Adaptive Tuning of MIMO-Enabled 802.11e WLANs with Network Utility Maximization
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".