Joint Power Allocation and Antenna Placement for Pinching-Antenna Systems under User Location Uncertainty
🤖 AI Summary
This work addresses the challenge of Gaussian-distributed user location uncertainty in high-frequency pinching antenna systems by proposing a robust resource allocation framework that jointly optimizes transmit power allocation and reconfigurable antenna placement to maximize energy efficiency under probabilistic outage constraints. For the first time, Gaussian localization error is explicitly incorporated into the system model, enabling the derivation of a closed-form power allocation policy. The optimal antenna deployment is efficiently solved using a particle swarm optimization (PSO) algorithm. Simulation results demonstrate that the proposed approach significantly outperforms fixed-antenna benchmarks, achieving higher energy efficiency while enhancing communication reliability, thereby validating its effectiveness in practical high-frequency wireless scenarios.
Application Category
📝 Abstract
Pinching antenna systems have attracted much attention recently owing to its capability to maintain reliable line-of-sight (LoS) communication in high-frequency bands. By guiding signals through a waveguide and emitting them via a movable pinching antenna, these systems enable dynamic control of signal propagation and spatial adaptability. However, their performance heavily depends on effective resource allocation-encompassing power, bandwidth, and antenna positioning-which becomes challenging under imperfect channel state information (CSI) and user localization uncertainty. Existing studies largely assume perfect CSI or ideal user positioning, while our prior work considered uniform localization errors, an oversimplified assumption. In this paper, we develop a robust resource allocation framework for multiuser downlink pinching antenna systems under Gaussian-distributed localization uncertainty, which more accurately models real-world positioning errors. An energy efficiency (EE) maximization problem is formulated subject to probabilistic outage constraints, and an analytical power allocation strategy is derived under given antenna positions. On this basis, the heuristic particle swarm optimization (PSO) algorithm is employed to identify the antenna position that achieves the global EE configuration. Simulation results illustrate that the proposed scheme greatly enhances both EE and system reliability compared with fixed-antenna benchmark, validating its effectiveness for practical high-frequency wireless deployments.
Problem
Research questions and friction points this paper is trying to address.
Pinching antenna systems
User location uncertainty
Energy efficiency
Resource allocation
Probabilistic outage constraints
Innovation
Methods, ideas, or system contributions that make the work stand out.
Pinching antenna
Energy efficiency
Localization uncertainty
Robust resource allocation
Particle swarm optimization
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