期刊
IEEE WIRELESS COMMUNICATIONS LETTERS
卷 12, 期 7, 页码 1125-1129出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LWC.2023.3256006
关键词
Semantic communication; Internet of Things; person re-identification; deep joint source and channel coding; collaborative image retrieval
We propose two novel deep learning-based joint source and channel coding (JSCC) schemes for collaborative image retrieval problem at the wireless edge. The proposed schemes outperform the single-device JSCC and separation-based multiple-access benchmarks. We also propose a channel state information-aware JSCC scheme with attention modules to adapt to varying channel conditions.
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 a channel state information-aware JSCC scheme with attention modules to enable our method to adapt to varying channel conditions.
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