Reinforcement Learning for Radio Resource Management in RAN Slicing: A Survey
Why this work is in the frame
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Bibliographic record
Abstract
Dynamic radio resource allocation to network slices in mobile networks is challenging due to the diverse requirements of RAN slices and the dynamic environment of wireless networks. Reinforcement learning (RL) has been successfully applied to solve different network resource allocation problems where an agent learns how to choose the best action from the interactions with the environment. This survey studies the state-of-the-art RL approaches that address radio resource management in radio access network slicing. To this end, we first categorize different problem definitions based on the network environment. Then we explain how each environment can be modeled as a Markov decision process and what RL algorithms can be used to solve them. In addition, we discuss the challenges present in existing works and suggest strategies to address them.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| 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 it