Social, technical and economical challenges of 5G technology in Indian prospective: Still 4G auction not over, but time to think about 5G in India
Bibliographic record
Abstract
The objective of present research is comprehensive study related to 5G technology of mobile communication for year 2020 in Indian prospective. The European and American forums started thinking seriously about the next generation of wireless and mobile communication for 2020. A European forum named METIS, Mobile and wireless communications Enablers for the Twenty-twenty (2020). Information Society started working on funded project to identify the 5G future scenarios especially in European environment. METIS funded project also involves many leading technology companies. The guidelines decided by these projects will become basis of shape of fifth generation of wireless technologies. The 4G concept shave already moved to standardization phase, we must begin to work on the building blocks of the 5G networks.5G technology will provide low battery consumption, more secure features and 4A's paradigm ie any rate, anytime, anywhere and affordable. There is a need to study local scenarios and information technology related local demand to give a direction to 5G technology development. The future technology is required to be indigenous, capable to handle local social and economic issues so that use of Information technology may be able to address our issues more effectively. Presently, most of the Europe and USA may not be compared with India, China and third world. Better infrastructure, better economy and less population are some features of Europe and USA. There challenges are different and they develop technology for their growth. In most of the cases we adopt it large cost. Applying technologies in core and routed social aspect is big challenge for nation like India. As our technology and our needs and wants are different from front runners, we need to design the technology more judiciously and economically. We need a technology which enables poor person percentage to grow fast. In this view, we have indentified our own scenario and technological requirement to implement 5th generation of mobile and wireless technologies.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".