3D compressive sensing for nodes localization in WNs based on RSS
Bibliographic record
Abstract
Compressive sensing (CS) intends to recover signals at a sampling rate significantly (much) lower than that classically used according to the Nyquist theorem. This allows avoiding unnecessary sampling and complexity. In this paper, a Three-Dimensional Compressive Sensing (3D-CS) approach is proposed for nodes localization in wireless networks. In 3D-CS-R2S2 approach, which is based on the ratio of received signal strength (RSS), a 3D sparsity basis and a 3D measurement matrix are used as radio map and noisy measurements respectively in order to recover the target position. A specific multi-linear algebra procedure was developed using N-way array products, together with an adequate decomposition. Both allow formulating the localization problem in a way that is solvable by an ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -minimization algorithm based on CS theory. 3D-CS-R2S2 improves localization accuracy even if propagation conditions change significantly and/or the effective isotropic radiated power (EIRP) is unknown. Additionally, it enables practical Real Time Localization Systems (RTLS) development since 3D-CS-R2S2 can be functional with a reduced number of base stations without compromising position recovery accuracy. The simulation results show the efficiency of the method that not only succeeds to recover a target position but also improves localization accuracy in presence of noise.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 teacher head, 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".