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Record W2490336733 · doi:10.1049/iet-com.2016.0372

Dual‐hop signal space cooperative systems using multiple DF relays

2016· article· en· W2490336733 on OpenAlexaff
Muhammad Ajmal Khan, Raveendra K. Rao, Xianbin Wang, Asrar U. H. Sheikh

Bibliographic record

VenueIET Communications · 2016
Typearticle
Languageen
FieldComputer Science
TopicCooperative Communication and Network Coding
Canadian institutionsWestern University
Fundersnot available
KeywordsComputer scienceHop (telecommunications)Dual (grammatical number)RelayTelecommunicationsSIGNAL (programming language)Computer networkTopology (electrical circuits)Electrical engineeringPhysics

Abstract

fetched live from OpenAlex

In a dual‐hop relaying system without a direct link between the source and the destination, the source broadcasts information signal to the relay and relay broadcasts it to the destination, thus it uses two phases to transmit one symbol. This study proposes a novel scheme to incorporate signal space diversity into a dual‐hop relaying system with multiple decode‐and‐forward (DF) relays to enhance its spectral efficiency. The proposed dual‐hop signal space cooperative relaying scheme transmits two symbols in three phases while the conventional dual‐hop DF relaying system uses four phases to transmit the same two symbols. Therefore, the proposed scheme improves the spectral efficiency without additional complexity, bandwidth or transmit power. The proposed scheme is analysed over Rayleigh fading channel and error probability performance is derived. Moreover, an asymptotic approximation for the error probability is obtained to illustrate the impact of different system parameters and diversity gain. In addition, this study discusses the power allocation optimisation, relay position optimisation and the joint optimisation of both. Furthermore, closed‐form expression for the average channel capacity is derived. In the end, analytical results are compared and validated through Monte Carlo simulations.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.959
Threshold uncertainty score0.849

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 studies0.0010.000
Scholarly communication0.0000.001
Open science0.0030.002
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 head0.098
GPT teacher head0.313
Teacher spread0.214 · 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 designTheoretical or conceptual
Domainnot available
GenreMethods

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

Citations7
Published2016
Admission routes1
Has abstractyes

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