CNN and Attention-Based Joint Source Channel Coding for Semantic Communications in WSNs

• Published in: Sensors (Basel, Switzerland) Vol. 24; no. 3; p. 957
• Main Authors: Liu, Xinyue, Huang, Zhen, Zhang, Yulu, Jia, Yunjian, Wen, Wanli
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 01.02.2024
• Subjects: attention mechanism; Codes; Communication; Computer peripherals industry; deep neural network; Error-correcting codes; joint source channel coding; mobile edge computing; Neural networks; Performance evaluation; Random variables; semantic communications; Semantics; Sensors; Transmitters; Wireless sensor networks; United States; semantic communications; mobile edge computing; wireless sensor networks; joint source channel coding; attention mechanism; deep neural network
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s24030957

Wireless Sensor Networks (WSNs) have emerged as an efficient solution for numerous real-time applications, attributable to their compactness, cost-effectiveness, and ease of deployment. The rapid advancement of 5G technology and mobile edge computing (MEC) in recent years has catalyzed the transition towards large-scale deployment of WSN devices. However, the resulting data proliferation and the dynamics of communication environments introduce new challenges for WSN communication: (1) ensuring robust communication in adverse environments and (2) effectively alleviating bandwidth pressure from massive data transmission. In response to the aforementioned challenges, this paper proposes a semantic communication solution. Specifically, considering the limited computational and storage resources of WSN devices, we propose a flexible Attention-based Adaptive Coding (AAC) module. This module integrates window and channel attention mechanisms, dynamically adjusts semantic information in response to the current channel state, and facilitates adaptation of a single model across various Signal-to-Noise Ratio (SNR) environments. Furthermore, to validate the effectiveness of this approach, the paper introduces an end-to-end Joint Source Channel Coding (JSCC) scheme for image semantic communication, employing the AAC module. Experimental results demonstrate that the proposed scheme surpasses existing deep JSCC schemes across datasets of varying resolutions; furthermore, they validate the efficacy of the proposed AAC module, which is capable of dynamically adjusting critical information according to the current channel state. This enables the model to be trained over a range of SNRs and obtain better results.