On the Performance of Image Recovery in Massive MIMO Communications

TL;DR

This paper explores the use of Massive MIMO technology for transmitting and recovering image data in wireless networks, proposing a novel decoding framework and noise reduction techniques to improve image quality.

Contribution

It introduces a new framework for image decoding in Massive MIMO systems using ADMM and post-filtering, addressing the challenge of image transmission quality.

Findings

Post-filtering significantly improves image quality.

The proposed decoding framework effectively reconstructs images from noisy signals.

Noise reduction techniques enhance overall transmission performance.

Abstract

Massive MIMO (Multiple Input Multiple Output) has demonstrated as a potential candidate for 5G-and-beyond wireless networks. Instead of using Gaussian signals as most of the previous works, this paper makes a novel contribution by investigating the transmission quality of image data by utilizing the Massive MIMO technology. We first construct a framework to decode the image signal from the noisy received data in the uplink Massive MIMO transmission by utilizing the alternating direction method of multipliers (ADMM) approach. Then, a low-pass filter is exploited to enhance the efficiency of the remaining noise and artifacts reduction in the recovered image. Numerical results demonstrate the necessity of a post-filtering process in enhancing the quality of image recovery.