Deep Learning-Based Beamforming Design Using Target Beam Patterns

TL;DR

This paper introduces a deep learning framework that designs beamforming vectors directly from target beam patterns, effectively handling hardware constraints and limited channel information.

Contribution

It presents a novel encoder-decoder neural network architecture with a two-stage training process for robust beamforming design across various antenna architectures.

Findings

Achieves near-optimal spectral efficiency with limited CSI.

Outperforms existing beamforming methods in simulations.

Works across digital, analog, and hybrid beamforming architectures.

Abstract

This paper proposes a deep learning-based beamforming design framework that directly maps a target beam pattern to optimal beamforming vectors across multiple antenna array architectures, including digital, analog, and hybrid beamforming. The proposed method employs a lightweight encoder-decoder network where the encoder compresses the complex beam pattern into a low-dimensional feature vector and the decoder reconstructs the beamforming vector while satisfying hardware constraints. To address training challenges under diverse and limited channel station information (CSI) conditions, a two-stage training process is introduced, which consists of an offline pre-training for robust feature extraction using an auxiliary module, followed by online training of the decoder with a composite loss function that ensures alignment between the synthesized and target beam patterns in terms of the main lobe shape and side lobe suppression. Simulation results based on NYUSIM-generated channels show that the proposed method can achieve spectral efficiency close to that of fully digital beamforming under limited CSI and outperforms representative existing methods.