Fluid Antenna Channel Reconstruction with Non-Negative Least Square Detector

Yang Chen; Jian Dang; Zaichen Zhang

Home/Archive/arXiv

arXiv · 2609.22137

Fluid Antenna Channel Reconstruction with Non-Negative Least Square Detector

Yang Chen·Jian Dang·Zaichen Zhang

View original source ↗Read source document ↗Export metadata ↓

Abstract

Reconstructing the full-aperture channel of a fluid antenna system (FAS) from a few activated ports requires inferring unobserved responses from limited spatial samples. This paper proposes a covariance-domain FAS channel-reconstruction method based on nonnegative least squares (NNLS). A nonuniform port arrangement constructs a difference coarray with an enlarged aperture. Vectorizing the sample covariance maps the physical array to a virtual steering dictionary, and the resulting complex covariance-fitting model is recast as a strictly equivalent real-valued NNLS problem. A projected-gradient iteration initialized at zero estimates the nonnegative angular-occurrence coefficients, and the L_s strongest well-separated peaks yield the angles of arrival (AoAs). Simulations show that the proposed FAS-NNLS estimator exploits the enlarged virtual aperture and achieves lower AoA mean squared errors than uniform linear array (ULA) multiple signal classification (ULA-MUSIC) and ULA estimation of signal parameters via rotational invariance techniques (ULA-ESPRIT). Its non-line-of-sight (NLoS) path accuracy matches FAS covariance matching pursuit, while its line-of-sight (LoS) estimation is significantly improved.

Explore related subjects

eess.SP cs.IT math.IT

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

Yang Chen, Jian Dang, Zaichen Zhang. 2026-08-24. Fluid Antenna Channel Reconstruction with Non-Negative Least Square Detector. https://arxiv.org/abs/2609.22137

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

KEEP EXPLORING

Related papers

arXiv2026

Subspace Learning with Interval-Censored Likelihoods for Dequantizing Percept PC LFP Snapshots

Implanted neurostimulators that sense local field potentials now enable chronic electrophysiology based biomarker tracking in patients at home. The Medtronic Percept PC, the only commercially available sensing-enabled deep brain stimulation (DBS) device, stores spectral amplitudes as 16-bit integers at approximately 0.1 $μ$V per bit (quantum $q \approx 0.11$ $μ$Vp). At frequencies where the true amplitude spans only a few quantization levels, consecutive bins round to the same stored value. Standard spectral parameterization (FOOOF, fitting oscillations and one over f), which separates periodic peaks from the aperiodic 1/f activity, treats every value as exact and fits oscillatory peaks to these plateaus. Because these spectra feed clinical biomarker pipelines and spectral foundation models for symptom decoding, spurious peaks can corrupt downstream inference. Across 9,438 spectra from 14 hemispheres in 7 subcallosal cingulate DBS patients, 20.6% of peaks detected at [2, 45] Hz have no match in ground truth synthesized by quantizing clean in-clinic BrainSense recordings, while aggregate beta band power and the aperiodic exponent are preserved. We formalize dequantization as interval-censored subspace estimation and compare five classes of correction methods. Quantized probabilistic PCA is the only tested method that reduces the spurious rate (20.6% to 18.3%) while preserving true peak detection and keeping noise floor RMSE below $q/\sqrt{12}$.

eess.SP↗

arXiv2026

TiamiTwin: A Digital Twin for Bistatic ISAC Drone Sensing, Validated Against Measurements

Monitoring lower airspace over critical infrastructure using cellular signals of opportunity is highly practical because transmitters are pre-deployed, licensed, and continuously active. Digital twins can evaluate the feasibility of such integrated sensing and communication (ISAC) architectures, but their predictive accuracy must be validated against real-world data. This paper reports validation results for TiamiTwin, a digital twin developed for bistatic ISAC drone sensing, using empirical measurements from an operational 5G deployment featuring a commercial band n41 gNB and a receiver separated by 572.8 m over a non-line-of-sight (NLOS) channel. TiamiTwin incorporates three parallel channel representations evaluated on a 240-subcarrier grid: the 3GPP TR 38.901 (Release 19) bistatic ISAC model, a ray-traced site model, and the captured field measurements. Empirical results demonstrate that both statistical and ray-tracing models under-predict the measured root-mean-square (RMS) delay spread by approximately a factor of three. Furthermore, target reflections sit 68 dB below static clutter in power, making target detection entirely dependent on Doppler separation to isolate the drone from zero-Doppler background returns. Despite this severe clutter environment, the target remains separable along 88% of the flight path in the delay, Doppler, or joint delay-Doppler domains.

eess.SP↗

arXiv2026

Extracting Physiological Numeric Values from French Pediatric ICU Notes: A Multi-Objective Representation Learning Approach

Numeric values in clinical narratives, such as heart rate, oxygen saturation, and pressure gradients, carry diagnostic meaning that Transformer models trained on generic text do not capture. Objective: We categorize numerical values in French pediatric intensive care unit (PICU) notes into eight physiological categories using CamemBERT-bio, under two constraints that make large-scale LLMs impractical: only 1,072 real, annotated clinical samples are available for this rare, single-site condition, and training must run on GPUs shared concurrently with other hospital workloads rather than a dedicated cluster. Methods: We compare fine-tuning CamemBERT-bio with Label Embedding for Self-Attention (LESA) against combining LESA with Xval, a magnitude-aware number embedding, under a multi-objective training loss. Results: Standard fine-tuning did not improve F1 score, but CamemBERT-bio + LESA raised it by over 13%, and adding Xval matched this gain while approaching GPT-4's performance. Conclusion: LESA and Xval let a compact encoder achieve reliable physiological value extraction under limited real data and shared hospital compute, offering a practical alternative to large-scale LLMs. Significance: Under limited-data and shared-compute constraints, this compact BERT-based language model remains effective without the resource trade-offs of trillion-parameter LLMs.

eess.SP↗