AI-Enabled Radio Resource Allocation in 5G for URLLC and eMBB Users
Bibliographic record
Abstract
The fifth generation (5G) network is expected to accommodate heterogeneous traffic with diverse QoS demands. In this paper, we address the coexistence of Ultra-Reliable Low-Latency communications (URLLC) and enhanced Mobile Broad-Band (eMBB) users in 5G networks. We propose an AI-enabled approach that uses a reinforcement learning-based algorithm to balance the Key Performance Indicators (KPIs) of both URLLC and eMBB users. The proposed algorithm aims to jointly optimize both latency and reliability of URLLC users as well as the throughput of eMBB users. To achieve this, the algorithm utilizes the flexibility of the time-frequency grid of 5G standard to jointly perform power and resource block allocations to users. We compare our results with two baseline algorithms; a priority-based proportional fairness algorithm with fixed power allocation (PPF) that gives priority to URLLC users and a Q-learning algorithm (LR-Q) that performs joint power and resource allocation with the objective of improving reliability and latency performance of URLLC users only. Our results show that the proposed algorithm outperforms LR-Q by 29% increase and PPF by 21 times increase in throughput. Meanwhile, less than 0.5 ms degradation in URLLC's latency at the 10-4percentile is observed, compared to both LR-Q and PPF.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".