal Processing"},{"key":"2026032900522741900_ref091","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/DySPAN.2019.8935749","article-title":"MIMO radar privacy protection through gradient enforcement in shared spectrum scenarios,","author":"Hilli","year":"2019","journal-title":"IEEE International Symposium on Dynamic Spectrum Access Networks"},{"issue":"2","key":"2026032900522741900_ref092","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1109\/LWC.2019.2945022","article-title":"Detection of eavesdropping attack in uav-aided wireless systems: Unsupervised learning with one-class svm and k-means clustering,","volume":"9","author":"Hoang","year":"2020","journal-title":"IEEE Wireless Communications Letters"},{"issue":"5","key":"2026032900522741900_ref093","doi-asserted-by":"crossref","first-page":"359","DOI":"10.1016\/0893-6080(89)90020-8","article-title":"Multilayer Feedforward Networks Are Universal Approximators,","volume":"2","author":"Hornik","year":"1989","journal-title":"Neural Networks"},{"key":"2026032900522741900_ref094","doi-asserted-by":"publisher","first-page":"15 754","DOI":"10.1109\/ACCESS.2018.2802450","article-title":"Full spectrum sharing in cognitive radio networks toward 5G: A survey,","volume":"6","author":"Hu","year":"2018","journal-title":"IEEE Access"},{"issue":"7","key":"2026032900522741900_ref095","doi-asserted-by":"publisher","first-page":"25","DOI":"10.23919\/JCC.2021.07.003","article-title":"A joint power and bandwidth allocation method based on deep reinforcement learning for V2V communications in 5G,","volume":"18","author":"Hu","year":"2021","journal-title":"China Communications"},{"key":"2026032900522741900_ref096","first-page":"1","article-title":"Expected q-learning for self-organizing resource allocation in lte-u with downlink-uplink decoupling,","author":"Hu","year":"2017","journal-title":"European Wireless 2017; 23th European Wireless Conference"},{"key":"2026032900522741900_ref097","article-title":"IMT vision - framework and overall objectives of the future development of IMT for 2020 and beyond,","author":"ITU","year":"2015","journal-title":"Recommendation ITU-R M.2083-0"},{"key":"2026032900522741900_ref098","article-title":"Report ITU-R M.2410-0 - Minimum requirements related to technical performance for IMT-2020 radio interface(s),","author":"ITU-R","year":"2017","journal-title":"InternationalTelecommunication Union (ITU), Tech. Rep."},{"key":"2026032900522741900_ref099","article-title":"Report ITU-R M.2160 - Framework And Overall Objectives Of The Future Development Of IMT for 2030 and Beyond,","author":"ITU-R","year":"2023","journal-title":"InternationalTelecommunication Union (ITU), Tech. Rep."},{"issue":"4","key":"2026032900522741900_ref100","doi-asserted-by":"crossref","DOI":"10.1002\/cpe.7534","article-title":"Support vector machine based spectrum hand-off scheme for seamless handover in cognitive radio networks,","volume":"35","author":"Iyer","year":"2023","journal-title":"Concurrency and Computation: Practice and Experience"},{"key":"2026032900522741900_ref101","doi-asserted-by":"publisher","first-page":"137","DOI":"10.1109\/TAFGEN.2018.8580478","article-title":"A spectrum handoff scheme based on joint location and channel state prediction in cognitive radio,","author":"Jaffar","year":"2018","journal-title":"Proc. in International Conference on Telematics and Future Generation Networks"},{"key":"2026032900522741900_ref102","doi-asserted-by":"crossref","DOI":"10.1016\/j.adhoc.2019.101913","article-title":"Machine learning for wireless communications in the internet of things: A comprehensive survey,","volume":"93","author":"Jagannath","year":"2019","journal-title":"Ad Hoc Networks"},{"key":"2026032900522741900_ref103","doi-asserted-by":"publisher","first-page":"385","DOI":"10.1109\/CyberC.2018.00075","article-title":"Performance analysis of support vector machine-based classifier for spectrum sensing in cognitive radio networks,","author":"Jan","year":"2018","journal-title":"Proc. International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery"},{"key":"2026032900522741900_ref104","doi-asserted-by":"publisher","DOI":"10.1002\/ett.4352","article-title":"Machine learning for cooperative spectrum sensing and sharing: A survey,","author":"Janu","year":"2021","journal-title":"Transactions on Emerging Telecommunications Technologies"},{"issue":"3","key":"2026032900522741900_ref105","doi-asserted-by":"publisher","first-page":"866","DOI":"10.1109\/LCOMM.2023.3241664","article-title":"Hierarchical cooperative LSTM-based spectrum sensing,","volume":"27","author":"Janu","year":"2023","journal-title":"IEEE Communications Letters"},{"issue":"5","key":"2026032900522741900_ref106","doi-asserted-by":"publisher","first-page":"5973","DOI":"10.1109\/TVT.2022.3226799","article-title":"Cooperative beamforming with nonlinear power amplifiers: A deep learning approach for distributed networks,","volume":"72","author":"Jee","year":"2023","journal-title":"IEEE Transactions on Vehicular Technology"},{"key":"2026032900522741900_ref107","doi-asserted-by":"publisher","first-page":"51 380","DOI":"10.1109\/ACCESS.2021.3068977","article-title":"Extending 5G TDD coverage with XDD: Cross division duplex,","volume":"9","author":"Ji","year":"2021","journal-title":"IEEE Access"},{"issue":"10","key":"2026032900522741900_ref108","doi-asserted-by":"publisher","first-page":"13 447","DOI":"10.1109\/TVT.2023.3275546","article-title":"Multi-agent reinforcement learning resources allocation method using dueling double deep Q-network in vehicular networks,","volume":"72","author":"Ji","year":"2023","journal-title":"IEEE Transactions on Vehicular Technology"},{"issue":"11","key":"2026032900522741900_ref109","doi-asserted-by":"publisher","first-page":"3008","DOI":"10.1109\/LCOMM.2023.3323387","article-title":"An access control scheme combining Q-learning and compressive random access for satellite IoT,","volume":"27","author":"Jiang","year":"2023","journal-title":"IEEE Communications Letters"},{"key":"2026032900522741900_ref110","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/IJCNN.2018.8489563","article-title":"Q-learning for non-cooperative channel access game of cognitive radio networks,","author":"Jiang","year":"2018","journal-title":"Proc, International Joint Conference on Neural Networks"},{"key":"2026032900522741900_ref111","author":"Jiang","year":"2021","journal-title":"Multi-agent reinforcement learning based joint cooperative spectrum sensing and channel access for cognitive UAV networks"},{"key":"2026032900522741900_ref112","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/ICSCS.2009.5412697","article-title":"Multi-armed bandit based policies for cognitive radio\u2019s decision making issues,","author":"Jouini","year":"2009","journal-title":"Proc. International Conference on Signals, Circuits and Systems"},{"key":"2026032900522741900_ref113","doi-asserted-by":"publisher","first-page":"276","DOI":"10.1109\/ICOIN53446.2022.9687254","article-title":"Deep reinforcement learning-based context-aware redundancy mitigation for vehicular collective perception services,","author":"Jung","year":"2022","journal-title":"Proc. International Conference on Information Networking"},{"issue":"6","key":"2026032900522741900_ref114","doi-asserted-by":"publisher","first-page":"1443","DOI":"10.1109\/72.809089","article-title":"A dynamic channel assignment policy through Q-learning,","volume":"10","author":"Nie","year":"1999","journal-title":"IEEE Transactions on Neural Networks"},{"key":"2026032900522741900_ref115","volume-title":"Speech and Language Processing:","author":"Jurafsky","year":"2020"},{"key":"2026032900522741900_ref116","doi-asserted-by":"publisher","first-page":"278","DOI":"10.1109\/ISPACS.2018.8923234","article-title":"AI-aided 3-D beamforming for millimeter wave communications,","author":"Kao","year":"2018","journal-title":"Proc. International Symposium on Intelligent Signal Processing and Communication Systems"},{"key":"2026032900522741900_ref117","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/PIMRC48278.2020.9217051","article-title":"Multiagent deep stochastic policy gradient for event based dynamic spectrum access,","author":"Kassab","year":"2020","journal-title":"International Symposium on Personal, Indoor and Mobile Radio Communications"},{"issue":"1","key":"2026032900522741900_ref118","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1080\/0952813X.2020.1818291","article-title":"A comprehensive survey on machine learning approaches for dynamic spectrum access in cognitive radio networks,","volume":"34","author":"Kaur","year":"2022","journal-title":"Journal of Experimental & Theoretical Artificial Intelligence"},{"issue":"2","key":"2026032900522741900_ref119","doi-asserted-by":"publisher","first-page":"281","DOI":"10.1109\/TCCN.2023.3234276","article-title":"Deep recurrent reinforcement learning-based distributed dynamic spectrum access in multichannel wireless networks with imperfect feedback,","volume":"9","author":"Kaur","year":"2023","journal-title":"IEEE Transactions on Cognitive Communications and Networking"},{"issue":"3","key":"2026032900522741900_ref120","doi-asserted-by":"publisher","first-page":"293","DOI":"10.1109\/TNSE.2018.2842246","article-title":"Efficient resource allocation utilizing Q-learning in multiple UA communications,","volume":"6","author":"Kawamoto","year":"2019","journal-title":"IEEE Transactions on Network Science and Engineering"},{"key":"2026032900522741900_ref121","doi-asserted-by":"crossref","first-page":"104 369","DOI":"10.1016\/j.dsp.2023.104369","article-title":"Secure spectrum sharing and power allocation by multi agent reinforcement