用于支持6G的联网自动驾驶车辆的基于VAE的多任务卫星辅助语义通信框架
A VAE-Driven Multi-Task Satellite-Aided Semantic Communication Framework for 6G-Enabled Connected Autonomous Vehicles
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中文总结 AI 辅助
针对智能交通系统中联网自动驾驶车辆的通信需求，提出基于VAE的多任务卫星辅助语义通信框架，利用概率潜在表示编码，经噪声信道传输特征进行交通标志重建与分类，能显著降低带宽并保持性能稳定。
中文摘要 AI 辅助
智能交通系统的发展和6G无线通信技术的引入显著改变了车辆网络拓扑结构。未来的联网自动驾驶（CAV）网络需要高效带宽、可靠且低延迟的通信以用于交通标志识别和决策等安全关键应用。传统通信系统传输原始数据，在卫星信道资源受限的情况下效率低下。语义通信通过传输任务相关信息解决此局限。我们提出了基于变分自编码器（VAE）的多任务语义通信框架用于卫星辅助自动驾驶。该模型使用概率潜在表示进行更稳健高效的编码，通过噪声无线信道传输学习到的特征以执行交通标志重建和分类，端到端训练以联合优化两个任务。结果表明该方法在不同信噪比条件下保持稳定性能的同时可显著降低带宽达87.23%至98.17%。
英文摘要
The development of smart transportation systems and the introduction of 6G wireless communication technologies have significantly changed vehicle network topologies. Future connected autonomous vehicle (CAV) networks require bandwidth-efficient, reliable, and low-latency communication for safety-critical applications such as traffic sign recognition and decision-making. Conventional communication systems transmit raw data regardless of task relevance, which is inefficient in resource-constrained satellite channels where uplink bandwidth is scarce and propagation losses are large. Semantic communication addresses this limitation by transmitting task-relevant information instead of full signal representations. It extracts and conveys essential semantic features and leverages deep learning to optimize task performance at the receiver. Therefore, we present a Variational Autoencoder (VAE)-based multi-task semantic communication framework for satellite-assisted autonomous driving. Unlike deterministic autoencoder-based methods, the proposed model uses probabilistic latent representations for more robust and efficient encoding. The learned features are transmitted over noisy wireless channels to perform traffic sign reconstruction and classification. The framework is trained end-to-end to jointly optimize both tasks. Results show that the proposed approach achieves significant bandwidth reduction of up to 87.23\% to 98.17\% while maintaining stable performance across varying signal-to-noise ratio conditions.
