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arXiv 2604.09255eess.SP
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语义特征多址接入赋能的集成学习与通信网络

Semantic Feature Multiple Access Empowered Integrated Learning and Communication Networks

King’s College London（伦敦国王学院）；Zhejiang University（浙江大学）；Tsinghua University（清华大学）；University of Miami（迈阿密大学）

Jiaxiang Wang, Zhouxiang Zhao, Yahao Ding, Zhijin Qin, Zhaohui Yang, Mingzhe Chen, Mohammad Shikh-Bahaei

备注：
13 pages, 8 figures
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AI总结：

本文提出基于Swin Transformer的SC-SFMA传输器，通过双条件相似性模ulator实现跨用户特征融合，结合二元对决策与连续资源变量，解决混合整数非凸优化问题，提升系统吞吐量和质量指标。

摘要：

集成学习与通信（ILAC）将学习的收发器与无线资源管理统一，其中语义特征多址接入（SFMA）使配对用户能将学习到的表示叠加在共享的时间-频率资源上。不同于传统多址方案，SFMA干扰出现在学习特征空间，并依赖于用户对、发射功率和压缩比。这种耦合将二进制配对决策与连续资源变量联系起来，产生混合整数非凸优化问题。为此，我们首先提出相似性条件的SFMA（SC-SFMA），一种基于Swin Transformer的收发器，其双条件相似性模ulator（DC-SimM）根据用户间语义相似性门控跨用户特征融合。我们随后通过双变量逻辑函数参数化发射功率和压缩比，从而将学习收发器与网络级优化联系起来。在此基础上，我们提出一个在用户失真、延迟、能量、功率和带宽约束下的总速率最大化问题。为解决此问题，我们开发了三块交替优化算法，整合双分解辅助的压缩比分配、信任区域逐次凸逼近（SCA）用于联合功率-带宽优化，以及动态可行图基于的用户配对。仿真结果表明，SC-SFMA在深度联合源-信道编码（JSCC）和基于分离的基线中实现了显著的峰值信噪比（PSNR）和多尺度结构相似性指数度量（MS-SSIM）增益。所提出的优化框架在传统多址基线中实现了显著的总速率提升。

英文摘要：

Integrated learning and communication (ILAC) unifies learned transceivers with radio resource management, where semantic feature multiple access (SFMA) enables paired users to superpose their learned representations over shared time-frequency resources. Unlike conventional multiple access schemes, SFMA interference arises in the learned feature space and depends jointly on the user pair, the transmit power, and the compression ratio. This coupling ties binary pairing decisions to continuous resource variables, yielding a mixed-integer non-convex optimization problem. To address this problem, we first propose similarity-conditioned SFMA (SC-SFMA), a Swin Transformer-based transceiver whose dual-conditioned similarity modulator (DC-SimM) gates cross-user feature fusion according to the inter-user semantic similarity. We then characterize the resulting pair-dependent interference by a bivariate logistic function parameterized by transmit power and compression ratio, thereby bridging the learned transceiver with network-level optimization. On this basis, we formulate a sum-rate maximization problem subject to per-user distortion, latency, energy, power, and bandwidth constraints. To solve this problem, we develop a three-block alternating optimization algorithm that integrates dual-decomposition-assisted compression ratio allocation, trust-region successive convex approximation (SCA) for joint power-bandwidth optimization, and dynamic feasible graph-based user pairing. Simulation results show that SC-SFMA achieves considerable peak signal-to-noise ratio (PSNR) and multi-scale structural similarity index measure (MS-SSIM) gains over deep joint source-channel coding (JSCC) and separation-based baselines. The proposed optimization framework attains significant sum rate improvements over conventional multiple access baselines.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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