Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 2610.05520

One-Shot Beam Tracking for Stacked Intelligent Metasurfaces-Assisted LEO Broadcasting

Chandan Kumar Sheemar·Giovanni Iacovelli·Sourabh Solanki·Wali Ullah Khan·Symeon Chatzinotas
View original source ↗Read source document ↗Export metadata ↓

Abstract

This letter develops a physics-informed one-shot beam tracking method for stacked intelligent metasurface (SIM)-enabled low-Earth-orbit (LEO) links with $K$ users and an $L$-layer SIM with $N$ meta-atoms per layer. The predictable orbital motion is propagated through the line-of-sight (LoS) geometry to forecast the finite-interval variation of the user signal-to-noise ratio (SNR) profile, and an analytical multilayer Jacobian relates this variation to the $LN$ SIM phase coefficients. Beam tracking is formulated as normalized SNR-profile preservation and solved through a single regularized Gauss--Newton correction that requires one Jacobian evaluation and a $K\times K$ linear solve, thereby avoiding iterative reoptimization. A tracking-error bound separates the regularization residual from the nonlinear Taylor remainder and establishes a second-order local error under a full-row-rank Jacobian. Numerical results show that the proposed method retains approximately $94\%$ and $92\%$ of the iterative-reoptimization rate for $L=2$ and $L=4$, respectively, while running about $350\times$ and $260\times$ faster at $N=225$, and confirm the predicted second-order residual error behavior.

Explore related subjects

eess.SPcs.ITmath.IT

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Chandan Kumar Sheemar, Giovanni Iacovelli, Sourabh Solanki, Wali Ullah Khan, Symeon Chatzinotas. 2026-10-04. One-Shot Beam Tracking for Stacked Intelligent Metasurfaces-Assisted LEO Broadcasting. https://arxiv.org/abs/2610.05520

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

KEEP EXPLORING

Related papers

arXiv2026

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↗
arXiv2026

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↗