Power Allocation Using Geometric Water Filling for OFDM-Based Cognitive Radio Networks
Bibliographic record
Abstract
Cognitive radio (CR) is a promising wireless paradigm that provides efficient spectral usage. Orthogonal frequency division multiplexing (OFDM) is a potential technology providing many advanced functionalities in terms of power and rate control for cognitive radio networks (CRNs). Power allocation for CRNs is a crucial task for better interference management. In this paper, a subcarrier assignment scheme and a novel power allocation algorithm using geometric water filling is presented for OFDM based CRNs. This algorithm is optimized such a way to maximize the sum rate of secondary users by allocating power more efficiently, while constraining the 1) total transmit power, 2) individual subchannel transmit power as well as 3) individual subcarrier peak power of secondary users, for a given interference level to the primary users. Numerical results show that this algorithm provides better utilization of power resources thus maximizes the sum rate than the existing algorithms.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".