Existing studies on generalized pinching-antenna systems are predominantly link-level, which optimize system parameters for a given user set with objectives defined by per-user performance metrics. Such designs do not capture network-level requirements, e.g., region-wide coverage and location fairness, and may require frequent re-optimization as users move or enter/leave, incurring control overhead and sensitivity to localization errors. Motivated by this gap, this two-part paper aims to develop an environment-aware network-level design framework for generalized pinching-antenna systems. Part I focuses on the traffic-aware case, where user presence is modeled statistically by a spatial traffic map and performance is optimized and evaluated in a traffic-aware sense; Part II addresses the geometry-aware case in obstacle-rich environments. In Part~I, we introduce traffic-weighted average SNR metrics and formulate two traffic-aware deployment problems: (i) maximizing the traffic-weighted network average SNR, and (ii) a fairness-oriented traffic-restricted max--min average-SNR design over traffic-dominant grids. To solve these nonconvex problems with low complexity, we reveal and exploit their separable structures. For the network-average objective, we establish unimodality properties of the hotspot-induced components and develop a candidate-based global maximization method that only needs to evaluate the objective at a small set of candidate antenna positions. For the traffic-restricted max--min objective, we develop a block coordinate decent framework where each coordinate update reduces to a globally solvable one-dimensional subproblem via an epigraph reformulation and bisection. Simulations show that traffic-aware pinching-antenna positioning consistently outperforms representative fixed and heuristic traffic-aware deployments in the considered setups.

翻译：现有关于广义夹持天线系统的研究主要集中于链路级，其针对给定用户集合优化系统参数，目标由单用户性能指标定义。此类设计未能捕获网络级需求，例如区域全覆盖和位置公平性，且可能因用户移动或进出而需要频繁重新优化，导致控制开销增大并对定位误差敏感。基于此研究空白，本两系列论文旨在为广义夹持天线系统开发一种环境感知的网络级设计框架。第一部分聚焦于流量感知场景，其中用户存在性通过空间流量图进行统计建模，性能在流量感知意义上进行优化与评估；第二部分将解决障碍物密集环境中的几何感知场景。在第一部分中，我们引入了流量加权平均信噪比度量，并构建了两个流量感知部署问题：(i) 最大化流量加权网络平均信噪比；(ii) 在流量主导网格上实现面向公平性的流量受限最大-最小平均信噪比设计。为以低复杂度求解这些非凸问题，我们揭示并利用了其可分离结构。针对网络平均目标，我们建立了热点诱导分量的单峰性性质，并开发了一种基于候选位置的全局最大化方法，该方法仅需在少量候选天线位置处评估目标函数。对于流量受限的最大-最小目标，我们提出了块坐标下降框架，其中每个坐标更新通过上镜图重构和二分法转化为可全局求解的一维子问题。仿真表明，在所考虑的场景中，流量感知的夹持天线定位方案持续优于代表性的固定部署及启发式流量感知部署方法。