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

2026-02-18

AI Summary

This work proposes the first geometry- and occlusion-aware network-level optimization framework for pinching-antenna systems, addressing the limitations of conventional link-level approaches that are sensitive to user geometry and struggle with mobility and localization errors—particularly in obstacle-dense environments where robustness is lacking. The framework leverages a grid-based average SNR model incorporating a deterministic line-of-sight indicator and a discrete waveguide activation architecture, combined with offline precomputed geometric terms to jointly optimize coverage and fairness. An efficient solution is achieved through an algorithmic fusion of mixed-integer linear programming, coordinate ascent, epigraph reformulation, and binary search. Simulations demonstrate that the proposed method significantly enhances both overall coverage and worst-case grid performance across diverse environments and parameter settings, while maintaining scalability and practical deployability.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Mixed Discrete/Continuous Search
• Machine Learning: Optimization

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

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 signal-to-noise (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.

Problem

• pinching-antenna systems
• environment-aware design
• user mobility
• localization errors
• obstacle-rich environments

Innovation

• geometry-aware optimization
• pinching-antenna systems
• network-level design
• blockage-aware SNR modeling
• coordinate-ascent algorithm