A Comparative Analysis of Open-Source Software in an E2E 5G Standalone Platform
Bibliographic record
Abstract
Open-source 5G cellular networks are becoming important to reduce cost and enable more vendors to enter the booming 5G market. Many open-source software are available for both the Radio Access Network (RAN) and the core network and verifying their interoperability and their performance when combined is critical. This has not been studied as yet and it is the topic of this paper. We have deployed a 5G-standalone platform to assess the interoperability of various core and RAN open-source software developed by different developers and to study how different testbeds comprising different core and RAN open-source software perform. For our experiments, we have selected srsRAN _Project, and OpenAirInterface SG RAN, as the leading open-source RAN soft-ware. Similarly, we have selected OpenSGS, and OpenAirInterface SG Core to be the open-source 5G core software. Our performance results show that in the RAN domain OpenAirInterface SG RAN wins on downlink rate, and latency while srsRAN _Project wins on uplink rate. Moreover, we did not see a significant difference in the performance of the two 5G core software.
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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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".