Edge computing based vector quantized semantic communication system for wireless image transmission

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Lokumarambage, Maheshi and Sivalingam, Thushan and Rezaei, Hossein and Rajatheva, Premanandana and Fernando, Anil (2026) Edge computing based vector quantized semantic communication system for wireless image transmission. IEEE Transactions on Consumer Electronics. ISSN 0098-3063 (https://doi.org/10.1109/tce.2026.3683921)

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Abstract

The rapid growth of multimedia content in consumer electronics devices in edge scenarios, such as smart camera sensors, autonomous navigation, and wireless security systems, necessitates more efficient communication systems. Semantic communication (SemCom) systems enhance transmission efficiency by focusing on semantic content. However, design challenges persist, including limitations in generalizing semantic source coding, maintaining channel-agnostic performance, and ensuring robustness against channel noise. We propose a novel SemCom architecture leveraging vector quantization(VQ) and edge inference to enhance semantic content representation. This architecture models the underlying data distribution, enabling efficient and accurate predictions of unseen data. By transmitting only the indices of relevant codebook vectors extracted at the edge device, our SemCom architecture minimizes communication overhead while maintaining robust semantic content representation. The system focuses on two receiver tasks, reconstruction and classification. Evaluations demonstrate the system’s superior performance over better portable graphics (BPG) and joint source-channel coding (JSCC) in terms of perceptual quality, with low-density parity-check (LDPC) coding across both additive white Gaussian noise (AWGN) and Rayleigh flat fading channels, as well as classification accuracy with polar coding over the AWGN channel. Experimental results reveal a 25% reduction in transmission overhead compared to JSCC with LDPC coding with a code rate of 0.5 for varying signal-to-noise ratio (SNR)s, while maintaining lower overall computational complexity. Additionally, the model is validated using out-of-distribution and high-resolution datasets, validating its adaptability and scalability to diverse consumer electronics scenarios. The classification model achieved 85.52% training accuracy, with 72.25% and 50.88% accuracy at high and low SNR levels, respectively.

ORCID iDs

Lokumarambage, Maheshi, Sivalingam, Thushan, Rezaei, Hossein, Rajatheva, Premanandana and Fernando, Anil ORCID logoORCID: https://orcid.org/0000-0002-2158-2367;
CORE (COnnecting REpositories)