{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T00:46:57Z","timestamp":1760230017489,"version":"build-2065373602"},"reference-count":36,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2022,7,2]],"date-time":"2022-07-02T00:00:00Z","timestamp":1656720000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003725","name":"the National Research Foundation of Korea (NRF)","doi-asserted-by":"publisher","award":["2021R1A2C2014333"],"award-info":[{"award-number":["2021R1A2C2014333"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The spectrum allocation in any auctioned wireless service primarily depends upon the necessity and the usage of licensed primary users (PUs) of a certain band of frequencies. These frequencies are utilized by the PUs as per their needs and requirements. When the allocated spectrum is not being utilized in the full efficient manner, the unused spectrum is treated by the PUs as white space without believing much in the concept of spectrum scarcity. There are techniques invented and incorporated by many researchers, such as cognitive radio technology, which involves software-defined radio with reconfigurable antennas tuned to particular frequencies at different times. Cognitive radio (CR) technology realizes the logic of the utility factor of the PUs and the requirements of the secondary users (SU) who are in queue to utilize the unused spectrum, which is the white space. The CR technology is enriched with different frequency allocation engines and with different strategies in different parts of the world, complying with the regulatory standards of the FCC and ITU. Based on the frequency allocation made globally, the existing CR technology understands the nuances of static and dynamic spectrum allocation and also embraces the intelligence in time allocation by scheduling the SUs whenever the PUs are not using the spectrum, and when the PUs pitch in the SUs have to leave the band without time. This paper identifies a few of the research gaps existing in the earlier literature. The behavioral aspects of the PUs and SUs have been analyzed for a period of 90 days with some specific spectrum ranges of usage in India. The communal habits of utilizing the spectrum, not utilizing the spectrum as white space, different time zones, the requisites of the SUs, the necessity of the applications, and the improvement of the utility factor of the entire spectrum have been considered along with static and dynamic spectrum usage, the development of the spectrum policy engine aligned with cooperative and opportunistic spectrum sensing, and access techniques indulging in artificial intelligence (AI). This will lead to fine-tuning the PU and SU channel mapping without being hindered by predefined policies. We identify the cognitive radio transmitter and receiver parameters, and resort to the same in a proposed channel adaption algorithm. We also analyze the white spaces offered by spectrum ranges of VHF, GSM-900, and GSM-1800 by a real-time survey with a spectrum analyzer. The identified parameters and white spaces are mapped with the help of a swotting algorithm. A sample policy has been stated for ISM band 2.4 GHz where such policies can be excited in a policy server. The policy engine is suggested to be configured over the 5G CORE spectrum management function.<\/jats:p>","DOI":"10.3390\/s22135011","type":"journal-article","created":{"date-parts":[[2022,7,4]],"date-time":"2022-07-04T23:38:55Z","timestamp":1656977935000},"page":"5011","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Elite-CAM: An Elite Channel Allocation and Mapping for Policy Engine over Cognitive Radio Technology in 5G"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5059-5247","authenticated-orcid":false,"given":"C. Rajesh","family":"Babu","sequence":"first","affiliation":[{"name":"Department of CSE, SRM Institute of Science and Technology, Chennai 603203, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9875-4852","authenticated-orcid":false,"given":"Amutha","family":"Balakrishnan","sequence":"additional","affiliation":[{"name":"Department of CSE, SRM Institute of Science and Technology, Chennai 603203, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4604-846X","authenticated-orcid":false,"given":"Kadiyala","family":"Ramana","sequence":"additional","affiliation":[{"name":"Department of IT, Chaitanya Bharathi Institute Technology, Hyderabad 500075, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1118-9569","authenticated-orcid":false,"given":"Saurabh","family":"Singh","sequence":"additional","affiliation":[{"name":"Department of Industrial and System Engineering, Dongguk University, Seoul 04620, Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3936-1116","authenticated-orcid":false,"given":"In-Ho","family":"Ra","sequence":"additional","affiliation":[{"name":"School of Computer, Information and Communication Engineering, Kunsan National University, Gunsan 54150, Korea"}]}],"member":"1968","published-online":{"date-parts":[[2022,7,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"101469","DOI":"10.1016\/j.phycom.2021.101469","article-title":"Clustering and power optimization in mmWave massive MIMO\u2013NOMA systems","volume":"49","author":"Wang","year":"2021","journal-title":"Phys. Commun."