Enhancing Reference Signal Received Power Prediction Accuracy in Wireless Outdoor Settings: A Comprehensive Feature Importance Study
Bibliographic record
Abstract
Predicting the reference signal received power in wireless communication is crucial for improving network performance, allocating resources, and ensuring good signal coverage, especially in advanced technologies like 5G and beyond. To create an accurate prediction model, we need to look at different aspects of the radio environment and understand the importance of each factor. In our study, we suggest using machine learning to predict reference signal received power. We analyze the importance of features by studying their impact on the received signal power. We developed a machine learning approach using 64 features taken from recent literature and new ones proposed in this study from real-world received signal power measurements in outdoor areas, including cities and suburbs. Using this data, we trained a Random Forest model for received signal power predictions. After training, we analyzed the importance of each feature to create a simpler machine learning model that maintains good prediction accuracy. Our results show that we can use only the 25 most important features to build a less complex model with a small error difference of 0.14 dB compared to the original model with 64 features.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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".