标题： 重新思考语义域中的多用户通信：基于洗牌正交化和扩散去噪的增强型OMDMA 显示英文标题

标题： Rethinking Multi-User Communication in Semantic Domain: Enhanced OMDMA by Shuffle-Based Orthogonalization and Diffusion Denoising

摘要： 用户间干扰仍然是无线通信系统中的关键瓶颈，特别是在语义通信（SemCom）这一新兴范式中。 与传统系统相比，SemCom中的用户间干扰会严重破坏关键的语义信息，通常在相同功率水平下表现比高斯噪声更差。 为解决这一挑战，我们受到最近提出的正交模型划分多址接入（OMDMA）概念的启发，该概念利用基于个性化联合源信道编码（JSCC）模型的语义正交性来区分用户，我们提出了一种新颖且可扩展的框架，不再需要像原始OMDMA那样使用用户特定的JSCC模型。 我们的关键创新在于基于打乱的正交化，其中随机打乱JSCC特征向量的位置将用户间干扰转化为类似高斯噪声。 通过为每个用户分配唯一的打乱模式，干扰被当作信道噪声处理，从而可以使用扩散模型（DMs）进行有效缓解。 这种方法不仅通过只需要一个通用的JSCC模型简化了系统设计，还增强了隐私性，因为打乱模式充当隐式的私钥。 此外，我们将该框架扩展到涉及语义相关数据的场景。 通过基于语义相似性对用户进行分组，引入了一种协作波束成形策略，以利用相关数据中的冗余，进一步提高系统性能。 大量仿真结果表明，所提出的方法优于最先进的多用户SemCom框架，在不增加额外训练开销的情况下，实现了更高的语义保真度、抗干扰能力和可扩展性。 显示英文摘要

摘要： Inter-user interference remains a critical bottleneck in wireless communication systems, particularly in the emerging paradigm of semantic communication (SemCom). Compared to traditional systems, inter-user interference in SemCom severely degrades key semantic information, often causing worse performance than Gaussian noise under the same power level. To address this challenge, inspired by the recently proposed concept of Orthogonal Model Division Multiple Access (OMDMA) that leverages semantic orthogonality rooted in the personalized joint source and channel (JSCC) models to distinguish users, we propose a novel, scalable framework that eliminates the need for user-specific JSCC models as did in original OMDMA. Our key innovation lies in shuffle-based orthogonalization, where randomly permuting the positions of JSCC feature vectors transforms inter-user interference into Gaussian-like noise. By assigning each user a unique shuffling pattern, the interference is treated as channel noise, enabling effective mitigation using diffusion models (DMs). This approach not only simplifies system design by requiring a single universal JSCC model but also enhances privacy, as shuffling patterns act as implicit private keys. Additionally, we extend the framework to scenarios involving semantically correlated data. By grouping users based on semantic similarity, a cooperative beamforming strategy is introduced to exploit redundancy in correlated data, further improving system performance. Extensive simulations demonstrate that the proposed method outperforms state-of-the-art multi-user SemCom frameworks, achieving superior semantic fidelity, robustness to interference, and scalability-all without requiring additional training overhead.