语义特征多址网络中的联合设备配对与带宽分配优化
Joint Device Pairing and Bandwidth Allocation Optimisation for Semantic Feature Multiple Access Networks
AI总结：
本文提出基于语义特征多址网络的联合设备配对与带宽分配优化方法，通过交叉用户注意模块实现受控语义特征交换，降低干扰并提升重建质量。
摘要：
本文提出了一种语义特征多址（SFMA）框架，用于下行无线系统中的多用户语义通信。通过将SwinJSCC扩展为双用户叠加范式，SFMA实现了在共享时频资源上的多用户同时语义传输。关键创新是交叉用户注意（CUA）模块，该模块通过利用图像间相似性实现配对用户间的受控语义特征交换，同时缓解干扰。我们提出一个联合用户配对和资源分配问题，以在带宽、端到端延迟和能量约束下最小化全局语义失真。该混合整数非凸问题被分解为最小权完美匹配（MWPM）子问题和凸带宽分配可行性检查，其中半闭合形式的带宽界限从严格凹率表达式中推导出来。提出了一种基于Blossom匹配和二分搜索的多项式时间算法。在ImageNet-100上的广泛模拟显示，SFMA在配对模式下显著提高了重建质量，所提出的优化有效减少了总体失真，同时满足物理层约束。
英文摘要：
This paper presents a Semantic Feature Multiple Access (SFMA) framework for multi-user semantic communication in downlink wireless systems. By extending SwinJSCC to a two-user superimposition paradigm, SFMA enables simultaneous semantic transmission to multiple users over shared time-frequency resources. A key innovation is the Cross-User Attention (CUA) module, which facilitates controlled semantic feature exchange between paired users by leveraging inter-image similarity while mitigating interference. We formulate a joint user pairing and resource allocation problem to minimize global semantic distortion under constraints on bandwidth, end-to-end latency, and energy. This mixed-integer non-convex problem is decomposed into a Minimum-Weight Perfect Matching (MWPM) sub-problem and a convex bandwidth allocation feasibility check, with semi-closed-form bandwidth bounds derived from a strictly concave rate expression. A polynomial-time algorithm based on Blossom matching and bisection search is proposed. Extensive simulations on ImageNet-100 show that SFMA significantly improves reconstruction quality across pairing modes, and the proposed optimization effectively reduces overall distortion while satisfying physical-layer constraints.
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