DRL-based Dolph-Tschebyscheff Beamforming in Downlink Transmission for Mobile Users

TL;DR

This paper introduces a deep reinforcement learning-based blind beamforming method using Dolph-Tschebyscheff arrays, enabling adaptive beam pattern control for mobile users in dynamic environments, achieving near-optimal data rates.

Contribution

It presents a novel DRL-based beamforming approach with a learnable Dolph-Tschebyscheff array, addressing complexity and performance issues in multi-user, high-dimensional scenarios.

Findings

Supports data rates close to optimal

Effective in dynamic, multi-user environments

Demonstrates robustness with increasing antenna dimensions

Abstract

With the emergence of AI technologies in next-generation communication systems, machine learning plays a pivotal role due to its ability to address high-dimensional, non-stationary optimization problems within dynamic environments while maintaining computational efficiency. One such application is directional beamforming, achieved through learning-based blind beamforming techniques that utilize already existing radio frequency (RF) fingerprints of the user equipment obtained from the base stations and eliminate the need for additional hardware or channel and angle estimations. However, as the number of users and antenna dimensions increase, thereby expanding the problem's complexity, the learning process becomes increasingly challenging, and the performance of the learning-based method cannot match that of the optimal solution. In such a scenario, we propose a deep reinforcement learning-based blind beamforming technique using a learnable Dolph-Tschebyscheff antenna array that can change its beam pattern to accommodate mobile users. Our simulation results show that the proposed method can support data rates very close to the best possible values.