Development of a Machine Learning Based Fault Detection Model for Received Signal Level in Telecommunication Enterprise Infrastructure
Bibliographic record
Abstract
This research develops a machine-learning fault detection model for received signal levels in telecommunication infrastructure. The methodology involves modeling an enterprise point-to-multipoint wireless network using pathloss 5.0 software. Data from the simulated network, including free space pathloss, transmit power output, transmit antenna gain, transmitter loss, miscellaneous loss, and receiver loss, is used to train three regression models: gradient boosting regression (GBR), random forest regression (RFR), and KNearest Neighbor (KNN). The algorithm compares the received signal levels (RSL) of new data with a threshold value, triggering a "Fault" or "No-fault" condition. A "Fault" indicates a deviation in the RSL, prompting maintenance by the field support team. A "No-fault" means the RSL is within the accepted range, requiring no maintenance. Performance evaluation metrics such as mean absolute error (MAE), mean square error (MSE), R-squared, and root mean square error (RMSE) were compared to select the optimal model. Experimental results show that the RFR model outperforms GBR and KNN with MAE: 0.007101, MSE: 0.000610, R-squared: 0.999992, and RMSE: 0.024697. Leveraging these machine learning-based fault detection models enables telecom service providers to optimize network performance, reduce downtime, and increase customer satisfaction.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".