Fair Coexistence in Unlicensed Band for Next Generation Multiple Access: The Art of Learning

Opening the unlicensed bands provides additional spectrum resources for the next generation wireless network, while severe unfairness and performance degradation occur when one coexists with the incumbent users of these bands. Therefore, plenty of efforts have been made towards fair coexistence, mainly focusing on parameter tuning of listen-before-talk (LBT) and duty-cycle (DC) mechanisms. For better utilization of the unlicensed bands, it is of paramount importance to establish an access mechanism that guarantees the fairness objective among feasible mechanisms. Such access mechanism and the corresponding benchmark, nevertheless, remain largely unknown. To address this issue, this paper considers the coexistence between WiFi and the other unlicensed nodes, and aims to maximize the ff-fairness between them. A benchmark is first given by solving the optimization problem. Then we propose a deep reinforcement learning (DRL) mechanism to help the unlicensed nodes make access decisions, such that they coexist with WiFi harmoniously. Extensive simulations have been carried out, and the results show that the DRL mechanism can approach the benchmark.