Optimized Physical Carrier Sensing Threshold in High Density CSMA/CA Networks
Bibliographic record
Abstract
We address the PCS (physical carrier sensing) threshold selection problem for dense wireless local area networks (WLANs). This important network parameter determines the spatial reuse permitted by the CSMA/CA protocol, which consequently determines the interference level. Using Poisson Point Processes (PPP) from stochastic geometry, we obtain a closedform expression for PCS threshold selection. Our primary aim is to find the PCS threshold value that improves the spatial density of throughput (SDT). Assuming a Rayleigh fading channel, we derive the PCS threshold as a function of node density and path loss exponent, parameters that are global and easily measured. That is, we determine the optimal PCS threshold based on the fundamental parameters of the network without requiring channel knowledge of each link. To assess the impact of the proposed method, simulation results reveal gains in throughput density over the conventional and the random selection methods.
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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 | 0.000 |
| 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".