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Record W2804523916 · doi:10.1109/wd.2018.8361719

M-ary beam angle shift keying modulation for MIMO channels

2018· article· en· W2804523916 on OpenAlexaff
Javad Hoseyni, Jacek Ilow

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Wireless Communication Technologies
Canadian institutionsDalhousie University
Fundersnot available
KeywordsTransmitterModulation (music)MIMOAngle of arrivalSpecular reflectionComputer scienceAntenna (radio)Channel (broadcasting)KeyingPhase-shift keyingElectronic engineeringSIGNAL (programming language)Rayleigh fadingAdditive white Gaussian noiseArray gainAntenna arrayTelecommunicationsOpticsFadingPhysicsBit error rateAcousticsEngineering

Abstract

fetched live from OpenAlex

This paper introduces Beam Angle Shift Keying (BASK) modulation that uses an antenna array to encode the information symbols into the angle of wave propagation (and arrival) in a Multiple-Input Multiple-Output (MIMO) wireless channel. Specifically, at the transmitter, the block of information bits is mapped into a "spatial" symbol selecting the beam or equivalently the direction of departure for the unmodulated carrier signal. Through the specular reflection paths, the carrier signal is assumed to arrive at the receiver from one direction over the duration of the spatial symbol indexing the transmitter/receiver beam. Using the receiver antenna array signals, we derive a maximum likelihood (ML) detection to differentiate between the direction-of-arrival from different beams. Mathematical analysis and simulation results are presented to demonstrate the performance of BASK in AWGN and Rayleigh fading channels. The selection of angles of arrivals is optimized to obtain the lowest symbol error rates for different modulation levels.

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: none
Teacher disagreement score0.874
Threshold uncertainty score0.342

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 head0.024
GPT teacher head0.259
Teacher spread0.234 · 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

Citations1
Published2018
Admission routes1
Has abstractyes

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