Secondary user access based on stochastic link estimation in cognitive radio with fibre‐connected distributed antennas
Bibliographic record
Abstract
In this study, the authors consider the application of a system architecture called cognitive radio (CR) with fibre‐connected distributed antennas in IEEE 802.22 wireless regional area networks (WRANs) as it could bring the benefits of much shorter wireless transmission distances, lower transmission power and the possibility of utilising multi‐antenna transmission techniques. In this architecture, the authors study the secondary user (SU) access problem in uplink, where the SU to primary user (PU) link estimation is subject to random errors because PU could not assist link estimation of SU. This SU access problem is divided into two parts: antenna selection and access control. Thus, first antenna selection problem is modelled as a restless bandit problem, which is solved by the primal‐dual index heuristic algorithm based on first order relaxation. In addition, the access control problem is modelled as a stochastic knapsack (SASK) problem with random weight, and then relaxed to be a deterministic second order cone programming problem. With the deduced upper bound, the access control problem is solved by the branch and bound algorithm, which yields the SU access based on SASK scheme. Simulation results illustrate the significant performance improvement of SASK scheme, compared with existing SU access methods.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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".