Blind Matched Filter Receiver

Abstract

Matched filter receiver (MFR) of Forney in the presence of intersymbol interference (ISI) and additive white Gaussian noise is investigated from a blind perspective, which includes blind maximum likelihood sequence detection, blind decision feedback equalization and linear blind equalization. Various realizations of the blind MFR are discussed. In a blind MFR, the channel can be easily estimated using a simple algorithm such as the constantmodulus algorithm (CMA). In this sense, the CMA can be used to estimate the channel directly without complicated matrix decomposition techniques. Zero forcing and minimum mean-square estimation based designs of the blind MFR are explored. Channel estimation using the CMA deteriorates in the presence of noise. A new method to tackle this problem is developed and shown to have satisfactory behavior in low signal-to-noise ratios. Tracking performance of the blind MFR is found to be acceptable in representative Rayleigh fading channels of mobile communication.Index Terms-Blind channel estimation, blind decision feedback equalization, constant-modulus algorithm (CMA), matched filter receiver.

CMF-DFE Based Adaptive Blind Equalization Using Particle Swarm Optimization

“…The performance of proposed PSO-CMF-DFE has been compared with those using LMS-CMF-DFE and CMA-DFE algorithms. In the LMS-CMF-DFE, the blind training is carried out using the error function given by (19). In this study, the implemented PSO algorithm is the classic one and first presented by [23].…”

“…After obtaining the estimates of the incoming signal as given in (18), an error function based on the received data can be set as given in (19).…”

“…process of data x k . Equation (19) can be configured as an MMSE type training process with a cost function…”

“…The method which estimates data and channel by using the maximum likelihood approach is studied in [18]. For example, in studies [19], [20], Baykal implemented an effective blind LTE and DFE trainings using Fourier analysis to obtain the matched filter of the channel. The reference [19] is the first study using CMA algorithm in order to directly obtain the channel impulse response (CIR).…”

“…For example, in studies [19], [20], Baykal implemented an effective blind LTE and DFE trainings using Fourier analysis to obtain the matched filter of the channel. The reference [19] is the first study using CMA algorithm in order to directly obtain the channel impulse response (CIR). In [20] the amplitude response of the channel is obtained blindly by using the autocorrelation of the channel output.…”