Bi-Level Traffic Steering Decision in High-Mobile and Ultra-Dense Multi-RAT Networks
Bibliographic record
Abstract
Technological advancements in cellular networks have enabled to surpass many challenges in telecommunications, but some features remain restricted, such as throughput, packet loss, and latency. User equipment (UE), including mobile, smart devices, vehicles, IoT devices, and smart city infrastructure, requires seamless connectivity to share data and resources effectively. Multiple radio access technology (multi-RAT) scenarios offer a solution to the limitations of individual RATs by combining their strengths. Determining the optimal RAT for traffic steering (TS) in multi-RAT scenarios is challenging due to factors such as high mobility, ultra-dense networks, overall dynamic network conditions, and the unique needs of individual users. In this context, we propose a bi-level approach, called BIL-TS, which includes (i) centrally determining the optimality of RATs and (ii) locally making TS decisions. BIL-TS utilizes the Actor-Critic SARSA Reinforcement Learning (ACS-RL) in level (i) to evaluate the optimality of RATs by considering the entire network, and level (ii) leverages Linear Regression (LR) to make decisions of TS to the optimal RAT based on specific requirements of each UE. Simulation results show that our proposed BILTS approach significantly enhances efficiency in TS, resulting in higher throughput, reduced packet loss, and lower latency.
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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 | 0.001 |
| 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".