Deep Unfolding-Empowered MmWave Massive MIMO Joint Communications and Sensing

TL;DR

This paper introduces UPGANet, a deep unfolding neural network for hybrid beamforming in joint communications and sensing, achieving significant performance improvements and reduced complexity over traditional methods.

Contribution

It develops a deep unfolding approach for hybrid beamforming that enhances performance and reduces complexity in joint communications and sensing systems.

Findings

UPGANet achieves 33.5% higher sum rate.

It reduces beampattern error by 2.5 dB.

It cuts runtime and complexity by up to 65%.

Abstract

In this paper, we propose a low-complexity and fast hybrid beamforming design for joint communications and sensing (JCAS) based on deep unfolding. We first derive closed-form expressions for the gradients of the communications sum rate and sensing beampattern error with respect to the analog and digital precoders. Building on this, we develop a deep neural network as an unfolded version of the projected gradient ascent algorithm, which we refer to as UPGANet. This approach efficiently optimizes the communication-sensing performance tradeoff with fast convergence, enabled by the learned step sizes. UPGANet preserves the interpretability and flexibility of the conventional PGA optimizer while enhancing performance through data training. Our simulations show that UPGANet achieves up to a 33.5% higher communications sum rate and 2.5 dB lower beampattern error compared to conventional designs based on successive convex approximation and Riemannian manifold optimization. Additionally, it reduces runtime and computational complexity by up to 65% compared to PGA without unfolding.