Bibliographic record
Abstract
As the number of wireless devices explodes in the lead up to the release of 5G communications technology, it is expected that the demands for usage of the wireless spectrum will increase to the point where current spectrum allocation methods will no longer be sufficient. Dynamic Spectrum Management (DSM) uses cognitive radio methods to sense the current state of spectrum usage by other devices, and then makes use of that data to allocate spectrum in time and space to best meet users communication requirements in near real time. Our previous work on a Spectrum Environment Awareness (SEA) sensor system suggests that static tasking of the large number of sensors required for 5G would consume large amounts of storage and communications resources. Intelligent and collaborative tasking of sensors would reduce both these overheads as well as the burden on data analysis. In this paper, we describe an intelligent distributed collaborative sensing system with centralized control for use in DSM-based automated spectrum assignment. We use simulation results to estimate the amount of sensor reading duplication in such a system. These results provide direction on how intelligent sensing systems can optimize the tasking of sensors in this domain.
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How this classification was reachedexpand
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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".