Direct Learning Localization in the Presence of Multiple Access Interference
Bibliographic record
Abstract
This article demonstrates that the performance degradation in wireless communication localization can result from multiple access interference (MAI). To address this issue, we introduce a new supervised machine-learning technique named direct learning localization (DLL). This approach determines user locations by analyzing the statistical model of received signal strength (RSS) at base stations. Our method utilizes the probability density function (PDF) of RSS rather than its instantaneous values. It operates independently of the prior knowledge of channel statistics and MAI profiles, as the RSS PDF is derived from direct observations. Consequently, our method obviates the need for interference cancellation algorithms and incorporates interference profiles as a part of the localization fingerprint. DLL calculates the Kullback-Leibler (KL) divergence between the RSS PDFs of training and test users as a measure of dissimilarity. Utilizing these dissimilarities, it then predicts user locations with a logistic regression model. We demonstrate that DLL significantly enhances localization accuracy in the presence of MAI and outperforms other methods in the literature. Additionally, we present an analysis of the algorithm's performance to prove its reliability.
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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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".