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Performance Analysis of Zero-Forcing for Two-Way MIMO AF Relay Networks

2012· article· en· W2119612043 on OpenAlexaff
Gayan Amarasuriya, C. Tellambura, Masoud Ardakani

Bibliographic record

VenueIEEE Wireless Communications Letters · 2012
Typearticle
Languageen
FieldComputer Science
TopicCooperative Communication and Network Coding
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMIMORelayComputer scienceMultiplexingRelay channelTransmission (telecommunications)Linear network codingOutage probabilitySignal-to-noise ratio (imaging)Channel (broadcasting)Topology (electrical circuits)TelecommunicationsComputer networkMathematicsFading

Abstract

fetched live from OpenAlex

Transmit/receive zero-forcing (ZF) is studied for multiple-input multiple-output (MIMO) amplify-and-forward (AF) two-way relay networks (TWRNs). Specifically, two sources employ transmit and receive ZF during two consecutive time-slots for transmission and reception, respectively, while the relay performs analog network coding. Each source then requires only the instantaneous respective source-to-relay channel knowledge, and hence, the complexity of practical implementation is significantly reduced. The performance of this system set-up is studied by deriving the upper and lower bounds of the overall outage probability, their high signal-to-noise ratio approximations and diversity order. To obtain valuable insights into practical MIMO TWRN implementation, the diversity-multiplexing trade-off is also quantified.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.047
GPT teacher head0.295
Teacher spread0.247 · 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 source (direct Gemma or distilled Codex), 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

Citations42
Published2012
Admission routes1
Has abstractyes

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