Outage capacity optimisation for cognitive radio networks with cooperative communications
Bibliographic record
Abstract
Cognitive radio networks with cooperative communications can effectively improve spectrum efficiency and data rate. Under this network scenario, there are three possible transmission modes: direct transmission, multi-hop transmission and cooperative communication. To optimise the system performance, three issues should be carefully considered: whether or not and which relay is needed, which channel is selected and which transmission mode is used. Therefore in this study, the authors solve these three problems jointly to optimise the outage capacity for cognitive radio networks with cooperative communications. Particularly, they emphasise on the imperfect channel sensing situation. Under these setup and the constraints, the authors formulate the problem of relay determination, transmission channel and corresponding transmission mode selection to maximise the outage capacity as a discrete optimisation problem. Then a discrete stochastic optimisation algorithm is proposed to maximise the outage capacity to adaptively determine relay and select the optimal transmission channel and transmission mode. The proposed algorithm has fast convergence rate and low computation complexity. Moreover, the time-varying radio environment scenario is also considered, and they show that the proposed algorithm has good tracking capability for time-varying radio environment. Finally, simulation results are presented to demonstrate the performance of proposed scheme.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".