Deep Unfolding Beamforming and Power Control Designs for Multi-Port Matching Networks

TL;DR

This paper introduces deep unfolding neural network designs for multi-port matching networks in 6G systems, improving beamforming and power control efficiency by modeling complex antenna and channel interactions.

Contribution

It develops novel deep unfolding frameworks for hybrid beamforming and power control in multi-port matching networks, incorporating impedance effects and port interactions.

Findings

PGD-Net achieves low complexity hybrid beamforming.

GNN-based AO-Net provides faster power control.

Numerical results highlight the importance of insertion loss modeling.

Abstract

The key technologies of sixth generation (6G), such as ultra-massive multiple-input multiple-output (MIMO), enable intricate interactions between antennas and wireless propagation environments. As a result, it becomes necessary to develop joint models that encompass both antennas and wireless propagation channels. To achieve this, we utilize the multi-port communication theory, which considers impedance matching among the source, transmission medium, and load to facilitate efficient power transfer. Specifically, we first investigate the impact of insertion loss, mutual coupling, and other factors on the performance of multi-port matching networks. Next, to further improve system performance, we explore two important deep unfolding designs for the multi-port matching networks: beamforming and power control, respectively. For the hybrid beamforming, we develop a deep unfolding framework, i.e., projected gradient descent (PGD)-Net based on unfolding projected gradient descent. For the power control, we design a deep unfolding network, graph neural network (GNN) aided alternating optimization (AO)Net, which considers the interaction between different ports in optimizing power allocation. Numerical results verify the necessity of considering insertion loss in the dynamic metasurface antenna (DMA) performance analysis. Besides, the proposed PGD-Net based hybrid beamforming approaches approximate the conventional model-based algorithm with very low complexity. Moreover, our proposed power control scheme has a fast run time compared to the traditional weighted minimum mean squared error (WMMSE) method.