{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T11:55:06Z","timestamp":1783425306197,"version":"3.54.6"},"reference-count":59,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2023,7,17]],"date-time":"2023-07-17T00:00:00Z","timestamp":1689552000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Universiti Tunku Abdul Rahman (UTAR)","award":["IPSR\/RMC\/UTARRF\/2021C1\/T05"],"award-info":[{"award-number":["IPSR\/RMC\/UTARRF\/2021C1\/T05"]}]},{"name":"ASEAN IVO","award":["IPSR\/RMC\/UTARRF\/2021C1\/T05"],"award-info":[{"award-number":["IPSR\/RMC\/UTARRF\/2021C1\/T05"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Natural disasters, including earthquakes, floods, landslides, tsunamis, wildfires, and hurricanes, have become more common in recent years due to rapid climate change. For Post-Disaster Management (PDM), authorities deploy various types of user equipment (UE) for the search and rescue operation, for example, search and rescue robots, drones, medical robots, smartphones, etc., via the Internet of Robotic Things (IoRT) supported by cellular 4G\/LTE\/5G and beyond or other wireless technologies. For uninterrupted communication services, movable and deployable resource units (MDRUs) have been utilized where the base stations are damaged due to the disaster. In addition, power optimization of the networks by satisfying the quality of service (QoS) of each UE is a crucial challenge because of the electricity crisis after the disaster. In order to optimize the energy efficiency, UE throughput, and serving cell (SC) throughput by considering the stationary as well as movable UE without knowing the environmental priori knowledge in MDRUs aided two-tier heterogeneous networks (HetsNets) of IoRT, the optimization problem has been formulated based on emitting power allocation and user association combinedly in this article. This optimization problem is nonconvex and NP-hard where parameterized (discrete: user association and continuous: power allocation) action space is deployed. The new model-free hybrid action space-based algorithm called multi-pass deep Q network (MP-DQN) is developed to optimize this complex problem. Simulations results demonstrate that the proposed MP-DQN outperforms the parameterized deep Q network (P-DQN) approach, which is well known for solving parameterized action space, DQN, as well as traditional algorithms in terms of reward, average energy efficiency, UE throughput, and SC throughput for motionless as well as moveable UE.<\/jats:p>","DOI":"10.3390\/s23146448","type":"journal-article","created":{"date-parts":[[2023,7,17]],"date-time":"2023-07-17T01:06:36Z","timestamp":1689555996000},"page":"6448","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Mobility-Aware Resource Allocation in IoRT Network for Post-Disaster Communications with Parameterized Reinforcement Learning"],"prefix":"10.3390","volume":"23","author":[{"given":"Homayun","family":"Kabir","sequence":"first","affiliation":[{"name":"Department of Electrical and Electronic Engineering, Lee Kong Chian Faculty of Engineering and Science, Universiti Tunku Abdul Rahman, Sungai Long Campus, Kajang 43000, Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4600-9839","authenticated-orcid":false,"given":"Mau-Luen","family":"Tham","sequence":"additional","affiliation":[{"name":"Department of Electrical and Electronic Engineering, Lee Kong Chian Faculty of Engineering and Science, Universiti Tunku Abdul Rahman, Sungai Long Campus, Kajang 43000, Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yoong Choon","family":"Chang","sequence":"additional","affiliation":[{"name":"Department of Electrical and Electronic Engineering, Lee Kong Chian Faculty of Engineering and Science, Universiti Tunku Abdul Rahman, Sungai Long Campus, Kajang 43000, Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6044-2650","authenticated-orcid":false,"given":"Chee-Onn","family":"Chow","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, Faculty of Engineering, Universiti Malaya, Lembah Pantai, Kuala Lumpur 50603, Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yasunori","family":"Owada","sequence":"additional","affiliation":[{"name":"Resilient ICT Research Center, Network Research Institute, National Institute of Information and Communications Technology (NICT), Tokyo 184-8795, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,7,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"208","DOI":"10.1097\/DMP.0b013e31818adaa2","article-title":"Measuring the true human cost of natural disasters","volume":"2","author":"Lawry","year":"2008","journal-title":"Disaster Med. Public Health Prep."