Cooperative Resource Management in Cognitive WiMAX with Femto Cells
Bibliographic record
Abstract
WiMAX with femto cells is a cost-effective next generation broadband wireless communication system. Cognitive Radio (CR) has recently emerged as a promising technology to improve spectrum utilization by allowing dynamic spectrum access. There will be large potential benefits by applying the CR technique to WiMAX with femto cells, which are barely explored in the literature. In this paper, we propose a novel cognitive WiMAX architecture with femto cells, where the base station and users are equipped with CRs and intelligently adjusts power, channel, and other resources to accommodate the entire network ecosystem. In this new design, we develop an optimization framework for location-aware cooperative resource management, by jointly employing multi-hop cooperative communication, power control, channel assignment, primary user protection, buffer management, and fairness, and incorporating user, channel, and cooperative diversities. To achieve optimality, it is designed based on stochastic Lyapunov optimization, aiming to take advantage of the radio flexibility and fully utilize the spectrum. Evaluated by the rigorous analysis and extensive simulations, our resource management protocol is near-optimal with closed-form bounds, with which cognitive WiMAX achieves substantial performance improvement.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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 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".