Energy-efficient relaying for cooperative cellular wireless networks
Bibliographic record
Abstract
Introduction The continuously growing demand for ubiquitous network access has led to the rapid development of wireless cellular networks during the last decade. The subscriber number and service traffic in cellular networks have explosively escalated. It is reported that there are now more than 5 billion mobile phone connections worldwide, and more than a billion mobile phone connections have been added globally in just 18 months [1]. The Asia-Pacific region including India and China is the main source of growth, accounting for 47% of global mobile connections at the end of June 2010. The penetration of mobile phones is growing rapidly in developing countries, and the number of subscribers will be astounding, since the world population is expected to reach 9.15 billion in 2050. In parallel with the rapid growth of the number of connections, the service types and traffic load have experienced significant evolvement from voice and short messaging service (SMS) to video and multimedia internet. Such tremendous growth in the information and communication technology (ICT) industry has made it become one of the leading sources of world energy consumption and it is expected to grow dramatically in the future. There are currently more than 4 million base stations (BSs) serving mobile users, each consuming an average of 25 MWh per year. In 2007, four Chinese operators consumed 20 billion KWh, which is equivalent to 8 million tons of coal combustion. ICT already represents around 2%of total carbon emissions, and this is expected to increase from 0.53 billion tonnes (Gt) carbon dioxide equivalent (CO2e) in 2002 to 1.43GtCO2e in 2020 [2].
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.006 | 0.002 |
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".