Topic Enhanced Semantic Communication System for Reliable Semantic Recovery

Semantic communications, powered by the ability to efficiently extract and transmit crucial semantic features, have shown attractive potential in supporting intelligent tasks and personalized communication. Although existing researches have achieved remarkable performance by leveraging local semantic relationships to reconstruct data, the important role of global semantic information in semantic recovery remains unexplored. In this paper, we introduce a vital global semantic information, topic, into semantic communications to enhance semantic recovery, which plays a crucial role in ensuring contextual consistency and semantic understanding. Specifically, we propose a topic enhanced semantic communication system (TESC) that integrates topic and content representations to ensure reliable communication. First, to realize effective information fusion, we develop two topic-semantic fusion mechanisms that generate a more comprehensive semantic representation. Second, to obtain topic information at the receiver without increasing transmission overhead, we introduce a pre-decoder based topic extractor that pre-decodes the sentences and extracts the corresponding topic embeddings. Third, a two-phase training algorithm has been devised to guarantee the convergence of each network module. Simulation results demonstrate the effectiveness of incorporating topic information into semantic communications. Compared to other benchmarks, the proposed TESC significantly improves communication reliability without increasing the amount of transmitted data.