learning,","volume":"146","author":"Kazemi","year":"2024","journal-title":"Digital Signal Processing"},{"key":"2026032900522741900_ref122","doi-asserted-by":"publisher","first-page":"1120","DOI":"10.1109\/IWCMC.2017.7986442","article-title":"When machine learning meets compressive sampling for wideband spectrum sensing,","author":"Khalfi","year":"2017","journal-title":"International Wireless Communications and Mobile Computing Conference"},{"issue":"6","key":"2026032900522741900_ref123","doi-asserted-by":"publisher","first-page":"34","DOI":"10.1109\/MNET.2014.6963802","article-title":"Carrier aggregation\/channel bonding in next generation cellular networks: Methods and challenges,","volume":"28","author":"Khan","year":"2014","journal-title":"IEEE Network"},{"issue":"4","key":"2026032900522741900_ref124","doi-asserted-by":"publisher","first-page":"441","DOI":"10.1016\/S0167-9236(02)00110-0","article-title":"A comparative assessment of classification methods,","volume":"35","author":"Kiang","year":"2003","journal-title":"Decision Support Systems"},{"issue":"4","key":"2026032900522741900_ref125","doi-asserted-by":"publisher","first-page":"2315","DOI":"10.1109\/COMST.2020.3008765","article-title":"Dynamic TDD systems for 5G and beyond: A survey of cross-link interference mitigation,","volume":"22","author":"Kim","year":"2020","journal-title":"IEEE Communications Surveys & Tutorials"},{"issue":"10","key":"2026032900522741900_ref126","doi-asserted-by":"publisher","first-page":"13 706","DOI":"10.1109\/TVT.2023.3276786","article-title":"Joint beamforming and learning rate optimization for over-the-air federated learning,","volume":"72","author":"Kim","year":"2023","journal-title":"IEEE Transactions on Vehicular Technology"},{"key":"2026032900522741900_ref127","doi-asserted-by":"publisher","first-page":"47 863","DOI":"10.1109\/ACCESS.2023.3268754","article-title":"Multi-agent learning and bargaining scheme for cooperative spectrum sharing process,","volume":"11","author":"Kim","year":"2023","journal-title":"IEEE Access"},{"issue":"9","key":"2026032900522741900_ref128","doi-asserted-by":"publisher","first-page":"80","DOI":"10.1109\/MCOM.2016.7565192","article-title":"Full-duplex mobile device: Pushing the limits,","volume":"54","author":"Korpi","year":"2016","journal-title":"IEEE Communications Magazine"},{"issue":"2","key":"2026032900522741900_ref129","doi-asserted-by":"publisher","first-page":"1726","DOI":"10.1109\/TVT.2021.3134272","article-title":"Multi-agent deep reinforcement learning-empowered channel allocation in vehicular networks,","volume":"71","author":"Kumar","year":"2022","journal-title":"IEEE Transactions on Vehicular Technology"},{"key":"2026032900522741900_ref130","doi-asserted-by":"publisher","first-page":"496","DOI":"10.1109\/ICAIIC.2019.8669027","article-title":"Machine learning-based beamforming in two-user MISO interference channels,","author":"Kwon","year":"2019","journal-title":"Proc. International Conference on Artificial Intelligence in Information and Communication"},{"key":"2026032900522741900_ref131","doi-asserted-by":"publisher","first-page":"752","DOI":"10.1109\/AINA.2018.00113","article-title":"A machine learning approach for detecting spoofing attacks in wireless sensor networks,","author":"Pinto","year":"2018","journal-title":"Proc. IEEE International Conference on Advanced Information Networking and Applications"},{"key":"2026032900522741900_ref132","doi-asserted-by":"publisher","first-page":"91 597","DOI":"10.1109\/ACCESS.2022.3202640","article-title":"A machine learning adaptive beamforming framework for 5G millimeter wave massive MIMO multicellular networks,","volume":"10","author":"Lavdas","year":"2022","journal-title":"IEEE Access"},{"issue":"3","key":"2026032900522741900_ref133","doi-asserted-by":"publisher","first-page":"3005","DOI":"10.1109\/TVT.2019.2891291","article-title":"Deep cooperative sensing: Cooperative spectrum sensing based on convolutional neural networks,","volume":"68","author":"Lee","year":"2019","journal-title":"IEEE Transactions on Vehicular Technology"},{"issue":"2","key":"2026032900522741900_ref134","doi-asserted-by":"publisher","first-page":"224","DOI":"10.1109\/TCCN.2019.2899871","article-title":"Deep learning classification of 3.5-GHz band spectrograms with applications to spectrum sensing,","volume":"5","author":"Lees","year":"2019","journal-title":"IEEE Transactions on Cognitive Communications and Networking"},{"issue":"6","key":"2026032900522741900_ref135","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1109\/MNET.111.2100206","article-title":"Dynamic-adaptive ai solutions for network slicing management in satellite-integrated b5g systems,","volume":"35","author":"Lei","year":"2021","journal-title":"IEEE Network"},{"issue":"8","key":"2026032900522741900_ref136","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1109\/MCOM.2019.1900271","article-title":"The Roadmap to 6G: AI Empowered Wireless Networks,","volume":"57","author":"Letaief","year":"2019","journal-title":"IEEE Communications Magazine"},{"issue":"5","key":"2026032900522741900_ref137","doi-asserted-by":"publisher","first-page":"878","DOI":"10.1109\/JPROC.2009.2015716","article-title":"Cooperative communications for cognitive radio networks,","volume":"97","author":"Letaief","year":"2009","journal-title":"Proceedings of the IEEE"},{"key":"2026032900522741900_ref138","doi-asserted-by":"publisher","first-page":"1893","DOI":"10.1109\/ICSMC.2009.5346172","article-title":"Multi-agent Q-learning of channel selection in multi-user cognitive radio systems: A two by two case,","author":"Li","year":"2009","journal-title":"Proc. IEEE International Conference on Systems, Man and Cybernetics"},{"key":"2026032900522741900_ref139","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/ICCW.2018.8403505","article-title":"Deep Q-learning based dynamic resource allocation for self-powered ultra-dense networks,","author":"Li","year":"2018","journal-title":"Proc. IEEE International Conference on Communications Workshops"},{"key":"2026032900522741900_ref140","doi-asserted-by":"publisher","first-page":"1477","DOI":"10.1109\/INFOCOM.2019.8737630","article-title":"PeDSS: Privacy enhanced and database-driven dynamic spectrum sharing,","author":"Li","year":"2019","journal-title":"Proc. IEEE Conference on Computer Communications"},{"issue":"4","key":"2026032900522741900_ref141","doi-asserted-by":"publisher","first-page":"659","DOI":"10.1109\/LWC.2023.3238073","article-title":"Double deep learning for joint phase-shift and beamforming based on cascaded channels in RIS-assisted MIMO networks,","volume":"12","author":"Li","year":"2023","journal-title":"IEEE Wireless Communications Letters"},{"key":"2026032900522741900_ref142","doi-asserted-by":"publisher","first-page":"87 615","DOI":"10.1109\/ACCESS.2023.3305483","article-title":"Cooperative spectrum sensing based on LSTM-CNN combination network in cognitive radio system,","volume":"11","author":"Li","year":"2023","journal-title":"IEEE Access"},{"key":"2026032900522741900_ref143","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/ConTEL58387.2023.10199063","article-title":"Cooperative spectrum sensing approach in C-V2X based on multi-agent reinforcement learning,","author":"Li","year":"2023","journal-title":"Proc. International Conference on Telecommunications"},{"key":"2026032900522741900_ref144","doi-asserted-by":"publisher","first-page":"25 463","DOI":"10.1109\/ACCESS.2018.2831240","article-title":"Intelligent power control for spectrum sharing in cognitive radios: A deep reinforcement learning approach,","volume":"6","author":"Li","year":"2018","journal-title":"IEEE Access"},{"issue":"8","key":"2026032900522741900_ref145","doi-asserted-by":"publisher","first-page":"8810","DOI":"10.1109\/TVT.2022.3173057","article-title":"Federated multi-agent deep reinforcement learning for resource allocation of vehicle-to-vehicle communications,","volume":"71","author":"Li","year":"2022","journal-title":"IEEE Transactions on Vehicular Technology"},{"key":"2026032900522741900_ref146","doi-asserted-by":"publisher","first-page":"5447","DOI":"10.1109\/TIFS.2023.3307950","article-title":"Dynamic spectrum anti-jamming access with fast convergence: A labeled deep reinforcement learning approach,","volume":"18","author":"Li","year":"2023","journal-title":"IEEE Transactions on Information Forensics and Security"},{"issue":"2","key":"2026032900522741900_ref147","doi-asserted-by":"publisher","first-page":"464","DOI":"10.1109\/TCCN.2020.2982895","article-title":"Deep reinforcement learning for dynamic spectrum sensing and aggregation in multi-channel wireless networks,","volume":"6","author":"Li","year":"2020","journal-title":"IEEE Transactions on Cognitive Communications and Networking"},{"issue":"2","key":"2026032900522741900_ref148","doi-asserted-by":"publisher","first-page":"1828","DOI":"10.1109\/TVT.2019.2961405","article-title":"Multi-agent deep reinforcement learning based spectrum allocation for D2D underlay communications,","volume":"69","author":"Li","year":"2020","journal-title":"IEEE Transactions on Vehicular Technology"},{"issue":"19","key":"2026032900522741900_ref149","doi-asserted-by":"publisher","first-page":"2485","DOI":"10.1049\/iet-com.2018.5245","article-title":"Improved cooperative spectrum sensing model based on machine learning for cognitive radio networks,","volume":"12","author":"Li","year":"2018","journal-title":"IET Communications"},{"issue":"10","key":"2026032900522741900_ref150","doi-asserted-by":"publisher","first-page":"2282","DOI":"10.1109\/JSAC.2019.2933962","article-title":"Spectrum sharing in vehicular networks based on multi-agent reinforcement