Gossip-Enabled Stochastic Channel Negotiation for Cognitive Radio Ad Hoc Networks
Bibliographic record
Abstract
The presence of a predefined control channel in ad hoc wireless networks is a common assumption widely accepted by the research community. However, it may not always be the case in some future networking scenarios with high-network dynamics and strong user diversity, such as cognitive radio (CR) ad hoc networks. This paper investigates channel negotiation in CR ad hoc networks without a predefined control channel by introducing a novel gossip-enabled stochastic channel negotiation (GES-CN) framework. The channel negotiation process is first formulated as an optimization problem, aiming to improve the probability of successful channel negotiation in the CR network while achieving sufficient suppression on the interferences to the primary networks. With the GES-CN framework, we develop an analytical model on the probability of successful channel negotiation as well as the resultant overhead in terms of the number of channel negotiation attempts made before achieving a successful channel negotiation process via an absorbing Markov chain. Numerical results demonstrate the merits of the proposed GES-CN framework and validate the developed analytical model. We conclude that the proposed GES-CN framework is an excellent candidate for the future distributed CR ad hoc networks with high dynamics and heterogeneity.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| 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".