Distributed Multi-Interface Multi-Channel Random Access
Bibliographic record
Abstract
The aggregate capacity of wireless ad-hoc networks can be substantially increased if each wireless node is equipped with multiple network interface cards (NICs) and each NIC operates over a distinct orthogonal frequency channel. Most of the recently proposed channel assignment algorithms are based on formulating combinatorial channel assignment problems. The key is to assign exactly one frequency channel to each NIC. However, combinatorial channel assignment models may result in computationally complicated algorithms as well as inefficient utilization of the available frequency spectrum. In this paper, we revisit channel assignment problem by formulating a novel continuous multi-interface multi-channel random access model. This includes elaborate modeling of the link data rates for various multi-interface multi-channel networking scenarios. We then propose a fast, fully distributed and easy to implement multi- interface multi-channel random access algorithm. Simulation results show that our proposed algorithm significantly outperforms combinatorial channel assignment algorithms in terms of achieved network utility and aggregate network throughput.
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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 | 0.002 | 0.001 |
| 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".