PCS Threshold Selection for Spatial Reuse in High Density CSMA/CA MIMO Wireless Networks
Bibliographic record
Abstract
Multiple-input multiple-out (MIMO) equipment improves the spectral efficiency of wireless local area network (WLAN) systems. However, in a large-scale or dense MIMO WLANs, overlapping radio cells or basic service sets (BSSs) are inherent. This prevents multiple concurrent transmissions and degrades spatial reuse. The inability to separate multiple simultaneous transmissions in space is detrimental to overall system performance. The carrier sense multiple access collision avoidance (CSMA/CA) protocol uses the physical carrier sensing (PCS) threshold to determine channel state at the physical layer (PHY) and decide on the number of concurrent transmissions allowed per time slot. Since the PCS threshold determines the spatial reuse under the CSMA/CA protocol, which consequently determines the interference level and the network aggregate throughput, we address PCS threshold selection for dense uplink (UL) MIMO WLAN systems. A closed-form expression is derived for selecting the PCS threshold based on the fundamental parameters of the network where nodes are randomly placed according to a Poisson point process (PPP). We obtain the PCS threshold value that maximizes the spatial density of throughput (SDT) and study the effectiveness of the proposed framework under moderate to high node density. In addition, we analyze the effect of WLAN density on interference from concurrent transmitters. The key observation is that PCS threshold selection should take into account key network characteristics including node density, target SINR or threshold, path-loss exponent and antenna configuration.
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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".