Direct Learning Localization in the Presence of Multiple Access Interference
Why this work is in the frame
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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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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 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it