MétaCan
Menu
Back to cohort

Ergodic Capacity Analysis for Interference-Limited AF Multi-Hop Relaying Channels in Nakagami-m Fading

2013· article· en· W2072997002 on OpenAlexaff
Imène Trigui, Sofiène Affes, Alex Stéphenne

Bibliographic record

VenueIEEE Transactions on Communications · 2013
Typearticle
Languageen
FieldComputer Science
TopicCooperative Communication and Network Coding
Canadian institutionsInstitut National de la Recherche Scientifique
Fundersnot available
KeywordsFadingNakagami distributionErgodic theoryTopology (electrical circuits)Computer scienceInterference (communication)Moment-generating functionChannel capacityCo-channel interferenceMathematicsTelecommunicationsChannel (broadcasting)Random variableStatisticsCombinatoricsMathematical analysis

Abstract

fetched live from OpenAlex

An analytical characterization of the ergodic capacity of interference-limited multihop wireless networks with amplify-and forward (AF) relaying is presented. In our analysis, we consider that transmissions are performed over Nakagami-m fading where channel state information is only known at the receiving terminals. We derive an exact expression for the ergodic capacity by exploiting the moment generating function (MGF) of the inverse signal-to-interference ratio (SIR). The result is applicable for arbitrary numbers of interfering signals at the receiving terminals and can be efficiently evaluated. Furthermore, considering the special case of dual-hop transmission, we propose a more refined characterization where the high-SIR capacity is expanded as an affine function. The zero-order term or the power offset for which we find insightful closed-form expressions, is shown to play a chief role in understanding the impact of interference and power on the system's capacity. Finally, some simulation results sustaining our analysis are provided.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.913
Threshold uncertainty score1.000

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 studies0.0010.000
Scholarly communication0.0000.001
Open science0.0030.000
Research integrity0.0000.001
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 head0.137
GPT teacher head0.317
Teacher spread0.180 · 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.

Study designSimulation or modeling
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

Citations24
Published2013
Admission routes1
Has abstractyes

Explore more

Same venueIEEE Transactions on CommunicationsSame topicCooperative Communication and Network CodingFrench-language works237,207