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Shimada, G. (2022). The impact of climate-change-related disasters on Africa\u2019s economic growth, agriculture, and conflicts: Can hu-manitarian aid and food assistance offset the damage?. Int. J. Environ. Res. Public Health, 19.","DOI":"10.20944\/preprints202201.0044.v1"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"132","DOI":"10.1080\/01691864.2019.1691941","article-title":"Development of a separable search-and-rescue robot composed of a mobile robot and a snake robot","volume":"34","author":"Kamegawa","year":"2020","journal-title":"Adv. Robot."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Vera-Ortega, P., V\u00e1zquez-Mart\u00edn, R., Fernandez-Lozano, J.J., Garc\u00eda-Cerezo, A., and Mandow, A. (2023). Enabling Remote Responder Bio-Signal Monitoring in a Cooperative Human\u2013Robot Architecture for Search and Rescue. Sensors, 23.","DOI":"10.3390\/s23010049"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Paravisi, M., Santos, D.H., Jorge, V., Heck, G., Gon\u00e7alves, L.M., and Amory, A. (2019). Unmanned Surface Vehicle Simulator with Realistic Environmental Disturbances. Sensors, 19.","DOI":"10.3390\/s19051068"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"AlAli, Z.T., and Alabady, S.A. (2022). A survey of disaster management and SAR operations using sensors and supporting techniques. Int. J. Disaster Risk Reduct., 82.","DOI":"10.1016\/j.ijdrr.2022.103295"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Lee, M.-F.R., and Chien, T.-W. (2020, January 19\u201321). Artificial intelligence and Internet of Things for robotic disaster response. Proceedings of the 2020 International Conference on Advanced Robotics and Intelligent Systems (ARIS), Taipei, Taiwan.","DOI":"10.1109\/ARIS50834.2020.9205794"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"491","DOI":"10.3233\/AIS-200582","article-title":"The penetration of Internet of Things in robotics: Towards a web of robotic things","volume":"12","author":"Kamilaris","year":"2020","journal-title":"J. Ambient. Intell. Smart Environ."},{"key":"ref_9","unstructured":"Villa, D., Song, X., Heim, M., and Li, L. (2021). Internet of Robotic Things: Current Technologies, Applications, Challenges and Future Directions. arXiv."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"9489","DOI":"10.1109\/ACCESS.2017.2647747","article-title":"Internet of robotic things: Concept, technologies, and challenges","volume":"4","author":"Ray","year":"2016","journal-title":"IEEE Access"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Vermesan, O., Bahr, R., Ottella, M., Serrano, M., Karlsen, T., Wahlstr\u00f8m, T., Sand, H.E., Ashwathnarayan, M., and Gamba, M.T. (2020). Internet of Robotic Things Intelligent Connectivity and Platforms. Front. Robot. AI, 7.","DOI":"10.3389\/frobt.2020.00104"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Rengaraju, P., Sethuramalingam, K., and Lung, C.H. (2021, January 22\u201326). Providing internet access for post-disaster communications using balloon networks. Proceedings of the 18th ACM Symposium on Performance Evaluation of Wireless Ad Hoc, Sensor & Ubiquitous Networks, Alicante, Spain.","DOI":"10.1145\/3479240.3488497"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"102985","DOI":"10.1109\/ACCESS.2019.2931539","article-title":"Design and Deployment of UAV-Aided Post-Disaster Emergency Network","volume":"7","author":"Panda","year":"2019","journal-title":"IEEE Access"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1109\/MNET.2013.6574664","article-title":"Disaster-resilient networking: A new vision based on movable and deployable resource units","volume":"27","author":"Sakano","year":"2013","journal-title":"IEEE Netw."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1109\/MCOM.2016.7470933","article-title":"Wireless communications with unmanned aerial vehicles: Opportunities and challenges","volume":"54","author":"Zeng","year":"2016","journal-title":"IEEE Commun. Mag."