Modified spider monkey optimization—An enhanced optimization of spectrum sharing in cognitive radio networks
Bibliographic record
Abstract
Summary At present, the demand for wireless communications is growing tremendously. Cognitive radio network plays an important role in making the spectrum to be used effectively. Uncertainty in channels and interference were generally occurs that reduced the system efficiency. To overcome the load usage problem, the proposed system of spectrum sensing and scheduling algorithms is introduced, where the available spectrums are sensed or detected and scheduled to load the free spectrum. Spectrum sensing is a cognitive radio basic function to thwart the harmful interference with licensed users and detect the available spectrum for enhancing the spectrum's utilization. In the proposed technique, initially, the spectrum sharing and sensing method is put forward to raise the throughput and quality of service necessity. At this time, spectrum sharing in common with scheduling process is presented, where the available spectrum, the load is scheduled and sensed to free the spectrum. Here, the modified spider monkey optimization (MSMO) technique is used for spectrum sensing and detecting free spectrums, thereby enhancing the energy efficiency of the available spectrum. This technique will found the optimal solution and increases the expectation of some decisions. Modified round robin algorithm is used for scheduling load. In this algorithm, every packet flow has its packet queue presented in the network interface controller. The performance analysis is finally measured using metrics such as throughput, handoff, success probability, and false alarm probability.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".