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

arXiv: https://arxiv.org/abs/2602.14018 · HTML: https://mlsanity.com/paper/2602.14018/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Feb 2026.

## Built on by (most cited first, 1 of 1)

- [Markov-Enforced Discrete Diffusion Model for Digital Semantic Symbol Error Correction](https://mlsanity.com/paper/2603.22983/) (2026)

## Abstract (arXiv)

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.