AI 中文总结
本文针对语义通信中多用户并发传输的语义冲突等问题，提出SIDMA技术，通过置换算子白化语义特征并结合ImpPA模块，在交织域分散核心特征并自适应优化功率，相比传统及先进方案，提升了多用户并发传输的通信质量和鲁棒性。
AI 中文摘要
多址（MA）技术一直是移动通信发展的核心驱动力。语义通信作为下一代通信的一种有前途的范式，通过挖掘信息的深层含义探索全新的语义空间资源。然而，语义特征固有的空间相关性和重要性异质性在多用户并发传输场景中常导致语义冲突和语义崩溃。为应对这些挑战，本文提出语义交织多址（SIDMA）技术。通过使用置换算子对语义特征进行结构白化，并将其与重要性感知功率分配（ImpPA）模块相结合以进行差异化保护，SIDMA在交织域中分散核心特征，并根据实时信道条件自适应优化功率水平。仿真结果表明，与传统MA技术和包括正交模型多址（OMDMA）、深度多址（DeepMA）和共享嵌入（SE）在内的先进语义多址方案相比，所提出的SIDMA在多用户并发传输中表现出卓越的重建保真度和可扩展性，有效提高了资源受限环境下的通信质量和鲁棒性。
英文摘要
Multiple Access (MA) technology has consistently served as the core driving force behind the evolution of mobile communications. As a promising paradigm for next-generation communications, Semantic Communication explores entirely new semantic spatial resources by mining the deep meaning of information. However, the inherent spatial correlation and importance heterogeneity of semantic features often cause semantic collisions and semantic collapse in multi-user concurrent transmission scenarios. To address these challenges, this paper proposes a Semantic Interleaved Division Multiple Access (SIDMA) technique. By utilizing a permutation operator to perform structural whitening on semantic features and combining it with an Importance-aware Power Allocation (ImpPA) module for differentiated protection, SIDMA scatters core features across the interleaving domain and adaptively optimizes power levels based on real-time channel conditions. Simulation results demonstrate that, compared with traditional MA techniques and advanced semantic multiple access schemes including Orthogonal-Model Division Multiple Access (OMDMA), Deep Multiple Access (DeepMA), and Shared Embedding (SE), the proposed SIDMA exhibits superior reconstruction fidelity and scalability in multi-user concurrent transmissions, effectively enhancing the communication quality and robustness in resource-constrained environments.
Comments13 pages, 7 figures, journal
