Adaptive blind equalizers with automatically controlled parameters

Signals when pass through a channel undergo various forms of distortion, most common of which is Inter-symbol-interference, so called ISI. Inter-symbol interference induced errors can cause the receiver to misinterpret the received samples. Equalizers are important parts of receivers, which minimizes the linear distortion produced by the channel. If channel characteristics are known a priori, then optimum setting for equalizers can be computed. But in practical systems the channel characteristics are not known a priori, so adaptive equalizers are used. Adaptive equalizers adapt or change the value of its taps as time progresses. There are two main types of adaptive equalizers, trained equalizers and blind equalizers. In trained equalizers there is a pseudo-random pattern of bits called training sequence known both to receiver and transmitter. But equalizers for which such a initial training period can be avoided are called BLIND EQUALIZERS. Blind equalizer as opposed to data trained equalizer, is able to compensate amplitude and delay distortion of a communication channel using only channel output sample and knowledge of basic statistical properties of the data symbol. Among some algorithms of blind equalizers like CMA, Stop and Go, GSA, SGA, SRCA etc., Stop and Go is one of the most important algorithms. Unfortunately all blind equalizers converge very slowly. So there is a proposed method for automatic control of step size and filter length for a blind equalizer which is driven by stop and go directed algorithm. This idea was presented by Krzysztof Wesolowski in his paper “Adaptive Blind Equalizers With Automatically Controlled Parameters”. This proposed method for varying the step size results substantial shortening of the convergence time.