Prediction and Modeling of Spectrum Occupancy for Dynamic Spectrum Access Systems
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Bibliographic record
Abstract
In a dynamic spectrum allocation (DSA) system, reliable prediction of spectrum occupancy based on a spectrum consumption model (SCM) is critical for system design, performance analysis, and evaluation. In this article, we focus on a low-level abstracted measured dataset from a massive campaign and investigate the occupancy of representative frequency bands. First, we apply an autoregressive-moving-average (ARMA) model combined with a low-pass filter, given the stationarity of the channel measurement dataset and thanks to the computational simplicity of the model. The average received power and off-state probability are extracted from the measured data. According to the results, the measured and predicted data are in good agreement. Comparing the proposed model-based ARMA with the popular long short-term memory learning algorithm, they have similar error accuracy with pre-processed data, while ARMA has a much lower training complexity. In the second step, we develop an SCM describing the spectrum usage for designing and examining the DSA system. We extract the periodic, aperiodic low-frequency, and burst components of the time series. Also, a binary sequence is extracted from a sparse occupancy channel, and modelled by a non-homogeneous Markov chain. Results show that the model-generated data can maintain the same statistics as the measured data.
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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 | 0.001 | 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 it