Deep Learning-based Limited Feedback Designs for MIMO Systems

TL;DR

This paper introduces a deep learning approach to limited feedback in MIMO systems, replacing traditional procedures with neural networks that improve error rates and reduce complexity.

Contribution

It proposes a novel deep neural network framework for limited feedback in MIMO systems, integrating channel training, codebook design, and beamforming vector selection.

Findings

Achieves 1 dB SER gain over conventional methods.

Reduces computational complexity in feedback procedures.

Demonstrates effectiveness of DL in MIMO feedback design.

Abstract

We study a deep learning (DL) based limited feedback methods for multi-antenna systems. Deep neural networks (DNNs) are introduced to replace an end-to-end limited feedback procedure including pilot-aided channel training process, channel codebook design, and beamforming vector selection. The DNNs are trained to yield binary feedback information as well as an efficient beamforming vector which maximizes the effective channel gain. Compared to conventional limited feedback schemes, the proposed DL method shows an 1 dB symbol error rate (SER) gain with reduced computational complexity.