5G DIY: Impact of Different Elements on the Performance of an E2E 5G Standalone Testbed
Bibliographic record
Abstract
5G, the fifth generation of mobile networks, promises new services, faster speeds, lower latency, and increased network capacity. A 5G network has three main elements: the Radio Access Network (RAN) which can be further divided into a hardware component, called software-defined radio (SDR), a software component, the core network and the User Equipment (UE). Recent years have seen the emergence of an “open” paradigm where the different elements of a 5G network are designed by different developers, and as a result can be separately modified and then integrated to enhance network functionality. This paper presents a framework to compare the impact of different elements on the performance of an end-to-end 5G standalone testbed. In particular, using open5GS as the core and 5G modems as the UE(s), we compare the performance of the recently released “O-RAN native suite, srsRAN-Project” to its srsRAN predecessor for two different SDRs (Ettus USRPs B210 and X410), over wireless and wired channels, in a single cell with one or two UEs. It is concluded that srsRAN-Project, with X410 as the SDR, provides the most stable and consistent performance over wired and wireless channels in both single-UE as well as multi-UE testbeds.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".