Reinforcement Learning for Random Access in Multi-cell Networks

In this paper, our goal is to maximize the system throughput in a time-slotted uplink multi-cell random access communication system. To this end, we propose a two-stage reinforcement learning (RL)-based algorithm based on the exponential-weight algorithm for exploration and exploitation (EXP3). In each macro-time slot that consists of multiple time slots, users run the RL-based algorithm to choose the associated access point (AP). Then, a transmission policy determines the sub-time slot that user will transmit data in each time slot. Another RL-based learning algorithm is used to obtain an optimal transmission policy. To show that our method is efficient, we compare our proposed algorithm with the $\epsilon$-greedy algorithm in two different scenarios. The simulation results show that the average system throughput of our algorithm outperforms that of $\epsilon$-greedy exploration.