Resource Allocation in V2X Communications Based on Multi-Agent Reinforcement Learning with Attention Mechanism

Yuanfeng Ding, Yan Huang, Li Tang, Xizhong Qin () and Zhenhong Jia
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Yuanfeng Ding: College of Information Science and Engineering, Xinjiang University, Urumqi 830000, China
Yan Huang: Network Department, China Mobile Communications Group Xinjiang Co., Ltd., Urumqi 830000, China
Li Tang: Network Department, China Mobile Communications Group Xinjiang Co., Ltd., Urumqi 830000, China
Xizhong Qin: College of Information Science and Engineering, Xinjiang University, Urumqi 830000, China
Zhenhong Jia: College of Information Science and Engineering, Xinjiang University, Urumqi 830000, China

Mathematics, 2022, vol. 10, issue 19, 1-19

Abstract: In this paper, we study the joint optimization problem of the spectrum and power allocation for multiple vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) users in cellular vehicle-to-everything (C-V2X) communication, aiming to maximize the sum rate of V2I links while satisfying the low latency requirements of V2V links. However, channel state information (CSI) is hard to obtain accurately due to the mobility of vehicles. In addition, the effective sensing of state information among vehicles becomes difficult in an environment with complex and diverse information, which is detrimental to vehicles collaborating for resource allocation. Thus, we propose a framework of multi-agent deep reinforcement learning based on attention mechanism (AMARL) to improve the V2X communication performance. Specifically, for vehicle mobility, we model the problem as a multi-agent reinforcement learning process, where each V2V link is regarded an agent and all agents jointly intercommunicate with the environment. Each agent allocates spectrum and power through its deep Q network (DQN). To enhance effective intercommunication and the sense of collaboration among vehicles, we introduce an attention mechanism to focus on more relevant information, which in turn reduces the signaling overhead and optimizes their communication performance more explicitly. Experimental results show that the proposed AMARL-based approach can satisfy the requirements of a high rate for V2I links and low latency for V2V links. It also has an excellent adaptability to environmental change.

Keywords: vehicle-to-everything; resource allocation; attention mechanism; multi-agent reinforcement learning; low latency (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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