Deep Reinforcement Learning for Multiple Access in Dynamic IoT Networks Using Bi-GRU

In the next-generation wireless communication systems, learning-based dynamic spectrum access strategy at the medium access control layer and physical layer shows its powerful capability of achieving optimal resources allocation, and it has become a hot research topic for the harmonious coexistence of heterogeneous wireless networks. In this paper, we propose a multiple access control method to achieve high network throughput by combining deep reinforcement learning and memory module. In specific, we introduce the bidirectional gated recurrent unit (Bi-GRU) in deep Q-learning (DQL) to utilize the information of varying environment observation at each time-step. Furthermore, we apply the method in a freeway scenario with real-world datasets, where the DQL node contends the same wireless channel with other nodes. Evaluated results demonstrate that the proposed approach learns an optimal policy without using complex mechanism or prior. Moreover, we consider realistic cases involving saturated or unsaturated uplink traffic flows of nodes on a freeway segment, and the on-line training strategies of the DQL node near the roadside facilities. The experimental results show that the proposed scheme leads to the highest throughput in all cases compared with the competing approaches.