Search arXivSearch

arXiv · 2003.02697

Position Based Compressed Channel Estimation and Pilot Design for High Mobility OFDM Systems

Abstract

With the development of high speed trains (HST) in many countries, providing broadband wireless services in HSTs is becoming crucial. Orthogonal frequency-division multiplexing (OFDM) has been widely adopted for broadband wireless communications due to its high spectral efficiency. However, OFDM is sensitive to the time selectivity caused by high-mobility channels, which costs large spectrum or time resources to obtain the accurate channel state information (CSI). Therefore, the channel estimation in high-mobility OFDM systems has been a long-standing challenge. In this paper, we first propose a new position-based high-mobility channel model,in which the HST's position information and Doppler shift are utilized to determine the positions of the dominant channel coefficients. %In this way, we can reduce the estimation complexity and to design the transmitted pilot.Then, we propose a joint pilot placement and pilot symbol design algorithm for compressed channel estimation. It aims to reduce the coherence between the pilot signal and the proposed channel model, and hence can improve the channel estimation accuracy. Simulation results demonstrate that the proposed method achieves better performances than existing channel estimation methods over high-mobility channels. Furthermore, we give an example of the designed pilot codebook to show the practical applicability of the proposed scheme.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Xiang Ren, Wen Chen, Meixia Tao. 2020-03-01. Position Based Compressed Channel Estimation and Pilot Design for High Mobility OFDM Systems. https://arxiv.org/abs/2003.02697

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

KEEP EXPLORING

Related papers

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

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

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