Optimal Channel Assignment in Multi-Hop Cellular Networks
Bibliographic record
Abstract
Wireless networks have made great gains in usability and popularity. However, inherent limitations on cell capacity and coverage still exist. There are also dead-spots and hotspots problems in these networks. Ad hoc multi-hop relaying enhances cell capacity and coverage, alleviates the dead-spots problem, and helps to ease congestion in hotspots. However, multi-hopping also increases packet delay. Effective channel assignment is key to reducing delay. Existing channel assignment schemes are heuristics and may not guarantee optimal solutions in terms of minimum delay. In this paper, we provide an optimal channel assignment (OCA) scheme for ad hoc TDD W-CDMA multi-hop cellular networks (MCN) to minimize packet delay. OCA can also be used as an un-biased tool for the comparison among different network topologies, network densities, and protocols. To the best of our knowledge, this is the first time that a minimum delay optimal channel assignment is proposed in a multi-hop cellular environment.
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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 | 0.001 | 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 | 0.000 |
| 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".