Rate Adaptable Multi-Task-Oriented Semantic Communication: An Extended Rate-Distortion Theory Based Scheme

Semantic communication, as a new paradigm for next-generation communication, aims to transmit semantic symbols for artificial intelligence (AI) tasks. Existing research typically requires extracting and transmitting specialized semantics for each AI task when multiple target AI tasks exist. Considering that each AI task may share common semantics, this paper proposes a joint source-channel coding scheme for a multi-task semantic communication system, which can extract the common semantics required by multiple AI tasks, and thus reduce the overall semantic transmission. To this end, we first formulate the multi-task semantic communication problem as a rate-distortion problem that simultaneously considers the rate of extracted semantics and the distortion of multiple AI tasks. Then, we derive a new form of rate-distortion, called extended rate-distortion, which can guide the compact semantics extraction of multiple AI tasks simultaneously. Additionally, we derive a self-consistent equation for this extended rate-distortion form, theoretically proving the effectiveness of this approach. To ensure proper trade-off between the rates and distortions of multiple AI tasks, we further propose a rate adjustment module that can dynamically adjust the rate according to channel conditions. We validate our experimental results on multiple datasets, which show that the proposed method can reduce transmission overhead by 40% to 50% and achieve a 7.6% improvement in multi-task performance.