Network Coding‐Based D2D Transmission for Public Safety Networks over LTE HetNets and 5G Networks
Bibliographic record
Abstract
Device to Device (D2D) communications appear like an emergency solution for the Public Safety Network (PSN) when the LTE cell range is limited. D2D networks can use the unlicensed frequency bands, as this makes their transmissions cheaper and easier to deploy. Therefore, the development of this technology must deal with the security challenge. On the one hand, it is important to know how to design a secure D2D solution within the small cells, and on the other hand, the new scheme needs to deal with the problem of radio resources limit, since it will be used during emergency situations. This paper develops a new algorithm, named Generalized Secure Network Coding‐based Data splitting algorithm (G‐SNCDS), to ensure a secure data transmission for Public Safety D2D communications over LTE Heterogeneous Networks (HetNets) and 5G networks, without using additional radio resources. Our approach consists of performing Network Coding (NC) data packets transmissions based on a new Data Splitting (DS) technique, which we developed, based on a constructed butterfly effect that uses a new Butterfly network algorithm that we propose. Our solution enhances the security without affecting the level of Quality of Service (QoS). Thus, it is more suitable when network resources are limited. The simulation results show that our approach provides a secure D2D communication without increasing the overhead in the network.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".