Reinforcement Learning-Based Joint Power and Resource Allocation for URLLC in 5G
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Bibliographic record
Abstract
Next-generation wireless networks are moving rapidly towards supporting heterogeneous services that bring along several challenges in radio resource allocation. In this paper, we address the problem of multiplexing Ultra- Reliable Low- Latency Communication (URLLC) users and enhanced Mobile Broadband (eMBB) users on a shared channel of 5G New Radio (NR).We propose a joint power and resource allocation algorithm based on Q-learning. The proposed algorithm is crafted carefully to improve reliability and latency of URLLC users without hindering throughput of eMBB users. In particular, the algorithm rewards the actions that mitigate inter-cell interference as well as improve transmission and scheduling delays. We compare our results with a priority-based proportional fairness algorithm with fixed power allocation that relies on giving URLLC users priority in resource scheduling. Simulation results reveal that our algorithm is able to achieve 4% increase in reliability as well as lower latency results in high traffic load scenarios.
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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 | 0.000 | 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 | 0.000 | 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 it