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Record W2115626298 · doi:10.1109/vetec.1998.686396

Adaptive arrays for high rate data communications

2002· article· en· W2115626298 on OpenAlexaff
Y. Wang, H. Scheving

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicWireless Communication Networks Research
Canadian institutionsNortel (Canada)
Fundersnot available
KeywordsMultipath propagationComputer scienceDelay spreadAntenna arrayOmnidirectional antennaIntersymbol interferenceAngle of arrivalWidebandElectronic engineeringAntenna (radio)Diversity combiningAntenna diversityInterference (communication)Multipath interferenceBase stationChannel (broadcasting)FadingTelecommunicationsEngineering

Abstract

fetched live from OpenAlex

A wideband TDMA system for high rate communications with an adaptive antenna array at base stations is proposed. This system is based on the recursive least squares (RLS) adaptive algorithm. The adaptive array provides a way to suppress the co-channel interference (CCI) and intersymbol interference (ISI). A method to generate a reference signal for the RLS algorithm is presented. We also propose two multipath diversity schemes, tapped delay lines (TDL) and parallel array processors (PAP), to cope with the urban cellular multipath environment and enhance the performance of the adaptive array system. The performance of the proposed system is quantified by comparison with an one-element omnidirectional antenna system. It is shown that the adaptive antenna system has a substantial improvement in performance over the one-element antenna system. Two different multipath diversity schemes are compared corresponding to signal-to-noise ratio (SNR) and multipath angle of arrival (AOA) spread. The simulation results illustrate that, for a wide AOA spread, the PAP outperforms the TDL scheme. However, the TDL is a better scheme for a narrow AOA spread.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesOpen science
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.935
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0130.005
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 head0.273
GPT teacher head0.353
Teacher spread0.080 · 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 designSimulation or modeling
Domainnot available
GenreMethods

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
Published2002
Admission routes1
Has abstractyes

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