Robust Single- and Multi-Pinching Antenna Systems Under User Location Uncertainty

2026-04-10

AI Summary

This work addresses the challenge of ensuring quality-of-service (QoS) in near-field antenna systems when user locations are subject to unknown but bounded errors. To tackle this issue, the study introduces worst-case robust design into such systems for the first time. For single-antenna scenarios, it proposes a convex semidefinite programming (SDP) reformulation based on the S-procedure. In multi-antenna settings, the paper develops a closed-form power allocation scheme coupled with an efficient antenna placement optimization algorithm that integrates block coordinate descent and worst-case channel gain evaluation. The proposed framework rigorously guarantees QoS robustness while achieving power consumption comparable to outage-probability-based benchmark schemes, thereby significantly enhancing both system practicality and energy efficiency.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Non-convex Optimization
• Multiagent Systems: Mechanism Design

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Security and Privacy: Large-scale security measurements
• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems

Abstract

Pinching antenna (PA) systems have recently emerged as a promising architecture for reconfigurable wireless communications by enabling flexible antenna placement along a dielectric waveguide. However, existing works typically assume perfect knowledge of user locations, which is impractical in real systems where location estimation errors are inevitable. In this paper, we investigate robust power allocation and antenna placement for PA systems under user location uncertainty. We consider both single-antenna and multi-antenna configurations, where the true user locations are unknown but lie within bounded uncertainty regions. For the single-antenna case, we adopt a worst-case robust design and leverage the S-procedure to transform the joint power allocation and antenna placement problem into a convex semidefinite program (SDP), ensuring that quality-of-service (QoS) constraints are satisfied for all possible user locations. For the multi-antenna case, we address the additional challenges arising from the superposition of channel components from multiple antennas by developing an efficient numerical procedure to evaluate the worst-case channel gain. Then, we derive a closed-form solution for optimal power allocation and develop a block coordinate descent algorithm to optimize antenna placement. Simulation results show that the proposed framework provides robustness to location uncertainty while achieving power consumption close to that of outage-based benchmark schemes.

Problem

• pinching antenna
• user location uncertainty
• robust design
• power allocation
• antenna placement

Innovation

• pinching antenna
• location uncertainty
• robust optimization
• semidefinite programming
• block coordinate descent