Transmission time minimization of an energy harvesting cognitive radio system
Bibliographic record
Abstract
Telecommunication systems consume significant amount of energy from the grid. Energy harvesting wireless systems utilize energy from the environment and provide a green solution. On the other hand, cognitive radio (CR) wireless systems harvest spectrum from licensed users reducing spectrum crunch. These two techniques can be synthesized in an energy harvesting cognitive radio (EHCR) system to efficiently manage precious resources, i.e., power and spectrum. However, since the availability of both spectrum and energy is not deterministic, such a system can suffer from large delay at the transmission stage. This paper aims at providing an exact solution to the transmission time minimization problem of an EHCR system. The challenge comes from the multiple constraints. In addition to the available energy and spectrum constraints, the CR system will have to have peak power constraint to avoid interference to the licensed user. This results in a non-convex mixed-integer optimization problem for which we develop an exact solution using geometric water-filling with peak power constraints (GWFPP). We also use a recursion machinery to solve the delay minimization problem. Such a system will save energy for the power grid and satisfy user with low-latent communication. Numerical example and complexity analysis illustrate exactness and efficiency of our proposed algorithm.
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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) | 0.001 | 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) | 0.003 | 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".