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Record W2187647323 · doi:10.1109/tcomm.2015.2475417

Closed-Form Expressions for the BER/SER of OFDM Systems With an Integer Time Offset

2015· article· en· W2187647323 on OpenAlexafffund
Ahmed M. Hamza, J.W. Mark

Bibliographic record

VenueIEEE Transactions on Communications · 2015
Typearticle
Languageen
FieldEngineering
TopicAdvanced Wireless Communication Techniques
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsOrthogonal frequency-division multiplexingPhase-shift keyingBit error rateCarrier frequency offsetRayleigh fadingQuadrature amplitude modulationAdditive white Gaussian noiseQAMAlgorithmMathematicsFadingModulation (music)Integer (computer science)Offset (computer science)Computer scienceFrequency offsetTelecommunicationsWhite noiseStatisticsChannel (broadcasting)Decoding methodsPhysics

Abstract

fetched live from OpenAlex

This paper provides a detailed mathematical analysis of the impact of integer time offsets on the performance of OFDM systems. Although a number of approximate expressions for the probability of error in OFDM systems with integer time offset can be found in the literature, to the best of our knowledge, this work is the first that provides exact expressions. In particular, we derive exact closed-form expressions for the bit error rate (BER) and the symbol error rate (SER) of BPSK, QPSK, and 16-QAM modulation for transmission over both AWGN and Rayleigh fading channels. The effect of the fractional carrier frequency offset (CFO) is taken into consideration in the derivations. This makes the derived expressions more useful in evaluating the probability of error for OFDM systems with CFO only, especially in cases like 16-QAM modulation for transmission over Rayleigh fading channels where exact closed-form expressions are not available. For OFDM systems with a large number of subcarriers, an approximate method for evaluating the BER/SER is given. Finally, numerical results are included to demonstrate the exactness of the derived expressions and the accuracy of the approximate method.

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: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.965
Threshold uncertainty score0.556

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 science0.0010.000
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 head0.052
GPT teacher head0.292
Teacher spread0.240 · 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

Citations12
Published2015
Admission routes2
Has abstractyes

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