Online Beam Learning with Interference Nulling for Millimeter Wave MIMO Systems

TL;DR

This paper introduces an online reinforcement learning algorithm for beamforming in mmWave MIMO systems that learns to null interference without channel knowledge, improving performance in dense networks.

Contribution

It presents a novel, sample-efficient reinforcement learning method for adaptive beam pattern design that suppresses interference without requiring explicit channel information or coordination.

Findings

The algorithm effectively learns interference-nullifying beam patterns.

Simulation results show significant interference suppression.

Hardware prototype demonstrates real-world applicability.

Abstract

Employing large antenna arrays is a key characteristic of millimeter wave (mmWave) and terahertz communication systems. Due to the hardware constraints and the lack of channel knowledge, codebook based beamforming/combining is normally adopted to achieve the desired array gain. However, most of the existing codebooks focus only on improving the gain of their target user, without taking interference into account. This can incur critical performance degradation in dense networks. In this paper, we propose a sample-efficient online reinforcement learning based beam pattern design algorithm that learns how to shape the beam pattern to null the interfering directions. The proposed approach does not require any explicit channel knowledge or any coordination with the interferers. Simulation results show that the developed solution is capable of learning well-shaped beam patterns that significantly suppress the interference while sacrificing tolerable beamforming/combing gain from the desired user. Furthermore, a hardware proof-of-concept prototype based on mmWave phased arrays is built and used to implement and evaluate the developed online beam learning solutions in realistic scenarios. The learned beam patterns, measured in an anechoic chamber, show the performance gains of the developed framework and highlight a promising machine learning based beam/codebook optimization direction for mmWave and terahertz systems.