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Record W3094826937 · doi:10.1049/iet-com.2019.1039

Optimal transceiver design for energy harvesting two‐way relay networks

2020· article· en· W3094826937 on OpenAlexaff
Xue Jiang, Baoyu Zheng, Wei‐Ping Zhu, Lei Wang, Yulong Zou

Bibliographic record

VenueIET Communications · 2020
Typearticle
Languageen
FieldEngineering
TopicEnergy Harvesting in Wireless Networks
Canadian institutionsConcordia University
FundersNanjing UniversityNanjing University of Posts and TelecommunicationsGovernment of Jiangsu ProvinceNatural Science Foundation of Jiangsu ProvinceNational Natural Science Foundation of China
KeywordsEnergy harvestingRelayTransceiverComputer scienceEnergy (signal processing)Computer networkTelecommunicationsWirelessMathematicsPhysicsStatistics

Abstract

fetched live from OpenAlex

Simultaneous wireless information and power transfer (SWIPT) is a promising solution for future wireless networks as it provides convenient and perpetual energy supplies to wireless users. This study proposes three new transceiver design schemes in two‐way relay networks with SWIPT. Firstly, a transmit power minimisation solution is proposed with an aim to minimise the transmit power while meeting an energy harvesting requirement. Then, a harvested energy maximisation solution that has the same complexity as the first solution but provides a much better sum harvested energy is proposed. Finally, a signal‐to‐interference‐plus‐noise ratio maximisation solution is developed, which gives the best sum‐rate performance for the two‐way relay networks while ensuring a desired level of energy harvesting. Furthermore, the complexity of the three new transceiver solutions is discussed. Simulation results have demonstrated the effectiveness of the proposed transceiver design schemes in two‐way relay networks with SWIPT.

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: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.735
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
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.054
GPT teacher head0.250
Teacher spread0.196 · 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

Citations6
Published2020
Admission routes1
Has abstractyes

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