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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 OpenAlex
Jun Zhu, Ye Li, Ning Wang, Wei Xu

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

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.

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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.309
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 communication0.0000.001
Open science0.0010.000
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 head0.011
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