Courteous Priority Access to the Shared Commercial Radio for Public Safety in LTE Heterogeneous Networks
Bibliographic record
Abstract
This paper presents a solution to allow priority access to the Shared Commercial Radio (SRC) for Public Safety (PS) in LTE Heterogeneous Networks (LTE HetNets). This access is tolerated only during the emergency situations. In fact, when the radio resources are available, the Allocation Retention Priority (ARP) scheme accepts the establishment of all new bearers, but when the resources are limited, the ARP mechanism bloc some bearers which have low priorities. Therefore, the lower priority traffics may suffer significantly from packet loss due to the blocking of the low priority bearers. To improve the quality of service of these traffics, a new approach is developed in this paper, namely Courteous Priority Access (CPA) to the Shared Commercial Radio for Public Safety in LTE Heterogeneous networks. In addition, as the Public Safety Network (PSN) and Commercial Network (CN) share a part of radio resources, it will be relevant to manage the bearer's access to the SRC by developing a new mechanism of radio resources allocation with constraints. These constraints are depending of the priority of bearers which request the resources. Thus, the Courteous Allocation Constraints model for Frequencies (CAMF) has been developed in this paper to define the different quantities of radio resources which may be reserved to the two types of networks, namely, PSN and CN. These resources will be allocated to the arrival bearers by using CPA algorithm. The simulation results show that on one hand CPA reduces the number of the commercial blocked bearers and increase the number of commercial active bearers, and on the other hand it keep an acceptable level of PS blocking bearers.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".