Cross-Layer Design of MIMO-Enabled WLANs With Network Utility Maximization
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Bibliographic record
Abstract
Wireless local area networks (WLANs) have become a ubiquitous high-speed data-access technology. The recent IEEE 802.11e standard provides quality-of-service (QoS) support, and the pending 802.11n standard further increases the transmission rate by using the multiple-input-multiple-output (MIMO) technique. Multiple antennas can be used to improve the performance gain by either increasing the transmission reliability through spatial diversity or increasing the transmission rate through spatial multiplexing. This new characteristic at the wireless physical (PHY) layer requires the corresponding adaptation at the medium access control (MAC) layer to reach the best performance gain. In this paper, we propose cross-layer design schemes for WLANs under two different MAC protocols: the carrier sense multiple access with collision avoidance (CSMA/CA)-based 802.11e MAC and the slotted Aloha MAC. For the 802.11e MAC, two different contention window (CW) size adaptation schemes, namely, <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">U-MAC</i> and <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">D-MAC</i> , are proposed, which facilitate the MAC protocol to jointly adapt its CW size with the PHY layer's MIMO operating parameters. For the slotted Aloha MAC, a cross-layer optimization framework <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">NUM-O</i> is proposed to jointly optimize the MIMO configuration at the PHY layer and the persistent probabilities for different classes of multimedia traffic at the MAC layer. A distributed algorithm <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">NUM-D</i> based on dual decomposition and a simplified version <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">NUM-S</i> are also proposed. Simulation results are presented to show the effectiveness of the proposed methods.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it