DSP: A Deep Neural Network Approach for Serving Cell Positioning in Mobile Networks
Bibliographic record
Abstract
Positioning remains a crucial aspect with wide-ranging applications, despite the availability of various solutions and extensive research. The lack of transparency surrounding the infrastructure topology and precise locations of base stations operated by telecommunication companies further complicates the positioning process. Moreover, as wireless networks continue to evolve with advancements like 5G and 6G, accurate positioning becomes increasingly important, leveraging vast amounts of data and artificial intelligence techniques. This paper proposes a Deep Neural Network (DNN) based approach called DSP for accurately determining the position of telecommunication operator serving cells in outdoor spaces. The proposed method incorporates fingerprint data collection, machine learning algorithms, and big data analysis to enhance accuracy and expand the range of evaluatable parameters. The data was collected and labeled using the drive test method. By employing deep learning techniques, the proposed method successfully predicts the location of the base station connected to the user device. Through effective data preprocessing and optimized hyperparameters, the average distance error was reduced to 20 meters.
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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 | 0.001 |
| 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".