Mobile location estimation for DS‐CDMA systems using self‐organizing maps
Bibliographic record
Abstract
Abstract In this paper, a self‐organizing map (SOM) scheme for mobile location estimation in a direct‐sequence code division multiple access (DS‐CDMA) system is proposed. As a feedforward neural network with unsupervised or supervised and competitive learning algorithm, the proposed scheme generates a number of virtual neurons over the area covered by the corresponding base stations (BSs) and performs non‐linear mapping between the measured pilot signal strengths from nearby BSs and the user's location. After the training is finished, the location estimation procedure searches for the virtual sensor which has the minimum distance in the signal space with the estimated mobile user. Analytical results on accuracy and measurement reliability show that the proposed scheme has the advantages of robustness and scalability, and is easy for training and implementation. In addition, the scheme exhibits superior performance in the non‐line‐of‐sight (NLOS) situation. Numerical results under various terrestrial environments are presented to demonstrate the feasibility of the proposed SOM scheme. Copyright © 2006 John Wiley & Sons, Ltd.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".