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"9472075","DOI":"10.1155\/2018\/9472075","article-title":"An enhanced PEGASIS algorithm with mobile sink support for wireless sensor networks","volume":"2018","author":"Wang","year":"2018","journal-title":"Wirel. Commun. Mob. Comput."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"14460","DOI":"10.1109\/ACCESS.2020.2966271","article-title":"5G Technology: Towards Dynamic Spectrum Sharing Using Cognitive Radio Networks","volume":"8","author":"Ahmad","year":"2020","journal-title":"IEEE Access"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Do, D.T., Le, A.T., Hoang, T.A., and Lee, B.M. (2020). Cognitive radio-assisted NOMA broadcasting for 5G cellular V2X communications: Model of roadside unit selection and SWIPT. Sensors, 20.","DOI":"10.3390\/s20061786"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"2405141","DOI":"10.1155\/2019\/2405141","article-title":"SenPUI: Solutions for sensing and primary user interference in cognitive radio implementation of a wireless sensor network","volume":"2019","author":"Besher","year":"2019","journal-title":"Wirel. Commun. Mob. Comput."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"7367028","DOI":"10.1155\/2019\/7367028","article-title":"Discrete-Time Analysis of Cognitive Radio Networks with Nonsaturated Source of Secondary Users","volume":"2019","author":"Pla","year":"2019","journal-title":"Wirel. Commun. Mob. Comput."},{"key":"ref_7","first-page":"252","article-title":"Cooperative spectrum sensing in cognitive radio using Bayesian updating with multiple observations","volume":"17","author":"Huang","year":"2019","journal-title":"J. Electron. Sci. Technol."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"70811","DOI":"10.1109\/ACCESS.2019.2918380","article-title":"Novel QoS-Aware Proactive Spectrum Access Techniques for Cognitive Radio Using Machine Learning","volume":"7","author":"Ozturk","year":"2019","journal-title":"IEEE Access"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Wasilewska, M., and Bogucka, H. (2019). Machine learning for LTE energy detection performance improvement. Sensors, 19.","DOI":"10.3390\/s19194348"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"110884","DOI":"10.1109\/ACCESS.2019.2932016","article-title":"Deep Reinforcement Learning Based Intelligent User Selection in Massive MIMO Underlay Cognitive Radios","volume":"7","author":"Shi","year":"2019","journal-title":"IEEE Access"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"544","DOI":"10.1109\/LWC.2016.2600576","article-title":"A Markov Decision Process-Based Opportunistic Spectral Access","volume":"5","author":"Arunthavanathan","year":"2016","journal-title":"IEEE Wirel. Commun. Lett."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"564","DOI":"10.1080\/00051144.2019.1674512","article-title":"Priority-based reserved spectrum allocation by multi-agent through reinforcement learning in cognitive radio network","volume":"60","author":"Jaishanthi","year":"2019","journal-title":"Automatika"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Jamoos, A., and Abdou, A. (2019, January 16\u201317). Spectrum Measurements and Analysis for Cognitive Radio Applications in Palestine. Proceedings of the 2019 6th International Conference on Electrical and Electronics Engineering (ICEEE), Istanbul, Turkey.","DOI":"10.1109\/ICEEE2019.2019.00042"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1109\/MWC.01.1900525","article-title":"A Secure Federated Learning Framework for 5G Networks","volume":"27","author":"Liu","year":"2020","journal-title":"IEEE Wirel. Commun."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Wang, W., Lv, T., Wang, T., and Yu, X. (2010, January 6\u20139). Primary user activity based channel allocation in cognitive radio networks. Proceedings of the IEEE Vehicular Technology Conference, Ottawa, ON, Canada.","DOI":"10.1109\/VETECF.2010.5594260"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"212","DOI":"10.1109\/MWC.001.1900323","article-title":"Artificial Intelligence-Enabled Cellular Networks: A Critical Path to Beyond-5G and 6G","volume":"27","author":"Shafin","year":"2020","journal-title":"IEEE Wirel. Commun."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Zhou, H., Elsayed, M., and Erol-Kantarci, M. (2021, January 13\u201316). RAN Resource Slicing in 5G Using Multi-Agent Correlated Q-Learning. Proceedings of the IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC, Helsinki, Finland.","DOI":"10.1109\/PIMRC50174.2021.9569358"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1186\/s13638-019-1433-1","article-title":"Reinforcement learning-based dynamic band and channel selection in cognitive radio ad-hoc networks","volume":"2019","author":"Jang","year":"2019","journal-title":"J. Wirel. Commun. Netw."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1109\/98.788210","article-title":"Cognitive radio: Making software radios more personal","volume":"6","author":"Mitola","year":"1999","journal-title":"IEEE Pers. Commun."