Adaptive Uplink OFDMA Random Access Grouping Scheme for Ultra-Dense Networks in IEEE 802.11ax
Bibliographic record
Abstract
IEEE 802.11ax, which is the next-generation WLAN standard, aims at providing highly efficient communication in ultra-dense networks. However, due to the high quantity of stations (STAs) in dense deployment scenarios, the potential high collision rate significantly degrades the network efficiency of WLAN. In this paper, we propose an adaptive grouping scheme to overcome this challenge in IEEE 802.11ax using Uplink OFD-MA Random Access (UORA). In order to achieve the optimal utilization efficiency of resource units (RUs), we first analyze the relationship between group size and RU efficiency. Based on this result, an adaptive STA grouping algorithm is proposed to cope with the performance fluctuation of 802.11ax due to remainder stations after grouping. The analysis and simulation results demonstrate that our adaptive grouping algorithm dramatically improves the performance of both the system and each STA in the ultra-dense network.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 source (direct Gemma or distilled Codex), 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".