Extended Universal Joint Source-Channel Coding for Digital Semantic Communications: Improving Channel-Adaptability

TL;DR

This paper introduces euJSCC, a novel deep learning-based joint source-channel coding framework that adaptively adjusts to varying SNRs and modulation schemes, significantly enhancing digital semantic communication robustness.

Contribution

euJSCC integrates a hypernetwork normalization and dynamic codebook generation to enable SNR- and modulation-adaptive transmission within a single model, improving flexibility over fixed schemes.

Findings

Outperforms state-of-the-art digital JSCC schemes in experiments.

Effective in both block fading and AWGN channels.

Enhances robustness and adaptability of semantic communication systems.

Abstract

Recent advances in deep learning (DL)-based joint source-channel coding (JSCC) have enabled efficient semantic communication in dynamic wireless environments. Among these approaches, vector quantization (VQ)-based JSCC effectively maps high-dimensional semantic feature vectors into compact codeword indices for digital modulation. However, existing methods, including universal JSCC (uJSCC), rely on fixed, modulation-specific encoders, decoders, and codebooks, limiting adaptability to fine-grained SNR variations. We propose an extended universal JSCC (euJSCC) framework that achieves SNR- and modulation-adaptive transmission within a single model. euJSCC employs a hypernetwork-based normalization layer for fine-grained feature vector normalization and a dynamic codebook generation (DCG) network that refines modulation-specific base codebooks according to block-wise SNR. To handle block fading channels, which consist of multiple coherence blocks, an inner-outer encoder-decoder architecture is adopted, where the outer encoder and decoder capture long-term channel statistics, and the inner encoder and decoder refine feature vectors to align with block-wise codebooks. A two-phase training strategy, i.e., pretraining on AWGN channels followed by finetuning on block fading channels, ensures stable convergence. Experiments on image transmission demonstrate that euJSCC consistently outperforms state-of-the-art channel-adaptive digital JSCC schemes under both block fading and AWGN channels.