Performance evaluation of multiband multi-sensor spectrum sensing systems
Bibliographic record
Abstract
Quickest detection theory has previously been applied to the problem of spectrum sensing. By detecting the onset of an idle channel period, quickest detectors minimize the time required to search for an idle period. These methods are based on detection of a single change point, which implies that change in the channel's usage state is assumed to occur only once. Since the channel state may transition continuously between busy and idle states via an ON-OFF process, an alternative formulation based on partially observable Markov decision processes (POMDP) has been recently proposed. In this paper, Page's cumulative sum sequential analysis method (CUSUM) and multiband multi-sensor spectrum sensing (MMSSD) based on POMDP are brought into a similar context and compared. Next, the ON-OFF process model itself is assessed. The POMDP formulation assumes an ON-OFF process model where the busy and idle periods are geometrically distributed. While this model is desirable for its analytical tractability, its applicability to reflect the dynamics of actual spectral usage, which are derived from real data traffic, is assessed.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".