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Blind equalization and automatic modulation classification (AMC) have been of significant importance for cognitive radios when the receiver has no information about the channel or modulation type. Choosing an appropriate equalizer is difficult in the absence of channel information. In this paper, an AMC based on cyclostationary feature detection and a predictor-based recursive blind equalizer is used...
This paper presents a novel blind adaptive identification algorithm based on least-squares type arguments. Parameter estimates are recursively updated with each output measurement, without resorting to any matrix inversion operation. It is proved that the parameter estimates converge almost surely (a.s.) toward a scalar multiple of the true parameters. Possible application of this algorithm to the...
This paper considers the problem of blind adaptive equalization of single-input multiple-output IIR channels without requiring the channel diversity condition. That is, the parallel sub-channels in the fractionally sampled model can have common factors. We develop an equalizer based on IIR prediction of the received signal. The predictor parameters are estimated by using the recursive extended least...
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