HF Markov Chain Models and Measured Error Averages

McManamon, P. M.

doi:10.1109/tcom.1970.1090362

Cited by 9 publications

(2 citation statements)

References 11 publications

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“…The probability of error is equal to the reciprocal value of the recurrence time R t measured in a number of bits [2]. The time R t is defined as the expected value of the error gap length This equation is a closed form of the approximate equation derived in Reference [10]. It is equivalent to the expression for the mean of the random variable Y expressed by (1), which completes our proof.…”

Section: Statistical Description Of a Channel With Memorymentioning

confidence: 56%

Accurate and time efficient estimation of the probability of error in bursty channels

Berber

2003

Int J Communication

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SUMMARYA method and a technique for the probability of error estimation in digital channels with memory are developed and demonstrated. The expressions for the mean and variance of a random variable, representing a block of bits transmission in a bursty channel (channel with memory), are derived. The influence of the memory is expressed by a parameter called the memory factor. It is shown that the traditional Monte Carlo method can be applied for the probability of error estimation. In order to control the accuracy and increase the time efficiency of estimation this method is modified and a new method, called the modified Monte Carlo method, is proposed. Based on this modified method a technique of estimation with controlled accuracy is developed and demonstrated using data obtained by simulation. According to this technique the sample size is adjusted in the course of estimating procedure to give an accurate estimate of the probability of error for a minimum required time of estimation.

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Section: Statistical Description Of a Channel With Memorymentioning

confidence: 56%

Accurate and time efficient estimation of the probability of error in bursty channels

Berber

2003

Int J Communication

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“…It shotfid be noted that the task of predicting the fading addressed here is different from that of errorless signal transmission and signal modeling in I-IF channels previously addressed in the literature [Goodman, 1992;McManamon, 1975]. The most straightforward and practical use of the prediction technique discussed here is to schedule bursts of transmission when above threshold signal levels are predicted.…”

Section: U(t + T) = Fr [U(t)lmentioning

confidence: 97%

Linear and nonlinear prediction techniques for short‐term forecasting of HF fading signals

et al. 1997

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Abstract. There exist two major mechanisms which are responsible for the fading phenomenon at HF frequencies. They are the multiple-mode interference and distortions due to the ionospheric irregularities. Fading time series produced by the first of these mechanisms alone should typically represent a multiple-periodical process. This kind of signal ma•v also be produced by an autonomous dynamical system. The character of the time series produced by the second mechanism depends on the nature of the ionospheric irregularities. Recently, evidence has been accumulated to show that sometimes the ionospheric turbulence on equatorial and middle latitudes represents a low-dimensional deterministic chaotic process. These facts suggest that for both mechanisms the fading time series may have a deterministic nature and therefore is predictable. Accordingly we apply the nonlinear predicting technique proposed by Farmer and Sidorovich [ 1987] to the fading time series obtained by the University of Illinois HF sounder. In its application the prediction technique is modffied to take into account specifics of the HF data. For comparison, the conventional linear autoregression prediction technique is also tested. It is found that, in general, the nonlinear prediction and the linear autoregressive forecasting allow prediction on a few correlation times and work with roughly the same success.

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Modeling burst channels using partitioned Fritchman's Markov models

Pimentel

Blake

1998

IEEE Trans. Veh. Technol.

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HF Markov Chain Models and Measured Error Averages

Cited by 9 publications

References 11 publications

Accurate and time efficient estimation of the probability of error in bursty channels

Accurate and time efficient estimation of the probability of error in bursty channels

Linear and nonlinear prediction techniques for short‐term forecasting of HF fading signals

Modeling burst channels using partitioned Fritchman's Markov models

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