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

2025-07-27

AI Summary

In semantic communication, multi-user interference severely degrades critical semantic information—often more destructively than additive Gaussian noise. To address this, we propose a scalable multi-user semantic communication framework. First, we design a shuffle-based orthogonalization mechanism that transforms inter-user interference into Gaussian-like noise, eliminating the need for personalized joint source-channel models. Second, we introduce a lightweight universal diffusion denoising model to suppress residual interference within a single shared architecture. Third, we integrate semantic similarity-based user grouping with cooperative beamforming to jointly exploit redundancy and preserve privacy. Experiments demonstrate that our approach significantly outperforms state-of-the-art methods in semantic fidelity, interference resilience, and scalability, while incurring no additional training overhead—particularly advantageous for semantically correlated data scenarios.

Technology Category

• Machine Learning: Privacy
• Multiagent Systems: Agent Communication
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems
• Security and Privacy: Large-scale security measurements

Abstract

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.

Problem

• Eliminating inter-user interference in semantic communication systems
• Simplifying system design with a universal JSCC model
• Enhancing privacy and performance via shuffle-based orthogonalization

Innovation

• Shuffle-based orthogonalization for interference reduction
• Single universal JSCC model simplifying design
• Diffusion models mitigate transformed Gaussian-like noise