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Record W2368724693 · doi:10.1109/tvt.2016.2567602

Performance Analysis of Dual-Hop MIMO AF Relaying Network With Multiple Interferences

2016· article· en· W2368724693 on OpenAlexaff
Min Li, Min Lin, Wei‐Ping Zhu, Yongming Huang, Kai‐Kit Wong, Quan Yu

Bibliographic record

VenueIEEE Transactions on Vehicular Technology · 2016
Typearticle
Languageen
FieldComputer Science
TopicCooperative Communication and Network Coding
Canadian institutionsConcordia University
FundersNatural Science Foundation of Jiangsu ProvinceNational Natural Science Foundation of China
KeywordsRelayMIMOHop (telecommunications)TransmitterDiversity gainComputer scienceMaximal-ratio combiningArray gainSignal-to-noise ratio (imaging)Electronic engineeringInterference (communication)Transmission (telecommunications)Topology (electrical circuits)Computer networkEngineeringTelecommunicationsFadingAntenna arrayBeamformingDecoding methodsElectrical engineeringAntenna (radio)Power (physics)PhysicsChannel (broadcasting)

Abstract

fetched live from OpenAlex

In this paper, we investigate the performance of a dual-hop multiple-input-multiple-output (MIMO) amplify-and-forward (AF) relay network, where the source, relay, and destination are all equipped with multiple antennas. By using maximum ratio transmission (MRT) at the transmitter and maximum ratio combining (MRC) at the receiver, we first obtain the output signal-to-interference-plus-noise ratio (SINR) of the dual-hop AF relay system, considering multiple cochannel interferences (CCIs), as well as noise at the relay. Then, we derive an exact closed-form expression for the outage probability (OP), and the asymptotic result of OP at high SNR, which can be used to calculate the array gain and diversity order. Finally, computer simulations are conducted to validate the performance analysis. Our new analytical expressions not only provide a fast and efficient method to evaluate the system performance but enable us to gain valuable insights into the effects of key parameters on the MIMO AF relaying network performance that benefits from implementing multiple antennas at each of the three nodes as well.

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 score0.811
Threshold uncertainty score0.414

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
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 head0.019
GPT teacher head0.238
Teacher spread0.219 · 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

Citations12
Published2016
Admission routes1
Has abstractyes

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