{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T11:27:44Z","timestamp":1780054064820,"version":"3.54.0"},"reference-count":27,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2022,2,17]],"date-time":"2022-02-17T00:00:00Z","timestamp":1645056000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Laboratory Open Fund of Beijing Smart-Chip Microelectronics Technology Co., Ltd."}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Ultra-reliable and low-latency communication (URLLC) is considered as one of the major use cases in 5G networks to support the emerging mission-critical applications. One of the possible tools to achieve URLLC is the device-to-device (D2D) network. Due to the physical proximity of communicating devices, D2D networks can significantly improve the latency and reliability performance of wireless communication. However, the resource management of D2D networks is usually a non-convex combinatorial problem that is difficult to solve. Traditional methods usually optimize the resource allocation in an iterative way, which leads to high computational complexity. In this paper, we investigate the resource allocation problem in the time-sensitive D2D network where the latency and reliability performance is modeled by the achievable rate in the short blocklength regime. We first design a game theory-based algorithm as the baseline. Then, we propose a deep learning (DL)-based resource management framework using deep neural network (DNN). The simulation results show that the proposed DL-based method achieves almost the same performance as the baseline algorithm, while it is more time-efficient due to the end-to-end structure.<\/jats:p>","DOI":"10.3390\/s22041551","type":"journal-article","created":{"date-parts":[[2022,2,17]],"date-time":"2022-02-17T20:26:41Z","timestamp":1645129601000},"page":"1551","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Deep-Learning-Based Resource Allocation for Time-Sensitive Device-to-Device Networks"],"prefix":"10.3390","volume":"22","author":[{"given":"Zhe","family":"Zheng","sequence":"first","affiliation":[{"name":"State Grid Key Laboratory of Power Industrial Chip Design and Analysis Technology, Beijing Smart-Chip Microelectronics Technology Co., Ltd., Beijing 102299, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yingying","family":"Chi","sequence":"additional","affiliation":[{"name":"State Grid Key Laboratory of Power Industrial Chip Design and Analysis Technology, Beijing Smart-Chip Microelectronics Technology Co., Ltd., Beijing 102299, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guangyao","family":"Ding","sequence":"additional","affiliation":[{"name":"College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou 310058, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7296-1490","authenticated-orcid":false,"given":"Guanding","family":"Yu","sequence":"additional","affiliation":[{"name":"College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou 310058, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,2,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Zhang, S., Xu, X., Wu, Y., and Lu, L. (2014, January 19\u201321). 5G: Towards energy-efficient, low-latency and high-reliable communications networks. Proceedings of the 2014 IEEE International Conference on Communication Systems, Macau, China.","DOI":"10.1109\/ICCS.2014.7024793"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1109\/MCOM.2018.1701178","article-title":"Ultra-reliable low latency cellular networks: Use cases, challenges and approaches","volume":"56","author":"Chen","year":"2018","journal-title":"IEEE Commun. Mag."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"3098","DOI":"10.1109\/COMST.2018.2841349","article-title":"A survey on low latency towards 5G: RAN, core network and caching solutions","volume":"20","author":"Parvez","year":"2018","journal-title":"IEEE Commun. Surveys Tuts."},{"key":"ref_4","unstructured":"(2021). Service Requirements for Cyber-Physical Control Applications in Vertical Domains, Document TS 22.104, Version 18.2.0; 3rd Generation Partnership Project (3GPP)."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Soret, B., Mogensen, P., Pedersen, K.I., and Aguayo-Torres, M.C. (2014, January 8\u201312). Fundamental tradeoffs among reliability, latency and throughput in cellular networks. Proceedings of the 2014 IEEE Globecom Workshops (GC Wkshps), Austin, TX, USA.","DOI":"10.1109\/GLOCOMW.2014.7063628"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1109\/MCOM.2014.6815897","article-title":"Device-to-device communication in 5G cellular networks: Challenges, solutions, and future directions","volume":"52","author":"Tehrani","year":"2014","journal-title":"IEEE Commun. Mag."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1801","DOI":"10.1109\/COMST.2014.2319555","article-title":"A survey on device-to-device communication in cellular networks","volume":"16","author":"Asadi","year":"2014","journal-title":"IEEE Commun. Surveys Tuts."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"3814","DOI":"10.1109\/TCOMM.2014.2363092","article-title":"Joint mode selection and resource allocation for device-to-device communications","volume":"62","author":"Yu","year":"2014","journal-title":"IEEE Trans. Commun."