Throughput Optimization of Parallel Sensing and Energy Harvesting Cognitive Radio Network
Bibliographic record
Abstract
In cognitive radio, throughput of secondary user (SU) will depend on spectrum sensing performance and available power of secondary user to transmits data. As the secondary user dissipates energy for spectrum sensing operation and to maintain cooperation among multiple SUs can results in reduction of transmission power. To compensate this energy, an energy harvesting technique has introduced in cognitive radio by which SU can harvest energy from primary (PU) signal and this harvested energy will be utilized to transmit its data and increases the lifetime. In a traditional Energy Harvesting Cognitive Radio Network (EHCRN), SU can perform sensing and harvesting in separate slots which decrease the transmission time of secondary user results in reduction in throughput. To enhance the throughput of secondary user, a parallel operation of spectrum sensing and energy harvesting has been discussed. This parallel operation results in reduction of energy consumption and increases harvested energy that makes more energy to be available for transmission, which results in an increase of SU throughput. Simulation results using MATLAB shows that the proposed Parallel Sensing and Energy Harvesting CRN have improved the throughput compared to Traditional Energy Harvesting CRN and are analyzed with different parameters.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".