  In recent years, there has been significant progress in semantic
communication systems empowered by deep learning techniques. It has greatly
improved the efficiency of information transmission. Nevertheless, traditional
semantic communication models still face challenges, particularly due to their
single-task and single-modal orientation. Many of these models are designed for
specific tasks, which may result in limitations when applied to multi-task
communication systems. Moreover, these models often overlook the correlations
among different modal data in multi-modal tasks. It leads to an incomplete
understanding of complex information, causing increased communication overhead
and diminished performance. To address these problems, we propose a multi-modal
fusion-based multi-task semantic communication (MFMSC) framework. In contrast
to traditional semantic communication approaches, MFMSC can effectively handle
various tasks across multiple modalities. Furthermore, we design a fusion
module based on Bidirectional Encoder Representations from Transformers (BERT)
for multi-modal semantic information fusion. By leveraging the powerful
semantic understanding capabilities and self-attention mechanism of BERT, we
achieve effective fusion of semantic information from different modalities. We
compare our model with multiple benchmarks. Simulation results show that MFMSC
outperforms these models in terms of both performance and communication
overhead.
