Energy-Aware Dynamic DU Selection and NF Relocation in O-RAN Using Actor–Critic Learning
Bibliographic record
Abstract
Open radio access network (O-RAN) is one of the promising candidates for fulfilling flexible and cost-effective goals by considering openness and intelligence in its architecture. In the O-RAN architecture, a central unit (O-CU) and a distributed unit (O-DU) are virtualized and executed on processing pools of general-purpose processors that can be placed at different locations. Therefore, it is challenging to choose a proper location for executing network functions (NFs) over these entities by considering propagation delay and computational capacity. In this paper, we propose a Soft Actor-Critic Energy-Aware Dynamic DU Selection algorithm (SA2C-EADDUS) by integrating two nested actor-critic agents in the O-RAN architecture. In addition, we formulate an optimization model that minimizes delay and energy consumption. Then, we solve that problem with an MILP solver and use that solution as a lower bound comparison for our SA2C-EADDUS algorithm. Moreover, we compare that algorithm with recent works, including RL- and DRL-based resource allocation algorithms and a heuristic method. We show that by collaborating A2C agents in different layers and by dynamic relocation of NFs, based on service requirements, our schemes improve the energy efficiency by 50% with respect to other schemes. Moreover, we reduce the mean delay by a significant amount with our novel SA2C-EADDUS approach.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".