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Record W1574115911 · doi:10.1109/vetec.1996.504047

Tracking and diversity for a mobile communications base station array antenna

2002· article· en· W1574115911 on OpenAlexaff
S.P. Stapleton, X. Carbo, T. McKeen

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Wireless Communication Techniques
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsFadingAntenna diversityAntenna (radio)Base stationAntenna arrayComputer scienceDiversity schemeDiversity gainBit error rateElectronic engineeringFlexibility (engineering)Mobile telephonyMobile stationChannel (broadcasting)EngineeringMobile radioTelecommunicationsMathematics

Abstract

fetched live from OpenAlex

A simulation model for analyzing the performance of antenna arrays in a mobile flat-fading environment is derived and applied to particular problems of tracking, combining and angle diversity. The model introduces angular coordinates that allows for the flexibility of utilizing directional co-channel interferers as well as providing the flexibility to test various array adaptation algorithms in a two dimensional mobile environment. The model is applied to a base-station 7 element half-wavelength spacing antenna array, operating at a carrier frequency of 815 MHz. The angle diversity performance is investigated by observing the bit error rate (BER) improvement for a /spl pi//4 DQPSK operating at 10 Kb/s. A tracking algorithm is demonstrated for maintaining the directional beam on the mobile for various vehicle trajectories and velocities up to 80 km/hr. The diversity and tracking algorithms performed well in the real environment as was predicted by simulation. Angle diversity provided a factor of 2 improvement in the BER, for a /spl pi//4 DQPSK signal at an Eb/No of 31 dB.

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: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.932
Threshold uncertainty score0.277

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 head0.051
GPT teacher head0.269
Teacher spread0.218 · 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
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

Citations18
Published2002
Admission routes1
Has abstractyes

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