Benchmarking Semantic Communications for Image Transmission Over MIMO Interference Channels

TL;DR

This paper introduces a neural network-based semantic communication scheme for MIMO interference channels, demonstrating improved interference mitigation and performance over baselines, especially at low SNR levels.

Contribution

It proposes an interference-robust semantic communication scheme with a novel training loss and dynamic weighting, tailored for MIMO interference scenarios.

Findings

IRSC outperforms baseline methods in interference mitigation.

IRSC shows significant gains in low SNR regimes.

The scheme effectively learns to handle interference in MIMO channels.

Abstract

Semantic communications offer promising prospects for enhancing data transmission efficiency. However, existing schemes have predominantly concentrated on point-to-point transmissions. In this paper, we aim to investigate the validity of this claim in interference scenarios compared to baseline approaches. Specifically, our focus is on general multiple-input multiple-output (MIMO) interference channels, where we propose an interference-robust semantic communication (IRSC) scheme. This scheme involves the development of transceivers based on neural networks (NNs), which integrate channel state information (CSI) either solely at the receiver or at both transmitter and receiver ends. Moreover, we establish a composite loss function for training IRSC transceivers, along with a dynamic mechanism for updating the weights of various components in the loss function to enhance system fairness among users. Experimental results demonstrate that the proposed IRSC scheme effectively learns to mitigate interference and outperforms baseline approaches, particularly in low signal-to-noise (SNR) regimes.