Performance evaluation of multiband multi-sensor spectrum sensing systems
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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