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Record W2787046174 · doi:10.1109/vtcfall.2017.8288026

Beam Angle Channel Modulation

2017· article· en· W2787046174 on OpenAlexaff
Javad Hoseyni, Jacek Ilow

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Wireless Communication Technologies
Canadian institutionsDalhousie University
Fundersnot available
KeywordsAngle of arrivalBeamformingComputer scienceModulation (music)Transmission (telecommunications)Constellation diagramAntenna (radio)Channel (broadcasting)SIGNAL (programming language)Antenna arrayQuadrature amplitude modulationDirection of arrivalElectronic engineeringOpticsAlgorithmTelecommunicationsPhysicsAcousticsBit error rateEngineering

Abstract

fetched live from OpenAlex

This paper introduces a novel transmission technique named Beam Angle Channel Modulation (BACM) that uses an antenna array to encode the information symbols into the angle of propagation (and arrival) wave in the wireless channel. At the encoder, the block of information bits is mapped into two symbols: (i) a spatial symbol selecting the direction of arrival of the amplitude and phase modulated carrier signal and (ii) a temporal symbol chosen from the constellation diagram like QAM or PSK. The proposed scheme follows the model for concatenated modulation designs like recently introduced spatial modulation (SM). The use of the transmitted signal angle through antenna array beamforming increases the spectral efficiency of the single-input single-output (SISO) conventional (temporal) modulation by the base-two logarithm of the number of spatial beams that can be resolved (detected) at the receiver. Direction-of-arrival type detection for spatial symbols is developed using a linear array of receive antennas. It is demonstrated that the detection reliability of the spatial symbols in BACM is determined by the number of antennas used at the receiver and the actual angles of signal arrival.

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 score0.805
Threshold uncertainty score0.178

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 head0.025
GPT teacher head0.253
Teacher spread0.229 · 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

Citations5
Published2017
Admission routes1
Has abstractyes

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