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

TL;DR

A topological data analysis (TDA)-based error detection method is introduced, which capably digs out the inner topological and geometric information of images, to capture semantic information and determine the necessity for re-transmission in image transmission.

Abstract

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]) is 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, it emerges a strong incentive to revolutionize the CRC mechanism, so as to reap 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, so as 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.

Figures (16)

• Figure 1: The semantic similarity of images detected by Sim32 and TDA-based decision network during the training procedure.
• Figure 2: An example of a filtered cubical complex's PH. (a) A $3 \times 3$ grayscale image. (b) Different dimension of cubes that constitutes the cubical complex. (c) The filtration process of the image. (d) The corresponding persistence barcode. (e) The corresponding persistence diagram.
• Figure 3: The pipeline of the transformer-enabled SemCom system with IK-HARQ and TDA-based error detection.
• Figure 4: The workflow of semantic IK-HARQ at the receiver.
• Figure 5: The training procedures of SC-TDA-HARQ. Modules colored in yellow are designated to be actively trained. Modules in green indicate that their weights are pre-loaded but not subject to further training during this phase. The modules shown in gray are not included in the training process at this stage.