Balanced association algorithm for IEEE 802.11 extended service areas
Bibliographic record
Abstract
Abstract Most wireless local area network performance estimations are done with the assumption of uniformly distributed (UD) users. In practice, however, stations (STAs) are distributed unevenly among access points (APs) in an extended service area, causing congested hot‐spots (HS) and under‐utilized APs. Considering a typical network is made up of multiple APs, having some nodes carrying excessive loads degrades the overall network performance. The system performance can be improved by associating STAs efficiently throughout the network, in a sense sharing the network resources fairly among APs and thus relieving congestion. The association algorithm currently employed in IEEE 802.11 systems, that is specifically designed for residential and small office environments, takes into account signal strength as the only parameter and associates STAs to the closest (in signal strength sense) AP, ignoring its load. Novel user association algorithms are required to solve the problems commonly seen in corporate network environments spanning multiple APs, namely congestion relief and resource sharing. In this work, a distributed and online association algorithm is proposed that demonstrates improved average throughput performance, a balanced load distribution as well as fairness across the network compared to the conventional algorithm. Copyright © 2008 John Wiley & Sons, Ltd.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".