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Record W2154863954 · doi:10.1109/ccece.2005.1556909

Design of space-time trellis codes with multi-H CPM signals

2006· article· en· W2154863954 on OpenAlexaff
A.R. Ahmadi, Raveendra K. Rao

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Wireless Communication Techniques
Canadian institutionsWestern University
Fundersnot available
KeywordsContinuous phase modulationAlgorithmAdditive white Gaussian noiseMatched filterMIMOCoding gainMathematicsTransmitterComputer scienceTelecommunicationsWhite noiseChannel (broadcasting)Decoding methodsDetector

Abstract

fetched live from OpenAlex

Signal design for wireless data links modeled as quasi-static Rayleigh is approached using space-time trellis (STT) coding and power/bandwidth efficient multi-h phase-coded continuous phase modulation (CPM) signals. System configuration for achieving multiple-input multiple-output (MIMO) with arbitrary number of antennas at the transmitter and receiver is described. At the receiver, the received signal is modeled as a linear sum of Rayleigh faded multi-h CPM signals with additive white Gaussian noise (AWGN). The problem of recovering transmitted data from such a composite signal is addressed using the criterion of maximum likelihood sequence estimation (MLSE). The finite-state discrete-time properties of the received composite signal are exploited and using Euclidean distance criterion best STT codes are determined. Performance analysis is presented based on pair-wise error probabilities (PWEP) for all possible pairs of signals in signal-space. It is noted that PWEP is a function of signal matrix, hence on multi-h CPM, and channel gain matrix. The signal matrix corresponding to error events is presented as a function of multi-h CPM signal parameters. From this signal matrix, the rank and the minimum determinant (minimum product of eigenvalues) of STT coded multi-h CPM can be found. It is shown that for these STT codes coding gain of 1.9 dB can be achieved relative to identical coding and MSK.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.559
Threshold uncertainty score0.387

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 head0.014
GPT teacher head0.226
Teacher spread0.211 · 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 designBench or experimental
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

Citations8
Published2006
Admission routes1
Has abstractyes

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