Cooperative sensing delay minimization in cloud-assisted DSA networks
Bibliographic record
Abstract
Dynamic Spectrum Access (DSA) is considered as a promising solution to address the problem of spectrum scarcity in future wireless networks. However, the main challenges associated with this approach are to acquire accurate spectrum usage information in a timely manner and to deal with the dynamicity of channel occupancy. Although Cooperative Sensing (CS) can provide significant advantages over individual device-level sensing in terms of sensing efficiency and the achievable throughput, the acquired channel occupancy information may become outdated in dynamic channel conditions due to the involved latency. In this regard, we propose to utilize a collaborative cloud-edge processing framework to minimize the CS delay in DSA networks. In this framework, the cloud-center can estimate channel occupancy parameters such as duty cycle based on the available historical sensing data by using a suitable spectrum prediction technique, and subsequently this prior knowledge can be utilized to adapt the sensing mechanism employed at the edge-side of a DSA network. Motivated by this, we formulate and solve the problem of minimizing CS delay in cloud-assisted DSA networks. A two-stage bisection search method is employed to solve this CS delay minimization problem. Our results show that the proposed cloud-assisted CS scheme can significantly reduce the CS delay in DSA networks.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".