VQ-VAE Based Digital Semantic Communication with Importance-Aware OFDM Transmission

TL;DR

This paper introduces a digital semantic communication system using VQ-VAE and importance-aware OFDM, improving transmission efficiency and robustness by transmitting discrete semantic features and prioritizing key information.

Contribution

It proposes a novel VQ-VAE based digital SemCom framework with importance-aware OFDM, enhancing compatibility with digital systems and improving performance over low SNR conditions.

Findings

Outperforms conventional DeepSC in reconstruction quality.

Effective discrete semantic feature transmission via VQ-VAE.

Improved robustness in low SNR environments.

Abstract

Semantic communication (SemCom) significantly reduces redundant data and improves transmission efficiency by extracting the latent features of information. However, most of the conventional deep learning-based SemCom systems focus on analog transmission and lack in compatibility with practical digital communications. This paper proposes a vector quantized-variational autoencoder (VQ-VAE) based digital SemCom system that directly transmits the semantic features and incorporates the importance-aware orthogonal frequency division multiplexing (OFDM) transmission to enhance the SemCom performance, where the VQ-VAE generates a discrete codebook shared between the transmitter and receiver. At transmitter, the latent semantic features are firstly extracted by VQ-VAE, and then the shared codebook is adopted to match these features, which are subsequently transformed into a discrete version to adapt the digital transmission. To protect the semantic information, an importance-aware OFDM transmission strategy is proposed to allocate the key features near the OFDM reference signals, where the feature importance is derived from the gradient-based method. At the receiver, the features are rematched with the shared codebook to further correct errors. Finally, experimental results demonstrate that our proposed scheme outperforms the conventional DeepSC and achieves better reconstruction performance under low SNR region.