Channel Assignment for Multihop Cellular Networks: Minimum Delay
Bibliographic record
Abstract
Multihop cellular networks (MCNs) enhance the capacity and coverage and alleviate the dead-spot and hot-spot problems of cellular networks. They also allow faster and cheaper deployment of cellular networks. A fundamental issue of these networks is packet delay because multihop relaying for signals is involved. An effective channel assignment is the key to reducing delay. In this paper, we propose an optimal and a heuristic channel assignment scheme, called OCA and minimum slot waiting first (MSWF), respectively, for a time division duplex (TDD) wideband code division multiple access (W-CDMA) MCN. OCA provides an optimal solution in minimizing packet delay and can be used as an unbiased or benchmark tool for comparison among different network conditions or networking schemes. However, OCA is computationally expensive and, thus, inefficient for large real-time channel assignment problem. In this case, MSWF is more appropriate. Simulation results show that MSWF achieves on average 95 percent of the delay performance of OCA and is effective in achieving high throughput and low packet delay in conditions of different cell sizes.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".