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Record W2928285465 · doi:10.1109/tmtt.2019.2903051

Millimeter-Wave CMOS Sourceless Receiver Architecture for 5G-Served Ultra-Low-Power Sensing and Communication Systems

2019· article· en· W2928285465 on OpenAlexafffund
Pascal Burasa, Bilel Mnasri, Ke Wu

Bibliographic record

VenueIEEE Transactions on Microwave Theory and Techniques · 2019
Typearticle
Languageen
FieldEngineering
TopicMicrowave Engineering and Waveguides
Canadian institutionsPolytechnique Montréal
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBasebandElectronic engineeringDemodulationMIMOQuadrature amplitude modulationQAMLocal oscillatorTransmission (telecommunications)EngineeringOrthogonal frequency-division multiplexingRadio receiver designPhase-locked loopComputer scienceElectrical engineeringCMOSBit error rateChannel (broadcasting)Phase noiseTransmitterBeamforming

Abstract

fetched live from OpenAlex

In this paper, an extremely low-power and low-complexity CMOS sourceless millimeter-wave (mmW) receiver for the next-generation wireless communication and sensing systems including fifth-generation (5G) is proposed and demonstrated. It makes the use of injection-locked self-oscillating mixers (SOMs) in order to enable a direct conversion to baseband without resorting to any external local oscillator (LO) nor an IF processing block, therefore, greatly reducing power consumption, as well as the receiver's complexity. This architecture is created through multi-input and multi-output (MIMO) arrays in connection with slicing frequency spectrum or frequency band of interest. In this way, the proposed receiver supports a high data transmission throughput based on the frequency-diversified MIMO, which presents a unique feature in the architectural implementation of a low-power and high bit-rate communication and sensing systems. Transmission and demodulation of a digital modulated signal M-quadratic-amplitude modulation (M-QAM) at 40 GHz, is successfully demonstrated with simulated and measured results.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.601
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 science0.0000.000
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 head0.008
GPT teacher head0.199
Teacher spread0.191 · 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 designBench or experimental
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

Citations20
Published2019
Admission routes2
Has abstractyes

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