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Record W2117764780 · doi:10.1109/bsc.2006.1644600

Improving the Throughput Performance of IEEE 802.11 Distributed Coordination Function

2006· article· en· W2117764780 on OpenAlexaff
Mohammad Amjad, Abdallah Shami

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicWireless Networks and Protocols
Canadian institutionsWestern University
Fundersnot available
KeywordsDistributed coordination functionComputer scienceThroughputComputer networkCarrier sense multiple access with collision avoidanceNetwork packetOverhead (engineering)Network allocation vectorIEEE 802.11Channel (broadcasting)Transmission (telecommunications)IdleProtocol (science)WirelessTelecommunications

Abstract

fetched live from OpenAlex

Distributed coordination function (DCF) is the basis protocol for IEEE 802.11 standard wireless local area networks. It is based on carrier sense multilple access with collision avoidance (CSMA/CA) mechanism. DCF uses backoff process to avoid collisions on the wireless channel. The main drawback in DCF is that packets have to spend more time in the backoff process which is an additional overhead to 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 present our proposed modification to the original IEEE 802.11 standard DCF protocol so that the channel utilization can be improved with successful packet transmissions and thereby achieve higher throughput performance. We studied the performance of the proposed media access protocol (PMAP) both numerically and through simulations using NS-2 network simulator. Results showed that PMAP achieves considerable improvement in the throughput performance

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.891
Threshold uncertainty score0.197

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.010
GPT teacher head0.212
Teacher spread0.202 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2006
Admission routes1
Has abstractyes

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