Resource Slicing for eMBB and URLLC Services in Radio Access Network Using Hierarchical Deep Learning
Bibliographic record
Abstract
Network slicing is a promising technique for wireless service providers to support enhanced mobile broadband (eMBB) and ultra-reliable low-latency communication (URLLC) services in a shared radio access network (RAN) infrastructure. In this paper, we apply numerology, mini-slot based transmission, and punctured scheduling techniques to support eMBB and URLLC network slices. For efficient allocation of radio resources (e.g., physical resource blocks, transmit power) to the users, we formulate RAN slicing problem as a multi-timescale problem. To solve this problem and address the dynamics of the traffic, we propose a hierarchical deep learning framework. Specifically, in each long time slot, the service provider employs a deep reinforcement learning (DRL) algorithm to determine the slice configuration parameters. The eMBB and URLLC schedulers use their own attention-based deep neural network (DNN) algorithm to allocate radio resources to their corresponding users in each short and mini time slot, respectively. Simulation results show that the proposed framework can achieve a higher aggregate throughput and a higher service level agreement (SLA) satisfaction ratio compared to some other RAN slicing approaches, including the resource proportional placement algorithm, decomposition and relaxation based resource allocation algorithm, and distributed bandwidth optimization algorithm.
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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 | 0.000 | 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".