Collaborative Semantic Communication for Edge Inference

TL;DR

This paper introduces deep learning-based collaborative source and channel coding schemes for edge image retrieval, enhancing accuracy over shared wireless channels by leveraging multiple devices and adaptive techniques.

Contribution

It presents two novel JSCC schemes for collaborative edge image retrieval over wireless channels, including SNR-aware attention modules for robustness against channel mismatch.

Findings

Outperforms single-device JSCC and separation-based benchmarks

Effective across a wide range of SNRs

Improves robustness with SNR-aware attention modules

Abstract

We study the collaborative image retrieval problem at the wireless edge, where multiple edge devices capture images of the same object from different angles and locations, which are then used jointly to retrieve similar images at the edge server over a shared multiple access channel (MAC). We propose two novel deep learning-based joint source and channel coding (JSCC) schemes for the task over both additive white Gaussian noise (AWGN) and Rayleigh slow fading channels, with the aim of maximizing the retrieval accuracy under a total bandwidth constraint. The proposed schemes are evaluated on a wide range of channel signal-to-noise ratios (SNRs), and shown to outperform the single-device JSCC and the separation-based multiple-access benchmarks. We also propose two novel SNR-aware JSCC schemes with attention modules to improve the performance in the case of channel mismatch between training and test instances.