# Benchmarking Semantic Communications for Image Transmission Over MIMO Interference Channels

arXiv: https://arxiv.org/abs/2406.16878 · HTML: https://mlsanity.com/paper/2406.16878/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 12 references that are in the map.
- First posted on arXiv 10 Apr 2024.

## Builds on (most cited first, 7 of 7)

- [Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms](https://mlsanity.com/paper/1708.07747/) (2017)
- [Deep Learning Enabled Semantic Communication Systems](https://mlsanity.com/paper/2006.10685/) (2020)
- [A Unified Multi-Task Semantic Communication System for Multimodal Data](https://mlsanity.com/paper/2209.07689/) (2022)
- [Semantic-preserved Communication System for Highly Efficient Speech Transmission](https://mlsanity.com/paper/2205.12727/) (2022)
- [Semantic Importance-Aware Communications Using Pre-trained Language Models](https://mlsanity.com/paper/2302.07142/) (2023)
- [Signal Shaping for Semantic Communication Systems with A Few Message Candidates](https://mlsanity.com/paper/2202.02072/) (2022)
- [Look-Ahead Task Offloading for Multi-User Mobile Augmented Reality in Edge-Cloud Computing](https://mlsanity.com/paper/2305.19558/) (2023)

## Abstract (arXiv)

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.