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Record W2987951653 · doi:10.1109/vtcfall.2019.8891176

Generalized Beam Angle Channel Modulation with Space-Time Block Coding

2019· article· en· W2987951653 on OpenAlexaff
Javad Hoseyni, Jacek Ilow

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Wireless Communication Technologies
Canadian institutionsDalhousie University
Fundersnot available
KeywordsSpace–time block codeFadingComputer scienceMIMOBlock codeMultipath propagationEncoderPhase-shift keyingAngle of arrivalAlgorithmCoding gainElectronic engineeringDecoding methodsChannel (broadcasting)TelecommunicationsBit error rateAntenna (radio)Engineering

Abstract

fetched live from OpenAlex

This paper introduces modulation and coding schemes which encode data into beams of antenna arrays by exploring the angular models of wireless channels. First, the paper presents Generalized Beam Angle Channel Modulation (G-BACM) which maps the spatial symbols into multiple angles of wave propagation and arrival. Then, space-time block coding (STBC) strategies for G-BACM are investigated. In the case of G-BACM configuration with two simultaneous beams, the block of information bits is mapped into three symbols at the encoder: (i) one, spatial, symbol indexing two beams representing the angles of arrival of the two amplitude and phase modulated (APM) carrier signals and (ii) two APM symbols independently transmitted over two beams. Utilizing simultaneous transmissions on two or more beams improves the bandwidth efficiency and is also used to benefit the diversity aspect of the proposed system. Specifically, STBC strategies for G-BACM are presented where multiple copies of data streams are transmitted across a number of beams rather than antennas as in conventional multiple-input multiple-output (MIMO) systems. The performance of G-BACM with and without STBC is analyzed in multipath fading channels when trading-off between the system rate and reliability.

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: Empirical
Teacher disagreement score0.068
Threshold uncertainty score0.394

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 head0.009
GPT teacher head0.203
Teacher spread0.193 · 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

Citations2
Published2019
Admission routes1
Has abstractyes

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