Two-View Image Semantic Cooperative Nonorthogonal Transmission in Distributed Edge Networks

Abstract

With the wide application of deep learning (DL) across various fields, deep joint source–channel coding (DeepJSCC) schemes have emerged as a new coding approach for image transmission. Compared with traditional separated source and CC (SSCC) schemes, DeepJSCC is more robust to the channel environment. To address the limited sensing capability of individual devices, distributed cooperative transmission is implemented among edge devices. However, this approach significantly increases communication overhead. In addition, existing distributed DeepJSCC schemes primarily focus on specific tasks, such as classification or data recovery. In this paper, we explore the wireless semantic image collaborative nonorthogonal transmission for distributed edge networks, where edge devices distributed across the network extract features of the same target image from different viewpoints and transmit these features to an edge server. A two-view distributed cooperative DeepJSCC (two-view-DC-DeepJSCC) with or without information disentanglement scheme is proposed. In particular, the two-view-DC-DeepJSCC with information disentanglement (two-view-DC-DeepJSCC-D) is proposed for achieving balancing performance between multitasking of image semantic communication; while the two-view-DC-DeepJSCC without information disentanglement only pursues outstanding data recovery performance. Through curriculum learning (CL), the proposed two-view-DC-DeepJSCC-D effectively captures both common and private information from two-view data. The edge server uses the received information to accomplish tasks such as image recovery, classification, and clustering. The experimental results demonstrate that our proposed two-view-DC-DeepJSCC-D scheme is capable of simultaneously performing image recovery, classification, and clustering tasks. In addition, the proposed two-view-DC-DeepJSCC has better recovery performance compared to the existing schemes, while the proposed two-view-DC-DeepJSCC-D not only maintains a competitive advantage in image recovery but also has a significant improvement in classification and clustering accuracy. However, the proposed two-view-DC-DeepJSCC-D will sacrifice some image recovery performance to balance multiple tasks. Furthermore, two-view-DC-DeepJSCC-D exhibits stronger robustness across various signal-to-noise ratios.