Joint Routing and Packet Scheduling For URLLC and eMBB traffic in 5G O-RAN
Bibliographic record
Abstract
Open Radio Access Network (O-RAN) is an innovative RAN architecture designed to revolutionize 5G-and-beyond mobile networks. O-RAN virtualizes the fronthaul network functions into Open Centralized Unit (O-CU), Open Distributed Unit (O-DU) and Open Radio Unit (O-RU). Unfortunately, there is no standard data communication mechanism to disaggregate Quality of Service (QoS) flow traffic into multiple routes to access O-DUs to leverage the distributed computing capability. Furthermore, there is no centralized scheduler to coordinate processors that are processing O-DU functions efficiently to meet fifth generation (5G) QoS services. Therefore, O-RAN performance is still questionable. This paper investigates an optimized solution for joint Routing and Packet Scheduling (RPS) which is implemented in the O-DU pool to replace individual O-DUs. We formulate two joint RPS problems to coordinate multiple routes and multiple parallel processors in the centralized O-DU pool to accommodate the Ultra-Reliable Low Latency Communications (URLLC) and enhanced Mobile Broadband (eMBB) services. We propose a greedy algorithm and a Min-Max algorithm to approximate the optimal result. Numerical results show that our proposed solution improves significantly system processing delay compared with a scheme of individual O-DUs which are selfishly maximized.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".