Identification of Cellular Measurements: A Neural Network Approach
Bibliographic record
Abstract
The efficient utilization of the wireless spectrum is essential to fulfill the rising demand of scarce bandwidth resources. Identifying the cellular signal types occupying the spectrum allows for usage optimization. Neural networks (NNs) are a promising approach for the signals’ identification problems. This article proposes a hybrid convolutional and feedforward NN (HCFNN) that classifies the cellular signals from the power spectral density (PSD) of real measurements into their corresponding types: global system for mobile communications (GSM), universal mobile telecommunications service (UMTS), and long-term evolution (LTE). The measured dataset is collected based on two acquisition modes: multiple-band (MB) and in-band (IB) PSD acquisition modes. In the MB model, the data are collected, trained, and tested from various frequency bands, while in the IB model, the data are collected, trained, and tested from a single frequency band. The accuracy and the precision–recall (PR) metrics are used to evaluate the performance of the proposed HCFNN model. Moreover, the complexity analysis of the model is derived in terms of the number of real additions, real multiplications, and parameters. The extensive assessments of the over-the-air measurements show that the proposed HCFNN model accurately identifies the cellular signal types in all studied scenarios.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".