Maximizing Reliability in Overlay Radio Networks With Time Switching and Power Splitting Energy Harvesting
Bibliographic record
Abstract
Cognitive radio networks (CRNs) are acknowledged for their ability to tackle the issue of spectrum under-utilization. In the realm of CRNs, this paper investigates the energy efficiency issue and addresses the critical challenge of optimizing system reliability for overlay CRN access mode. Randomly dispersed secondary users (SUs) serving as relays for primary users (PUs) are considered, in which one of these relays is designated to harvest energy through the time switching-energy harvesting (EH) protocol. Moreover, this relay amplifies-and-forwards (AF) the PU’s messages and broadcasts them along with its own across cascaded κ-μ fading channels. The power splitting protocol is another EH approach utilized by the SU and PU receivers to enhance the amount of energy in their storage devices. In addition, the SU transmitters and the SU receiver are deployed with multiple antennas for reception and apply the maximal ratio combining approach. The outage probability is utilized to assess both networks’ reliability. Then, an energy efficiency evaluation is performed to determine the effectiveness of EH on the system. Finally, an optimization problem is provided with the goal of maximizing the data rate of the SUs by optimizing the time switching and the power allocation parameters of the SU relay.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 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".