Search arXiv⌕ Search

arXiv · 2508.19540

Pinching Antenna Systems for Integrated Sensing and Communications

Abstract

Recently, the pinching antenna system (PASS) has attracted considerable attention due to their advantages in flexible deployment and reduction of signal propagation loss. In this work, a multiple waveguide PASS assisted integrated sensing and communication (ISAC) system is proposed, where the base station (BS) is equipped with transmitting pinching antennas (PAs) and receiving uniform linear array (ULA) antennas. The full-duplex (FD) BS transmits the communication and sensing signals through the PAs on waveguides and collects the echo sensing signals with the mounted ULA. Based on this configuration, a target sensing Cramer Rao Bound (CRB) minimization problem is formulated under communication quality-of-service (QoS) constraints, power budget constraint, and PA deployment constraints. The alternating optimization (AO) method is employed to address the formulated non-convex optimization problem. In each iteration, the overall optimization problem is decomposed into a digital beamforming sub-problem and a pinching beamforming sub-problem. The sensing covariance matrix and communication beamforming matrix at the BS are optimized by solving the digital beamforming sub-problem with semidefinite relaxation (SDR). The PA deployment is updated by solving the pinching beamforming sub-problem with the successive convex approximation (SCA) method, penalty method, and element-wise optimization. Simulation results show that the proposed PASS assisted ISAC framework achieves superior performance over benchmark schemes, is less affected by stringent communication constraints compared to conventional MIMO-ISAC, and benefits further from increasing the number of waveguides and PAs per waveguide.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Haochen Li, Ruikang Zhong, Jiayi Lei, Yuanwei Liu. 2025-12-31. Pinching Antenna Systems for Integrated Sensing and Communications. https://arxiv.org/abs/2508.19540

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Low-Interference N-Continuous OFDM via Optimized Time-Domain Smoothing

A novel basis signal optimization method is proposed for reducing the interference in the N-continuous orthogonal frequency division multiplexing (NC-OFDM) system. Compared to conventional NC-OFDM, the proposed scheme is capable of improving the transmission performance while maintaining an identical sidelobe suppression performance imposed by the linear combination of two groups of basis signals. Our performance results demonstrate that with a low-complexity overhead, the proposed scheme is capable of striking a better trade-off among the bit error rate (BER), complexity, and the sidelobe suppression performance compared to its conventional counterparts.

eess.SP↗

Self-Localizing MIMO Beam Mapping with Continuously Evolving Channel Memory

Machine learning has greatly advanced data-driven channel modeling and resource optimization. However, most existing methods require accurately location-labeled datasets, which are costly to collect and maintain in dynamic environments. This paper develops a self-localizing multiple-input multiple-output (MIMO) beam map framework that constructs a hierarchical wireless memory from highly sparse channel state information (CSI) measurements without explicit location labels. To reduce acquisition and processing overhead, we use beamdomain received signal strength (RSS) as compact inputs and theoretically show that they enable asymptotically unbiased spatial signature estimation. A dual-scale extractor captures intrasnapshot angular dependencies and inter-sample correlations for incomplete observations, and a hybrid temporal encoder is designed to consolidate recent CSI into stable short-term context for physical anchor inference. The inferred anchors spatially index a physically structured radio map embedding that stores long-term channel knowledge, which conditions a diffusion decoder for location-consistent full CSI reconstruction. Such a radio map embedding provides a persistent wireless knowledge representation that can be continuously updated and reused without full CSI acquisition. Experiments show that the proposed framework improves physical-anchor recovery accuracy by over 30% under sparse measurements and achieves more than 20% channel-capacity gain in non-line-of-sight (NLOS) beam tracking over Kalman-filter-based methods.

eess.SP↗

Few-Shot Specific Emitter Identification via Integrated Complex Variational Mode Decomposition and Spatial Attention Transfer

Specific emitter identification (SEI) utilizes passive hardware characteristics to authenticate transmitters, providing a robust physical-layer security solution. However, most deep-learning-based methods rely on extensive data or require prior information, which poses challenges in real-world scenarios with limited labeled data. We propose an integrated complex variational mode decomposition algorithm that decomposes and reconstructs complex-valued signals to approximate the original transmitted signals, thereby enabling more accurate feature extraction. We further utilize a temporal convolutional network to effectively model the sequential signal characteristics, and introduce a spatial attention mechanism to adaptively weight informative signal segments, significantly enhancing identification performance. Additionally, the branch network allows leveraging pre-trained weights from other data while reducing the need for auxiliary datasets. Ablation experiments on the simulated data demonstrate the effectiveness of each component of the model. An accuracy comparison on a public dataset reveals that our method achieves 96% accuracy using only 10 symbols without requiring any prior knowledge.

eess.SP↗