A New Low Complexity NLOS Identification Approach Based on Maximum Slope and Skewness of Energy Block for 60GHz System
Bibliographic record
Abstract
The major problem of indoor localization is the presence of non-line-of-sight (NLOS) channels. In order to perform the NLOS identification, in this paper, we propose a novel NLOS identification technique based on the ratio values of the maximum slope and skewness of energy block of the received signal using energy detector. In particular, the IEEE 802.15.3c 60 GHz channel models are used as examples and the above statistics is found to be explained in detail. The simplicity of the proposed approach lies in the use of the parameters of the energy-based time of arrival (TOA) Estimation algorithm. The CM1 (LOS) and CM2 (NLOS) channel models of the standard IEEE 802.15.3c channel models are used. Numerical simulations results show that the correct identification of channel models with the proposed approach is better than with the multipath channel statistics based approach.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".