Improved channel assignment for WLANs by exploiting partially overlapped channels with novel CIR-based user number estimation
Bibliographic record
Abstract
Aiming at solving the problem of frequency scarcity in dense IEEE 802.11 wireless local area networks (WLANs), a novel channel assignment scheme is proposed in this paper where we explore the partially overlapped channels for additional frequency resources. In our proposed algorithm, we first introduce a user number estimation algorithm at physical layer where the number of users is determined by the number of different channel impulse responses (CIRs). Then, the IEEE 802.11 channels are allocated to the users in a distributed way with the purpose of maximizing system capacity using the information of the number of users for each channel. The interferences caused by the channel partial overlap are mathematically evaluated and involved in the channel assignment. Simulations verify that the proposed algorithm can estimate the number of users accurately while at the same time, significantly improving the system performance.
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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 | 0.000 | 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 | 0.000 | 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".