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.
In modern wireless deployments, the key performance question is often not how fast the best link can be, but how well the network serves the whole region. Operators are expected to provide broadly uniform service, avoiding persistent weakcoverage areas and large location-to-location disparities, under practical constraints such as limited site availability, irregular layouts, and evolving usage patterns. This motivates networklevel design approaches that treat performance as a spatial quantity and evaluate service quality over a region rather than at a small set of scheduled user locations [1], [2]. Despite this shift, current network-level planning is still largely built upon fixed-site infrastructures, where base stations (BS) and antenna panels are installed at predetermined locations and the network can mainly adapt through antenna tilt/beam patterns, transmit powers, and scheduling [3]- [8]. While effective in many settings, fixed-site deployments inherently lack spatial agility. Especially, when the environment is irregular (e.g., cluttered indoor spaces, partitioned halls, industrial plants, or dense urban canyons), location-dependent propagation conditions can create persistent weak-coverage regions that are difficult to remedy without costly site densification or infrastructure reconfiguration. In such cases, improving link-level performance for served users does not necessarily eliminate coverage holes or enhance the worst-location experience, because the effective radiation points remain unchanged.
In this context, generalized pinching antennas introduce a fundamentally different network-level degree of freedom by enabling reconfigurable radiation points along supporting structures, such as dielectric waveguides, leaky coaxial cables, and other pinching-inspired platforms [9]- [11]. By adjusting where energy is effectively radiated or collected along these structures, the network can shift the transmit/receive locations within the service region in a flexible and lightweight manner. This capability complements classical beam-domain adaptation by providing a new lever in the geometry domain. Instead of only shaping beams from fixed sites, the network can also reshape the physical emission/reception locations to better align with region-wide service objectives. Consequently, generalized pinching antennas open up new opportunities for environment-aware network optimization, enabling deployments that improve region-wide coverage and robustness without relying solely on dense fixed-site installations.
This two-part work aims to develop an environment-aware, network-level design methodology for generalized pinchingantenna systems by capturing two fundamental drivers of spatial performance variability. Specifically, Part I focuses on the traffic-aware setting, where user presence is modeled statistically through a spatial traffic map and network performance is assessed via traffic-aware region-wide metrics. However, traffic heterogeneity is only one side of the story. Even with identical demand distributions, service quality can differ sharply across locations due to the propagation environment, which determines where reliable links are feasible in the first place.
In many practical scenarios, such as partitioned indoor spaces, warehouses, shopping malls, and industrial floors-walls, partitions, shelves, and other obstacles create strongly geometry-dependent channel conditions. These structures give rise to line-of-sight (LoS) corridors, abrupt shadow boundaries, and persistent coverage holes that cannot be captured by traffic statistics alone. An example is shown in Fig. 1(a), which shows that the conventional fixed BS cannot guarantee an efficient region-wide coverage. This motivates Part II, which studies geometry-aware network-level generalized pinching-antenna design. Here, “geometry-aware” means that the deployment explicitly incorporates blockage-induced LoS/non-line-of-sight (NLoS) transitions (or LoS feasibility) over the service region based on environment information (e.g., obstacle maps), enabling optimization of region-wide service quality without relying on instantaneous user geometry.
Beyond the numerous studies that focus on LoS-dominated scenarios and demonstrate the benefits of generalized pinching antennas in improving spectral efficiency [12], [13], enhancing energy efficiency [14], [15], and reducing outage probability [16], [17], a growing body of work has begun to investigate blockage-aware modeling and optimization for generalized pinching-antenna systems [18]- [28]. Specifically, a first line of studies adopted probabilistic channel model to characterize LoS blockages and NLoS fading and revealed how blockage reshapes performance trends and design insights. Representative examples include analyzing the impact of LoS blockage on link reliability/outage behavior and developing optimization formulations that explicitly incorporate probabilistic LoS availability into pinching-an
This content is AI-processed based on open access ArXiv data.