Evaluating Open-Source 5G SA Testbeds: Unveiling Performance Disparities in RAN Scenarios
Bibliographic record
Abstract
Fifth generation (5G) standalone (SA) mobile networks are rapidly gaining prominence worldwide, and becoming increasingly prevalent as the telecommunication industry standard. Most published work concerning 5G applications relies on open-source 5G radio access network (RAN) simulation and emulation tools to evaluate various concepts, algorithms, and use cases. However, these tools are not always accurate in conveying a realistic representation of real-world RAN performance and expected quality of service (QoS). This paper discusses the deployment of a 5G SA testbed supporting three different RAN scenarios of real and simulated deployments using open- source software, commercial-off-the-shelf (COTS) hardware, and software defined radios (SDRs). We experimentally evaluate the performance of these scenarios for the RAN and quantify their differences in terms of computational resource utilization, throughput, latency, coverage, and power consumption. Specifically, we explore the emulation and simulation tools’ ability to reflect realistic RAN performance and highlight the differences compared to the SDR-based deployment. Through this analysis, this paper provides insights into the performance of each approach and sheds light on the feasibility of using open- source software for 5G testing and experimentation.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".