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Record W2593419552 · doi:10.1109/lwc.2017.2676109

Wireless Information and Power Transfer in Secure Massive MIMO Downlink With Phase Noise

2017· article· en· W2593419552 on OpenAlexaff
Jun Zhu, Ye Li, Ning Wang, Wei Xu

Bibliographic record

VenueIEEE Wireless Communications Letters · 2017
Typearticle
Languageen
FieldEngineering
TopicEnergy Harvesting in Wireless Networks
Canadian institutionsUniversity of British Columbia
FundersNational Natural Science Foundation of China
KeywordsTelecommunications linkComputer scienceBase stationWirelessArtificial noiseMIMOComputer networkSecrecyInformation leakageMaximum power transfer theoremInformation transferNoise (video)Channel (broadcasting)Electronic engineeringTelecommunicationsTransmitterPower (physics)Computer securityEngineeringPhysicsArtificial intelligence

Abstract

fetched live from OpenAlex

To support downlink simultaneous wireless information and power transfer (SWIPT), energy harvesting mobile terminals (EMTs) need to be deployed closer to the base station than information mobile terminals (IMTs), in order to meet higher received power requirement. However, this raises a critical issue that the messages sent to IMTs are potentially eavesdropped on by EMTs, which experience better channels. In this letter, we study the effect of phase noise on the downlink SWIPT in secure massive MIMO systems, which degrades accuracy of the channel state information and in turn causes potential information leakage. We derive closed-form lower bounds on the worst-case secrecy rate achieved by each IMT and the energy harvested by each EMT. Numerical results reveal that the secrecy rate monotonically decreases as the phase noise variance increases, but the monotonicity does not hold for the harvested energy.

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)0.003
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 score0.001
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
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 science0.0000.001
Research integrity0.0010.001
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 head0.011
GPT teacher head0.234
Teacher spread0.223 · 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

Citations27
Published2017
Admission routes1
Has abstractyes

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