Chernoff Bound on the Probability of Error for Combined Linear-decision Feedback Sequence Estimation
نویسندگان
چکیده
Decision feedback sequence estimation (DFSE) is a reduced state alternative to maximum likelihood sequence estimation (MLSE). One of the most popular technique to improve the performance of DFSE is to process the received signal by a linear pre-filter prior to DFSE. This technique is referred to as combinedlinear DFSE (CLDFSE). In this paper, we derive a tight upper bound on the error performance of CLDFSE. To the best of our knowledge, this bound is new and has not been yet considered in the literature. The simulation results show that the derived upper bound can be used to approximate the true BER performance of CLDFSE systems for a wide range of communication channels.
منابع مشابه
A Comprehensive Performance Analysis of Direct Detection Receivers inWDMASystems
In this work the performance of a wavelength division multiple access (WDMA) system with direct detection receiver is investigated. For this purpose, the probability of error in a WDMA network with OOK modulation considering crosstalk, ISI, photo detector noise and thermal noise is calculated and the effect of each on system performance is investigated. The system performance in presence of PIN...
متن کاملWavelet Based Estimation of the Derivatives of a Density for a Discrete-Time Stochastic Process: Lp-Losses
We propose a method of estimation of the derivatives of probability density based on wavelets methods for a sequence of random variables with a common one-dimensional probability density function and obtain an upper bound on Lp-losses for such estimators. We suppose that the process is strongly mixing and we show that the rate of convergence essentially depends on the behavior of a special quad...
متن کاملProbability of error, equivocation, and the Chernoff bound
-I4 ET US consider the usual decision-theory problem of classifying an observation X ss coming from one of m possible classes (hypotheses) C,, CZ, * , C,. Let nl, .a * , 7~,,, denote the a priori probabilities on the hypotheses, and let p,(x), * * * , p,(x) denote the conditional probability density functions given the true hypothesis. Let us assume that these are known. Then it is well knoti t...
متن کاملWavelet Based Estimation of the Derivatives of a Density for m-Dependent Random Variables
Here, we propose a method of estimation of the derivatives of probability density based wavelets methods for a sequence of m−dependent random variables with a common one-dimensional probability density function and obtain an upper bound on Lp-losses for the such estimators.
متن کاملToward a tight upper bound for the error probability of the binary Gaussian classification problem
It is well known that the error probability, of the binary Gaussian classification problem with different class covariance matrices, cannot be generally evaluated exactly because of the lack of closed-form expression. This fact pointed out the need to find a tight upper bound for the error probability. This issue has been for more than 50 years ago and is still of interest. All derived upper-bo...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2001