Multi-Agent Actor Critic for Channel Allocation in Heterogeneous Networks

Abstract

Heterogeneous networks (HetNets) can equalize traffic loads and cut down the cost of deploying cells. Thus, it is regarded to be the significant technique of the next-generation communication networks. Due to the non-convexity nature of the channel allocation problem in HetNets, it is difficult to design an optimal approach for allocating channels. To ensure the user quality of service as well as the long-term total network utility, this article proposes a new method through utilizing multi-agent reinforcement learning. Moreover, for the purpose of solving computational complexity problem caused by the large action space, deep reinforcement learning is put forward to learn optimal policy. A nearly-optimal solution with high efficiency and rapid convergence speed could be obtained by this learning method. Simulation results reveal that this new method has the best performance than other methods.

Multi-Agent Reinforcement Learning-Based Resource Management for V2X Communication

“…To address the challenges caused by obtaining the global channel status information, we adopt the reinforcement learning (RL) method (Zhao et al, 2021;Zhao et al, 2020) in this paper. Currently one of the most powerful machine learning tools, RL is usually applied to time-varying dynamic systems (Wu et al, 2018;Yan et al, 2018) and the wireless network (Simsek et al, 2018;Zhao et al, 2018).…”