Prompt-Based Transceiver Cooperation for Semantic Communications with Domain-Incremental Background Knowledge
Lan Zhang, Madhureeta Das, Yao Sun, Dusit Tao Niyato, Xiaoyong Yuan · 2023
Semantic communication (SemCom) has gained significant attention to extracting and delivering semantic information based on transceivers' background knowledge. While successful, most existing works assume a fixed knowledge base (KB) shared between transceivers, limiting their applicability to the ever-increasing domain knowledge. To address this limitation, we propose an innovative transceiver cooperation framework, Prompt-SC, using prompt learning techniques to achieve domain-incremental SemCom. To alleviate catastrophic forgetting of domain incremental learning (DIL) and avoid the need to store data for all domains, we first reconstruct the SemCom model, i.e., the semantic and channel encoders/decoders, to be composed of a pretrained base model and domain-specific prompts. This way, a transceiver freezes its base model and learns prompts independently across domains to achieve the best for each domain, enabling rehearsal-free DIL. Additionally, we introduce the control-/data-plane decoupling design to align transceivers with heterogeneous or asynchronously evolved domain knowledge. Since the prompt size is small, transceivers can efficiently share the domain-specific prompt with each other, thereby aligning their background knowledge with low communication overhead and preserving the data privacy of individual KBs. Furthermore, we introduce a new metric, semantic spectrum efficiency, to evaluate Prompt-SC based on its communication cost and achieved SemCom gain, which suggests applicable scenarios for Prompt-SC. Finally, we conduct extensive experiments to demonstrate the effectiveness and efficiency of Prompt-SC.