# ESC-MVQ: End-to-End Semantic Communication With Multi-Codebook Vector Quantization

arXiv: https://arxiv.org/abs/2504.11709 · HTML: https://mlsanity.com/paper/2504.11709/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Apr 2025.

## Built on by (most cited first, 4 of 4)

- [Context-Aware Iterative Token Detection and Masked Transmission for Wireless Token Communication](https://mlsanity.com/paper/2601.17770/) (2026)
- [Context-Aware Wireless Token Communication via Joint Token Masking and Detection](https://mlsanity.com/paper/2605.02123/) (2026)
- [Robust Nonlinear Transform Coding: A Framework for Generalizable Joint Source-Channel Coding](https://mlsanity.com/paper/2511.18884/) (2025)
- [VQ-DSC-R: Robust Vector Quantized-Enabled Digital Semantic Communication With OFDM Transmission](https://mlsanity.com/paper/2602.15045/) (2026)

## Abstract (arXiv)

This paper proposes a novel end-to-end digital semantic communication framework based on multi-codebook vector quantization (VQ), referred to as ESC-MVQ. Unlike prior approaches that rely on end-to-end training with a specific power or modulation scheme, often under a particular channel condition, ESC-MVQ models a channel transfer function as parallel binary symmetric channels (BSCs) with trainable bit-flip probabilities. Building on this model, ESC-MVQ jointly trains multiple VQ codebooks and their associated bit-flip probabilities with a single encoder-decoder pair. To maximize inference performance when deploying ESC-MVQ in digital communication systems, we devise an optimal communication strategy that jointly optimizes codebook assignment, adaptive modulation, and power allocation. To this end, we develop an iterative algorithm that selects the most suitable VQ codebook for semantic features and flexibly allocates power and modulation schemes across the transmitted symbols. Simulation results demonstrate that ESC-MVQ, using a single encoder-decoder pair, outperforms existing digital semantic communication methods in both performance and memory efficiency, offering a scalable and adaptive solution for realizing digital semantic communication in diverse channel conditions.