Intelligent CW Selection Mechanism Based on Q-Learning (MISQ)
Bibliographic record
Abstract
Mobile ad hoc networks (MANETs) consist of self-configured mobile wireless nodes capable of communicating with each other without any fixed infrastructure or centralized administration using the medium radio. Wireless technology is based on standard IEEE.802.11. The IEEE 802.11 Distributed Coordination Function (DCF) MAC layer uses the Binary Exponential Backoff (BEB) algorithm to deal with wireless network collisions. BEB is considered effective in reducing the probability of collisions but at the expense of numerous network performance measures, such as throughput and packets delivery ratio, mainly in high traffic load. Deep Reinforcement Learning (DRL) is a DL technique in which an agent can achieve a goal by interacting with the environment. In this paper, using one of the DRL models, we propose Q-learning (QL) to optimize MAC protocols' performance based on the contention window (CW) in MANETs. The intelligent proposed MISQ takes into account the number of packets to be transmitted and the collisions committed by each station to select the appropriate contention window. The performance of the proposed mechanism is evaluated by using in-depth simulations. The outputs indicate that the intelligent proposal mechanism learns various MANETS environments and optimizes performance over standard MAC protocol. The performance of MISQ is evaluated in various networks with throughput, channel access delay, and packets delivery rate as performance measures.
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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 | 0.002 |
| Open science | 0.003 | 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".