6G机器人车用语义通信框架，提升图像传输质量与能效。
Knowledge Distillation Driven Semantic NOMA with GAN Refinement for 6G Robotic Vehicle Networks
- 用知识蒸馏让模型轻量化，减少通信干扰
- 引入条件生成对抗网络，修复图像失真细节
- 适合低带宽高精度的智能网联车场景
为实现可持续智能出行，6G赋能的机器人车辆（RVs）需在严苛带宽与能耗约束下实现高保真视觉感知。语义通信虽具频谱效率优势，但在上行非正交多址（NOMA）RV网络中面临严重干扰问题。为此，提出一种基于知识蒸馏与生成模型增强的鲁棒绿色通信框架——KDG-SemNOMA。首先，设计基于ConvNeXt的深度联合源信道编码（DeepJSCC）架构，并引入增强注意力特征（AF）模块以实现动态信道适应。其次，通过两阶段知识蒸馏策略，由正交传输教师模型指导NOMA学生模型，有效抑制干扰且无推理开销。最后，针对像素级优化带来的过平滑伪影，引入通道条件生成对抗网络（cGAN）。该模块以第一阶段初始重建结果和信道状态为条件输入，将粗略输出精细化为具备真实纹理的高质量图像。在FFHQ-256数据集上的实验表明，KDG-SemNOMA在像素级准确率与感知保真度方面均显著优于现有先进方法。
原文摘要 · Abstract (English)
To achieve sustainable intelligent mobility, 6G-empowered robotic vehicles (RVs) require high-fidelity visual perception under stringent bandwidth and energy constraints. Semantic communication offers a spectral-efficient solution but suffers from severe interference in uplink non-orthogonal multiple access (NOMA) RV networks. To address this, we propose a knowledge distillation-driven and generative models-enhanced NOMA framework for robust and green RV communications, named KDG-SemNOMA. First, we develop a ConvNeXt-based deep joint source-channel coding (DeepJSCC) architecture with an enhanced attention feature (AF) module for dynamic channel adaptation. Second, to mitigate interference without inference overhead, an orthogonal transmission teacher model guides the NOMA student model via a two-stage knowledge distillation strategy. Finally, to address the over-smoothing artifacts of pixel-wise optimization, we introduce a channel-conditional GAN (cGAN). By explicitly taking the Stage-I initial reconstruction and channel states as conditional inputs, this module refines coarse outputs into high-fidelity images with realistic textures. Experiments on FFHQ-256 demonstrate that KDG-SemNOMA significantly outperforms state-of-the-art methods in both pixel-level accuracy and perceptual fidelity.
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