Mobile Network Planing Process Case Study - 3G Network
Bibliographic record
Abstract
Third Generation cellular networks (3G) were developed with the aim of offering high data rates up to 2Mbps and 384Kbps for stationary and mobile users respectively which allows the operators to offer a multimedia connectivity and other data services to the end customers. In this work we apply techniques to design a 3G radio network, in particular we study the planning and implementation in developing countries, and state of Palestine as a case study. In order to carry a 3G radio network planning for a selected regions, we must follow a roadmap consists of set of phases; First of all, we must determine the region under study on the digitized map in order to obtain some useful information, such as the area distribution; thus using digital maps gives a good clearness for areas classification, land use, land terrain, heights, vectors, etc. Also we must forecast subscriber profile to perform coverage and capacity dimensioning process to achieve nominal cell plan. This paper work studies Nablus as one of the major Palestinian cities. then, subscriber forecast profile is applied in order to calculate the service traffic demand, the capacity and and coverage requirements, this study is carried in corporation with Wataniya which is one of the leading mobile telecommunication service provider in Palestine. For Nablus city we’ve found that 28 sites are required to be installed to meet the given capacity requirements, on the other hand 46 sites are for coverage. At this point we should make decision about how many site will be implemented. In general we have to select number of sites relative to coverage requirement; so to serve Nablus city by 3G services we should implement 46 sites. At final stage, we have to be sure that our proposed 3G network is suitable not only for the first year of running the 3G services over the deployed network, our design takes into consideration the growth of subscribers number and their demands, so periodically, the networking administrators and the network planning department, assess the network current status and upgrade the network to meet the future demands.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.009 | 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".