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A Comparative Study on Higher Order Statistics and Cyclostationarity in the Context of Identification and Equalization

In many radio communication environments, frequency selectivity caused by multipath propagation degrades the performance of digital communication channels by causing intersymbol interference (ISI). To achieve highspeed reliable communication, channel identification and equalization are necessary. In contrast to the traditional identification and equalization algorithms, the so-called blind identification and equalization methods do not require a training sequence. The focus of this paper is on two particular families of blind identification and equalization methods: Methods based on the higher-order spectra and methods based on the cyclostationarity of the received signal. These methods are used in conjunction with least-squares (LS) and FFT based algorithms. Different algorithms are compared from mean-squared error (MSE) and bit error rate (BER) points of view.