Cross-modal Semantic Communications in 6G
Mingkai Chen, Minghao Liu, Wenjun Wang, Haie Dou, Lei Wang
Abstract
In 6G communication, semantic communication is considered one of the most promising directions to fulfill users’ demands for immersive multi-modal experiences, low latency, and high reliability. We proposes a cross-modal semantic communication approach based on deep learning, where both semantic coding and decoding are carefully crafted to provide optimum performance. Firstly, cross-modal semantic fusion is designed to enable end-to-end data transmission, driven by various task requirements of multi-modal business users. In addition, the proposed approach for evaluation on the semantic similarity is highly effective. It consists of a siamese network and a pseudo-siamese network, which can accurately obtain the matching loss between modal contents. Finally, the simulation results show that the proposed cross-modal semantic communication approach outperforms traditional communication systems, especially in low SNR scenarios. The similarity of cross-modal semantic communication improves by more than 53% compared to the traditional approaches, demonstrating its superiority and feasibility. Overall, our solution can meet the increasing demands of modern communication and facilitate seamless and intuitive experiences for users.