Securing UWB Communications under NLOS Indoor Propagation Conditions
Bibliographic record
Abstract
Ultra-wideband (UWB) communications can be achieved under non-line-of-sight (NLOS) propagation conditions using carefully selected and located microwave scatterers together with a multi-carrier modulation technique such as orthogonal frequency-division multiplexing (OFDM). One key factor in achieving high data throughputs is the average signal intensity of the scattered signals that may be achieved by using one or multiple scatterers. Precise scattered signal measurements carried out in a 1 GHz band centered around 29.5 GHz with single or multiple scatterers have led to the conclusion that there exists an approximate relationship of proportionality between the average received signal intensity expressed in dB and the sum of scatterer cross section per unit length. Measurements of the frequency response of NLOS indoor propagation channels were performed for three different laterally-positioned receive antenna locations and used for single-input multiple output (SIMO) studies. Based on these measurements, OFDM data transmission streams were simulated. It was determined that individual subchannels of the OFDM data transmission stream suffered distortions to various degrees resulting in the unsuitability of some subchannels for data transmission. Using the technique of post-discrete Fourier transform (post-DFT) multiple subcarrier selection (MSCS), it was found that data throughputs comparable to those achievable under quasi-perfect propagation conditions could be obtained in NLOS indoor channels provided that the sum of the scatterer cross sections was of sufficient magnitude. Signal-level and error-rate metrics were used for the selection of OFDM subcarriers.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".