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Joint Power Allocation and Antenna Placement for Pinching-Antenna Systems under User Location Uncertainty

Hao Feng, Ming Zeng, Xingwang Li, Wenwu Xie, Nian Xia, Octavia A. Dobre

TL;DR

This work addresses robust EE optimization for downlink pinching-antenna systems under Gaussian user-location uncertainty. It derives an analytical per-user power rule under outage constraints by modeling the location error with a non-central chi-squared distribution via the Marcum Q-function, and then solves for the optimal pinching-antenna position using PSO to minimize total transmit power. Numerical results show that pinching-antenna configurations substantially improve EE and reliability over fixed antennas, with PSO achieving near-exhaustive-search performance. The approach offers a practical framework for robust resource allocation and dynamic antenna positioning in high-frequency wireless networks.

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.

Joint Power Allocation and Antenna Placement for Pinching-Antenna Systems under User Location Uncertainty

TL;DR

This work addresses robust EE optimization for downlink pinching-antenna systems under Gaussian user-location uncertainty. It derives an analytical per-user power rule under outage constraints by modeling the location error with a non-central chi-squared distribution via the Marcum Q-function, and then solves for the optimal pinching-antenna position using PSO to minimize total transmit power. Numerical results show that pinching-antenna configurations substantially improve EE and reliability over fixed antennas, with PSO achieving near-exhaustive-search performance. The approach offers a practical framework for robust resource allocation and dynamic antenna positioning in high-frequency wireless networks.

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.
Paper Structure (9 sections, 1 theorem, 18 equations, 4 figures)

This paper contains 9 sections, 1 theorem, 18 equations, 4 figures.

Table of Contents

  1. Introduction
  2. System Model and Problem Formulation
  3. System Model
  4. Problem Formulation
  5. Proposed Solution
  6. Power Optimization under given Pinching Antenna Position
  7. Pinching Antenna Position Optimization
  8. Numerical Results
  9. Conclusion

Key Result

Theorem 1

The probability of the true user location lies within the circle with radius $r$ is given by where $Q_1(a, b)=\int_{b}^{\infty} x\, e^{-\frac{x^2 + a^2}{2}}\, I_0(a x)\, dx$ is the Marcum Q-function of order 1, with $I_0(\cdot)$ denoting the modified Bessel function of the first kind and order 0. Besides, $l=\sqrt{(\hat{x}_n-x^{\text{Pin}})^2+ \hat{y}_n^2}$ denotes the Euclidean distance be

Figures (4)

  • Figure 1: Illustration of a) the antenna sphere achieving the target rate and its intersection with the user plane; and b) the user circle showing the center, the radius and the estimated user location.
  • Figure 2: EE versus target data rate at the users.
  • Figure 3: EE versus users' outage probability $\varepsilon_n$.
  • Figure 4: EE versus the standard deviation of the user locations.

Theorems & Definitions (1)

  • Theorem 1