Collaborative Semantic Communication for Edge Inference
Collaborative Semantic Communication for Edge Inference
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DOI：
10.1109/lwc.2023.3256006
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发表时间：
2023-01
影响因子：
6.3
通讯作者：
W. F. Lo;N. Mital;Haotian Wu;Deniz Gündüz
中科院分区：
文献类型：
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作者：
W. F. Lo;N. Mital;Haotian Wu;Deniz Gündüz
我们研究了无线边缘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捕获同一物体的图像，然后通过共享多址通道（MAC）在边缘服务器上联合使用这些图像来检索相似的图像。我们提出了两种基于深度学习的联合源信道编码（JSCC）方案，用于加性高斯白噪声（AWGN）和瑞利慢衰落信道上的任务，目的是在总带宽约束下最大化检索精度。所提出的方案在大范围的信道信噪比（SNRs）上进行了评估，并显示出优于单设备JSCC和基于分离的多址基准测试。我们还提出了一个具有关注模块的信道状态信息感知JSCC方案，使我们的方法能够适应不同的信道条件。
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.