LMR and LTE for Public Safety in 700 MHz Spectrum
Bibliographic record
Abstract
This paper presents a concise overview of current public safety communication networks known as LMR (Land Mobile Radio) and emerging LTE- (Long-Term Evolution-) based broadband public safety networks to be deployed in the 700 MHz band. A broadband nationwide network for public safety based on LTE is inevitable where shared or dedicated types of LTE-based public safety networks are possible. Current LTE services do not meet mission-critical requirements and several enhancements have been defined by 3GPP to address this in Releases 12 and 13. First responders are familiar with LMR and consider it to be a reliable technology with massive deployment everywhere. Therefore, it is expected that LMR will continue to exist alongside any new LTE-based broadband public safety network. Recent LTE releases (particularly Release 15) addressed the LMR-LTE interoperability issue and described comprehensive interworking facilities. New and upcoming features and services of LTE in Releases 14 and 15, such as mission-critical data, mission-critical video, and aerial user equipments, are also directly applicable to public safety. The paper endeavours to provide a quick yet meaningful review of all these issues. It also offers a look ahead at the new and rapidly advancing virtualization technologies, such as software-defined radio access network, and radio access network slicing, as enablers for future public safety networks.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".