Reinforcement Learning Assisted Beamforming for Inter-cell Interference Mitigation in 5G Massive MIMO Networks

TL;DR

This paper introduces a reinforcement learning-based dynamic beamforming method to mitigate inter-cell interference in 5G massive MIMO networks, improving signal quality without requiring channel estimation.

Contribution

It presents a novel joint beamforming and Q-learning approach for ICI mitigation that is low-complexity and does not rely on channel estimation.

Findings

Enhanced SINR performance compared to existing methods

Reduced computational complexity

Effective ICI mitigation in 5G massive MIMO systems

Abstract

Beamforming is an essential technology in the 5G massive multiple-input-multiple-output (MMIMO) communications, which are subject to many impairments due to the nature of wireless transmission channel, i.e. the air. The inter-cell interference (ICI) is one of the main impairments faced by 5G communications due to frequency-reuse technologies. In this paper, we propose a reinforcement learning (RL) assisted full dynamic beamforming for ICI mitigation in 5G downlink. The proposed algorithm is a joint of beamforming and full dynamic Q-learning technology to minimize the ICI, and results in a low-complexity method without channel estimation. Performance analysis shows the quality of service improvement in terms of signal-to-interference-plus-noise-ratio (SINR) and computational complexity compared to other algorithms.