Optimal Power Allocation for CR MIMO Energy Harvesting Coexisting Systems
Bibliographic record
Abstract
Cognitive radio (CR) can be combined with energy harvesting and multiple antenna mechanics to lift the spectrum efficiency and make use of green energy. The allocated power for the secondary user (SU), equipped with multiple antennas, needs to have peak power constraints to restrict the interference with the primary user (PU). On the other side, the energy harvesting property of the nodes leads to the causality feature when allocating the harvested energy. In this paper, we apply our recently proposed geometric water-filling with group upper bounded power constraints (GWFGUP) and recursion machinery to form the proposed algorithm for solving the target throughput maximization problem. This proposed CR multiple input multiple output energy harvesting power allocation algorithm (CRMPA) is precisely defined. It provides the exact optimal solution via efficient finite computation. Significant throughput gain of our proposed algorithm can be observed over the existing optimization methods, e.g., the well-known primal- dual interior point method (PD-IPM), although the used PD-IPM is based on our proposed equivalent real problem.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 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".