High-speed Millimeter-wave 5G/6G Image Transmission via Artificial Intelligence

TL;DR

This paper introduces DL-CSNet, an AI-based system that optimizes millimeter-wave compressed sensing for high-speed 5G/6G image transmission, achieving significant speed improvements with a 94-GHz prototype.

Contribution

The paper presents a novel neural network that jointly learns image dictionaries, optimizes measurement matrices, and reconstructs images for millimeter-wave communications.

Findings

Achieved up to tenfold increase in image transmission speed.

Developed a 94-GHz prototype demonstrating practical feasibility.

Successfully reconstructed lossless images with learned dictionaries.

Abstract

Artificial Intelligence (AI) has been used to jointly optimize a mmWave Compressed Sensing (CS) for high-speed 5G/6G image transmission. Specifically, we have developed a Dictionary Learning Compressed Sensing neural Network (DL-CSNet) to realize three key functionalities: 1) to learn the dictionary basis of the images for transmission; 2) to optimize the Hadamard measurement matrix; and 3) to reconstruct the lossless images with the learned dictionary basis. A 94-GHz prototype has been built and up to one order of image transmission speed increase has been realized for letters ``A" to ``Z".