Reinforcement Learning Based Inter-User-Interference Suppression in Full-Duplex Networks

In this work, we propose a reinforcement learning (RL) based solution for managing inter-user-interference in wireless full-duplex networks. In particular, the RL algorithm is trained to divide half-duplex capable UEs into two groups such that a full-duplex capable base station can serve one group in uplink while the other one is in downlink. Moreover, it can do the grouping based on only the realized data rates of the UEs, without any explicit knowledge of the mutual interference patterns. With proper training, it is shown that the RL algorithm can identify groups where the interference from the uplink UEs to the downlink UEs is minimized. The results indicate that such a scheme can achieve 92% of the optimal sum data rate, thereby providing over a 60% throughput gain in comparison to a corresponding half-duplex network. A purely random UE grouping will only yield a 48% gain over a half-duplex network.