AoA-Based Pilot Assignment in Massive MIMO Systems Using Deep Reinforcement Learning

TL;DR

This paper proposes a deep reinforcement learning approach for adaptive pilot assignment in massive MIMO systems, effectively reducing pilot contamination by leveraging AoA information and achieving near-optimal performance with low complexity.

Contribution

It introduces a novel DRL-based pilot assignment strategy that adapts to channel variations using AoA data, improving over traditional static methods.

Findings

DRL scheme tracks environmental changes effectively

Achieves near-optimal pilot assignment performance

Maintains low computational complexity

Abstract

In this paper, the problem of pilot contamination in a multi-cell massive multiple input multiple output (M-MIMO) system is addressed using deep reinforcement learning (DRL). To this end, a pilot assignment strategy is designed that adapts to the channel variations while maintaining a tolerable pilot contamination effect. Using the angle of arrival (AoA) information of the users, a cost function, portraying the reward, is presented, defining the pilot contamination effects in the system. Numerical results illustrate that the DRL-based scheme is able to track the changes in the environment, learn the near-optimal pilot assignment, and achieve a close performance to that of the optimum pilot assignment performed by exhaustive search, while maintaining a low computational complexity.