UE Centric DU Placement with Carrier Aggregation in O-RAN using Deep Q-Network Algorithm
Bibliographic record
Abstract
Open Radio Access Network (O-RAN) provides the capability to efficiently distribute the RAN Network Functions (NFs) such as the Radio Unit (RU), Distributed Unit (DU) and Centralized Unit (CU) in O-RAN Cloud (O-Cloud) nodes using virtualization, automation, intelligence and open interface specifications. In addition, Carrier Aggregation (CA) technology enhances the throughput of the users by aggregating Component Carriers (CCs) and allocating one Primary Cell (PCell) and multiple Secondary Cells (SCell) to each user. Finding the DU NFs placement of each CC (PCell or SCell) while minimizing the number of used O-Cloud nodes and the average user end to end delay is our aim in this paper. Thus, we model the average delay and propose an algorithm using Deep Q Network (DQN) based Deep Reinforcement Learning (DRL) algorithm to find the solution to the problem. Simulation results demonstrate that our proposed scheme reduces the average end user delay and the number of employed O-Cloud nodes at least 90% and 20% with respect to the baselines.
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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".