Impact of access contention on cooperative sensing optimisation in cognitive radio networks
Bibliographic record
Abstract
The tradeoff between decreasing interference to primary user (PU) and increasing secondary user's (SU's) throughput is of great importance for cooperative sensing in cognitive radio networks. Non‐ideal spectrum sensing in PHY and multiple SUs' access contention in MAC jointly impact SUs' transmission and the tradeoff. In this study, the authors investigate the joint impact from a cross‐layer perspective. First, they quantify the reliability of cooperative sensing and compute SUs' transmission probability under the conditions of non‐ideal sensing and access contention. Closed‐form expressions of the interference probability to PU and SUs' throughput are derived. Specially, two widely‐used contention‐based MAC protocols, i.e. slotted Aloha and distributed coordination function, are studied. Then, they formulate the sensing‐throughput tradeoff problem by using interference probability to PU, rather than the detection probability, as the constraint. Finally, a 2‐dimension search algorithm is proposed to obtain the optimal solution, including the optimal fusion rule, sensing duration and detection threshold. Simulation results validate the outperformance of the cross‐layer scheme. They also demonstrate how the optimal solution varies with some key parameters, i.e. PU's signal‐to‐noise ratio and the number of contending SUs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".