Collaborative Semantic Communication for Edge Inference

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 propos...

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Bibliographic Details
Published in:IEEE wireless communications letters Vol. 12; no. 7; pp. 1125 - 1129
Main Authors: Lo, Wing Fei, Mital, Nitish, Wu, Haotian, Gunduz, Deniz
Format: Journal Article
Language:English
Published: Piscataway IEEE 01.07.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Collaboration
collaborative image retrieval
deep joint source and channel coding
Image edge detection
Image retrieval
Internet of Things
Multiple access
person re-identification
Random noise
Receivers
Retrieval
Semantic communication
Semantics
Task analysis
Wireless communication
ISSN:2162-2337, 2162-2345
Online Access:Get full text
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Summary: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.
Bibliography:ObjectType-Article-1
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ISSN:2162-2337
2162-2345
DOI:10.1109/LWC.2023.3256006