关键词: Wi-Fi7, 多链路, 通信感知一体化, 多智能体, 深度强化学习

Abstract:

To solve the problem of joint power and channel resource allocation for integrated communication and sensing in multi-link transmission of next-generation Wi-Fi7 devices， a multi-link multi-agent reinforcement learning algorithm based on QMIX（Q-learning Mixing Network） for Joint Power Control and channel allocation （JPCQMIX） was proposed on the basis of special upper and lower Media Access Control （MAC） layer structure of Multi-Link Device （MLD）. In the algorithm， each lower-layer MAC， i.e.， each link， was regarded as an agent， and mixing network was set up in the upper-layer MAC to process all the local value functions of lower-layer MACs， thereby achieving the effect of centralized training. After the training， each lower-layer MAC entered the distributed execution mode and interacted with its local environment independently to perform power control and channel allocation decision making. Simulation results show that the proposed algorithm improves the communication throughput performance by 20.51% and 29.10% respectively compared with Multi-Agent Deep Q Network （MADQN） algorithm and the traditional heuristic Particle Swarm Optimization （PSO） algorithm. Meanwhile， the proposed algorithm demonstrates better robustness when facing with different sensing accuracy thresholds and different link minimum Signal-to-Interference-plus-Noise Ratio （SINR）. It can be seen that JPCQMIX enhances the system’s communication throughput under the condition of satisfying the sensing accuracy effectively.

Key words: Wi-Fi7, multi-link, integrated communication and sensing, multi-agent, deep reinforcement learning