Guest Editorial: LTE in Unlicensed Spectrum
Bibliographic record
Abstract
The articles in this special section focus on the deployment of Long Term Evolution in unlicensed spectrum. These articles cover various design issues such as network architecture, protocol development, network coexistence, unlicensed spectrum access, and practical implementation. Fifth generation (5G) cellular networks will face a rigorous challenge in the ever increasing data rate requirement. To meet such anticipated data growth demand, the industry and academia have developed many cutting edge techniques to improve spectrum utilization. However, the scarcity of spectral resources is still a fundamental bottleneck for network capacity enhancement. Recently, the rich available bandwidth on the 5.8 GHz unlicensed national information infrastructure (U-NII) spectrum has stimulated substantial interest from cellular operators to use the unlicensed spectrum for LTE. In 2015, the LTE-Unlicensed (LTE-U) Forum formally launched the LTE-U specification, and the Third Generation Partnership Project (3GPP) has pushed the standardization of licensed assisted access (LAA)into its Releases 13 and 14. However, the LTE-U technology is still in its infancy, and there are lots of challenges that need to be solved, such as network coexistence among different radio access technologies, unlicensed spectrum sharing and access, and quality of service (QoS) provision on unlicensed spectrum. This Feature Topic aims to provide a comprehensive overview of this appealing technology, harmonizing recent results and key challenges, as well as highlighting future important directions.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.011 | 0.009 |
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".