User-AP Association for Performance Gains in Dense Full Duplex CSMA/CA Networks
Bibliographic record
Abstract
The spectral efficiency of wireless LANs (WLANs) can be improved by allowing access points (APs) and stations (STAs) to transmit concurrently in a bidirectional mode using full duplex (FD) radio technology. However, the high density of today's WLANs necessitates the need to optimally coordinate the association of stations (STAs) or users with the access points (APs) to minimize the effect of interference among multiple pairs of FD transmissions, and to achieve better throughput gain. Hence, we seek a set of user-AP associations that improve throughput gain in FD WLAN based on spatial channel statistics. Using tools from stochastic geometry, this problem is formulated as an optimization problem with the objective of maximizing the mean rate utility and the sum rate. We perform the analysis of throughput gain when FD is used in dense WLAN with an optimized user-AP association and the assumption that self- interference (SI) is reduced close to the noise floor level. From our evaluation, we infer that FD WLAN yields potential significant gains over half duplex (HD) WLAN. Also, efficient distribution of users among APs further improves throughput gain in FD WLAN in the worst-case mean interference when compared with the legacy user-AP association. Overall, our analysis reveals that FD could at most double the throughput of WLAN and additional throughput gain is possible when FD is combined with an optimized user-AP association.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".