User-Based Channel Assignment Algorithm in a Load-Balanced IEEE 802.11 WLAN
Bibliographic record
Abstract
A new load balancing algorithm is presented based on power management of Access Points (APs) to reduce congestion at hot spots in Wireless Local Area Networks (WLANs) and to assign channels to APs The algorithm first finds the Most Congested Access Point (MCAP), then decreases its transmitted power in discrete steps, and the process continues until the users’ assignment which leads to a high balance index is reached. A new mathematical programming formulation is then applied to assign channels to the APs such that the Signal-to-Interference Ratio (SIR) at the users’ level is maximized. Results show that the algorithm is capable of reducing the overall congestion at hot spots in a WLAN and increases the SIR significantly for cases involving relatively large WLANs. In the process, network throughput is increased.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| 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".