Searcharxiv⌕ Search

arXiv · 2610.04769

Spatially Reconfigurable Pinching-Antenna Systems: Experimental Validation and ISAC Applications

Abstract

Future integrated sensing and communication (ISAC) networks require wireless platforms that can adapt not only their signals but also the physical locations from which they radiate and observe. This article presents pinching-antenna systems (PASS) as a spatially adaptive platform for ISAC. PASS adopts dielectric waveguides as signal-transport media and reconfigurable dielectric pinching antennas to realize programmable radiation and observation points along these waveguides. This unique architecture introduces new spatial degrees of freedom, allowing the network to dynamically reconfigure its interaction geometry with users, targets, and the surrounding environment, rather than solely optimizing signals over a fixed antenna geometry. Leveraging a terahertz testbed, we experimentally validate the spatial control and spatially selective reception capabilities of PASS and demonstrate constructive field combining between two coherent radiation points. We further explore how such spatial reconfigurability can enable radio-map-assisted communication, environment division multiple access, and closed-loop active radio perception. Finally, we identify key challenges and research directions toward practical PASS-ISAC networks.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Shaokang Hu, Ruotong Zhao, Qigejian Wang, Shaghik Atakaramians, Derrick Wing Kwan Ng, Jinhong Yuan. 2026-10-03. Spatially Reconfigurable Pinching-Antenna Systems: Experimental Validation and ISAC Applications. https://arxiv.org/abs/2610.04769

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

KEEP EXPLORING

Related papers

Resource-Efficient Wi-Fi CSI-Based Sensing via Exploiting the Age of Samples

Wi-Fi channel state information (CSI)-based sensing must coexist with data communications, limiting the availability of temporally-dense CSI measurements. We formulate CSI-based human activity and identity recognition under an average sensing budget that limits the fraction of CSI measurement and reporting opportunities within a sensing session. The budget captures sensing-communication resource sharing, packet loss, and traffic-induced irregularity, which we model using deterministic (accumulated) and stochastic (Bernoulli) sampling policies. We propose a low-cost, age-aware WiFi sensing framework that encodes the age of each retained CSI sample and multiplicatively fuses it with the CSI embedding. On the NTU-Fi human activity recognition and person identification datasets, the proposed model outperforms both a CSI-only baseline and the time-aware attention model of the UniFi benchmark across most operating regimes. For person identification, it improves over UniFi by more than 10 percentage points, with the largest gains under strict sensing budgets.

eess.SP↗

Graph Learning for Cross-Subject, Cross-Population EEG Emotion Decoding and Model-Derived Spatial-Spectral Neural Signatures

Cross subject emotion decoding from electroencephalography EEG requires representations that accommodate individual variability while preserving spatial spectral structure for interpretation. This study introduces EmoDiPyraTrans, a differential graph Transformer that integrates adaptive graph recurrence, differential attention, pyramid fusion and distribution regularization over sequential relative power spectral density graphs. Across SEED, FACED, MAHNOB HCI, DEAP and DREAMER, the model achieved the highest participant mean accuracy and positive class F1 among the evaluated methods, with accuracy and F1 both reaching 0.928 on SEED. On DEP EEG, positive versus neutral accuracy reached 0.802 within healthy controls and 0.704 within participants with depression, compared with 0.591 under healthy to depression transfer and 0.581 with mixed population development. Complementary SEED analyses identified distributed spatial weighting and an alpha centred spectral preference, while configurations averaging six channels retained near full performance. These findings link generalization assessment with model derived candidate signatures to support interpretable EEG emotion decoding, with code available at https://github.com/hdy6438/EmoDiPyraTrans.

eess.SP↗