learning,","volume":"37","author":"Liang","year":"2019","journal-title":"IEEE Journal on Selected Areas in Communications"},{"issue":"8","key":"2026032900522741900_ref151","doi-asserted-by":"crossref","first-page":"2488","DOI":"10.1007\/s10489-020-01637-z","article-title":"Deep reinforcement learning for imbalanced classification,","volume":"50","author":"Lin","year":"2020","journal-title":"Applied Intelligence"},{"key":"2026032900522741900_ref152","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/CCNC.2010.5421750","article-title":"Distributed spectrum sharing in cognitive radio networks - game theoretical view,","author":"Lin","year":"2010","journal-title":"Proc. IEEE Consumer Communications and Networking Conference"},{"key":"2026032900522741900_ref153","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/ICC.2019.8761360","article-title":"Deep CNN for spectrum sensing in cognitive radio,","author":"Liu","year":"2019","journal-title":"Proc. IEEE International Conference on Communications"},{"issue":"10","key":"2026032900522741900_ref154","doi-asserted-by":"publisher","first-page":"2306","DOI":"10.1109\/JSAC.2019.2933892","article-title":"Deep cm-cnn for spectrum sensing in cognitive radio,","volume":"37","author":"Liu","year":"2019","journal-title":"IEEE Journal on Selected Areas in Communications"},{"key":"2026032900522741900_ref155","doi-asserted-by":"publisher","first-page":"465","DOI":"10.1109\/ICOIN48656.2020.9016474","article-title":"Dynamic channel allocation for satellite internet of things via deep reinforcement learning,","author":"Liu","year":"2020","journal-title":"Proc. International Conference on Information Networking"},{"key":"2026032900522741900_ref156","doi-asserted-by":"publisher","first-page":"182 814","DOI":"10.1109\/ACCESS.2019.2956805","article-title":"Learning based adaptive network immune mechanism to defense eavesdropping attacks,","volume":"7","author":"Liu","year":"2019","journal-title":"IEEE Access"},{"issue":"1","key":"2026032900522741900_ref157","doi-asserted-by":"publisher","first-page":"641","DOI":"10.1109\/TVT.2018.2883669","article-title":"Deep learning-inspired message passing algorithm for efficient resource allocation in cognitive radio networks,","volume":"68","author":"Liu","year":"2019","journal-title":"IEEE Transactions on Vehicular Technology"},{"key":"2026032900522741900_ref158","doi-asserted-by":"publisher","first-page":"15 733","DOI":"10.1109\/ACCESS.2018.2809581","article-title":"Deep reinforcement learning based dynamic channel allocation algorithm in multibeam satellite systems,","volume":"6","author":"Liu","year":"2018","journal-title":"IEEE Access"},{"key":"2026032900522741900_ref159","doi-asserted-by":"publisher","first-page":"169 204","DOI":"10.1109\/ACCESS.2019.2954531","article-title":"Pattern-aware intelligent anti-jamming communication: A sequential deep reinforcement learning approach,","volume":"7","author":"Liu","year":"2019","journal-title":"IEEE Access"},{"issue":"4","key":"2026032900522741900_ref160","doi-asserted-by":"publisher","DOI":"10.3390\/app11041884","article-title":"Dynamic cooperative spectrum sensing based on deep multi-user reinforcement learning,","volume":"11","author":"Liu","year":"2021","journal-title":"Applied Sciences"},{"key":"2026032900522741900_ref161","doi-asserted-by":"publisher","first-page":"155","DOI":"10.1109\/EExPolytech58658.2023.10318765","article-title":"Federated deep reinforcement learning-based spectrum sharing and power allocation for mobile communication system,","author":"Liu","year":"2023","journal-title":"Proc. International Conference on Electrical Engineering and Photonics"},{"issue":"5","key":"2026032900522741900_ref162","doi-asserted-by":"publisher","first-page":"998","DOI":"10.1109\/LCOMM.2018.2815018","article-title":"Anti-jamming communications using spectrum waterfall: A deep reinforcement learning approach,","volume":"22","author":"Liu","year":"2018","journal-title":"IEEE Communications Letters"},{"issue":"1","key":"2026032900522741900_ref163","doi-asserted-by":"publisher","first-page":"805","DOI":"10.1109\/TITS.2022.3175570","article-title":"Joint collaborative big spectrum data sensing and reinforcement learning based dynamic spectrum access for cognitive internet of vehicles,","volume":"25","author":"Liu","year":"2024","journal-title":"IEEE Transactions on Intelligent Transportation Systems"},{"issue":"6","key":"2026032900522741900_ref164","doi-asserted-by":"crossref","first-page":"4244","DOI":"10.1109\/TII.2021.3113949","article-title":"Reinforcement-learningbased dynamic spectrum access for software-defined cognitive industrial internet of things,","volume":"18","author":"Liu","year":"2021","journal-title":"IEEE Transactions on Industrial Informatics"},{"key":"2026032900522741900_ref165","doi-asserted-by":"publisher","first-page":"471","DOI":"10.1109\/ICUFN.2017.7993829","article-title":"Dynamic resource allocation using reinforcement learning for LTE-U and WiFi in the unlicensed spectrum,","author":"Liu","year":"2017","journal-title":"Proc. International Conference on Ubiquitous and Future Networks"},{"issue":"3","key":"2026032900522741900_ref166","doi-asserted-by":"publisher","first-page":"1697","DOI":"10.1109\/TCOMM.2022.3143122","article-title":"Deep unsupervised learning for joint antenna selection and hybrid beamforming,","volume":"70","author":"Liu","year":"2022","journal-title":"IEEE Transactions on Communications"},{"key":"2026032900522741900_ref167","doi-asserted-by":"publisher","first-page":"253","DOI":"10.1109\/RPIC.2019.8882140","article-title":"Hybrid beamforming algorithm using reinforcement learning for millimeter wave wireless systems,","author":"Lizarraga","year":"2019","journal-title":"Proc. Workshop on Information Processing and Control"},{"issue":"2","key":"2026032900522741900_ref168","doi-asserted-by":"publisher","first-page":"340","DOI":"10.1109\/JSTSP.2018.2818649","article-title":"Data-drivenbased analog beam selection for hybrid beamforming under mmwave channels,","volume":"12","author":"Long","year":"2018","journal-title":"IEEE Journal of Selected Topics in Signal Processing"},{"issue":"1","key":"2026032900522741900_ref169","doi-asserted-by":"publisher","first-page":"425","DOI":"10.1109\/COMST.2022.3224279","article-title":"Reinforcement learning-based physical cross-layer security and privacy in 6G,","volume":"25","author":"Lu","year":"2023","journal-title":"IEEE Communications Surveys & Tutorials"},{"key":"2026032900522741900_ref170","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/WCNC.2016.7564840","article-title":"Machine learning techniques with probability vector for cooperative spectrum sensing in cognitive radio networks,","author":"Lu","year":"2016","journal-title":"2016 IEEE Wireless Communications and Networking Conference"},{"key":"2026032900522741900_ref171","doi-asserted-by":"publisher","first-page":"100 158","DOI":"10.1016\/j.jii.2020.100158","article-title":"6g: A survey on technologies, scenarios, challenges, and the related issues,","volume":"19","author":"Lu","year":"2020","journal-title":"Journal of Industrial Information Integration"},{"key":"2026032900522741900_ref172","doi-asserted-by":"publisher","first-page":"1333","DOI":"10.1109\/ICASSP.2019.8683478","article-title":"Deep learning for fast adaptive beamforming,","author":"Luijten","year":"2019","journal-title":"Proc. IEEE International Conference on Acoustics, Speech and Signal Processing"},{"issue":"3","key":"2026032900522741900_ref173","doi-asserted-by":"publisher","first-page":"701","DOI":"10.1109\/LCOMM.2021.3137809","article-title":"DQN-based predictive spectrum handoff via hybrid priority queuing model,","volume":"26","author":"Luo","year":"2022","journal-title":"IEEE Communications Letters"},{"key":"2026032900522741900_ref174","doi-asserted-by":"publisher","first-page":"385","DOI":"10.1109\/ICCWAMTIP.2014.7073432","article-title":"Dynamic resource allocations based on q-learning for d2d communication in cellular networks,","author":"Luo","year":"2014","journal-title":"Proc. International Computer Conference on Wavelet Actiev Media Technology and Information Processing"},{"issue":"05","key":"2026032900522741900_ref175","doi-asserted-by":"publisher","first-page":"1892","DOI":"10.1109\/TMC.2020.3030061","article-title":"When attackers meet AI: Learning-empowered attacks in cooperative spectrum sensing,","volume":"21","author":"Luo","year":"2022","journal-title":"IEEE Transactions on Mobile Computing"},{"issue":"1","key":"2026032900522741900_ref176","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1002\/j.1538-7305.1979.tb02209.x","article-title":"Advanced mobile phone service: The cellular concept,","volume":"58","author":"Mac Donald","year":"1979","journal-title":"The bell system technical Journal"},{"key":"2026032900522741900_ref177","doi-asserted-by":"publisher","first-page":"68","DOI":"10.1016\/j.jnca.2017.11.003","article-title":"A hardware testbed for learning-based spectrum handoff in cognitive radio networks,","volume":"106","author":"Manjunatha","year":"2018","journal-title":"Journal of Network and Computer Applications"},{"key":"2026032900522741900_ref178","doi-asserted-by":"publisher","first-page":"1810","DOI":"10.1109\/IranianCEE.2017.7985346","article-title":"Spectrum sharing in cognitive radio networks using beamforming and two-path successive relaying,","author":"Masrour","year":"2017","journal-title":"Proc. Iranian Conference on Electrical Engineering"},{"issue":"10","key":"2026032900522741900_ref179","doi-asserted-by":"publisher","first-page":"6255","DOI":"10.1109\/TWC.2020.3001736","article-title":"Power allocation in multi-user cellular networks: