Effective channel assignment in multi-hop W-CDMA cellular networks
Bibliographic record
Abstract
Multi-hop relaying is an important concept in tackling the inherent problems of limited capacity and coverage in cellular networks. It helps to solve the dead-spots problem and to ease congestion in hotspots. However, to obtain good performance of multi-hop relaying, an effective channel assignment scheme is needed. In this paper, we study the design goals of a good channel assignment scheme. We then propose a channel assignment scheme, called Extended Delay-Sensitive Slot Assignment (E-DSSA), which achieves all these goals. The distinctive feature of E-DSSA is the use of a novel transmission zone testing technique which allows high flexibility and precision in channel assignment in TDD W-CDMA multi-hop cellular environment. Performance evaluation shows that E-DSSA outperforms its existing counterparts in terms of data throughput with low delay for both sparse and dense networks. E-DSSA is also shown to adapt to different cell sizes achieving high throughput and call acceptance ratios.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 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 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".