Enhancing Reasoning Ability in Semantic Communication Through Generative AI-Assisted Knowledge Construction

Semantic communication (SemCom), a pioneering paradigm that places emphasis on conveying the meaning of information, faces challenges in constructing background knowledge to drive precise reasoning of semantic coding models. Fortunately, the recent emergence of Generative Artificial Intelligence (GAI) technology is promising to create high-quality content that can be harnessed to assist knowledge construction in SemCom, enhancing the reasoning ability of semantic coding models. In this letter, we propose a GAI-assisted SemCom framework, named Gen-SC, where sufficient samples for training SemCom transceivers are generated using GAI as per user contextual information. In addition, to guide the GAI model in producing contextually relevant content, a discriminator is incorporated into Gen-SC to measure the disparity between generated samples and actual samples. The simulation results demonstrate that the Gen-SC achieves higher semantic accuracy, especially when the original training samples are insufficient, in contrast to traditional SemCom without knowledge enhancement.