Deep reinforcement learning approaches,","volume":"19","author":"Meng","year":"2020","journal-title":"IEEE Transactions on Wireless Communications"},{"key":"2026032900522741900_ref180","doi-asserted-by":"publisher","first-page":"1275","DOI":"10.1109\/ICISCE.2016.273","article-title":"Centralized spectrum sharing using reinforcement learning,","author":"Miao","year":"2016","journal-title":"Proc. International Conference on Information Science and Control Engineering"},{"key":"2026032900522741900_ref181","doi-asserted-by":"publisher","first-page":"429","DOI":"10.1109\/CyberC.2014.80","article-title":"Machine learning to data fusion approach for cooperative spectrum sensing,","author":"Mikaeil","year":"2014","journal-title":"Proc. International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery"},{"key":"2026032900522741900_ref182","volume-title":"Proc. of International Conference on Neural Information Processing Systems (NIPS) - Deep Learning Workshop, ser. NIPS\u201913","author":"Mnih","year":"2013"},{"issue":"7540","key":"2026032900522741900_ref183","doi-asserted-by":"crossref","first-page":"529","DOI":"10.1038\/nature14236","article-title":"Human-level control through deep reinforcement learning,","volume":"518","author":"Mnih","year":"2015","journal-title":"nature"},{"issue":"10","key":"2026032900522741900_ref184","doi-asserted-by":"publisher","first-page":"19 691","DOI":"10.1109\/TITS.2022.3173153","article-title":"A novel machine learning-based scheme for spectrum sharing in virtualized 5G networks,","volume":"23","author":"Morgado","year":"2022","journal-title":"IEEE Transactions on Intelligent Transportation Systems"},{"issue":"10","key":"2026032900522741900_ref185","doi-asserted-by":"publisher","first-page":"4694","DOI":"10.1109\/TNNLS.2017.2766162","article-title":"Brain-inspired wireless communications: Where reservoir computing meets MIMO-OFDM,","volume":"29","author":"Mosleh","year":"2018","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"key":"2026032900522741900_ref186","volume-title":"Machine Learning: A Probabilistic Perspective","author":"Murphy","year":"2013"},{"key":"2026032900522741900_ref187","doi-asserted-by":"publisher","first-page":"31 823","DOI":"10.1109\/ACCESS.2020.2973140","article-title":"Deep reinforcement learning-based channel allocation for wireless LANs with graph convolutional networks,","volume":"8","author":"Nakashima","year":"2020","journal-title":"IEEE Access"},{"issue":"8","key":"2026032900522741900_ref188","doi-asserted-by":"publisher","first-page":"1708","DOI":"10.1109\/LCOMM.2018.2841378","article-title":"Secure beamforming for MIMO-NOMA-based cognitive radio network,","volume":"22","author":"Nandan","year":"2018","journal-title":"IEEE Communications Letters"},{"issue":"1","key":"2026032900522741900_ref189","doi-asserted-by":"publisher","first-page":"310","DOI":"10.1109\/TWC.2018.2879433","article-title":"Deep multi-user reinforcement learning for distributed dynamic spectrum access,","volume":"18","author":"Naparstek","year":"2019","journal-title":"IEEE Transactions on Wireless Communications"},{"key":"2026032900522741900_ref190","doi-asserted-by":"crossref","DOI":"10.1109\/LWC.2023.3315230","article-title":"Multi-agent DRL-based RIS-assisted spectrum sensing in cognitive satellite-terrestrial networks,","author":"Ngo","year":"2023","journal-title":"IEEE Wireless Communications Letters"},{"key":"2026032900522741900_ref191","article-title":"White paper 5G evolution and 6G,","author":"NTT Docomo","year":"2021"},{"issue":"1","key":"2026032900522741900_ref192","doi-asserted-by":"publisher","first-page":"168","DOI":"10.1109\/JSTSP.2018.2797022","article-title":"Over-the-air deep learning based radio signal classification,","volume":"12","author":"O\u2019Shea","year":"2018","journal-title":"IEEE Journal of Selected Topics in Signal Processing"},{"issue":"5","key":"2026032900522741900_ref193","doi-asserted-by":"publisher","first-page":"26","DOI":"10.1109\/MCOM.2014.6815890","article-title":"Scenarios for 5G mobile and wireless communications: The vision of the METIS project,","volume":"52","author":"Osseiran","year":"2014","journal-title":"IEEE Communications Magazine"},{"key":"2026032900522741900_ref194","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/ICC.2019.8761575","article-title":"Empowering reinforcement learning on big sensed data for intrusion detection,","author":"Otoum","year":"2019","journal-title":"Proc. IEEE International Conference on Communications"},{"issue":"2","key":"2026032900522741900_ref195","doi-asserted-by":"publisher","first-page":"68","DOI":"10.1109\/LNET.2019.2901792","article-title":"On the feasibility of deep learning in sensor network intrusion detection,","volume":"1","author":"Otoum","year":"2019","journal-title":"IEEE Networking Letters"},{"issue":"6","key":"2026032900522741900_ref196","doi-asserted-by":"publisher","first-page":"1395","DOI":"10.3390\/s19061395","article-title":"An effective spectrum handoff based on reinforcement learning for target channel selection in the industrial internet of things,","volume":"19","author":"Oyewobi","year":"2019","journal-title":"Sensors"},{"key":"2026032900522741900_ref197","doi-asserted-by":"publisher","first-page":"100","DOI":"10.1109\/MeditCom49071.2021.9647572","article-title":"Implementation and latency assessment of a prototype for C-ITS collective perception,","author":"Pacella","year":"2021","journal-title":"Proc. IEEE International Mediterranean Conference on Communications and Networking"},{"key":"2026032900522741900_ref198","doi-asserted-by":"crossref","DOI":"10.1002\/9781119551539","volume-title":"Spectrum Sharing: The Next Frontier in Wireless Networks","author":"Papadias","year":"2020"},{"issue":"3","key":"2026032900522741900_ref199","doi-asserted-by":"publisher","first-page":"270","DOI":"10.1109\/JPROC.2014.2301972","article-title":"Security and enforcement in spectrum sharing,","volume":"102","author":"Park","year":"2014","journal-title":"Proceedings of the IEEE"},{"issue":"4","key":"2026032900522741900_ref200","doi-asserted-by":"publisher","first-page":"2325","DOI":"10.1109\/TWC.2022.3210955","article-title":"Intelligent access to unlicensed spectrum: A mean field based deep reinforcement learning approach,","volume":"22","author":"Pei","year":"2023","journal-title":"IEEE Transactions on Wireless Communications"},{"key":"2026032900522741900_ref201","doi-asserted-by":"crossref","first-page":"551","DOI":"10.1007\/978-3-030-32388-2_47","volume-title":"Machine Learning and Intelligent Communications","author":"Pei","year":"2019"},{"key":"2026032900522741900_ref202","doi-asserted-by":"publisher","first-page":"9205","DOI":"10.1109\/ICASSP40776.2020.9053157","article-title":"Exploitation of 3D city maps for hybrid 5G RTT and GNSS positioning simulations,","author":"del Peral-Rosado","year":"2020","journal-title":"Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)"},{"key":"2026032900522741900_ref203","doi-asserted-by":"publisher","first-page":"669","DOI":"10.1109\/ICOIN56518.2023.10048946","article-title":"A review on reinforcement learning enabled cooperative spectrum sensing,","author":"Pham","year":"2023","journal-title":"Proc. International Conference on Information Networking"},{"key":"2026032900522741900_ref204","doi-asserted-by":"publisher","first-page":"146","DOI":"10.4108\/icst.5gu.2014.258154","article-title":"Ultra-reliable communication in 5G wireless systems,","author":"Popovski","year":"2014","journal-title":"Proc. International Conference on 5G for Ubiquitous Connectivity"},{"issue":"2","key":"2026032900522741900_ref205","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1109\/MNET.2018.1700258","article-title":"Wireless access for ultra-reliable low-latency communication: Principles and building blocks,","volume":"32","author":"Popovski","year":"2018","journal-title":"IEEE Network"},{"key":"2026032900522741900_ref206","doi-asserted-by":"publisher","first-page":"555","DOI":"10.1109\/ICTC46691.2019.8939986","article-title":"Reinforcement learning based 5G enabled cognitive radio networks,","author":"Puspita","year":"2019","journal-title":"Proc. International Conference on Information and Communication Technology Convergence"},{"issue":"12","key":"2026032900522741900_ref207","doi-asserted-by":"publisher","first-page":"7910","DOI":"10.1109\/TWC.2018.2872712","article-title":"Channel energy statistics learning in compressive spectrum sensing,","volume":"17","author":"Qi","year":"2018","journal-title":"IEEE Transactions on Wireless Communications"},{"key":"2026032900522741900_ref208","article-title":"Federated reinforcement learning: Techniques, applications, and open challenges,","author":"Qi","year":"2021","journal-title":"arXiv preprint arXiv:2108.11887"},{"key":"2026032900522741900_ref209","first-page":"85","article-title":"Deep-reinforcement-learning-based resource allocation for energy harvesting D2D communication,","author":"Qi","year":"2023","journal-title":"Proc. International Conference on Electronic Communication and Artificial Intelligence, IEEE"},{"issue":"2","key":"2026032900522741900_ref210","doi-asserted-by":"publisher","first-page":"257","DOI":"10.1109\/5.18626","article-title":"A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition,","volume":"77","author":"Rabiner","year":"1989","journal-title":"Proceedings of the IEEE"},{"issue":"1","key":"2026032900522741900_ref211","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1109\/JSTSP.2018.2798920","article-title":"Spectrum access in cognitive radio using a two-stage reinforcement learning approach,","volume":"12","author":"Raj","year":"2018","journal-title":"IEEE Journal of Selected