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Record W2102055072 · doi:10.1109/pimrc.2009.5450147

Performance evaluation of distributed STBC in wireless relay networks with imperfect CSI

2009· article· en· W2102055072 on OpenAlexaff
Wael Jaafar, Wessam Ajib, David Haccoun

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicCooperative Communication and Network Coding
Canadian institutionsUniversité du Québec à MontréalPolytechnique Montréal
Fundersnot available
KeywordsComputer scienceRelaySpace–time block codeComputer networkMIMOChannel state informationBit error rateCooperative diversityLinear network codingTransmission (telecommunications)WirelessAntenna diversityChannel (broadcasting)TelecommunicationsFading

Abstract

fetched live from OpenAlex

It has been shown that cooperative communication techniques have a great potential to increase the diversity in wireless relay networks and hence improve the Bit Error Rate (BER). When exploiting many users as relay nodes, a multi-antenna network called virtual-MIMO (Multiple Input Multiple Output) is set up. This special technique helps to solve the problem of transmission error occurrences when sending information through a low quality radio channel. Consequently, the transmission gets a better reliability and higher transmission rate. In this work, we focus on the distributed Space-Time-Block- Coding (STBC) with Amplify-and-Forward (AF) and Decode-and-Forward (DF) relays, for various network configurations and channel knowledge conditions. We investigate and evaluate the performance - in term of BER - of a cooperative communication system using multiple relays equipped with multiple antennas when DSTBC coding is employed at the relays with AF (or DF) relaying. Also, we examine the behavior of these cooperative communication techniques when the Channel State Information (CSI) available at the receivers is imperfect.

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: Empirical
Teacher disagreement score0.762
Threshold uncertainty score0.285

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.029
GPT teacher head0.280
Teacher spread0.250 · 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

Citations5
Published2009
Admission routes1
Has abstractyes

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