Blind channel estimation and equalization using Viterbi algorithms
In this paper, a trellis based blind channel estimation and equalization technique is presented. First, blind channel estimation is accomplished by incorporating a list parallel Viterbi algorithm with the least mean square (LMS) updating approach. In this operation, multiple trellis mappings are preserved and ranked in terms of path metrics. Equivalently, multiple channel estimates are maintained and updated once a single symbol is received. Second, at a certain point, which is determined by the evolution of path metrics and the linear constraint possessed in the trellis mapping, only the best channel estimate is maintained. Third, this channel estimate is adopted to construct the whole trellis that is used by a conventional adaptive Viterbi algorithm. Signal detection and further channel updating are then conducted alternatively. To alleviate the noise impact, a small delay is introduced before the feedback of detected symbols to further update the channel estimate. Simulation has shown the overall good performance of the proposed scheme in terms of mean square error (MSE) convergence of the channel estimation, stability to the initial channel guess, and computational complexity etc.