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Record W1554697419 · doi:10.1109/glocom.2004.1378119

Optimization of pilot symbol-assisted RAKE receivers for DS-CDMA systems

2005· article· en· W1554697419 on OpenAlexaff
Tao Cui, Chintha Tellambura

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicWireless Communication Networks Research
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsRake receiverRakeCode division multiple accessMaximal-ratio combiningRayleigh fadingComputer scienceChannel (broadcasting)AlgorithmSpread spectrumFrame (networking)WidebandDelay spreadPower delay profileFadingBit error rateElectronic engineeringMathematicsTelecommunicationsEngineering

Abstract

fetched live from OpenAlex

For optimizing pilot sequences for a general wideband direct-sequence (DS) code division multiple access (CDMA) system in a slow fading Rayleigh channel, we derive a design criterion by minimizing the mean square error (MSE) of the channel estimate. We analyze the effects of imperfect channel estimation (CE) on a CDMA system, based on the maximal ratio combining (MRC) RAKE receiver in both uniform power delay profile (UPDP) and non-uniform power delay profile (NPDP) channels. Published results on the effect of CE errors hold only for UPDP channels. We therefore use a characteristic function method to derive new closed-form expressions for the BER of RAKE receivers in NPDP channels. Constraining the energy per data frame to be constant, we optimize the length of the pilot symbols by minimizing the BER of the MRC receiver. We show an elegant result that the optimal number of pilot symbols is equal to the square root of the frame length for UPDP channels and for NPDP channels in the high SNR region.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.770
Threshold uncertainty score0.336

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 communication0.0000.000
Open science0.0010.000
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 head0.057
GPT teacher head0.301
Teacher spread0.244 · 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

Citations0
Published2005
Admission routes1
Has abstractyes

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