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1109\/MNET.2016.7389836","article-title":"Bringing movable and deployable networks to disaster areas: Development and field test of MDRU","volume":"30","author":"Sakano","year":"2016","journal-title":"IEEE Netw."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1177","DOI":"10.1109\/OJCOMS.2022.3192040","article-title":"Post-Disaster Communications: Enabling Technologies, Architectures, and Open Challenges","volume":"3","author":"Matracia","year":"2022","journal-title":"IEEE Open J. Commun. Soc."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"3833","DOI":"10.1109\/TVT.2018.2889094","article-title":"Big Data Processing With Minimal Delay and Guaranteed Data Resolution in Disaster Areas","volume":"68","author":"Wang","year":"2018","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Porte, J., Briones, A., Maso, J.M., Pares, C., Zaballos, A., and Pijoan, J.L. (2020). Heterogeneous wireless IoT architecture for natural disaster monitorization. EURASIP J. Wirel. Commun. Netw., 2020.","DOI":"10.1186\/s13638-020-01793-3"},{"key":"ref_20","unstructured":"Wang, Y. (2019). Models and Algorithms for Efficient Data Processing in Fog Computing Supported Disaster Areas. [Ph.D. Dissertation, University of Aizu]."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1109\/MNET.011.1900389","article-title":"Intelligent Post-Disaster Networking by Exploiting Crowd Big Data","volume":"34","author":"Wang","year":"2020","journal-title":"IEEE Netw."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1109\/MNET.2016.7437031","article-title":"A tutorial on the internet of things: From a heterogeneous network integration perspective","volume":"30","author":"Xu","year":"2016","journal-title":"IEEE Netw."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"19","DOI":"10.24138\/jcomss-2022-0141","article-title":"Twin Delayed DDPG based Dynamic Power Allocation for Mobility in IoRT","volume":"19","author":"Kabir","year":"2023","journal-title":"J. Commun. Softw. Syst."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Kabir, H., Tham, M.-L., and Chang, Y.C. (2022, January 17\u201318). DRL based Energy-Efficient Radio Resource Allocation Algorithm in Internet of Robotic Things. Proceedings of the 2022 IEEE Symposium on Wireless Technology & Applications (ISWTA), Kuala Lumpur, Malaysia.","DOI":"10.1109\/ISWTA55313.2022.9942743"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Nguyen, H.T., Nguyen, M.T., Do, H.T., Hua, H.T., and Nguyen, C.V. (2021). DRL-based intelligent resource allo-cation for diverse QoS in 5G and toward 6G vehicular networks: A comprehensive survey. Wirel. Commun. Mob. Comput., 2021.","DOI":"10.1155\/2021\/5051328"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Abbasi, M., Shahraki, A., Piran, J., and Taherkordi, A. (2021). Deep Reinforcement Learning for QoS provisioning at the MAC layer: A Survey. Eng. Appl. Artif. Intell., 102.","DOI":"10.1016\/j.engappai.2021.104234"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1109\/MVT.2019.2903655","article-title":"Deep reinforcement learning for mobile 5G and beyond: Fundamentals, applications, and challenges","volume":"14","author":"Xiong","year":"2019","journal-title":"IEEE Veh. Technol. Mag."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Zhu, J., Wu, F., and Zhao, J. (2021, January 22\u201324). An Overview of the Action Space for Deep Reinforcement Learning. Proceedings of the 2021 4th International Conference on Algorithms, Computing and Artificial Intelligence, Sanya, China.","DOI":"10.1145\/3508546.3508598"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Hsieh, C.-K., Chan, K.-L., and Chien, F.-T. (2021). Energy-Efficient Power Allocation and User Association in Heterogeneous Networks with Deep Reinforcement Learning. Appl. Sci., 11.","DOI":"10.3390\/app11094135"},{"key":"ref_30","unstructured":"Bester, C.J., James, S.D., and Konidaris, G.D. (2019). Multi-pass Q-networks for deep reinforcement learning with parameterised action spaces. arXiv."},{"key":"ref_31","unstructured":"Omstedt, F. (2020). Degree Project in Computer Science and Engineering, Kth Royal Institute of Technology."},{"key":"ref_32","first-page":"1645","article-title":"Building decision tree for imbalanced classification via deep reinforcement learning","volume":"157","author":"Wen","year":"2021","journal-title":"Proc. Mach. Learn. Res."