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"201","DOI":"10.1109\/JSAC.2004.839380","article-title":"Cognitive radio: Brain-empowered wireless communications","volume":"23","author":"Haykin","year":"2005","journal-title":"IEEE J. Sel. Areas Commun."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1258","DOI":"10.1109\/TMC.2016.2592917","article-title":"Channel Selection Algorithm for Cognitive Radio Networks with Heavy-Tailed Idle Times","volume":"16","author":"Sengottuvelan","year":"2017","journal-title":"IEEE Trans. Mob. Comput."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1178","DOI":"10.1007\/s12083-019-00786-4","article-title":"Blockchain Meets VANET: An Architecture for Identity and Location Privacy Protection in VANET","volume":"12","author":"Li","year":"2019","journal-title":"Peer-Peer Netw. Appl."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Jiang, H., Wang, T., and Wang, S. (2019, January 20\u201324). Multi-Agent Reinforcement Learning for Dynamic Spectrum Access. Proceedings of the 2019 IEEE International Conference on Communications (ICC), Shanghai, China.","DOI":"10.1109\/ICC.2019.8761786"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Rajesh Babu, C., and Amutha, B. (2020). Blockchain and extreme learning machine based spectrum management in cognitive radio networks. Trans. Emerg. Telecommun. Technol., e4174.","DOI":"10.1002\/ett.4174"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"2452","DOI":"10.1007\/s12083-020-00990-7","article-title":"A novel energy-efficient data aggregation protocol for cognitive radio based wireless multimedia networks","volume":"14","author":"Babu","year":"2021","journal-title":"Peer-Peer Netw. Appl."},{"key":"ref_26","first-page":"2278","article-title":"Performance Analysis and Comparison of Different Modulation Schemes with Channel Estimation Methods for MIMO-OFDM System","volume":"8","author":"Jeya","year":"2019","journal-title":"Int. J. Innov. Technol. Explor. Eng."},{"key":"ref_27","first-page":"7692630","article-title":"Intelligent Process of Spectrum Handoff\/Mobility in Cognitive Radio Networks","volume":"2019","author":"Yawada","year":"2019","journal-title":"J. Electr. Comput. Eng."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Long, Q., Chen, Y., Zhang, H., and Lei, X. (2019). Software Defined 5G and 6G Networks: A Survey. Mob. Netw. Appl., 1\u201321.","DOI":"10.1007\/s11036-019-01397-2"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"5710834","DOI":"10.1155\/2019\/5710834","article-title":"Practical Aspects for the Integration of 5G Networks and IoT Applications in Smart Cities Environments","volume":"2019","author":"Minoli","year":"2019","journal-title":"Wirel. Commun. Mob. Comput."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Shakeel, A., Hussain, R., Iqbal, A., Khan, I.L., Hasan, Q.U., and Malik, S.A. (2019). Spectrum handoff based on imperfect channel state prediction probabilities with collision reduction in cognitive radio ad hoc networks. Sensors, 19.","DOI":"10.3390\/s19214741"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"101287","DOI":"10.1016\/j.phycom.2021.101287","article-title":"Equivalent channel-based joint hybrid precoding\/combining for large-scale MIMO systems","volume":"47","author":"Wang","year":"2021","journal-title":"Phys. Commun."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1186\/s13673-020-00258-2","article-title":"The shift to 6G communications: Vision and requirements","volume":"10","author":"Akhtar","year":"2020","journal-title":"Hum. Cent. Comput. Inf. Sci."},{"key":"ref_33","first-page":"3","article-title":"A comprehensive survey on core technologies and services for 5G security: Taxonomies, issues, and solutions","volume":"11","author":"Park","year":"2021","journal-title":"Hum. Cent. Comput. Inf. Sci."},{"key":"ref_34","first-page":"1","article-title":"Research on Emotion Simulation Method of Large-Scale Crowd Evacuation under Particle Model","volume":"11","author":"Mei","year":"2021","journal-title":"Hum. Cent. Comput. Inf. Sci."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"2507","DOI":"10.1109\/JSYST.2019.2910409","article-title":"Interference-aware multisource transmission in multiradio and multichannel wireless network","volume":"13","author":"He","year":"2019","journal-title":"IEEE Syst. J."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1186\/s13638-015-0503-2","article-title":"An optimal data service providing framework in cloud radio access network","volume":"2016","author":"Luo","year":"2016","journal-title":"EURASIP J. Wirel. Commun. Netw."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/13\/5011\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:42:11Z","timestamp":1760139731000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/13\/5011"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,7,2]]},"references-count":36,"journal-issue":{"issue":"13","published-online":{"date-parts":[[2022,7]]}},"alternative-id":["s22135011"],"URL":"https:\/\/doi.org\/10.3390\/s22135011","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2022,7,2]]}}}