On Virtualization and Security-Awareness Performance Analysis in 5G Cellular Networks
Bibliographic record
Abstract
Recently, Fifth Generation (5G) cellular networks have gained promise as a paradigm that could provide rich computational resources for users. Virtualization is a key technology for wireless communications, especially in standard Long Term Evolution (LTE) systems, which enable cloud based multi-tenancy business models through providing a shared scalable resource platform for all users. Despite the potential significance of virtualization for cellular networks, several challenges remain to be addressed. For cellular networks, providing multiple levels of security is essential to support different levels in information sensitivity. However, placing different customers' services requirements on a virtualized evolved Node B's (eNB's) scheduler may lead to noticeable security vulnerabilities. In this work, we present an overview of cellular network security issues in a fully virtualized environment along with their preventative measures. Virtualization is implemented by allowing service providers to share their resources while performing different scheduling policies and sharing one eNB. To evaluate the considered framework, the average delays for different traffic types were measured. The results of the simulation showed that virtualization could noticeably reduce average user equipment delay compared with the non-sharing scheme.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".