Energy and QoS-Aware Functional Split Selection in 5G and Beyond O-RAN Networks
Bibliographic record
Abstract
The next generation radio access network (RAN), open RAN (O-RAN), has been introduced as an industry-level standard, supporting interoperability between different vendors' equipment and offering flexibility by including efficient network slicing with different functional split options in 5 G and beyond mobile networks. Nevertheless, strictly satisfying quality-ofservice (QoS) requirements while taking into account the energy consumption when placing network functions within the RAN and allocating resources for each network slice remains a key research problem. In this paper, we propose an O-RAN UserCentric Split Selection (O-RAN-UCSS) that aims at minimizing the deployment cost and power consumption, while considering infrastructure computation cost, link capacities as well as QoS requirements in a three-layer O-RAN architecture. The problem is formulated as an Integer Linear Problem (ILP), then solved using a Branch-and-Cut (B&C) algorithm in a realistic RAN scenario including eMBB and URLLC services. Simulation results show that our proposal outperforms the baseline approaches in terms of deployment cost, power consumption, and latency penalty at the expense of a reasonable increase of the computation time.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".