Blind channel equalization via multiobjective optimization

Blind equalisation has the potential to improve the efficiency of communication systems by eliminating training signals. Difficulties of its application in wireless communications, however, are due largely to the characteristics of the propagation media-multipath delays and fast fading. The challenge is achieving blind equalisation using only a limited amount of data. We present a multiple objective optimization approach to fast blind channel equalization. By investigating first the performance (mean-square error) of the standard fractionally spaced constant modulus algorithm (CMA) equalizer in the presence of noise, we show that the CMA local minima exist near the minimum mean-square error (MMSE) equalizers. Consequently, the CMA may converge to a local minimum corresponding to a poorly designed MMSE receiver with a considerably large mean-square error. Based on the multiple objective optimization techniques, we propose next a blind channel estimator by exploiting simultaneously the second-order cyclostationary statistics and the constant modulus of QAM-type communication signals. Such a channel estimation-based blind equalization scheme has the advantage of designing FIR minimum mean-square error equalizer with the optimal delay.