Abstract
Recently, a new blind adaptive deconvolution algorithm was proposed based on a new closed-form approximated expression for the conditional expectation (the expectation of the source input given the equalized or deconvolutional output) where the output and input probability density function (pdf) of the deconvolutional process were approximated with the maximum entropy density approximation technique. The Lagrange multipliers for the output pdf were set to those used for the input pdf. Although this new blind adaptive deconvolution method has been shown to have improved equalization performance compared to the maximum entropy blind adaptive deconvolution algorithm recently proposed by the same author, it is not applicable for the very noisy case. In this paper, we derive new Lagrange multipliers for the output and input pdfs, where the Lagrange multipliers related to the output pdf are a function of the channel noise power. Simulation results indicate that the newly obtained blind adaptive deconvolution algorithm using these new Lagrange multipliers is robust to signal-to-noise ratios (SNR), unlike the previously proposed method, and is applicable for the whole range of SNR down to 7 dB. In addition, we also obtain new closed-form approximated expressions for the conditional expectation and mean square error (MSE).
Highlights
In this paper, we consider a blind adaptive deconvolution problem in which we observe the output of an unknown, possibly non-minimum phase, linear system from which we want to recover its input using an adjustable linear filter [1]
We consider the application in digital communications where the received symbol sequence has been affected by intersymbol interference (ISI), whereby symbols transmitted before and after a given symbol corrupt the detection of that symbol [2]
According to simulation results carried out in [52] for the 16QAM constellation input case, the equalization method based on the conditional expectation from [52] has better equalization performance compared to the maximum entropy equalization technique [20], which was shown to have significant equalization improvement compared to Godard’s [39], the reduced constellation algorithm (RCA) [54], the sign reduced constellation algorithm (SRCA) [55] and others
Summary
We consider a blind adaptive deconvolution problem in which we observe the output of an unknown, possibly non-minimum phase, linear system from which we want to recover its input using an adjustable linear filter (equalizer) [1]. Minimizing this cost function with respect to the equalizer’s coefficients reduces the ISI to such a level that the sent symbol can be recovered According another approach, the conditional expectation (the expectation of the source input given the equalized or deconvolutional output) is the nonlinear function. According to simulation results carried out in [52] for the 16QAM constellation input case, the equalization method based on the conditional expectation from [52] has better equalization performance compared to the maximum entropy equalization technique [20], which was shown to have significant equalization improvement compared to Godard’s [39], the reduced constellation algorithm (RCA) [54], the sign reduced constellation algorithm (SRCA) [55] and others.
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
