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Record W1542631986 · doi:10.1109/iccw.2015.7247310

Performance analysis of interference-limited AF Relay Systems with Antenna Correlation

2015· article· en· W1542631986 on OpenAlexaff
Jian Ouyang, Min Lin, Wei‐Ping Zhu, Jun Yan

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicCooperative Communication and Network Coding
Canadian institutionsConcordia University
Fundersnot available
KeywordsRelayBeamformingInterference (communication)Computer scienceHop (telecommunications)Diversity gainRelay channelAntenna (radio)Antenna diversitySignal-to-noise ratio (imaging)Expression (computer science)Topology (electrical circuits)Channel (broadcasting)TelecommunicationsElectronic engineeringMathematicsMIMOEngineeringPhysicsPower (physics)

Abstract

fetched live from OpenAlex

In this paper, we investigate the performance of a dual-hop amplify-and-forward (AF) relay system with beamforming (BF), where the source and destination are both equipped with multi-antenna, which are correlated in space, while the relay has a single antenna corrupted by multiple co-channel interferences (CCIs). We first derive the closed-form expression for the outage probability (OP) of the output signal-to-interference-plus-noise ratio (SINR). Then, we present an approximate yet accurate expression for the average symbol error rate (ASER) of the AF relaying. Moreover, asymptotic expressions of OP and ASER at high signal-to-noise ratio (SNR) are also provided to reveal the diversity order and array gain of the considered relay system. Finally, computer simulations are given to validate the analytical results and demonstrate the effects of antenna correlation and CCI on the dual-hop AF relay network.

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)0.001
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 score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.062
GPT teacher head0.264
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 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

Citations0
Published2015
Admission routes1
Has abstractyes

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