Topics in Signal Processing"},{"issue":"10","key":"2026032900522741900_ref212","doi-asserted-by":"crossref","DOI":"10.1002\/ett.4174","article-title":"Blockchain and extreme learning machine based spectrum management in cognitive radio networks,","volume":"33","author":"Rajesh Babu","year":"2022","journal-title":"Transactions on Emerging Telecommunications Technologies"},{"key":"2026032900522741900_ref213","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/DySPAN.2018.8610489","article-title":"A machine learning algorithm for unlicensed lte and wifi spectrum sharing,","author":"Rastegardoost","year":"2018","journal-title":"Proc. IEEE International Symposium on Dynamic Spectrum Access Networks"},{"key":"2026032900522741900_ref214","doi-asserted-by":"publisher","first-page":"858","DOI":"10.1109\/AECE59614.2023.10428591","article-title":"Machine learning based cooperative spectrum sensing using regression methods,","author":"Reddy","year":"2023","journal-title":"Proc. International Conference on Advancement in Electronics & Communication Engineering"},{"issue":"2","key":"2026032900522741900_ref215","doi-asserted-by":"publisher","first-page":"106","DOI":"10.1109\/MCOM.2014.6736750","article-title":"Millimeter-wave beamforming as an enabling technology for 5G cellular communications: Theoretical feasibility and prototype results,","volume":"52","author":"Roh","year":"2014","journal-title":"IEEE Communications Magazine"},{"issue":"5","key":"2026032900522741900_ref216","doi-asserted-by":"publisher","first-page":"55","DOI":"10.1109\/MSEC.2023.3251888","article-title":"Challenges and opportunities for beyond-5G wireless security,","volume":"21","author":"Ruzomberka","year":"2023","journal-title":"IEEE Security & Privacy"},{"issue":"3","key":"2026032900522741900_ref217","doi-asserted-by":"crossref","first-page":"134","DOI":"10.1109\/MNET.001.1900287","article-title":"A vision of 6G wireless systems: Applications, trends, technologies, and open research problems,","volume":"34","author":"Saad","year":"2019","journal-title":"IEEE network"},{"issue":"3","key":"2026032900522741900_ref218","doi-asserted-by":"publisher","first-page":"183","DOI":"10.1109\/MNET.104.2100351","article-title":"AI-aided integrated terrestrial and non-terrestrial 6G solutions for sustainable maritime networking,","volume":"36","author":"Saafi","year":"2022","journal-title":"IEEE Network"},{"key":"2026032900522741900_ref219","doi-asserted-by":"publisher","first-page":"383","DOI":"10.1109\/SITIS.2019.00068","article-title":"An optimized spectrum sensing implementation based on SVM, KNN and tree algorithms,","author":"Saber","year":"2019","journal-title":"Proc. in International Conference on Signal-Image Technology Internet-Based Systems"},{"issue":"6","key":"2026032900522741900_ref220","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/LSENS.2023.3275215","article-title":"SDR-implementation of a support vector machine-assisted covariance-based spectrum sensing algorithm in the presence of correlated noise,","volume":"7","author":"Sabra","year":"2023","journal-title":"IEEE Sensors Letters"},{"key":"2026032900522741900_ref221","doi-asserted-by":"publisher","first-page":"2307","DOI":"10.1109\/ICCW.2015.7247525","article-title":"Learning-based coexistence for LTE operation in unlicensed bands,","author":"Sallent","year":"2015","journal-title":"Proc. IEEE International Conference on Communication Workshop"},{"issue":"3","key":"2026032900522741900_ref222","doi-asserted-by":"crossref","first-page":"395","DOI":"10.1049\/cit2.12114","article-title":"Scope of machine learning applications for addressing the challenges in next-generation wireless networks,","volume":"7","author":"Samanta","year":"2022","journal-title":"Transactions on Intelligence Technology"},{"key":"2026032900522741900_ref223","doi-asserted-by":"publisher","first-page":"2521","DOI":"10.1109\/INFOCOM.2019.8737534","article-title":"A practical underlay spectrum sharing scheme for cognitive radio networks,","author":"Sangdeh","year":"2019","journal-title":"Proc. IEEE Conference on Computer Communications"},{"issue":"7","key":"2026032900522741900_ref224","doi-asserted-by":"publisher","first-page":"1459","DOI":"10.1109\/LCOMM.2020.2984430","article-title":"Cooperative spectrum sensing meets machine learning: Deep reinforcement learning approach,","volume":"24","author":"Sarikhani","year":"2020","journal-title":"IEEE Communications Letters"},{"issue":"11","key":"2026032900522741900_ref225","doi-asserted-by":"publisher","first-page":"2673","DOI":"10.1109\/78.650093","article-title":"Bidirectional recurrent neural networks,","volume":"45","author":"Schuster","year":"1997","journal-title":"IEEE Transactions on Signal Processing"},{"issue":"11","key":"2026032900522741900_ref226","doi-asserted-by":"publisher","first-page":"1927","DOI":"10.1109\/TCOMM.2003.818096","article-title":"User cooperation diversity. part i. system description,","volume":"51","author":"Sendonaris","year":"2003","journal-title":"IEEE Transactions on Communications"},{"issue":"2","key":"2026032900522741900_ref227","doi-asserted-by":"publisher","first-page":"2439","DOI":"10.1109\/TVT.2022.3212966","article-title":"Mitigating jamming attack in 5G heterogeneous networks: A federated deep reinforcement learning approach,","volume":"72","author":"Sharma","year":"2023","journal-title":"IEEE Transactions on Vehicular Technology"},{"issue":"4","key":"2026032900522741900_ref228","doi-asserted-by":"publisher","first-page":"1858","DOI":"10.1109\/COMST.2015.2452414","article-title":"Cognitive radio techniques under practical imperfections: A survey,","volume":"17","author":"Sharma","year":"2015","journal-title":"IEEE Communications Surveys Tutorials"},{"issue":"25","key":"2026032900522741900_ref229","doi-asserted-by":"crossref","first-page":"1368","DOI":"10.1049\/el.2019.2259","article-title":"Reinforcement learning-based spectrum handoff scheme with measured PDR in cognitive radio networks,","volume":"55","author":"Shi","year":"2019","journal-title":"Electronics Letters"},{"issue":"3","key":"2026032900522741900_ref230","doi-asserted-by":"publisher","first-page":"149","DOI":"10.1109\/LNET.2023.3284665","article-title":"Deep reinforcement learning for nextG radio access network slicing with spectrum coexistence,","volume":"5","author":"Shi","year":"2023","journal-title":"IEEE Networking Letters"},{"issue":"11","key":"2026032900522741900_ref231","doi-asserted-by":"publisher","first-page":"2902","DOI":"10.1109\/JSAC.2016.2615259","article-title":"Spectrum sharing in mmwave cellular networks via cell association, coordination, and beamforming,","volume":"34","author":"Shokri-Ghadikolaei","year":"2016","journal-title":"IEEE Journal on Selected Areas in Communications"},{"issue":"1","key":"2026032900522741900_ref232","doi-asserted-by":"publisher","first-page":"44","DOI":"10.1109\/MWC.001.2000233","article-title":"Full duplex and dynamic TDD: Pushing the limits of spectrum reuse in multi-cell communications,","volume":"28","author":"da Silva","year":"2021","journal-title":"IEEE Wireless Communications"},{"issue":"3\u20134","key":"2026032900522741900_ref233","doi-asserted-by":"crossref","first-page":"200","DOI":"10.1561\/2000000102","article-title":"A Brief Introduction to Machine Learning for Engineers,","volume":"12","author":"Simeone","year":"2018","journal-title":"Foundations and Trends in Signal Processing"},{"key":"2026032900522741900_ref234","doi-asserted-by":"publisher","first-page":"169","DOI":"10.1109\/RWS47064.2019.8971827","article-title":"Physical layer security analysis in the priority-based 5G spectrum sharing systems,","volume":"1","author":"Soltani","year":"2019","journal-title":"Proc. Resilience Week"},{"key":"2026032900522741900_ref235","doi-asserted-by":"publisher","first-page":"221","DOI":"10.1109\/ACSSC.2006.356619","article-title":"The throughput potential of cognitive radio: A theoretical perspective,","author":"Srinivasa","year":"2006","journal-title":"Proc. Asilomar Conference on Signals, Systems and Computers"},{"issue":"11","key":"2026032900522741900_ref236","doi-asserted-by":"publisher","first-page":"2887","DOI":"10.1109\/JSAC.2016.2614952","article-title":"A Q-learning framework for user qoe enhanced self-organizing spectrally effi-cient network using a novel inter-operator proximal spectrum sharing,","volume":"34","author":"Srinivasan","year":"2016","journal-title":"IEEE Journal on Selected Areas in Communications"},{"issue":"4","key":"2026032900522741900_ref237","doi-asserted-by":"crossref","DOI":"10.3390\/s23042011","article-title":"Innovative spectrum handoff process using a machine learning-based metaheuristic algorithm,","volume":"23","author":"Srivastava","year":"2023","journal-title":"Sensors"},{"key":"2026032900522741900_ref238","doi-asserted-by":"publisher","first-page":"87 754","DOI":"10.1109\/ACCESS.2022.3199350","article-title":"Real-time dynamic SLAM algorithm based on deep learning,","volume":"10","author":"Su","year":"2022","journal-title":"IEEE Access"},{"key":"2026032900522741900_ref239","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1109\/ICRAMET.2018.8683930","article-title":"Spectrum sensing for cognitive radio using deep autoencoder neural network and SVM,","author":"Subekti","year":"2018","journal-title":"Proc. International Conference on Radar, Antenna, Microwave, Electronics, and Telecommunications"},{"key":"2026032900522741900_ref240","doi-asserted-by":"publisher","first-page":"6304","DOI":"10.1109\/ACCESS.2019.2963468","article-title":"A machine learning approach for beamforming in ultra dense network considering selfish and altruistic strategy,","volume":"8","author":"Sun","year":"2020","journal-title":"IEEE