Adaptive HARQ Design for Semantic Image Transmission

Semantic communication is a promising framework for the next generation communication systems, which generally adopts deep learning based joint source and channel coding and has been verified to offer superior efficacy. A key ingredient in augmenting the reliability of this framework is the incorporation of hybrid automatic repeat request (HARQ) techniques. However, existing semantic HARQ architectures, such as fixed-length HARQ or chase combining HARQ (CC-HARQ), utilize predefined retransmission code lengths, lacking the flexibility to adjust to different channel signal-to-noise ratio (SNR) conditions. To address this issue, this paper develops an adaptive HARQ scheme by leveraging the double deep Q-network (DDQN) to determine the retransmission code lengths. Specifically, we first propose a basic model which consists of an image reconstruction module and a performance estimation module. The performance estimation module replaces the conventional error detection method like cyclic redundancy check (CRC) to estimate the structural similarity index measure (SSIM) of the reconstructed image at the receiver. Building on this basic model, our proposed HARQ scheme works by feeding back an NACK signal and an appropriate code length determined by the proposed DDQN algorithm to the semantic transmitter for the next transmission, if the estimated SSIM performance of the previous transmission does not exceed a predefined threshold. Experimental results demonstrate that our HARQ scheme is able to achieve the same SSIM performance as the existing semantic HARQ schemes, but with significantly reduced communication cost.