  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.
