Power allocation in OFDM-based cognitive radio systems
Why this work is in the frame
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Bibliographic record
Abstract
In this thesis, we develop an subcarrier transmission suboptimal power allocation algorithm and an underlay subcarrier transmission optimal power allocation algorithm for the orthogonal frequency division multiplexing (OFDM)-based cognitive radio (CR) systems with different statistical interference constraints imposed by different primary users (PUs). Given the fact that the interference constraints are met in a statistical manner, the CR transmitter does not require the instantaneous channel quality feed-back from the PU receivers. First an alternative subcarrier transmission suboptimal algorithm with reduced complexity has been proposed and the performance has been investigated. Presented numerical results show that with our proposed suboptimal power allocation algorithm CR user can achieve 10 percent higher transmission capacity for given statistical interference constraints and a given power budget compared to the traditional suboptimal power allocation algorithms, uniform and water-filling power allocation algorithms. The proposed suboptimal algorithm outperforms traditional suboptimal algorithm, water-filling algorithm and uniform power loading algorithm. Second,We introduce an underlay subcarrier transmission optimal power allocation algorithms which allows the secondary users use the bandwidth used by Pus. And at the same time we consider the individual peak power constraint as the forth constraint added to the objective function which is the transmission capacity rate of the secondary users.Third, we propose suboptimal algorithm using GWF which has less complexity level than traditional water-filling algorithm instead of conventional water-filling algorithm in calculating the assigned power while considering the satisfaction of the total power constraint. The proposed suboptimal algorithm gives an option of using a low complexity power allocation algorithm where complexity is an issue.
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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 | 0.000 |
| 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 it