Blind channel identification from burst data using implicit matching of HOS

Abstract

In this paper we present a new approach for blind identification of non-minimum phase FIR channels for single input single output (SISO) and multiple inputs single output (MISO) scenarios. The received record of samples is processed by multiple parallel branches, each comprising an FIR filter followed by a non-linear function and an accumulator. Using an appropriate cost function, the obtained vector of averages is then best fitted by a function of the system model. The non-linear function used in each branch is selected so as to obtain an implicit matching of all higher order statistics (HOS) of certain orders. Choice of the cumulants generating function (CGF) as this non-linear function is also possible and can be interpreted as matching the joint probability density function (PDF) for selected samples in the vector space of the channel. With this interpretation the coefficients of the FIR filters are actually sampling points in a multidimensional frequency domain. The main advantages of this approach are its high probability of identification success, its ability to obtain reliable channel estimation in low SNR using a short record of samples and its relative insensitivity to overestimation of the channel order. The method is suitable for single transmit and receive antennas and has easy extensions to multiple antenna scenarios. The validity of the proposed approach is confirmed by simulations for BPSK and QPSK modulations and suitable design guidelines for these modulations are given.