期刊
IEEE OPEN JOURNAL OF THE COMMUNICATIONS SOCIETY
卷 3, 期 -, 页码 1719-1736出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/OJCOMS.2022.3211340
关键词
Resource management; Device-to-device communication; Q-learning; Games; Interference; Learning systems; Uplink; Cellular vehicle-to-everything (C-V2X) communication; PD-NOMA; resource allocation; learning algorithm
This paper investigates a resource allocation problem in a C-V2X network to improve energy efficiency. Self-organizing mechanisms and a multi-agent Q-learning algorithm are proposed for joint and disjoint subcarrier and power allocation in a distributed manner. Simulation results show significant performance gains of the multi-agent joint Q-learning algorithm in terms of energy efficiency.
In this paper, we investigate a resource allocation problem for a Cellular Vehicle to Everything (C-V2X) network to improve energy efficiency of the system. To address this problem, self-organizing mechanisms are proposed for joint and disjoint subcarrier and power allocation procedures which are performed in a fully distributed manner. A multi-agent Q-learning algorithm is proposed for the joint power and subcarrier allocation. In addition, for the sake of simplicity, it is decoupled into two sub-problems: a subcarrier allocation sub-problem and a power allocation sub-problem. First, to allocate the subcarrier among users, a distributed Q-learning method is proposed. Then, given the optimal subcarriers, a dynamic power allocation mechanism is proposed where the problem is modeled as a non-cooperative game. To solve the problem, a no-regret learning algorithm is utilized. To evaluate the performance of the proposed approaches, other learning mechanisms are used which are presented in Fig. 8. Simulation results show the multi-agent joint Q-learning algorithm yields significant performance gains of up to about 11% and 18% in terms of energy efficiency compared to proposed disjoint mechanism and the third disjoint Q-learning mechanism for allocating the power and subcarrier to each user; however, the multi-agent joint Q-learning algorithm uses more memory than disjoint methods.
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