Adaptive Tuning of MIMO-Enabled 802.11e WLANs with Network Utility Maximization
Why this work is in the frame
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Bibliographic record
Abstract
The IEEE 802.11-based wireless local area networks (WLANs) are widely used for high-speed wireless data access. With the recent 802.11e quality-of-service (QoS) extension, realtime applications such as voice over IP and video streaming are finding their way to be running over WLANs. The recent 802.11n proposal aims to provide higher throughput support for bandwidth-intensive multimedia applications. It uses the multiple-input-multiple-output (MIMO) technology at the physical layer to increase the transmission rate. MIMO introduces several new features at the physical layer such as the spatial diversity and spatial multiplexing gains. These new characteristics at the wireless physical layer require corresponding adaptation at higher layers to achieve a better performance. This paper proposes a joint adaptation of the MIMO physical layer and the 802.11e MAC layer through the formulation of a network utility maximization problem. The MIMO configuration at the physical layer and the contention window sizes for different access categories' traffic at the MAC layer are jointly optimized. Simulations are carried out in the ns-2 simulator to show the effectiveness of the proposed method.
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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 | 0.000 | 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 it