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.

翻译：本文研究无线边缘环境下的协同图像检索问题，其中多个边缘设备从不同角度和位置拍摄同一物体的图像，并通过共享多址接入信道（MAC）联合用于边缘服务器端的相似图像检索。针对加性白高斯噪声（AWGN）和瑞利慢衰落信道，我们提出两种基于深度学习的新型联合信源信道编码（JSCC）方案，旨在总带宽约束下最大化检索准确率。所提方案在广泛的信道信噪比（SNR）范围内进行评估，并显示出优于单设备JSCC和基于分离的多址接入基准方案。我们还提出了两种带有注意力模块的新型SNR感知JSCC方案，以改善训练与测试实例间存在信道失配时的性能。