Blind adaptive channel equalization using multichannel linear prediction-based cross-correlation vector estimation

Blind equalization of transmission channel is important in communication areas and signal processing applications because it does not need training sequences, nor does it require a priori channel information. In this paper, an adaptive blind MMSE channel equalization technique based on second-order statistics is investigated. We present an adaptive blind MMSE channel equalization using multichannel linear prediction error method for estimating cross-correlation vector. They can be implemented as RLS or LMS algorithms to recursively update the cross-correlation vector. Once cross-correlation vector is available, it can be used for MMSE channel equalization. Unlike many known subspace methods, our proposed algorithms do not require channel order estimation. Therefore, our algorithms are robust to channel order mismatch. Performance of our algorithms and comparison with existing algorithms are shown for real measured digital microwave channel.