Resource Management in Spectrum-Sharing Cognitive Radio Broadcast Channels: Adaptive Time and Power Allocation
Bibliographic record
Abstract
In this paper, we consider a primary/secondary spectrum-sharing system, and study adaptive resource management in cognitive radio (CR) fading broadcast channels (BC). Specifically, we propose utilizing spectrum sensing information about the primary's activity at the secondary base station for an efficient allocation of the resources, namely, transmission time and power, to the secondary users. Spectrum sensing information about the primary user, and secondary channel side information, are assumed available at the base station and receivers of the secondary CR network. The sensing information is obtained using spectrum-aware sensors deployed in the secondary network coverage area. Based on this information, we present an optimal time-sharing and power allocation policy to maximize the achievable capacity of fading cognitive radio broadcast channels, where transmission is limited by appropriate constraints on the average received-interference at the primary receiver and peak transmit-power pertaining to the secondary transmitter. Furthermore, considering that availability of full soft-sensing information at the CR transmitter may result in a severe load of feedback data and high system complexity, we present a quantized spectrum sensing mechanism wherein only restricted levels of primary activity are considered for the sensing observations. Our theoretical results are sustained by numerical and simulation analyses, and insightful discussions are provided.
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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 | 0.000 |
| 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".