Access"},{"issue":"2","key":"2026032900522741900_ref241","doi-asserted-by":"publisher","first-page":"74","DOI":"10.1109\/MWC.2013.6507397","article-title":"Wideband spectrum sensing for cognitive radio networks: A survey,","volume":"20","author":"Sun","year":"2013","journal-title":"IEEE Wireless Communications"},{"issue":"2","key":"2026032900522741900_ref242","doi-asserted-by":"publisher","first-page":"2220","DOI":"10.1109\/TVT.2021.3136197","article-title":"A cost-efficient skipping based spectrum sensing scheme via reinforcement learning,","volume":"71","author":"Sun","year":"2022","journal-title":"IEEE Transactions on Vehicular Technology"},{"key":"2026032900522741900_ref243","doi-asserted-by":"publisher","first-page":"42 191","DOI":"10.1109\/ACCESS.2023.3270316","article-title":"Joint DDPG and unsupervised learning for channel allocation and power control in centralized wireless cellular networks,","volume":"11","author":"Sun","year":"2023","journal-title":"IEEE Access"},{"key":"2026032900522741900_ref244","volume-title":"Reinforcement learning: An introduction","author":"Sutton","year":"2018"},{"key":"2026032900522741900_ref245","doi-asserted-by":"publisher","first-page":"89 591","DOI":"10.1109\/ACCESS.2023.3305388","article-title":"Deep neural networks for spectrum sensing: A review,","volume":"11","author":"Syed","year":"2023","journal-title":"IEEE Access"},{"issue":"8","key":"2026032900522741900_ref246","doi-asserted-by":"publisher","first-page":"2132","DOI":"10.1109\/LCOMM.2023.3289982","article-title":"An SVM-based feature detection scheme for spatial spectrum sensing,","volume":"27","author":"Tang","year":"2023","journal-title":"IEEE Communications Letters"},{"key":"2026032900522741900_ref247","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/WICOM.2010.5601105","article-title":"Artificial neural network based spectrum sensing method for cognitive radio,","author":"Tang","year":"2010","journal-title":"Proc. International Conference on Wireless Communications Networking and Mobile Computing"},{"issue":"10","key":"2026032900522741900_ref248","doi-asserted-by":"publisher","first-page":"11 353","DOI":"10.1109\/TVT.2020.3009746","article-title":"Hybrid beamforming\/combining for millimeter wave MIMO: A machine learning approach,","volume":"69","author":"Tao","year":"2020","journal-title":"IEEE Transactions on Vehicular Technology"},{"issue":"4","key":"2026032900522741900_ref249","doi-asserted-by":"publisher","first-page":"2591","DOI":"10.1109\/COMST.2016.2583499","article-title":"Licensed spectrum sharing schemes for mobile operators: A survey and outlook,","volume":"18","author":"Tehrani","year":"2016","journal-title":"IEEE Communications Surveys Tutorials"},{"issue":"11","key":"2026032900522741900_ref250","doi-asserted-by":"publisher","first-page":"42","DOI":"10.1109\/35.109664","article-title":"Handover and channel assignment in mobile cellular networks,","volume":"29","author":"Tekinay","year":"1991","journal-title":"IEEE Communications Magazine"},{"key":"2026032900522741900_ref251","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/VETECF.2010.5594301","article-title":"Reinforcement learning based auction algorithm for dynamic spectrum access in cognitive radio networks,","author":"Teng","year":"2010","journal-title":"Proc. IEEE 72nd Vehicular Technology Conference - Fall"},{"issue":"11","key":"2026032900522741900_ref252","doi-asserted-by":"publisher","first-page":"2209","DOI":"10.1109\/JSAC.2013.131120","article-title":"Machine learning techniques for cooperative spectrum sensing in cognitive radio networks,","volume":"31","author":"Thilina","year":"2013","journal-title":"IEEE Journal on Selected Areas in Communications"},{"issue":"2","key":"2026032900522741900_ref253","doi-asserted-by":"publisher","first-page":"390","DOI":"10.1109\/TCCN.2018.2833848","article-title":"Secure communication in spectrum-sharing massive MIMO systems with active eavesdropping,","volume":"4","author":"Timilsina","year":"2018","journal-title":"IEEE Transactions on Cognitive Communications and Networking"},{"key":"2026032900522741900_ref254","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/GLOCOM.2018.8647811","article-title":"Comparison of statistical signal processing and machine learning algorithms for spectrum sensing,","author":"Tiwari","year":"2018","journal-title":"Proc. IEEE Global Communications Conference"},{"key":"2026032900522741900_ref255","doi-asserted-by":"publisher","first-page":"564","DOI":"10.1109\/ICPCSI.2017.8391775","article-title":"Predictive learning model in cognitive radio using reinforcement learning,","author":"Tubachi","year":"2017","journal-title":"Proc. IEEE International Conference on Power, Control, Signals and Instrumentation Engineering"},{"issue":"1","key":"2026032900522741900_ref256","doi-asserted-by":"publisher","first-page":"491","DOI":"10.1109\/JSYST.2022.3197880","article-title":"Multi-agent deep reinforcement learning for enhancement of distributed resource allocation in vehicular network,","volume":"17","author":"Urmonov","year":"2023","journal-title":"IEEE Systems Journal"},{"issue":"21","key":"2026032900522741900_ref257","doi-asserted-by":"publisher","DOI":"10.3390\/s21217146","article-title":"Deep cooperative spectrum sensing based on residual neural network using feature extraction and random forest classifier,","volume":"21","author":"Valad\u00e3o","year":"2021","journal-title":"Sensors"},{"key":"2026032900522741900_ref258","article-title":"Deep reinforcement learning with double Q-learning,","author":"Van Hasselt","year":"2015","journal-title":"arXiv preprint arXiv:1509.06461"},{"issue":"2","key":"2026032900522741900_ref259","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1109\/53.665","article-title":"Beamforming: A versatile approach to spatial filtering,","volume":"5","author":"Veen","year":"1988","journal-title":"IEEE ASSP Magazine"},{"issue":"6","key":"2026032900522741900_ref260","doi-asserted-by":"publisher","first-page":"3148","DOI":"10.1109\/TVT.2010.2048766","article-title":"Opportunistic bandwidth sharing through reinforcement learning,","volume":"59","author":"Venkatraman","year":"2010","journal-title":"IEEE Transactions on Vehicular Technology"},{"key":"2026032900522741900_ref261","doi-asserted-by":"publisher","DOI":"10.1007\/s11277-019-06986-8","article-title":"Machine learning based intrusion detection systems for IoT applications,","author":"Verma","year":"2019","journal-title":"Wireless Personal Communications"},{"key":"2026032900522741900_ref262","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/PIMRC.2017.8292449","article-title":"Artificial neural network based hybrid spectrum sensing scheme for cognitive radio,","author":"Vyas","year":"2017","journal-title":"Proc. International Symposium on Personal, Indoor, and Mobile Radio Communications"},{"issue":"1","key":"2026032900522741900_ref263","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1109\/JSTSP.2010.2093210","article-title":"Advances in cognitive radio networks: A survey,","volume":"5","author":"Wang","year":"2011","journal-title":"IEEE J. Selected Topics in Signal Processing"},{"issue":"3","key":"2026032900522741900_ref264","doi-asserted-by":"publisher","first-page":"498","DOI":"10.1109\/LWC.2021.3133883","article-title":"Adversarial learning-based spectrum sensing in cognitive radio,","volume":"11","author":"Wang","year":"2022","journal-title":"IEEE Wireless Communications Letters"},{"issue":"3","key":"2026032900522741900_ref265","doi-asserted-by":"publisher","first-page":"54","DOI":"10.1109\/MWC.2019.1800353","article-title":"Intelligent cognitive radio in 5g: Ai-based hierarchical cognitive cellular networks,","volume":"26","author":"Wang","year":"2019","journal-title":"IEEE Wireless Communications"},{"key":"2026032900522741900_ref266","doi-asserted-by":"publisher","first-page":"1293","DOI":"10.1109\/CISP-BMEI.2016.7852915","article-title":"An novel spectrum sensing scheme combined with machine learning,","author":"Wang","year":"2016","journal-title":"Proc. International Congress on Image and Signal Processing, BioMedical Engineering and Informatics"},{"key":"2026032900522741900_ref267","article-title":"Hybrid hierarchical DRL enabled resource allocation for secure transmission in multi-IRS-assisted sensing-enhanced spectrum sharing networks,","author":"Wang","year":"2023","journal-title":"IEEE Transactions on Wireless Communications"},{"key":"2026032900522741900_ref268","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/GLOCOM.2018.8647707","article-title":"Efficient identity spoofing attack detection for IoT in mm-wave and massive MIMO 5G communication,","author":"Wang","year":"2018","journal-title":"Proc. IEEE Global Communications Conference"},{"key":"2026032900522741900_ref269","doi-asserted-by":"publisher","first-page":"176","DOI":"10.1109\/OJCOMS.2022.3146364","article-title":"When machine learning meets spectrum sharing security: Methodologies and challenges,","volume":"3","author":"Wang","year":"2022","journal-title":"IEEE Open Journal of the Communications Society"},{"issue":"2","key":"2026032900522741900_ref270","doi-asserted-by":"publisher","first-page":"257","DOI":"10.1109\/TCCN.2018.2809722","article-title":"Deep reinforcement learning for dynamic multichannel access in wireless networks,","volume":"4","author":"Wang","year":"2018","journal-title":"IEEE Transactions on Cognitive Communications and Networking"},{"issue":"3","key":"2026032900522741900_ref271","doi-asserted-by":"publisher","first-page":"1717","DOI":"10.1109\/COMST.2016.2539923","article-title":"A survey on applications of model-free strategy learning in