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.
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 by explicitly modeling line-of-sight blocking and optimizing region-wide robustness objectives. In Part I, we introduce trafficweighted average signal-to-noise ratio (SNR) metrics and formulate two traffic-aware deployment problems: (i) maximizing the traffic-weighted network average SNR, and (ii) a fairnessoriented 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.
Index Terms-Generalized pinching antenna, environmentaware design, traffic-weighted optimization, traffic-restricted optimization.
Wireless systems are increasingly assessed by area-wide service quality rather than the peak rate of a single scheduled link. In practice, operators must provide reliable and fair coverage over an entire service region, especially in deployments where both user demand and propagation conditions vary remarkably across space. User presence is often nonuniform and clustered around traffic hotspots [1], while blockage, scattering, and irregular layouts create location-dependent channels with LoS corridors and shadowed zones [2], [3]. Consequently, network performance is jointly governed by the spatial traffic distribution and geometry-induced propagation characteristics. This motivates a shift from link-level tuning to network-level optimization grounded in spatial statistics, where performance is evaluated over a discretized region using metrics such as (traffic-weighted) spatially averaged signal-tonoise ratio (SNR), SNR-threshold coverage probability, and worst-location robustness.
Conventional network-level optimization typically tunes configuration parameters of a fixed infrastructure, e.g., antenna tilts/beam patterns, transmit powers, and cell-level control knobs, to improve region-wide coverage and capacity. A representative example is the self-organizing-network line of work on coverage-and-capacity optimization, where remote electrical tilts and power settings are adapted based on network measurements (e.g., call traces) to address overshooting and coverage holes while balancing spectral efficiency; related formulations also optimize antenna tilts via utility/fairness criteria at the network level [4], [5]. In parallel, map-based paradigms such as radio environment maps (REMs) and channel-knowledge maps (CKMs) advocate learning locationtagged propagation statistics to enable environment-aware resource management across grids [6]. However, CKM-centric designs primarily capture propagation knowledge and do not explicitly encode where/when users appear and demand concentrates; as emphasized by the perception-embedding-map framework (PEMNet), network optimization fundamentally benefits from jointly embedding channel and fine-grained spatial-temporal traffic knowledge, since traffic maps determine the relevant operating points and bottlenecks that channel maps alone cannot reveal [7]. Recent data-driven “digital-twin” frameworks further aim to support network-level optimization by building measurement-grounded simulators and localized channel/coverage maps so that network behaviors can be evaluated and optimized off-line rather than through costly trial-and-error in live networks [8], [9].
Despite their success, these approaches are fundamentally built on a fixed infrastructure and therefore share an inherent limitation: the radiating sites remain physically anchored, so optimization can primarily reshape how a given site radiates (e.g., via tilt, power, or beam pattern adaptation) but cannot change where energy is injected into the environment. Consequently, both environment-induced and demand-induced nonuniformities can persist at the network level. In blockage-rich environments, coverage tails and worst-location performance are often dictated by a small set of severely shadowed or farcorner grids that are difficult to lift through parameter tuning alone. Meanwhile, when user traffic is highly heterogeneous, fixed-site configurations may struggle to deliver sufficient service to shifting hotspot
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