Dynamic Multichannel Access Based on Deep Reinforcement Learning in Distributed Wireless Networks

With the emergence of innovative applications in vertical industries such as smart home and industrial automation, machine communication has shown a spurt of development. Different from the traditional human-oriented cellular communication, machine communication is characterized by strong uncertainty and abruptness, large-scale concurrent device connection as well as uneven and unsaturated data traffic. This article investigates the dynamic multiple-devices multiple-channels access for unsaturated traffic with retransmission mechanism, which is aimed at reducing the long-term data packet loss resulting from buffer overflows and transmission failure. The instant channel selection will lead to a non-negligible impact on the future decision, motivating us to model this problem as a Markov decision process. Limited by the unknown environment knowledge, we proposed a dynamic access policy based on deep reinforcement learning algorithm to optimally select the channel for transmission or keep silent for Internet-of-Things devices. Simulation results confirm that our proposed channel access strategy can reduce the collision and the packet loss of network. Furthermore, it can also work well when coexisting with the devices that adopt time division multiple access (TDMA) or ALOHA protocol.