Mobility Aware Channel Allocation for 5G Vehicular Networks using Multi-Agent Reinforcement Learning
Bibliographic record
Abstract
Reinforcement learning is a machine learning technique that focuses on exploring an uncharted territory exploiting of current knowledge. This paper proposes a Mobility Aware Channel Allocation (MACA) algorithm for 5G Vehicular Networks using a combination of Multi-Agent Reinforcement Learning (MARL) and Semi-Markov Decision Process (SMDP). In this work, we use multiple autonomous agents operating in a common environment to address the sequential decision-making problem to optimize the long-term rewards. In MACA, first we predict the mobility of vehicles using Teammate-Learning model as it allows the vehicles to cooperate and collaborate with each other without prior coordination. Secondly, during SMDP resource allocation phase, MARL inputs are applied to the Action Selection model for each vehicle based on their priorities. This is done at Road-Side Units (RSUs). Through numerical results and evaluations, we verify that the proposed method demonstrates efficient channel allocation and high packet delivery ratio as compared in the scenario of vehicles with multiple (high, medium, and low) priorities to existing conventional SMDP and Greedy algorithms.
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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 | 0.001 |
| 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".