Traffic-Aware Hierarchical Beam Selection for Cell-Free Massive MIMO

TL;DR

This paper introduces a traffic-aware hierarchical beam selection scheme for cell-free massive MIMO systems that reduces training overhead and improves delay satisfaction using neural networks and reinforcement learning.

Contribution

It proposes a dual-timescale beam selection framework combining neural network prediction and reinforcement learning, with scalable distributed algorithms for improved efficiency.

Findings

Significantly reduces beam training overhead.

Enhances delay satisfaction rate.

Improves scalability and tradeoff between performance and overhead.

Abstract

Beam selection for joint transmission in cell-free massive multi-input multi-output systems faces the problem of extremely high training overhead and computational complexity. The traffic-aware quality of service additionally complicates the beam selection problem. To address this issue, we propose a traffic-aware hierarchical beam selection scheme performed in a dual timescale. In the long-timescale, the central processing unit collects wide beam responses from base stations (BSs) to predict the power profile in the narrow beam space with a convolutional neural network, based on which the cascaded multiple-BS beam space is carefully pruned. In the short-timescale, we introduce a centralized reinforcement learning (RL) algorithm to maximize the satisfaction rate of delay w.r.t. beam selection within multiple consecutive time slots. Moreover, we put forward three scalable distributed algorithms including hierarchical distributed Lyapunov optimization, fully distributed RL, and centralized training with decentralized execution of RL to achieve better scalability and better tradeoff between the performance and the execution signal overhead. Numerical results demonstrate that the proposed schemes significantly reduce both model training cost and beam training overhead and are easier to meet the user-specific delay requirement, compared to existing methods.