VQ-DeepISC: Vector Quantized-Enabled Digital Semantic Communication with Channel Adaptive Image Transmission

TL;DR

This paper introduces VQ-DeepISC, a novel vector quantized digital semantic communication system with channel adaptation, enabling efficient, robust image transmission by combining deep source-channel coding, hierarchical feature extraction, and adaptive index transmission.

Contribution

The paper presents a new VQ-enabled semantic communication framework with channel adaptation, addressing codebook collapse and optimizing index transmission for improved image reconstruction.

Findings

Superior reconstruction fidelity compared to benchmarks

Effective channel adaptation improves transmission robustness

Stable training with regularization and EMA techniques

Abstract

Discretization of semantic features enables interoperability between semantic and digital communication systems, showing significant potential for practical applications. The fundamental difficulty in digitizing semantic features stems from the need to preserve continuity and context in inherently analog representations during their compression into discrete symbols while ensuring robustness to channel degradation. In this paper, we propose a vector quantized (VQ)-enabled digital semantic communication system with channel adaptive image transmission, named VQ-DeepISC. Guided by deep joint source-channel coding (DJSCC), we first design a Swin Transformer backbone for hierarchical semantic feature extraction, followed by VQ modules projecting features into discrete latent spaces. Consequently, it enables efficient index-based transmission instead of raw feature transmission. To further optimize this process, we develop an attention mechanism-driven channel adaptation module to dynamically optimize index transmission. Secondly, to counteract codebook collapse during training process, we impose a distributional regularization by minimizing the Kullback-Leibler divergence (KLD) between codeword usage frequencies and a uniform prior. Meanwhile, exponential moving average (EMA) is employed to stabilize training and ensure balanced feature coverage during codebook updates. Finally, digital communication is implemented using quadrature phase shift keying (QPSK) modulation alongside orthogonal frequency division multiplexing (OFDM), adhering to the IEEE 802.11a standard. Experimental results demonstrate superior reconstruction fidelity of the proposed system over benchmark methods.