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Record W2410996924 · doi:10.1049/iet-com.2015.0943

Performance enhancement of multi‐hop relay‐based wireless systems based on outage probability

2016· article· en· W2410996924 on OpenAlexaff
Sami Baroudi, Yousef R. Shayan

Bibliographic record

VenueIET Communications · 2016
Typearticle
Languageen
FieldComputer Science
TopicCooperative Communication and Network Coding
Canadian institutionsConcordia University
Fundersnot available
KeywordsHop (telecommunications)RelayComputer scienceOutage probabilityWirelessComputer networkWireless networkTelecommunicationsFadingChannel (broadcasting)

Abstract

fetched live from OpenAlex

Wireless communication is now accessible throughout the world, with an increasing number of users. Engineers are putting extra efforts to evaluate and improve the performance of wireless systems. Multi‐hop wireless systems have drawn significant attention as a cost‐effective solution to improve the performance of wireless systems. In these systems, a certain number of relays is deployed over the cell to help in the transmission of signals from the base station (BS) to the users. In a multi‐hop system, a low‐quality long‐distance link is broken into two or more better‐quality links, minimising the outage probability and enhancing the system performance. Interference is one of the fundamental elements affecting the quality of service in wireless networks. The probability density function (PDF) of the signal‐to‐interference ratio (SIR) is evaluated in a circular cell with uniformly distributed users around a BS located at the centre of the cell. Then, the PDF for outage probability is assessed, based on the evaluated SIR. After that, the minimum number of relays and their placement is found over the studied area, which can achieve the desirable performance of the wireless system. Finally, simulation is performed based on long‐term evolution parameters to validate the derived analytical solution.

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)0.001
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 score0.962
Threshold uncertainty score0.628

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0030.001
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 head0.087
GPT teacher head0.305
Teacher spread0.218 · 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

Citations3
Published2016
Admission routes1
Has abstractyes

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