Prediction and Modeling of Spectrum Occupancy for Dynamic Spectrum Access Systems
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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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".