Failure Detection for Pinching-Antenna Systems
Chongjun Ouyang, Hao Jiang, Zhaolin Wang, Yuanwei Liu, Zhiguo Ding
TL;DR
This work tackles failure detection in segmented pinching-antenna systems (SWAN) by introducing a tagged-pilot framework that embeds segment-specific signatures into a single RF chain observation. It develops a per-segment ML detector for the overdetermined regime (pilot length $T \\ge M$) and a compressive-sensing (LASSO) detector for the underdetermined regime ($T < M$) that exploits sparsity in failures. With orthogonal tagging (e.g., Walsh-Hadamard) the overdetermined detector can match joint ML performance, while the underdetermined detector achieves reliable detection with short pilots and can outperform longer-pilot baselines. Numerical results demonstrate near-optimal performance in both regimes and highlight tagged pilots as a practical, scalable approach for maintenance and reliability in PASS deployments, reducing monitoring complexity and downtime.
Abstract
A signal processing-based framework is proposed for detecting random segment failures in segmented waveguide-enabled pinching-antenna systems. To decouple the passively combined uplink signal and to provide per-segment observability, tagged pilots are employed. A simple tag is attached to each segment and is used to apply a known low-rate modulation at the segment feed, which assigns a unique signature to each segment. Based on the tagged-pilot model, a low-complexity per-segment maximum-likelihood (ML) detector is developed for the case in which the pilot length is no smaller than the number of segments. For the case in which the pilot length is smaller than the number of segments, sparsity in the failure-indicator vector is exploited and a compressive sensing-based detector is adopted. Numerical results show that the per-segment detector approaches joint ML performance, while the compressive sensing-based detector achieves reliable detection with a short pilot and can outperform baselines that require much longer pilots.