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Record W2170783080 · doi:10.1109/icupc.1997.627255

Effect of transmit antenna pattern on RAKE reception in multipath fading channels

2002· article· en· W2170783080 on OpenAlexaff
Ming Lu, T. Lo, J. Litva

Bibliographic record

VenueProceedings of ICUPC 97 - 6th International Conference on Universal Personal Communications · 2002
Typearticle
Languageen
FieldComputer Science
TopicWireless Communication Networks Research
Canadian institutionsMcMaster University
Fundersnot available
KeywordsRake receiverMultipath propagationFadingBeamwidthDelay spreadComputer scienceElectronic engineeringAntenna (radio)RakeTelecommunicationsChannel (broadcasting)Engineering

Abstract

fetched live from OpenAlex

In cellular radio systems, one can take advantage of multi-beam antennas at the cell-site to increase the system capacity and improve the quality of service. The antenna pattern may have an effect on the channel's characteristics in the multipath propagation environments. On the one hand, the use of a sectorized antenna pattern tends to reduce the multipath diversity since a portion of multipath components are suppressed to a certain extent. On the other hand, sometimes the multipath fading can be mitigated for the same reason. This paper investigates the effect of the antenna pattern on the mobile RAKE receiver. We have observed that a higher SNR is required for RAKE reception in a multipath fading environment to guarantee an acceptable bit-error-rate performance and at a given BER, the excess SNR requirements depend on the beamwidth and sidelobe levels of the sectorized antenna, as well as the characteristics of the propagation environment. In a multipath scenario, although the adjacent beams have a relatively high correlation in the channel's impulse response, the RAKE receiver at the mobile can take advantage of the soft-handoff from beam to beam to greatly improve the RAKE reception in fast fading channels. Simulation results are provided.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.989
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0040.001
Research integrity0.0000.001
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 head0.087
GPT teacher head0.320
Teacher spread0.233 · 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
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

Citations3
Published2002
Admission routes1
Has abstractyes

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