SNR Adaptive Attention Joint Source Channel Coding for Enhanced Image Transmission in Wireless Vehicular System

Reliable image transmission is critical for applications such as autonomous driving, collision avoidance, and real time traffic monitoring in the field of vehicles. However, extreme and dynamic vehicular environments impose significant challenges on reliable image transmission. Traditional communication methods that separately design source and channel coding fail to fully exploit the signal-to-noise ratio (SNR) information in vehicular channels and lead to suboptimal performance. Although recent deep learning (DL) based approaches have shown promising results in enhancing image transmission, they are often constrained by high storage requirements and limited adaptability to the highly variable channel conditions in vehicular environments. In this paper, a novel DL-based SNR adaptive attention joint source channel coding (SAA-JSCC) framework tailored for vehicular wireless communication systems is proposed. The proposed SAA-JSCC dynamically adjusts encoding and decoding strategies in real time by leveraging instantaneous SNR feedback, thereby optimizing transmission efficiency and reliability. A deep neural network is employed to directly map input RGB images to complex-valued baseband samples, while the receiver integrates the decoding and decision-making processes within its neural architecture. Comprehensive simulations under diverse vehicular channel conditions demonstrate that the SAA-JSCC approach significantly outperforms existing JSCC methods in terms of Peak Signal-to-Noise Ratio (PSNR) for image transmission, particularly under low SNR scenarios prevalent in vehicular environments. Furthermore, the proposed method delivers significant PSNR gain with a modest increase in FLOPs and negligible parameter growth, while remaining far smaller model size than prior deep baselines, thereby facilitating more efficient and scalable real time communication in next-generation vehicular networks.