HiSO-CoMA: Hierarchical Self-Optimizing Framework for O-RAN Slicing Using Cooperative Multiple Agent Deep Reinforcement Learning
Bibliographic record
Abstract
Network slicing (NS) is a cornerstone technology for sixth-generation (6G) networks, enabling the support of heterogeneous services with diverse quality-of-service (QoS) requirements. However, existing radio access network (RAN) slicing schemes often rely on single-level resource allocation, limiting their adaptability to the dynamic nature of RAN and the efficient use of limited radio resources. This leads to challenges in satisfying service-level agreements (SLAs). Moreover, effective hierarchical slicing that operates under fluctuating traffic loads, and hardware impairments for multiple antenna systems remains a challenge. To address these issues, we propose a hierarchical self-optimization framework aimed at maximizing both the long-term QoS and the spectral efficiency. Specifically, the proposed framework consists of two slicing management schemes: a cooperative multiple actor-critic (CoMA2C) scheme to manage the power and bandwidth among heterogeneous slices on a large scale. Concurrently, a multiagent deep Q-network (MADQN) scheme manages the power and beamforming for active users within each slice on a small time scale, accounting for hardware impairments, user mobility, traffic fluctuations, and channel variations. The DQN and A2C algorithms are employed in the design of the proposed schemes owing to their proven effectiveness in real-time decision-making in dynamic environments. Furthermore, a promising scheme based on rate-splitting multiple access (RSMA) is investigated for heterogeneous services. Simulation results showcase the effectiveness of our proposed framework, demonstrating its ability to satisfy SLAs for heterogeneous services while reducing network overhead and outperforming existing state-of-the-art approaches.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".