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Record W2886970340 · doi:10.1109/icc.2018.8422300

Performance Analysis of Wireless-Powered Relaying with Ambient Backscattering

2018· article· en· W2886970340 on OpenAlexaff
Xiao Lu, Guangxia Li, Hai Jiang, Dusit Niyato, Ping Wang

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicEnergy Harvesting in Wireless Networks
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsRelayWirelessComputer scienceContext (archaeology)Wireless networkEnergy harvestingElectronicsComputer networkTransmission (telecommunications)Wearable computerTelecommunicationsEnergy (signal processing)Power (physics)Electrical engineeringEmbedded systemEngineering

Abstract

fetched live from OpenAlex

With the increasing use of smart objects, such as wearable health gadgets, household automation devices, and personal electronics, there is a growing demand for a globally interconnected information network, known as the Internet of Things (IoT). IoT is featured with low-power communications among a massive number of ubiquitously-deployed and energy-constrained electronics, like sensors and actuators. In this context, wireless-powered cooperative relaying emerges as a promising solution to extend coverage and solve energy scarcity problems for IoT devices. In this paper, we propose a novel hybrid relay by combining wireless-powered communications and ambient backscattering functions for improved applicability and performance. To well adapt the hybrid relay to the network environments, we design a mode selection protocol to coordinate between the two functions. Moreover, we analyze the successful transmission probability of a dual-hop relaying system with the hybrid relay. Through numerical results, we demonstrate the performance gain of the hybrid relay and the impact of the system parameters.

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 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: Empirical
Teacher disagreement score0.031
Threshold uncertainty score0.549

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.007
GPT teacher head0.194
Teacher spread0.186 · 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 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

Citations31
Published2018
Admission routes1
Has abstractyes

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