Enhancing channel utilization by improving media access coordination in wireless local area networks
Bibliographic record
Abstract
Abstract Distributed coordination function (DCF) is the basis protocol for IEEE 802.11 standard wireless local area networks. It is based on carrier sense multiple access with collision avoidance (CSMA/CA) mechanism. DCF uses backoff process to avoid collisions on the wireless channel. The main drawback with this process is that packets have to spend time in the backoff process which is an additional overhead in their transmission time. The channel is rendered idle when all the stations defer their transmissions due to their backoff process. Therefore, the channel utilization and the total throughput on the channel can be improved by reducing the average time spent by the packets in the backoff process. In this paper, we propose a new media access coordination function called proposed media access protocol (PMAP) that will improve the channel utilization for successful packet transmission and therefore, the total achievable throughput. In addition, we propose an analytical model for PMAP under saturated conditions. We use this model to analyze the performance of PMAP under saturated conditions. To substantiate the effectiveness of our model, we have verified the model by simulating PMAP in NS‐2. Simulation and analytical results show that under saturated conditions, PMAP shows profound improvement in the throughput performance compared to DCF. In addition, the throughput performance of PMAP under unsaturated conditions is presented. We have also presented the delay performance of PMAP and DCF through simulation in both saturated and unsaturated conditions. Simulation results show that the average delay experienced by the packets is less in PMAP compared to DCF. Further, the variance in the packet delay is same for both PMAP and DCF protocols under unsaturated conditions. From the performance results obtained for PMAP under both saturated and unsaturated conditions, it can be concluded that PMAP is superior in performance compared to DCF. Copyright © 2006 John Wiley & Sons, Ltd.
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How this classification was reachedexpand
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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".