Outage and Throughput Analysis of Cognitive Users in Underlay Cognitive Radio Networks With Handover
Bibliographic record
Abstract
Interference characterization in cognitive networks with handover has received less attention in stochastic geometry-based interference management and control, especially in cognitive radio networks, because of the possibility of complicating the analysis of various performance metrics of interest, such as outage probability and throughput. However, because of the possible mobility that is observed in real practical systems, some of the receivers may be located outside the coverage regions of their paired transmitters. In order to ensure that any receiver located outside the coverage region of its paired transmitter continues to receive its required service from its paired transmitter while still achieving tractable analysis for various performance metrics of interest, we adopted multiuser diversity via packet relaying. With this approach, any secondary nodes waiting to transmit can be used to sustain coverage between any typical active transmitter and receiver pair, while reducing their own waiting period in the process. We obtained tractable analysis for outage probability, spectral efficiency and throughput and showed the effect of handover rate over the network performance. The outcomes of the numerical results show that the proposed approach is capable of improving the overall network performance by improving coverage and throughput among network users in the cognitive radio networks.
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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 | 0.002 | 0.006 |
| 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 | 0.001 |
| 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".