Augmented Deep Unfolding for Downlink Beamforming in Multi-cell Massive MIMO With Limited Feedback

TL;DR

This paper introduces an augmented deep unfolding approach that jointly optimizes beamforming and channel quantization in multi-cell massive MIMO systems with limited feedback, significantly improving system throughput.

Contribution

It proposes a novel joint optimization framework using deep unfolding and variational information bottleneck techniques for limited feedback MIMO systems.

Findings

Outperforms benchmark schemes in average system rate

Effectively integrates beamforming and quantization tasks

Enhances performance under strict feedback constraints

Abstract

In limited feedback multi-user multiple-input multiple-output (MU-MIMO) cellular networks, users send quantized information about the channel conditions to the associated base station (BS) for downlink beamforming. However, channel quantization and beamforming have been treated as two separate tasks conventionally, which makes it difficult to achieve global system optimality. In this paper, we propose an augmented deep unfolding (ADU) approach that jointly optimizes the beamforming scheme at the BSs and the channel quantization scheme at the users. In particular, the classic WMMSE beamformer is unrolled and a deep neural network (DNN) is leveraged to pre-process its input to enhance the performance. The variational information bottleneck technique is adopted to further improve the performance when the feedback capacity is strictly restricted. Simulation results demonstrate that the proposed ADU method outperforms all the benchmark schemes in terms of the system average rate.