cognitive wireless networks,","volume":"18","author":"Wang","year":"2016","journal-title":"IEEE Communications Surveys Tutorials"},{"issue":"2","key":"2026032900522741900_ref272","doi-asserted-by":"publisher","first-page":"79","DOI":"10.1109\/MCOM.001.1900530","article-title":"Dynamic spectrum anti-jamming communications: Challenges and opportunities,","volume":"58","author":"Wang","year":"2020","journal-title":"IEEE Communications Magazine"},{"issue":"8","key":"2026032900522741900_ref273","doi-asserted-by":"publisher","first-page":"7279","DOI":"10.1109\/JIOT.2020.2982699","article-title":"DRL-based energy-efficient resource allocation frameworks for uplink NOMA systems,","volume":"7","author":"Wang","year":"2020","journal-title":"IEEE Internet of Things Journal"},{"issue":"2","key":"2026032900522741900_ref274","doi-asserted-by":"publisher","first-page":"916","DOI":"10.1109\/TGCN.2022.3186282","article-title":"Green spectrum sharing framework in B5G era by exploiting crowdsensing,","volume":"7","author":"Wang","year":"2023","journal-title":"IEEE Transactions on Green Communications and Networking"},{"key":"2026032900522741900_ref275","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-018-9639-x","article-title":"A survey of dynamic spectrum allocation based on reinforcement learning algorithms in cognitive radio networks,","volume":"51","author":"Wang","year":"2019","journal-title":"Artificial Intelligence Review"},{"key":"2026032900522741900_ref276","first-page":"1995","article-title":"Dueling network architectures for deep reinforcement learning,","author":"Wang","year":"2016","journal-title":"Proc. International conference on machine learning"},{"key":"2026032900522741900_ref277","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/PIMRC.2017.8292321","article-title":"Implications of decentralized q-learning resource allocation in wireless networks,","author":"Wilhelmi","year":"2017","journal-title":"Proc. IEEE Annual International Symposium on Personal, Indoor, and Mobile Radio Communications"},{"key":"2026032900522741900_ref278","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/ICCW.2019.8756639","article-title":"Deep learning based beamforming neural networks in down-link miso systems,","author":"Xia","year":"2019","journal-title":"Proc. IEEE International Conference on Communications Workshops"},{"key":"2026032900522741900_ref279","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/VTC2022-Fall57202.2022.10012889","article-title":"Multi-agent power and resource allocation for D2D communications: A deep reinforcement learning approach,","author":"Xiang","year":"2022","journal-title":"Proc. Vehicular Technology Conference"},{"issue":"1","key":"2026032900522741900_ref280","doi-asserted-by":"publisher","first-page":"195","DOI":"10.1109\/LCOMM.2022.3214792","article-title":"Multiagent reinforcement learning-based decentralized spectrum access in vehicular networks with emergent communication,","volume":"27","author":"Xiang","year":"2023","journal-title":"IEEE Communications Letters"},{"issue":"10","key":"2026032900522741900_ref281","doi-asserted-by":"publisher","first-page":"9499","DOI":"10.1109\/TVT.2018.2856854","article-title":"Two-dimensional antijamming mobile communication based on reinforcement learning,","volume":"67","author":"Xiao","year":"2018","journal-title":"IEEE Transactions on Vehicular Technology"},{"key":"2026032900522741900_ref282","doi-asserted-by":"publisher","first-page":"65","DOI":"10.1016\/j.ins.2021.04.053","article-title":"RTFN: A robust temporal feature network for time series classification,","volume":"571","author":"Xiao","year":"2021","journal-title":"Information Sciences"},{"issue":"5","key":"2026032900522741900_ref283","doi-asserted-by":"publisher","first-page":"5307","DOI":"10.1109\/TVT.2020.2982203","article-title":"Unsupervised deep spectrum sensing: A variational auto-encoder based approach,","volume":"69","author":"Xie","year":"2020","journal-title":"IEEE Transactions on Vehicular Technology"},{"issue":"6","key":"2026032900522741900_ref284","doi-asserted-by":"publisher","first-page":"1025","DOI":"10.1109\/LCOMM.2019.2910176","article-title":"Activity pattern aware spectrum sensing: A cnn-based deep learning approach,","volume":"23","author":"Xie","year":"2019","journal-title":"IEEE Communications Letters"},{"issue":"1","key":"2026032900522741900_ref285","doi-asserted-by":"publisher","first-page":"e4388","DOI":"10.1002\/ett.4388","article-title":"Spectrum sensing in cognitive radio: A deep learning based model,","volume":"33","author":"Xing","year":"2022","journal-title":"Transactions on Emerging Telecommunications Technologies"},{"key":"2026032900522741900_ref286","doi-asserted-by":"publisher","first-page":"18 797","DOI":"10.1109\/ACCESS.2020.2968595","article-title":"Deep deterministic policy gradient (DDPG)-based resource allocation scheme for NOMA vehicular communications,","volume":"8","author":"Xu","year":"2020","journal-title":"IEEE Access"},{"issue":"1","key":"2026032900522741900_ref287","doi-asserted-by":"publisher","first-page":"46","DOI":"10.1109\/MWC.001.1900246","article-title":"Intelligent spectrum sensing: When reinforcement learning meets automatic repeat sensing in 5G communications,","volume":"27","author":"Xu","year":"2020","journal-title":"IEEE Wireless Communications"},{"issue":"21","key":"2026032900522741900_ref288","doi-asserted-by":"publisher","first-page":"5634","DOI":"10.1109\/TSP.2018.2870379","article-title":"Mobile collaborative spectrum sensing for heterogeneous networks: A bayesian machine learning approach,","volume":"66","author":"Xu","year":"2018","journal-title":"IEEE Transactions on Signal Processing"},{"key":"2026032900522741900_ref289","doi-asserted-by":"publisher","first-page":"865","DOI":"10.1109\/MILCOM.2018.8599697","article-title":"Dealing with partial observations in dynamic spectrum access: Deep recurrent q-networks,","author":"Xu","year":"2018","journal-title":"MILCOM 2018 - 2018 IEEE Military Communications Conference (MILCOM)"},{"issue":"7","key":"2026032900522741900_ref290","doi-asserted-by":"publisher","first-page":"4494","DOI":"10.1109\/TWC.2020.2984227","article-title":"The application of deep reinforcement learning to distributed spectrum access in dynamic heterogeneous environments with partial observations,","volume":"19","author":"Xu","year":"2020","journal-title":"IEEE Transactions on Wireless Communications"},{"key":"2026032900522741900_ref291","doi-asserted-by":"publisher","first-page":"207","DOI":"10.1109\/MILCOM.2018.8599723","article-title":"Deep reinforcement learning for dynamic spectrum access in wireless networks,","author":"Xu","year":"2018","journal-title":"MILCOM 2018 - 2018 IEEE Military Communications Conference (MILCOM)"},{"issue":"9","key":"2026032900522741900_ref292","doi-asserted-by":"publisher","first-page":"6185","DOI":"10.1109\/TWC.2023.3240425","article-title":"Deep reinforcement learning for multi-objective resource allocation in multi-platoon cooperative vehicular networks,","volume":"22","author":"Xu","year":"2023","journal-title":"IEEE Transactions on Wireless Communications"},{"key":"2026032900522741900_ref293","doi-asserted-by":"publisher","first-page":"476","DOI":"10.1109\/CHINACOM.2015.7497987","article-title":"A machine learning based spectrum-sensing algorithm using sample covariance matrix,","author":"Xue","year":"2015","journal-title":"Proc. International Conference on Communications and Networking in China"},{"issue":"2","key":"2026032900522741900_ref294","doi-asserted-by":"publisher","first-page":"94","DOI":"10.1109\/MWC.2016.7462490","article-title":"Advanced spectrum sharing in 5G cognitive heterogeneous networks,","volume":"23","author":"Yang","year":"2016","journal-title":"IEEE Wireless Communications"},{"issue":"9","key":"2026032900522741900_ref295","doi-asserted-by":"publisher","first-page":"6935","DOI":"10.1109\/TWC.2022.3153175","article-title":"Distributed deep reinforcement learning-based spectrum and power allocation for heterogeneous networks,","volume":"21","author":"Yang","year":"2022","journal-title":"IEEE Transactions on Wireless Communications"},{"key":"2026032900522741900_ref296","doi-asserted-by":"publisher","first-page":"174","DOI":"10.1109\/MILCOM.2016.7795321","article-title":"Achieving secure spectrum sensing in presence of malicious attacks utilizing unsupervised machine learning,","author":"Li","year":"2016","journal-title":"Proc. IEEE Military Communications Conference"},{"issue":"4","key":"2026032900522741900_ref297","doi-asserted-by":"publisher","first-page":"1024","DOI":"10.1109\/LWC.2019.2904486","article-title":"A collaborative multi-agent reinforcement learning anti-jamming algorithm in wireless networks,","volume":"8","author":"Yao","year":"2019","journal-title":"IEEE Wireless Communications Letters"},{"key":"2026032900522741900_ref298","doi-asserted-by":"publisher","first-page":"1211","DOI":"10.1109\/TIFS.2023.3236788","article-title":"Jamming and eavesdropping defense scheme based on deep reinforcement learning in autonomous vehicle networks,","volume":"18","author":"Yao","year":"2023","journal-title":"IEEE Transactions on Information Forensics and Security"},{"issue":"4","key":"2026032900522741900_ref299","doi-asserted-by":"publisher","first-page":"3163","DOI":"10.1109\/TVT.2019.2897134","article-title":"Deep reinforcement learning based resource allocation for V2V communications,","volume":"68","author":"Ye","year":"2019","journal-title":"IEEE Transactions on Vehicular Technology"},{"issue":"23","key":"2026032900522741900_ref300","doi-asserted-by":"publisher","first-page":"23 995","DOI":"10.1109\/JIOT.2022.3188833","article-title":"Collaborative