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.

语义通信在增强数据传输效率方面具有很大潜力。本文针对干扰情景与基准方案进行了比较研究，提出了一种抗干扰的语义通信方案。该方案基于神经网络（NNs）开发了收发机，它可以单独在接收端或发送端和接收端都集成信道状态信息（CSI）。我们还建立了一个综合损失函数来训练 IRSC 收发机，并采用动态机制来更新损失函数中各个部分的权重，以提高系统对用户的公平性。实验结果表明，所提出的 IRSC 方案能有效地学习抑制干扰，在低信噪比（SNR）环境中优于基准方案。