Machine-Learning Beam Tracking and Weight Optimization for mmWave Multi-UAV Links

TL;DR

This paper presents a machine learning approach, specifically Q-learning, for efficient analog beam tracking and digital weight optimization in mmWave multi-UAV links, improving resilience and spectral efficiency in dynamic environments.

Contribution

It introduces a Q-learning-based beam tracking scheme and a hybrid beamforming method that maximizes SINR in highly dynamic multi-UAV mmWave systems.

Findings

Q-learning significantly improves beam tracking efficiency.

Hybrid beamforming maximizes SINR with adaptive analog/digital weights.

The method enhances resilience and spectral efficiency in UAV networks.

Abstract

Millimeter-wave (mmWave) hybrid analog-digital beamforming is a promising approach to satisfy the low-latency constraint in multiple unmanned aerial vehicles (UAVs) systems, which serve as network infrastructure for flexible deployment. However, in highly dynamic multi-UAV environments, analog beam tracking becomes a critical challenge. The overhead of additional pilot transmission at the price of spectral efficiency is shown necessary to achieve high resilience in operation. An efficient method to deal with high dynamics of UAVs applies machine learning, particularly Q-learning, to analog beam tracking. The proposed Q-learning-based beam tracking scheme uses current/past observations to design rewards from environments to facilitate prediction, which significantly increases the efficiency of data transmission and beam switching. Given the selected analog beams, the goal of digital beamforming is to maximize the SINR. The received pilot signals are utilized to approximate the desired signal and interference power, which yield the SINR measurements as well as the optimal digital weights. Since the selected analog beams based on the received power do not guarantee the hybrid beamforming achieving the maximization SINR, we therefore reserve additional analog beams as candidates during the beam tracking. The combination of analog beams with their digital weights achieving the maximum SINR consequently provides the optimal solution to the hybrid beamforming.