multiagent reinforcement learning aided resource allocation for uav anti-jamming communication,","volume":"9","author":"Yin","year":"2022","journal-title":"IEEE Internet of Things Journal"},{"key":"2026032900522741900_ref301","first-page":"84","article-title":"Resource allocation in vehicular networks based on federated multi-agent reinforcement learning,","author":"Yu","year":"2023","journal-title":"Prcoc. International Conference on Communication Technology, IEEE"},{"key":"2026032900522741900_ref302","doi-asserted-by":"publisher","first-page":"1112","DOI":"10.1109\/ICCT59356.2023.10419549","article-title":"Graph learning for multi-satellite based spectrum sensing,","author":"Yuan","year":"2023","journal-title":"Proc. IEEE International Conference on Communication Technology"},{"issue":"10","key":"2026032900522741900_ref303","doi-asserted-by":"publisher","first-page":"2337","DOI":"10.1109\/JSAC.2019.2933966","article-title":"Distributed learning for channel allocation over a shared spectrum,","volume":"37","author":"Zafaruddin","year":"2019","journal-title":"IEEE Journal on Selected Areas in Communications"},{"issue":"10","key":"2026032900522741900_ref304","doi-asserted-by":"publisher","first-page":"7331","DOI":"10.1109\/TCOMM.2019.2924010","article-title":"Wireless Networks Design in the Era of Deep Learning: Model-Based, AI-Based, or Both?","volume":"67","author":"Zappone","year":"2019","journal-title":"IEEE Transactions on Communications"},{"issue":"6","key":"2026032900522741900_ref305","doi-asserted-by":"crossref","first-page":"1784","DOI":"10.1109\/TCOMM.2009.06.070402","article-title":"Eigenvalue-based spectrum sensing algorithms for cognitive radio,","volume":"57","author":"Zeng","year":"2009","journal-title":"IEEE Transactions on Communications"},{"key":"2026032900522741900_ref306","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/wicom.2011.6040028","article-title":"SVM-based spectrum sensing in cognitive radio,","author":"Zhang","year":"2011","journal-title":"Proc. International Conference on Wireless Communications, Networking and Mobile Computing"},{"issue":"6","key":"2026032900522741900_ref307","doi-asserted-by":"publisher","first-page":"4209","DOI":"10.1109\/TWC.2020.2981320","article-title":"Power control based on deep reinforcement learning for spectrum sharing,","volume":"19","author":"Zhang","year":"2020","journal-title":"IEEE Transactions on Wireless Communications"},{"issue":"10","key":"2026032900522741900_ref308","doi-asserted-by":"publisher","first-page":"8205","DOI":"10.1109\/TWC.2022.3164800","article-title":"Machine learning empowered spectrum sensing under a sub-sampling framework,","volume":"21","author":"Zhang","year":"2022","journal-title":"IEEE Transactions on Wireless Communications"},{"issue":"3","key":"2026032900522741900_ref309","doi-asserted-by":"publisher","first-page":"132","DOI":"10.1109\/MWC.2018.1800259","article-title":"Spectrum sharing for internet of things: A survey,","volume":"26","author":"Zhang","year":"2019","journal-title":"IEEE Wireless Communications"},{"issue":"5","key":"2026032900522741900_ref310","doi-asserted-by":"publisher","first-page":"44","DOI":"10.1109\/MWC.2017.1700069","article-title":"A survey of advanced techniques for spectrum sharing in 5G networks,","volume":"24","author":"Zhang","year":"2017","journal-title":"IEEE Wireless Communications"},{"issue":"7","key":"2026032900522741900_ref311","doi-asserted-by":"publisher","first-page":"1543","DOI":"10.1109\/LWC.2022.3179362","article-title":"Hybrid beamforming based on an unsupervised deep learning network for downlink channels with imperfect CSI,","volume":"11","author":"Zhang","year":"2022","journal-title":"IEEE Wireless Communications Letters"},{"issue":"18","key":"2026032900522741900_ref312","doi-asserted-by":"publisher","first-page":"4899","DOI":"10.1109\/TSP.2019.2932866","article-title":"A learning-based two-stage spectrum sharing strategy with multiple primary transmit power levels,","volume":"67","author":"Zhang","year":"2019","journal-title":"IEEE Transactions on Signal Processing"},{"key":"2026032900522741900_ref313","doi-asserted-by":"publisher","first-page":"118 898","DOI":"10.1109\/ACCESS.2019.2937108","article-title":"Multi-agent deep reinforcement learning-based cooperative spectrum sensing with upper confidence bound exploration,","volume":"7","author":"Zhang","year":"2019","journal-title":"IEEE Access"},{"key":"2026032900522741900_ref314","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/ICCC57788.2023.10233366","article-title":"A joint scheme on spectrum sensing and access with partial observation: A multiagent deep reinforcement learning approach,","author":"Zhang","year":"2023","journal-title":"Proc. International Conference on Communications"},{"issue":"3","key":"2026032900522741900_ref315","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1109\/MVT.2019.2921208","article-title":"6G wireless networks: Vision, requirements, architecture, and key technologies,","volume":"14","author":"Zhang","year":"2019","journal-title":"IEEE Vehicular Technology Magazine"},{"key":"2026032900522741900_ref316","doi-asserted-by":"publisher","first-page":"177 254","DOI":"10.1109\/ACCESS.2019.2937438","article-title":"Joint power control and channel allocation for interference mitigation based on reinforcement learning,","volume":"7","author":"Zhao","year":"2019","journal-title":"IEEE Access"},{"issue":"3","key":"2026032900522741900_ref317","doi-asserted-by":"publisher","first-page":"79","DOI":"10.1109\/MSP.2007.361604","article-title":"A survey of dynamic spectrum access,","volume":"24","author":"Zhao","year":"2007","journal-title":"IEEE Signal Processing Magazine"},{"key":"2026032900522741900_ref318","doi-asserted-by":"publisher","first-page":"94","DOI":"10.1109\/DySPAN.2015.7343893","article-title":"Txminer: Identifying transmitters in real-world spectrum measurements,","author":"Zheleva","year":"2015","journal-title":"Proc. IEEE International Symposium on Dynamic Spectrum Access Networks"},{"issue":"7","key":"2026032900522741900_ref319","doi-asserted-by":"publisher","first-page":"1370","DOI":"10.1109\/LWC.2021.3058922","article-title":"Channel assignment for hybrid NOMA systems with deep reinforcement learning,","volume":"10","author":"Zheng","year":"2021","journal-title":"IEEE Wireless Communications Letters"},{"issue":"2","key":"2026032900522741900_ref320","doi-asserted-by":"publisher","first-page":"138","DOI":"10.23919\/JCC.2020.02.012","article-title":"Spectrum sensing based on deep learning classification for cognitive radios,","volume":"17","author":"Zheng","year":"2020","journal-title":"China Communications"},{"issue":"11","key":"2026032900522741900_ref321","doi-asserted-by":"publisher","first-page":"1856","DOI":"10.1109\/LWC.2023.3296438","article-title":"Spectrum sensing of NOMA signals using particle swarm optimization based channel estimation with a GMM model,","volume":"12","author":"Zhou","year":"2023","journal-title":"IEEE Wireless Communications Letters"},{"key":"2026032900522741900_ref322","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/GLOBECOM38437.2019.9013281","article-title":"Dynamic channel allocation for multi-UAVs: A deep reinforcement learning approach,","author":"Zhou","year":"2019","journal-title":"Proc. IEEE Global Communications Conference"},{"key":"2026032900522741900_ref323","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/ICCW.2018.8403653","article-title":"Cooperative spectrum sensing algorithm based on support vector machine against SSDF attack,","author":"Zhu","year":"2018","journal-title":"IEEE International Conference on Communications Workshops"},{"issue":"4","key":"2026032900522741900_ref324","doi-asserted-by":"publisher","first-page":"2375","DOI":"10.1109\/JIOT.2017.2759728","article-title":"A new deep-q-learningbased transmission scheduling mechanism for the cognitive internet of things,","volume":"5","author":"Zhu","year":"2018","journal-title":"IEEE Internet of Things Journal"},{"issue":"1","key":"2026032900522741900_ref325","doi-asserted-by":"publisher","first-page":"1178","DOI":"10.1109\/TITS.2022.3179442","article-title":"Federated deep reinforcement learning-based spectrum access algorithm with warranty contract in intelligent transportation systems,","volume":"24","author":"Zhu","year":"2023","journal-title":"IEEE Transactions on Intelligent Transportation Systems"},{"key":"2026032900522741900_ref326","doi-asserted-by":"publisher","first-page":"6733","DOI":"10.1109\/ACCESS.2018.2890210","article-title":"A distributed multi-agent RL-based autonomous spectrum allocation scheme in D2D enabled multi-tier hetnets,","volume":"7","author":"Zia","year":"2019","journal-title":"IEEE Access"}],"container-title":["Foundations and Trends\u00ae in Networking"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.emerald.com\/ftnet\/article-pdf\/14\/1-2\/1\/11044012\/1300000073en.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/www.emerald.com\/ftnet\/article-pdf\/14\/1-2\/1\/11044012\/1300000073en.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T18:13:46Z","timestamp":1777486426000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.emerald.com\/ftnet\/article\/14\/1-2\/1\/1328321\/Machine-Learning-for-Spectrum-Sharing-A-Survey"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,6]]},"references-count":326,"journal-issue":{"issue":"1-2","published-print":{"date-parts":[[2024,11,6]]}},"URL":"https:\/\/doi.org\/10.1561\/1300000073","relation":{},"ISSN":["1554-057X","1554-0588"],"issn-type":[{"value":"1554-057X","type":"print"},{"value":"1554-0588","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,11,6]]}}}