Topology Data Analysis-Based Error Detection for Semantic Image Transmission with Incremental Knowledge-Based HARQ

Semantic communication(SemCom) aims to achieve high-fidelity information delivery under low communication consumption by only guaranteeing semantic accuracy. Nevertheless, semantic communication still suffers from unexpected channel volatility and thus developing a re-transmission mechanism(e.g., hybrid automatic repeat request [HARQ]) becomes indispensable. In that regard, instead of discarding previously transmitted information, the incremental knowledge-based HARQ(IK-HARQ) is deemed as a more effective mechanism that could sufficiently utilize the information semantics. However,considering the possible existence of semantic ambiguity in image transmission, a simple bit-level cyclic redundancy check(CRC) might compromise the performance of IK-HARQ. Therefore, there emerges a strong incentive to revolutionize the CRC mechanism,thus more effectively reaping the benefits of both SemCom and HARQ. In this paper, built on top of swin transformer-based joint source-channel coding(JSCC) and IK-HARQ, we propose a semantic image transmission framework SC-TDA-HARQ. In particular, different from the conventional CRC, we introduce a topological data analysis(TDA)-based error detection method, which capably digs out the inner topological and geometric information of images, to capture semantic information and determine the necessity for re-transmission. Extensive numerical results validate the effectiveness and efficiency of the proposed SC-TDA-HARQ framework, especially under the limited bandwidth condition, and manifest the superiority of TDA-based error detection method in image transmission.