Optimal spectrum utilisation in cognitive radio networks based on processor sharing techniques
Bibliographic record
Abstract
Summary Cognitive radio networks have achieved higher efficiency in terms of spectrum usage; however they do not readily solve any competition for access among secondary users. Optimisation is applied to an underlay network to obtain the optimal solution for at least two secondary users operating simultaneously on the same channel. Performance measures are used as the target for optimisation. However, the objective function is difficult to obtain in closed form. For the performance measures, queueing theory, particularly weighted processor sharing techniques are employed to model the system dynamics and behaviour. Transmission power and the interference temperature limit are used to allocate weights to the secondary users. Queue length and waiting time functions obtained from the queuing models are used for optimisation. After establishing that the objective function can be considered to be pseudo‐convex, convex programming is then deployed to obtain the optimised solution. The results suggest that there is indeed an improvement in network performance after optimisation. The immediate benefits of such a system are firstly improved spectrum utilisation through adding multiple secondary users and secondly, through optimisation, higher performance that can be achieved by the secondary users.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".