Wireless SDN architecture Testbed to support IP Multimedia Subsystem
Bibliographic record
Abstract
The fifth generation of mobile communications, promises to offer very high achievable data rate, very low latency, ultra-high reliability, along with supporting a wide range of new applications and use cases. In order to increase network scalability, service flexibility and to improve mobility management in 5g wireless networks, two new concepts have emerged, namely Network Functions Virtualization (NFV) and Software Defined Wireless Networking (SDWN). In this study, we designed and implemented a network architecture based on an open-source software-based LTE implementation named as OpenAirInterface (OAI). OAI emulation platform is an integrated tool allowing large-scale networking experimentation. The latter can be used for prototyping innovation scheduling algorithms, making the majority of new architecture. SDWN network technology has emerged in order to deliver a high quality multiple performance system with cost benefits that includes several processes such as live monitoring, reconfiguration, control delegation and faster data-transfer. In order to test some key enablers and features of 5G mobile networks, we provide a topology that combines SDWN and NFV technologies to handle the fulfilment of network slices by running Mosaic 5g FlexRAN software on top of the OAI platform. Moreover, Clearwater IP Multimedia Subsystem (IMS) is integrated to provide voice over IP (VoIP) service between subscribers and a Wi-Fi Access point (AP) is added to network in order to establish a heterogeneous wireless network (HetNet). Our results can serve as a reference for future optimization by the open source community.
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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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".