A Q-Learning-based Approach for Distributed Beam Scheduling in mmWave Networks

TL;DR

This paper introduces a distributed Q-learning-based method for beam scheduling and power allocation in mmWave networks sharing spectrum among multiple base stations, outperforming existing game-theoretic approaches.

Contribution

It proposes a novel distributed Q-learning algorithm for interference management in mmWave networks, integrating with Lyapunov optimization for network utility maximization.

Findings

Q-learning approach adapts effectively to interference scenarios.

Proposed method achieves higher network payoff than game-based approaches.

Integration with Lyapunov framework enables automatic weight tuning.

Abstract

We consider the problem of distributed downlink beam scheduling and power allocation for millimeter-Wave (mmWave) cellular networks where multiple base stations (BSs) belonging to different service operators share the same unlicensed spectrum with no central coordination or cooperation among them. Our goal is to design efficient distributed beam scheduling and power allocation algorithms such that the network-level payoff, defined as the weighted sum of the total throughput and a power penalization term, can be maximized. To this end, we propose a distributed scheduling approach to power allocation and adaptation for efficient interference management over the shared spectrum by modeling each BS as an independent Q-learning agent. As a baseline, we compare the proposed approach to the state-of-the-art non-cooperative game-based approach which was previously developed for the same problem. We conduct extensive experiments under various scenarios to verify the effect of multiple factors on the performance of both approaches. Experiment results show that the proposed approach adapts well to different interference situations by learning from experience and can achieve higher payoff than the game-based approach. The proposed approach can also be integrated into our previously developed Lyapunov stochastic optimization framework for the purpose of network utility maximization with optimality guarantee. As a result, the weights in the payoff function can be automatically and optimally determined by the virtual queue values from the sub-problems derived from the Lyapunov optimization framework.