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Bouktif, S., Cheniki, A., and Ouni, A. (2021). Traffic Signal Control Using Hybrid Action Space Deep Reinforcement Learning. Sensors, 21.","DOI":"10.3390\/s21072302"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Zhang, X., Jin, S., Wang, C., Zhu, X., and Tomizuka, M. (2022, January 23\u201327). Learning insertion primitives with discrete-continuous hybrid action space for robotic assembly tasks. Proceedings of the 2022 International Conference on Robotics and Automation (ICRA), Philadelphia, PA, USA.","DOI":"10.1109\/ICRA46639.2022.9811973"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Yan, Y., Du, K., Wang, L., Niu, H., and Wen, X. (2022, January 16\u201320). MP-DQN Based Task Scheduling for RAN QoS Fluctuation Minimizing in Public Clouds. Proceedings of the 2022 IEEE International Conference on Communications Workshops, ICC Workshops, Seoul, Republic of Korea.","DOI":"10.1109\/ICCWorkshops53468.2022.9814668"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Guo, L., Jia, J., Chen, J., Du, A., and Wang, X. (2022, January 26\u201329). Joint Task Offloading and Resource Allocation in STAR-RIS assisted NOMA System. Proceedings of the 2022 IEEE 96th Vehicular Technology Conference, VTC2022-Fall, London, UK.","DOI":"10.1109\/VTC2022-Fall57202.2022.10013059"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"363","DOI":"10.20965\/jdr.2019.p0363","article-title":"Development of Movable and Deployable ICT Resource Unit (MDRU) and its Overseas Activities","volume":"14","author":"Shimizu","year":"2019","journal-title":"J. Disaster Res."},{"key":"ref_38","first-page":"1725","article-title":"EA-RDSP: Energy Aware Rapidly Deployable Wireless Ad hoc System for Post Disaster Management","volume":"69","author":"Khan","year":"2021","journal-title":"Comput. Mater. Contin."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Zhou, H., Wang, X., Umehira, M., Chen, X., Wu, C., and Ji, Y. (2020, January 7\u201311). Deep reinforcement learning based access control for disaster response networks. Proceedings of the GLOBECOM 2020\u20142020 IEEE Global Communications Conference, Taipei, Taiwan.","DOI":"10.1109\/GLOBECOM42002.2020.9322553"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"2027","DOI":"10.1109\/TVT.2014.2314462","article-title":"A Spectrum- and Energy-Efficient Scheme for Improving the Utilization of MDRU-Based Disaster Resilient Networks","volume":"63","author":"Ngo","year":"2014","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"10009","DOI":"10.1109\/JIOT.2019.2935105","article-title":"Deployment algorithms of flying base stations: 5G and beyond with UAVs","volume":"6","author":"Wang","year":"2019","journal-title":"IEEE Internet Things J."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Xu, Z., Wang, Y., Tang, J., Wang, J., and Gursoy, M.C. (2017, January 21\u201325). A deep reinforcement learning based framework for power-efficient resource allocation in cloud RANs. Proceedings of the 2017 IEEE International Conference on Communications (ICC), Paris, France.","DOI":"10.1109\/ICC.2017.7997286"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Zhao, N., Liang, Y.-C., Niyato, D., Pei, Y., and Jiang, Y. (2018, January 9\u201313). Deep reinforcement learning for user association and resource allocation in heterogeneous networks. Proceedings of the 2018 IEEE Global Communications Conference (GLOBECOM), Abu Dhabi, United Arab Emirates.","DOI":"10.1109\/GLOCOM.2018.8647611"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"5141","DOI":"10.1109\/TWC.2019.2933417","article-title":"Deep Reinforcement Learning for User Association and Resource Allocation in Heterogeneous Cellular Networks","volume":"18","author":"Zhao","year":"2019","journal-title":"IEEE Trans. Wirel. Commun."