Reinforcement Learning-Based Joint Power and Resource Allocation for URLLC in 5G
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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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 | 0.003 |
| 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".