文献类型：期刊文献

英文题名：Deep Reinforcement Learning-Based Resource Allocation for Integrated Sensing, Communication, and Computation in Vehicular Network

作者：Yang, Liu[1,2];Wei, Yifei[3];Feng, Zhiyong[4];Zhang, Qixun;Han, Zhu[5,6]

第一作者：Yang, Liu

通讯作者：Wei, YF[1]

机构：[1]Beijing Univ Posts & Telecommun, Sch Elect Engn, Beijing 100876, Peoples R China;[2]Beijing Union Univ, Coll Robot, Beijing Key Lab Informat Serv Engn, Beijing 100101, Peoples R China;[3]Beijing Univ Posts andTelecommunicat, Sch Elect Engn, Beijing Key Lab Work Safety Intelligent Monitoring, Beijing 100876, Peoples R China;[4]Beijing Univ Posts & Telecommun, Key Lab Universal Wireless Commun, Minist Educ, Beijing 100876, Peoples R China;[5]Univ Houston, Dept Elect & Comp Engn, Houston, TX 77004 USA;[6]Kyung Hee Univ, Dept Comp Sci & Engn, Seoul 446701, South Korea

第一机构：Beijing Univ Posts & Telecommun, Sch Elect Engn, Beijing 100876, Peoples R China

通讯机构：[1]corresponding author), Beijing Univ Posts andTelecommunicat, Sch Elect Engn, Beijing Key Lab Work Safety Intelligent Monitoring, Beijing 100876, Peoples R China.

年份：2024

卷号：23

期号：12

起止页码：18608-18622

外文期刊名：IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS

收录：;EI(收录号：20244317253500);WOS:【SCI-EXPANDED(收录号:WOS:001376971600026)】；

基金：This work was supported in part by the Fundamental Research Funds for Central Universities under Grant 24820232023YQTD01; in part by Beijing Municipal Natural Science Foundation under Grant L232003; in part by the National Natural Science Foundation of China (NSFC) under Grant 62341101 and Grant 62321001; in part by the National Key Research and Development Program of China under Grant 2022YFB4300403; in part by NSF under Grant CNS-2107216, Grant CNS-2128368, Grant CMMI-2222810, and Grant ECCS-2302469; and in part by the U.S. Department of Transportation, Toyota, Amazon and Japan Science and Technology Agency (JST) Adopting Sustainable Partnerships for Innovative Research Ecosystem (ASPIRE) under Grant JPMJAP2326.

语种：英文

外文关键词：Array signal processing; Resource management; Optimization; Robot sensing systems; Integrated sensing and communication; Wireless communication; Interference; Autonomous vehicles; 6G mobile communication; Federated learning; Integrated sensing; communication; and computation; beamforming; resource allocation; deep reinforcement learning

摘要：In developing the sixth-generation (6G) system, integrated sensing and communication technology is becoming increasingly essential, especially for applications like autonomous driving. This paper develops an architecture for integrated sensing, communication, and computation (ISCC) in the vehicular network, where vehicles perform environment sensing, sensing data computation, and transmission. To support low-latency cooperation between vehicles and extend vehicles' sensing range, over-air-computation federated learning is employed. The optimization problem of joint beamforming design and power resource allocation in the ISCC scenario is formulated to maximize the achievable data rate while ensuring sensing and computing performance. However, solving this joint optimization problem is a great challenge due to the high coupling resource and time-varying channel environment. Therefore, a hybrid reinforcement learning scheme is proposed in this work. First, the semidefinite relaxation and Gaussian randomization techniques are leveraged to obtain the approximate solution of the aggregation beamformer. Then, the deep deterministic policy gradient algorithm is proposed to tackle the transmit beamforming design and resource allocation problem in continuous action space. Extensive simulation results validated the admirable performance of the proposed scheme in convergence and achievable sum rate compared with the benchmark schemes. In addition, the impact of variables on the optimization performance is demonstrated via numerical results.