A Viterbi-Like Algorithm With Adaptive Clustering for Channel Assignment in Cellular Radio Networks
Bibliographic record
Abstract
A new channel assignment algorithm, called the Viterbi-like algorithm (VLA), is proposed to solve the channel assignment problem in cellular radio networks. The basic idea of the proposed algorithm is step-by-step (sequential) channel assignment with the objectives of minimum bandwidth required at every step, subject to adjacent channel and cochannel separation constraints. The VLA provides the benefits of minimum required bandwidth, stability of solution, and fast execution time. The performance of the VLA is evaluated by computer simulation, applied first to 19 benchmark problems on channel assignment and then applied to study cellular radio network performance. Results from computer simulation studies show that bandwidth requirements by VLA closely match or are sometimes better than those of the existing channel assignment algorithms. Furthermore, it is found that execution of VLA is approximately two times faster than the local search algorithm—the existing channel assignment algorithm with the least bandwidth requirements. The combined advantages of minimum required bandwidth, stability of solution, and fast execution time make the VLA a useful candidate for cellular radio network planning.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".