Received Signal Strength Indicator Prediction for Mesh Networks in a Real Urban Environment Using Machine Learning
Bibliographic record
Abstract
Mesh networks are self-managing wireless systems with dynamic topology. These networks differ from broadcast and mobile networks because their mesh nodes can directly exchange information without the intervention of any other infrastructure. However, the radio propagation environment in urban regions, characterized by dense building clusters and human-made structures, influences signal attenuation and path loss. Therefore, deploying these networks brings distinct challenges from the more intensively studied indoor or rural scenarios. In line with this, predicting radio signal propagation attenuation is crucial for planning and deploying reliable networks. The literature on received signal strength indicator (RSSI) prediction for mesh networks in urban areas is scarce. This paper proposes machine learning-based RSSI prediction models for highly urbanized areas. We highlight the most influential features, including the distance between the transmitter and receiver, obstruction details in the first Fresnel zone, and terrain variability measures. Considering data from two mesh networks in the Metropolitan Region of São Paulo, Brazil, owned by a power utility company, we trained a Random Forest and a Support Vector Regression model for the RSSI prediction task. Comparative analysis indicates an improvement of up to 66% in the RSSI prediction error using the Random Forest approach in comparison with classical and empirical models.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".