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"6119","DOI":"10.1109\/TVT.2015.2472995","article-title":"Energy-efficient joint resource allocation and power control for D2D communications","volume":"65","author":"Jiang","year":"2016","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"6633","DOI":"10.1109\/TVT.2017.2675451","article-title":"Analytical modeling of resource allocation in D2D overlaying multihop multichannel uplink cellular networks","volume":"66","author":"Dai","year":"2017","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1109\/MWC.2017.1500385WC","article-title":"Mode selection, radio resource allocation, and power coordination in D2D communications","volume":"24","author":"Ma","year":"2017","journal-title":"IEEE Wireless Commun."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"5256","DOI":"10.1109\/TVT.2016.2615718","article-title":"Energy-efficient matching for resource allocation in D2D enabled cellular networks","volume":"66","author":"Zhou","year":"2017","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"8461","DOI":"10.1109\/TVT.2015.2511924","article-title":"A stackelberg game model for overlay D2D transmission with heterogeneous rate requirements","volume":"65","author":"Lyu","year":"2016","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"8995","DOI":"10.1109\/TVT.2019.2931625","article-title":"Opportunistic spectrum sharing for D2D-based URLLC","volume":"68","author":"Chu","year":"2019","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"9960","DOI":"10.1109\/TVT.2020.3003944","article-title":"Contention-based radio resource management for URLLC-oriented D2D communications","volume":"69","author":"Wu","year":"2020","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"212","DOI":"10.1109\/LCOMM.2021.3121515","article-title":"Joint Reservation and Contention-Based Access for URLLC-Enabled D2D Communications","volume":"26","author":"Wu","year":"2021","journal-title":"IEEE Commun. Lett."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"5045","DOI":"10.1109\/TWC.2018.2836937","article-title":"A D2D-Based Protocol for Ultra-Reliable Wireless Communications for Industrial Automation","volume":"17","author":"Liu","year":"2018","journal-title":"IEEE Trans. Wireless Commun."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"543","DOI":"10.1109\/TVT.2020.3046747","article-title":"Two-Timescale Resource Allocation for Cooperative D2D Communication: A Matching Game Approach","volume":"70","author":"Yuan","year":"2021","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"962","DOI":"10.1109\/LWC.2018.2843359","article-title":"Deep neural networks for linear sum assignment problems","volume":"7","author":"Lee","year":"2018","journal-title":"IEEE Wireless Commun. Lett."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Zappone, A., Sanguinetti, L., and Debbah, M. (2018, January 28\u201331). User association and load balancing for massive MIMO through deep learning. Proceedings of the 2018 52nd Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, CA, USA.","DOI":"10.1109\/ACSSC.2018.8645483"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Gao, J., Hu, M., Zhong, C., Li, G., and Zhang, Z. (2021). An attention-aided deep learning framework for massive MIMO channel estimation. IEEE Trans. Wireless Commun., early access.","DOI":"10.1109\/TWC.2021.3107452"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1109\/MWC.2019.1800601","article-title":"Deep learning in physical layer communications","volume":"26","author":"Qin","year":"2019","journal-title":"IEEE Wireless Commun."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1942","DOI":"10.1109\/LCOMM.2018.2859392","article-title":"Resource allocation for multi-channel underlay cognitive radio network based on deep neural network","volume":"22","author":"Lee","year":"2018","journal-title":"IEEE Commun. Lett."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1363","DOI":"10.1109\/TWC.2020.3032991","article-title":"Deep reinforcement learning for joint channel selection and power control in D2D networks","volume":"20","author":"Tan","year":"2021","journal-title":"IEEE Trans. Wireless Commun."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"2307","DOI":"10.1109\/TIT.2010.2043769","article-title":"Channel coding rate in the finite blocklength regime","volume":"56","author":"Polyanskiy","year":"2010","journal-title":"IEEE Trans. Inf. Theory"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Schiessl, S., Gross, J., and Al-Zubaidy, H. (2015, January 2). Delay analysis for wireless fading channels with finite blocklength channel coding. Proceedings of the 18th ACM International Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems, New York, NY, USA.","DOI":"10.1145\/2811587.2811596"},{"key":"ref_27","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 Netw."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/4\/1551\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:21:22Z","timestamp":1760134882000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/4\/1551"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,2,17]]},"references-count":27,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2022,2]]}},"alternative-id":["s22041551"],"URL":"https:\/\/doi.org\/10.3390\/s22041551","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,2,17]]}}}