Techniques for Estimating the Bit Error Rate in the Simulation of Digital Communication Systems

Jeruchim, Michel C.

doi:10.1109/jsac.1984.1146031

Cited by 441 publications

(236 citation statements)

References 12 publications

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“…In each case, the number of runs is sufficiently large to ensure an error count of 30 or more. Observing an error count of 10 implies that, with a confidence level of 95%, the actual error is within a factor of of the estimated error [12], [13]. Thus our error estimates are obtained with an even greater accuracy.…”

Section: Finite Sample Probability Of Error Performancementioning

confidence: 91%

Two rank order tests for M-ary detection

Annampedu

Roganov

Viswanathan

2000

IEEE Trans. Inform. Theory

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Abstract-We consider a general -ary detection problem where, given groups of samples each, the problem is to identify which unique group of samples have come from the signal hypothesis. The optimal likelihood ratio test is unrealizable, when the joint distribution of samples is not completely known. In this paper we consider two rank order types of tests termed as the modified rank test (MRT) and the modified rank test with row sort (MRTRS). We examine through simulation, the small sample probability of error performances of MRT and MRTRS for detecting a signal among contaminants. Numerically computable closed -form error expressions are derived for some special cases. Asymptotic (large sample) error rate of MRT is also derived. The results indicate that MRTRS provides improved performance over other previously known rank tests.

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Section: Finite Sample Probability Of Error Performancementioning

confidence: 91%

Two rank order tests for M-ary detection

Annampedu

Roganov

Viswanathan

2000

IEEE Trans. Inform. Theory

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“…The performance was evaluated in terms of the required OSNR (0.1 nm resolution) to achieve a bit error rate (BER) of 510 -4 by direct error counting. 128,961 bits were sufficient to produce a confidence interval of [3.510 -4 710 -4 ] for this BER with 99% certainty (Jeruchim, 1984).…”

Section: Numerical Analysis For 10 Gbit/s Ffd-based Ook Systemsmentioning

confidence: 99%

Full-Field Detection with Electronic Signal Processing

Zhao¹,

Ellis²

2012

Optical Communications Systems

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“…Monte Carlo (Jeruchim, 1984) computer simulations using Importance Sampling (Shanmugam and Balaban, 1980) were performed to estimate the performance of the neural TCM decoder for 16-QAM. We characterize the performance by the probability of error P e : ARTICLE IN PRESS Estimates are given by the sample means over N samples…”

Section: Simulationsmentioning

confidence: 99%

“…* The IS probability of error, # P e ; is computed using the weighed errors with Eq. (43), which is the unbiased estimate (Jeruchim, 1984) of the probability of error.…”

Section: Article In Pressmentioning

confidence: 99%

TCM decoding using neural networks

Kaminsky

Deshpande²

2003

Engineering Applications of Artificial Intelligence

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This paper presents a neural decoder for trellis coded modulation (TCM) schemes. Decoding is performed with Radial Basis Function Networks and Multi-Layer Perceptrons. The neural decoder effectively implements an adaptive Viterbi algorithm for TCM which learns communication channel imperfections. The implementation and performance of the neural decoder for trellis encoded 16-QAM with amplitude imbalance are analyzed. r

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Techniques for Estimating the Bit Error Rate in the Simulation of Digital Communication Systems

Cited by 441 publications

References 12 publications

Two rank order tests for M-ary detection

Two rank order tests for M-ary detection

Full-Field Detection with Electronic Signal Processing

TCM decoding using neural networks

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