Blind equalization with generalized inverse channel estimation and fractional phase MLSE metrics for mobile communications

This paper proposes a new blind adaptive MLSE equalizer for frequency selective mobile radio channels. The proposed equalizer performs channel estimation for each survivor path of the Viterbi algorithm (VA), and restricts the number of symbol candidates for the channel estimation in order to reduce prohibitive complexity. In such channel estimation, autocorrelation matrices of the symbol candidates are likely to become singular, which increases the estimation error. To cope with the singularity, the proposed equalizer employs a recursive channel estimation algorithm using the Moore-Penrose generalized inverse of the autocorrelation matrix. As another problem, the blind channel estimation can yield plural optimal estimates of a channel impulse response, and the ambiguity of the estimates degrades the BER performance. To avoid this ambiguity, the proposed equalizer is enhanced so that it can take advantage of the fractional sampling. The enhanced equalizer performs symbol-spaced channel estimation for each fractional sampling phase. This equalizer combines separate channel estimation errors, and provides the sum to the VA processor as the branch metric, which tremendously reduces the probability that a correct estimate turns into a false one. Computer simulation demonstrates the effectiveness of the proposed equalizers in the frequency selective fading channels.