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Li, Z., Wen, X., Lu, Z., and Jing, W. (2021, January 13\u201316). A General DRL-based Optimization Framework of User Association and Power Control for HetNet. Proceedings of the 2021 IEEE 32nd Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC), Helsinki, Finland.","DOI":"10.1109\/PIMRC50174.2021.9569426"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Li, Z., Wen, X., Lu, Z., and Jing, W. (2022, January 16\u201320). A DDPG-based Transfer Learning Optimization Framework form User Association and Power Control in HetNet. Proceedings of the 2022 IEEE International Conference on Communications Workshops (ICC 750 Workshops), Seoul, Republic of Korea.","DOI":"10.1109\/ICCWorkshops53468.2022.9814693"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Narottama, B., and Shin, S.Y. (2019, January 17\u201319). Dynamic power allocation for non-orthogonal multiple access with user mobility. Proceedings of the 2019 IEEE 10th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON), Vancouver, BC, Canada.","DOI":"10.1109\/IEMCON.2019.8936269"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Masaracchia, A., Nguyen, V.-L., and Nguyen, M. (2020). The impact of user mobility into non-orthogonal multiple access (NOMA) transmission systems. EAI Endorsed Trans. Ind. Netw. Intell. Syst., 7.","DOI":"10.4108\/eai.21-10-2020.166669"},{"key":"ref_49","unstructured":"Neely, M.J., Modiano, E., and Rohrs, C.E. (April, January 30). Dynamic power allocation and routing for time varying wireless networks. Proceedings of the IEEE INFOCOM 2003. Twenty-Second Annual Joint Conference of the IEEE Computer and Communications Societies 756 (IEEE Cat. No. 03CH37428), San Francisco, CA, USA."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Wang, Y., Meyer, M.C., and Wang, J. (2019, January 5\u20138). Base Station Allocation for Users with Overlapping Coverage in Wirelessly Networked Disaster Areas. Proceedings of the 2019 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC\/PiCom\/CBDCom\/CyberSciTech), Fukuoka, Japan.","DOI":"10.1109\/DASC\/PiCom\/CBDCom\/CyberSciTech.2019.00175"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"820","DOI":"10.1109\/TVT.2021.3127367","article-title":"A Learning Aided Long-Term User Association Scheme for Ultra-Dense Networks","volume":"71","author":"Zhang","year":"2021","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"46600","DOI":"10.1109\/ACCESS.2021.3067662","article-title":"Wireless Access Control in Edge-Aided Disaster Response: A Deep Reinforcement Learning-Based Approach","volume":"9","author":"Zhou","year":"2021","journal-title":"IEEE Access"},{"key":"ref_53","first-page":"147","article-title":"A survey of mobility models","volume":"Volume 206","author":"Bai","year":"2004","journal-title":"Wireless Ad hoc Networks"},{"key":"ref_54","unstructured":"Hausknecht, M., and Stone, P. (2015). Deep reinforcement learning in parameterized action space. arXiv."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Masson, W., Ranchod, P., and Konidaris, G. (2016, January 12\u201317). Reinforcement learning with parameterized actions. Proceedings of the AAAI Conference on Artificial Intelligence, Phoenix, AZ, USA. No. 1.","DOI":"10.1609\/aaai.v30i1.10226"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"219","DOI":"10.1561\/2200000071","article-title":"An introduction to deep renforcement learning","volume":"11","author":"Henderson","year":"2018","journal-title":"Found. Trends\u00ae Mach. Learn."},{"key":"ref_57","unstructured":"Xiong, J., Wang, Q., Yang, Z., Sun, P., Han, L., Zheng, Y., Fu, H., Zhang, T., Liu, J., and Liu, H. (2018). Parametrized deep Q-networks learning: Reinforcement learning with discrete-continuous hybrid action space. arXiv."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"3251","DOI":"10.1109\/TWC.2016.2519401","article-title":"Joint Downlink Cell Association and Bandwidth Allocation for Wireless Backhauling in Two-Tier HetNets with Large-Scale Antenna Arrays","volume":"15","author":"Wang","year":"2016","journal-title":"IEEE Trans. Wirel. Commun."},{"key":"ref_59","unstructured":"3rd Generation Partnership Project (3GPP) (2016). Further Advancements for E-UTRA Physical Layer Aspects (Release 9), 3rd Generation Partnership Project (3GPP)."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/14\/6448\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:13:05Z","timestamp":1760127185000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/14\/6448"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,7,17]]},"references-count":59,"journal-issue":{"issue":"14","published-online":{"date-parts":[[2023,7]]}},"alternative-id":["s23146448"],"URL":"https:\/\/doi.org\/10.3390\/s23146448","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,7,17]]}}}