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

2025-02-03

AI Summary

To address the performance degradation of conventional blind beamforming in dynamic mobile downlink transmission under high-dimensional non-stationary channel conditions, this paper proposes an RF-fingerprint-driven adaptive beamforming method that requires neither explicit channel estimation nor additional hardware. The approach uniquely integrates learnable Dolph–Chebyshev antenna array modeling with proximal policy optimization (PPO)-based deep reinforcement learning to enable dynamic beam pattern reconfiguration, thereby overcoming limitations imposed by fixed array geometries and static beamforming policies. We establish an RF-fingerprint-driven end-to-end joint optimization framework supporting unsupervised blind beamforming. Simulation results demonstrate that, in a scenario with 50 users and a 64-antenna base station, the proposed method achieves 98.3% of the theoretical capacity upper bound—outperforming baseline learning-based methods by 12.7% in spectral efficiency.

Technology Category

• Intelligent Robots: Learning & Optimization for ROB
• Search and Optimization: Learning to Search
• Machine Learning: Online Learning & Bandits

Application Category

• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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.

Problem

• DRL-based Dolph-Tschebyscheff Beamforming
• Optimal Data Transmission
• Mobile Users or Large Antenna Arrays

Innovation

• Deep Reinforcement Learning
• Dolph-Tschebyscheff Beamforming
• Optimized Signal Transmission