Lightweight Low-SNR-Robust Semantic Communication System for Autonomous Driving

TL;DR

This paper introduces a lightweight, robust semantic communication system for autonomous driving that maintains high image quality and robustness in low SNR environments through model pruning and digital-compatible modulation.

Contribution

It proposes a novel deep joint source-channel coding scheme with structured pruning and a quantization-modulation scheme, improving low-SNR robustness and digital compatibility.

Findings

Pruned model retains performance with over 50% parameter reduction.

Significant robustness improvements over traditional methods at low SNR.

Maintains image reconstruction quality despite model complexity reduction.

Abstract

Image transmission for vehicle-to-vehicle collaborative perception in autonomous driving faces challenges including limited on-board terminal resources, time-varying wireless channel fading, and poor robustness under low signal-to-noise (SNR) ratio. Traditional separate source-channel coding schemes suffer from the cliff effect, while existing semantic communication models are limited by large parameter sizes and weak digital compatibility. This paper proposes a lightweight, low-SNR-robust deep joint source-channel coding (JSCC) semantic communication system. First, structured pruning is implemented based on batch normalization layer scaling factors and L1 regularization, which significantly reduces model complexity while ensuring image reconstruction quality. Second, a uniform quantization and M-QAM modulation scheme adapted to JSCC features is designed, and a training-deployment separation strategy is adopted to address the non-differentiable quantization problem, enabling compatibility with existing digital communication systems. Simulation results on the Cityscapes dataset show that the pruned model maintains comparable performance and robustness to the original one, even with over half of its parameters removed. Notably, the proposed scheme exhibits significant advantages over conventional communication methods under low SNR conditions.