Fair channel allocation and access design for cognitive ad hoc networks
Bibliographic record
Abstract
We investigate the fair channel assignment and access design problem for cognitive radio ad hoc network in this paper. In particular, we consider a scenario where ad hoc network nodes have hardware constraints which allow them to access at most one channel at any time. We investigate a fair channel allocation problem where each node is allocated a subset of channels which are sensed and accessed periodically by their owners by using a MAC protocol. Toward this end, we analyze the complexity of the optimal brute-force search algorithm which finds the optimal solution for this NP-hard problem. We then develop low-complexity algorithms that can work efficiently with a MAC protocol algorithm, which resolves the access contention from neighboring secondary nodes. Also, we develop a throughput analytical model, which is used in the proposed channel allocation algorithm and for performance evaluation of its performance. Finally, we present extensive numerical results to demonstrate the efficacy of the proposed algorithms in achieving fair spectrum sharing among traffic flows in the network.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".