Distributed Resource Allocation for Cognitive Radio Ad-Hoc Networks with Spectrum-Sharing Constraints
Bibliographic record
Abstract
In cognitive radio settings with highly dynamic primary activities and with small opportunities for secondary access, the requirement to fairly distribute the temporarily available spectral ranges among the unlicensed users turns out to be of particular relevance. The current paper addresses this issue by presenting a new design formulation that aims to optimize the performance of an orthogonal-frequency-division-multiple-access (OFDMA) ad-hoc cognitive radio network, by means of joint subcarrier assignment and power allocation. Besides important constraint on the tolerable interference induced to primary network, to efficiently implement spectrum-sharing fairness, the optimization problem considered here strictly enforces upper and lower bounds on the total amount of temporarily available bandwidth to be granted to individual secondary users. Specifically, the system throughput is maximized via the application of Lagrangian duality theory. More importantly, the dual decomposition framework also gives rise to the realization of distributed solution. As the proposed distributed protocol requires very limited cooperation among the participating network elements, it is especially applicable for the ad-hoc networking environment under investigation, to which any central processing or control is certainly inaccessible. While the computational complexity of the devised algorithm is affordable, its performance in practical scenarios also attains the actual global optimum. The potential of the proposed approach is verified through asymptotic complexity analysis and via numerical examples.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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".