Bibliographic record
Abstract
The 802.11ac version of the popular IEEE 802.11 protocol aims to boost performance by increasing the channel bandwidth and allowing simultaneous transmission to multiple clients in the downlink direction. This dissertation presents an innovative approach of performance evaluation of downlink multi-user multiple input multiple output (DL-MUMIMO) technique for differentiated quality of service (QoS) based traffic categories in IEEE 802.11ac protocol. We propose a novel analytical model based on discrete Markov chain (DMC) and E-limited M/G/1 queuing model to evaluate the performance improvements of multi-user transmission opportunity (MU-TXOP) sharing in DL-MU-MIMO under non-saturated load and non-ideal channel condition. We also evaluate MU-TXOP sharing probabilities among different QoS nodes to assess the performance improvement of different traffic categories. In this dissertation, we propose an Access Point controlled MAC protocol (A-MAC) that enables simultaneous transmissions from multiple stations (STA) in uplink to eliminate under utilization of network resources in uplink transmission due to single user communication. The protocol uses enhanced distributed channel access (EDCA) technique to initiate multi-user transmission and orthogonal frequency division multiple access (OFDMA) method to transmit multiple Ready-To-Send (RTS) messages simultaneously. The proposed protocol also introduces explicit channel sounding technique by using dedicated OFDM subcarrier blocks for each user. We evaluate the performance of the proposed protocol using a noble analytical model and validate the performance metrics by extensive simulation in different traffic conditions. In this dissertation we also propose the increase of carrier sensing threshold (CSTH) of nodes during association with access point (AP) to mitigate collision probability due to hidden nodes during uplink transmission and validate our proposal through extensive simulation. Finally, we propose a noble analytical model to evaluate the performance of restricted access window (RAW) mechanism of IEEE 802.11ah as the MAC layer protocol for internet of things (IoT) network. We evaluate detailed performance metrics of non-QoS IoT network and investigate the feasibility of using RAW mechanism to support differentiated QoS based heterogeneous IoT network
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".