A survey of overlay and underlay paradigms in cognitive radio networks
Bibliographic record
Abstract
Summary In the recent years, wireless applications and services have grown tremendously, resulting to a shortage of radio spectrum. On one hand, most of the available radio spectrum has already been allocated to different users and service providers. On another hand, research and statistics have revealed that the spectrum utilization usability is very limited. To address this dilemma, the concept of cognitive radio has emerged, which promotes the use of overlay and underlay transmission techniques to boost the utilization of radio spectrum resources. This paper provides a comprehensive survey of these 2 techniques and compares them qualitatively based on several network parameters. Next, this paper simulates overlay and underlay transmission techniques in OMNeT++ simulator on different network parameters, namely, Primary user arrival rate, throughput, sensing duration, and energy consumption. Our findings reveal that neither the overlay nor the underlay technique is sufficient itself to fulfill the demands for future wireless systems, and adopting a hybrid access technique consisting of a joint utilization of overlay and underlay approaches is desirable. Furthermore, the key challenges and open research issues in radio spectrum resources utilization are discussed.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| 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".