Q-Learning Based Predictive Relay Selection for Optimal Relay Beamforming

Wireless Autonomous Networks are expected to support communication between a source and a receiver, by constantly self-adapting to changes in their communication environment. This paper considers a scenario of relay beamforming, in which relays collaboratively retransmit the source signal so that they maximize the average signal-to-interference+noise ratio (SINR) at the destination. The relays are grouped into clusters, with each cluster having a single active relay at a time. The system evolves in time slots; in each time slot the clusters beamform to the destination, and at the same time, each cluster selects the relay to be active in the subsequent time slot. Relay selection is performed locally within each cluster, using a reinforcement learning approach, namely Q-learning. Compared to prior methods, the proposed scheme does not require any statistical knowledge on the channels, and achieves similar average SINR performance while involving lower complexity.