MétaCan
Menu
← Back to cohort
Record W2783939382 · doi:10.1109/uemcon.2017.8249104

Outage probability and system optimization of SSD-based dual-hop relaying system with multiple relays

2017· article· en· W2783939382 on OpenAlexaff
Muhammad Ajmal Khan, Raveendra K. Rao, Xianbin Wang

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicCooperative Communication and Network Coding
Canadian institutionsWestern University
Fundersnot available
KeywordsHop (telecommunications)Rayleigh fadingOutage probabilityComputer scienceDual (grammatical number)RelayWirelessFadingDiversity gainRadio repeaterElectronic engineeringChannel (broadcasting)Topology (electrical circuits)Computer networkPower (physics)TelecommunicationsElectrical engineeringEngineeringPhysicsCognitive radio

Abstract

fetched live from OpenAlex

The conventional dual-hop relaying system, without a direct link between the source and the destination, transmits one symbol in two time slots. However, if signal space diversity (SSD) is integrated into the conventional dual-hop relaying system, it transmits two symbols in three time slots to enhance the spectral efficiency of the conventional dual-hop relaying system. In this paper, the outage performance of an SSD-based dual-hop relaying system with multiple decode-and-forward (DF) relays over Rayleigh fading channel is analyzed, and its closed-form expression is derived. Moreover, an asymptotic approximation of the outage probability is obtained and it is used to investigate the impact of different system parameters. Furthermore, system optimization for power is investigated to enhance the outage performance by allocating optimal powers to transmitting nodes. In the end, extensive computer simulations are performed to validate the analytical results.

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)0.002
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 score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
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 head0.039
GPT teacher head0.250
Teacher spread0.211 · 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
Published2017
Admission routes1
Has abstractyes

Explore more

Same topicCooperative Communication and Network Coding→French-language works237,207→