APPLICATION OF MICROSOFT EXCEL SOLVER TECHNIQUES TO COMPUTE CLUSTER SIZE IN CELLULAR WIRELESS NETWORK
Bibliographic record
Abstract
Microsoft Excel is one of the most popular software programs that is used by individual and business to record, manipulate and analyze data.Microsoft Excel offers number of data analysis tools collectively called "what if analysis".Many business problems are solved using these tools.However, in Engineering and Computing Science these features of Excel are seldom used to solve appropriate problems.Instead most of the problems are solved using specific tools such as MATLAB or specific Computer programs.In cases where Microsoft Excel is used, only basic operations such as formula, mathematical functions and graphing or charting are utilized.In this paper author attempts to show with examples how Excel Solver techniques can effectively be used to model and solve common problems in Wireless Communication network.Many of the wireless network problems require extensive problem solving skills to manipulate mathematical expressions to produce solutions.This pedagogical approach of using Excel Solver in handling some difficult computation problem in Wireless Communication network is specially aimed at students who do not have strong mathematical aptitude in solving computational problems.A very common example of finding Cell clusters in Wireless Cellular network is used to demonstrate the usefulness of Excel Solver in modeling and solving complex optimization problems.The author also discusses the limitations and practical issues related to using Excel solver.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".