Dynamic bandwidth allocation with machine learning in dense WiFi network
Bibliographic record
Abstract
Efficient load balancing is a fundamental aspect within a densely populated Wireless Fidelity (WiFi) network, particularly when the goal is to evenly distribute bandwidth and regulate Internet usage. The distribution of network traffic for load balancing can be achieved by utilizing quality of service protocols and access control lists or filters. This document introduces the application of a machine learning-based prediction model to outline time intervals of congestion in a densely populated WiFi network employing dynamic load balancing. Historical data related to associated users and WiFi network bandwidth serve as key indicators to assess the network's congestion level. Dynamic load balancing is executed by allocating bandwidth to priority traffic based on observed congestion levels. To implement load balancing, access control lists based on time and quality of service parameters are configured within the network devices.The experimental findings reveal a notable improvement in the performance of a WiFi network through the implementation of our dynamic load balancing approach, resulting in a 50% reduction in lost packets. This innovative method allows for the definition of priority traffic types during different congestion periods.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".