SOIS-A2C Scheme: Facilitating Management of Multi-Radio Resources in Heterogeneous Inter-RAN Slicing in the Presence of Hardware Impairments
Bibliographic record
Abstract
Recent years have witnessed the emergence of the concept of network slicing (NS) that enables the creation of independent, virtualized logical networks on the same physical infrastructure. Each NS is tailored to meet the needs of a particular service or application. NS is widely considered a key enabling technology for end-to-end (E2E) automation in managing resources within radio access networks (RAN). To achieve E2E automation in RAN slicing, it is essential to automate resource allocation at both the intra- and inter-slice levels to meet the demands of future applications and services. In this context, the present study focuses on the automated management of multiple radio resources at the inter-slice level. More specifically, we propose a self-optimizing inter-slice scheme based on the deep reinforcement learning (DRL) advantage actor-critic (A2C) algorithm, named SOIS-A2C. Our goal is to maximize the spectral efficiency of the system while maintaining high service quality by considering the effects of hardware distortions and intra-slicing interference. The results highlight the effectiveness of the proposed SOIS-A2C scheme, which demonstrates superior performance in maximizing spectral efficiency as compared to benchmark schemes such as the SOIS-based deep Q-network (DQN) and hard slicing in highly fluctuating environments and under ideal and non-ideal hardware conditions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".