Guest Editorial: Unfolding the potential of 5G technologies for future wireless networks
Bibliographic record
Abstract
Abstract With the rapid advancements in mobile Internet and smartphones, data traffic in current mobile communication systems is growing exponentially. At the same time, demands for lower latency, increased robustness, and higher energy efficiency are becoming more stringent. In response, 5G technology promises to meet these demands and is currently garnering extensive research interest from both industry and academia. 5G is not just an incremental improvement over its predecessors; it is a transformative technology designed to revolutionise mobile communications. By offering significantly higher speeds, reduced latency, and the ability to connect a massive number of devices simultaneously, 5G stands to impact a wide range of applications from autonomous vehicles to smart cities, healthcare, and beyond. Significant progress has been made in the standardisation and field deployment of 5G networks. Organisations such as the 3rd Generation Partnership Project (3GPP) have been instrumental in developing the standards that define 5G technologies. Moreover, various pilot projects and commercial deployments have been initiated around the world, showcasing the practical capabilities of 5G in real‐world environments.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.011 | 0.017 |
| Insufficient payload (model declined to judge) | 0.011 | 0.010 |
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".