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Record W3014682316 · doi:10.1109/tgcn.2020.3035512

Analysis of Asymmetric Dual-Hop Energy Harvesting-Based Wireless Communication Systems in Mixed Fading Environments

2020· preprint· en· W3014682316 on OpenAlexaff
Elmehdi Illi, Faissal El Bouanani, Paschalis C. Sofotasios, Sami Muhaidat, Daniel Benevides da Costa, Fouad Ayoub, Ala Al‐Fuqaha

Bibliographic record

VenueIEEE Transactions on Green Communications and Networking · 2020
Typepreprint
Languageen
FieldEngineering
TopicEnergy Harvesting in Wireless Networks
Canadian institutionsCarleton University
FundersKhalifa University of Science, Technology and Research
KeywordsFadingRelayNakagami distributionHop (telecommunications)Computer scienceEnergy harvestingWirelessEnergy (signal processing)Node (physics)Topology (electrical circuits)Transmission (telecommunications)Monte Carlo methodBit error rateTelecommunicationsElectronic engineeringMathematicsPower (physics)Channel (broadcasting)StatisticsPhysicsEngineering

Abstract

fetched live from OpenAlex

This work investigates the performance of a dual-hop energy harvesting-based fixed-gain amplify-and-forward relaying communication system, subject to fading impairments. We consider a source node ( S) communicating with a destination node ( D), either directly or through a fixed distant relay ( R), which harvests energy from its received signals and uses it to amplify and forward the received signals to D. We also consider maximal-ratio combining at D to combine the signals coming from S and R. Both power-splitting and time-switching energy harvesting protocols are investigated. The S - R link is modeled by Nakagami- m fading model, while the R - D and S - D links experience α- μ fading. Closed-form expressions for the statistical properties of the total signal-to-noise ratio are derived, based on which novel closed-form expressions are then derived for the average symbol error rate as well as for the average channel capacity, considering four different adaptive transmission policies. The derived expressions are 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 categoriesMeta-epidemiology (narrow)
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 score0.955
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.003
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.002
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 head0.033
GPT teacher head0.236
Teacher spread0.203 · 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
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
Published2020
Admission routes1
Has abstractyes

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