As a promising 6G enabler beyond conventional bit-level transmission, semantic communication can considerably reduce required bandwidth resources, while its combination with multiple access requires further exploration. This paper proposes a knowledge distillation-driven and diffusion-enhanced (KDD) semantic non-orthogonal multiple access (NOMA), named KDD-SemNOMA, for multi-user uplink wireless image transmission. Specifically, to ensure robust feature transmission across diverse transmission conditions, we firstly develop a ConvNeXt-based deep joint source and channel coding architecture with enhanced adaptive feature module. This module incorporates signal-to-noise ratio and channel state information to dynamically adapt to additive white Gaussian noise and Rayleigh fading channels. Furthermore, to improve image restoration quality without inference overhead, we introduce a two-stage knowledge distillation strategy, i.e., a teacher model, trained on interference-free orthogonal transmission, guides a student model via feature affinity distillation and cross-head prediction distillation. Moreover, a diffusion model-based refinement stage leverages generative priors to transform initial SemNOMA outputs into high-fidelity images with enhanced perceptual quality. Extensive experiments on CIFAR-10 and FFHQ-256 datasets demonstrate superior performance over state-of-the-art methods, delivering satisfactory reconstruction performance even at extremely poor channel conditions. These results highlight the advantages in both pixel-level accuracy and perceptual metrics, effectively mitigating interference and enabling high-quality image recovery.

翻译：作为超越传统比特级传输的潜在6G使能技术，语义通信可显著降低所需带宽资源，但其与多址接入的结合仍需深入探索。本文提出一种知识蒸馏驱动且扩散增强（KDD）的语义非正交多址接入（NOMA）方案，命名为KDD-SemNOMA，用于多用户上行无线图像传输。具体而言，为确保在不同传输条件下的鲁棒特征传输，我们首先构建了基于ConvNeXt的深度联合信源信道编码架构，并配备增强型自适应特征模块。该模块融合信噪比与信道状态信息，可动态适应加性高斯白噪声与瑞利衰落信道。此外，为在不增加推理开销的前提下提升图像复原质量，我们引入两阶段知识蒸馏策略：在无干扰正交传输场景下训练的教师模型，通过特征亲和蒸馏与跨头预测蒸馏指导学生模型。进一步地，基于扩散模型的精炼阶段利用生成先验，将初始SemNOMA输出转化为具有增强感知保真度的高质量图像。在CIFAR-10与FFHQ-256数据集上的大量实验表明，本方法优于现有先进方案，即使在极端恶劣信道条件下仍能实现令人满意的重建性能。这些结果凸显了本方法在像素级精度与感知度量方面的双重优势，能有效抑制干扰并实现高质量图像恢复。