Team Learning-Based Resource Allocation for Open Radio Access Network (O-RAN)
Why this work is in the frame
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Bibliographic record
Abstract
Recently, the concept of open radio access network (O-RAN) has been proposed, which aims to adopt intelligence and openness in the next generation radio access networks (RAN). It provides standardized interfaces and the ability to host network applications from third-party vendors by x-applications (xAPPs), which enables higher flexibility for network management. However, this may lead to conflicts in network function implementations, especially when these functions are implemented by different vendors. In this paper, we aim to mitigate the conflicts between xAPPs for near-real-time (near-RT) radio intelligent controller (RIC) of O-RAN. In particular, we propose a team learning algorithm to enhance the performance of the network by increasing cooperation between xAPPs. We compare the team learning approach with independent deep Q-learning where network functions individually optimize resources. Our simulations show that team learning has better network performance under various user mobility and traffic loads. With 6 Mbps traffic load and 20 m/s user movement speed, team learning achieves 8% higher throughput and 64.8% lower PDR.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.000 | 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 it