We study the image retrieval problem at the wireless edge, where an edge device captures an image, which is then used to retrieve similar images from an edge server. These can be images of the same person or a vehicle taken from other cameras at different times and locations. Our goal is to maximize the accuracy of the retrieval task under power and bandwidth constraints over the wireless link. Due to the stringent delay constraint of the underlying application, sending the whole image at a sufficient quality is not possible. We propose two alternative schemes based on digital and analog communications, respectively. In the digital approach, we first propose a deep neural network (DNN) aided retrieval-oriented image compression scheme, whose output bit sequence is transmitted over the channel using conventional channel codes. In the analog joint source and channel coding (JSCC) approach, the feature vectors are directly mapped into channel symbols. We evaluate both schemes on image based re-identification (re-ID) tasks under different channel conditions, including both static and fading channels. We show that the JSCC scheme significantly increases the end-to-end accuracy, speeds up the encoding process, and provides graceful degradation with channel conditions. The proposed architecture is evaluated through extensive simulations on different datasets and channel conditions, as well as through ablation studies.

中文翻译：

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边缘无线图像检索

我们研究无线边缘的图像检索问题，其中边缘设备捕获图像，然后用于从边缘服务器检索相似图像。这些可以是同一个人或车辆在不同时间和地点从其他摄像机拍摄的图像。我们的目标是在无线链路上的功率和带宽限制下最大限度地提高检索任务的准确性。由于底层应用程序的严格延迟限制，以足够的质量发送整个图像是不可能的。我们分别提出了基于数字和模拟通信的两种替代方案。在数字方法中，我们首先提出了一种深度神经网络 (DNN) 辅助检索式图像压缩方案，其输出位序列使用传统的信道代码通过信道传输。在模拟联合源和信道编码 (JSCC) 方法中，特征向量直接映射到信道符号。我们在不同信道条件下评估基于图像的重新识别 (re-ID) 任务的两种方案，包括静态和衰落信道。我们表明 JSCC 方案显着提高了端到端的准确性，加快了编码过程，并提供了信道条件下的优雅降级。通过对不同数据集和通道条件的广泛模拟以及消融研究来评估所提出的架构。我们表明 JSCC 方案显着提高了端到端的准确性，加快了编码过程，并提供了信道条件下的优雅降级。通过对不同数据集和通道条件的广泛模拟以及消融研究来评估所提出的架构。我们表明 JSCC 方案显着提高了端到端的准确性，加快了编码过程，并提供了信道条件下的优雅降级。通过对不同数据集和通道条件的广泛模拟以及消融研究来评估所提出的架构。