Bibliographic record
Abstract
This thesis aims to investigate the incorporation of cooperative techniques in cognitive radio networks over Nakagami-m fading channels. These last years, spectrum sharing mechanisms has gained a lot of interest in the wireless communication domain. Using cooperation in a cognitive set up make the use of spectrum much more efficient. Moreover, it helps to extend the coverage area of the cognitive network and also to reduce the transmitting power and, thus, the generated interference. In this work, we consider two particular scenarios for cooperative cognitive radio systems. The first scenario consider multihop regenerative relaying in an underlay cognitive set up. The cooperation is performed in the secondary system, in the presence of multiple primary users. Both interference power and peak power constraints are taking into account. Closed-form expressions for the statistical characteristics and multiple end- to-end performance metrics are derived. Different scenarios are presented to illustrate the obtained results and Monte Carlo simulations confirm the accuracy of our analytical derivations. In the second part of this work, we consider an overlay cognitive network with the spectrally efficient two-phase two-way relaying protocol. Two relay selection techniques, optimizing both the primary and the secondary communication, are presented. The overall outage performance is investigated and an optimal power allocation scheme, that ameliorate the outage performance of the system, is proposed. Numerical simulations are presented to illustrate and compare the obtained results.
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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 | 0.000 | 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 | 0.000 |
| 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".