Cross-Layer Design of MIMO-Enabled WLANs With Network Utility Maximization
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,U-MACandD-MAC, 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 frameworkNUM-Ois 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 algorithmNUM-Dbased on dual decomposition and a simplified versionNUM-Sare also proposed. Simulation results are presented to show the effectiveness of the proposed methods.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".