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arXiv 2602.17032eess.SPcs.ITmath.IT
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面向环境的网络级通用捏缩天线系统设计——第二部分:几何感知案例

Environment-Aware Network-Level Design of Pinching-Antenna Systems--Part II: Geometry-Aware Case

The Chinese University of Hong Kong, Shenzhen(香港中文大学(深圳));Nanyang Technological University(南洋理工大学);South China University of Technology(华南理工大学);Pazhou Laboratory(琶洲实验室);Memorial University(纪念大学);Queen’s University Belfast(贝尔法斯特女王大学)

Yanqing Xu, Zhiguo Ding, Xiu Yin Zhang, Trung Q. Duong, Tsung-Hui Chang

AI总结:

本文提出面向环境的网络级设计框架,用于优化通用捏缩天线系统,通过几何和遮挡感知模型解决传统链路级优化的局限性。

摘要:

本文两部分论文旨在开发一种面向环境的网络级设计框架,用于通用捏缩天线系统,以克服传统链路级优化的局限性,后者紧密耦合于瞬时用户几何,并对用户移动性和定位误差敏感。第一部分研究了交通感知案例,其中用户存在通过空间交通图进行统计表征,并利用交通感知的网络级指标进行优化。第二部分则通过开发在障碍物丰富的环境中针对捏缩天线系统的几何感知和遮挡感知网络优化来补充第一部分。我们引入了一个网格级平均信噪比(SNR)模型,包含确定性的直视可见性指示器和离散激活架构,其中几何相关的项在预先计算中完成。在此模型基础上,我们提出了两个网络级激活问题:(i)平均SNR阈值覆盖最大化和(ii)公平导向的最差网格平均SNR最大化。在算法层面,我们证明了覆盖问题是NP难的,并通过二进制覆盖变量和线性SNR链接约束推导出等价的混合整数线性规划重述。为实现可扩展性,我们进一步开发了一种结构利用的坐标上升方法,通过预计算的每个候选SNR贡献逐步更新一个波导。对于最差网格目标,我们采用一个epigraph重述,并利用由此产生的目标SNR的单调可行性,从而实现一个高效的二分法求解器,具有在离散候选集上低复杂度的可行性检查。仿真结果验证了所提出的设计,并在不同环境和系统参数下量化了其增益。

英文摘要:

This two-part paper aims to develop an environment-aware network-level design framework for generalized pinching-antenna systems to overcome the limitations of conventional link-level optimization, which is tightly coupled to instantaneous user geometry and thus sensitive to user mobility and localization errors. Part I investigates the traffic-aware case, where user presence is characterized statistically by a spatial traffic map and deployments are optimized using traffic-aware network-level metrics. Part II complements Part I by developing geometry-aware, blockage-aware network optimization for pinching-antenna systems in obstacle-rich environments. We introduce a grid-level average SNR model with a deterministic LoS visibility indicator and a discrete activation architecture, where the geometry-dependent terms are computed offline in advance. Building on this model, we formulate two network-level activation problems: (i) average-SNR-threshold coverage maximization and (ii) fairness-oriented worst-grid average-SNR maximization. On the algorithmic side, we prove the coverage problem is NP-hard and derive an equivalent mix-integer linear programming reformulation through binary coverage variables and linear SNR linking constraints. To achieve scalability, we further develop a structure-exploiting coordinate-ascent method that updates one waveguide at a time using precomputed per-candidate SNR contributions. For the worst-grid objective, we adopt an epigraph reformulation and leverage the resulting monotone feasibility in the target SNR, enabling an efficient bisection-based solver with low-complexity feasibility checks over the discrete candidate set. Simulations results validate the proposed designs and quantify their gains under different environments and system parameters.

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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