Resource allocation and congestion control in clustered M2M communication using Q‐learning
Bibliographic record
Abstract
Abstract In this paper, we apply a Q‐learning algorithm to carry out slot assignment for machine type communication devices (MTCDs) in machine‐to‐machine communication. We first make use of a K‐means clustering algorithm to overcome the congestion problem in an machine‐to‐machine network where each MTCD is associated with one controller. Subsequently, we formulate the slot selection problem as an optimisation problem. Then, we present a solution using the Q‐learning algorithm to select conflict‐free slot assignment in a random access network with MTCD controllers. The performance of the solution is dependent on parameters such as learning rate and reward. We thoroughly analyse the performance of the proposed algorithm considering different parameters related to its operation. The convergence time, that is, the time required to reach a solution, decreases with increasing value of learning rate, whereas the convergence probability increases. In addition, for smaller values of learning rate, the convergence time decreases with increasing reward values. We also compare with simple ALOHA and channel‐based scheduled allocation and show that the proposed Q‐learning‐based technique has a higher probability of assigning slots compared with these techniques. Copyright © 2016 John Wiley & Sons, Ltd.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".