On Simultaneous System and Input Sequence Estimation

Abstract

Equalization is concerned with estimation of the input sequence of a linear system given noisy measurements of the output signal. In case the system description is unknown we have the problem of blind equalization. A scheme for blind equalization which is based on the assumption that the input signal belongs to a finite alphabet is proposed. A finite impulse response model can be directly estimated by the least-squares method if the input sequence is known. Since we know that the number of possible input sequences is limited, we can associate one system estimate to each possible input sequence. This allows us to determine the a posteriori probability of an input sequence given output observations. The maximum a posteriori (MAP) input sequence estimate is then taken as the most probable input sequence. Sufficient conditions for identifiability of the input signal and the system are given. The complexity of this scheme increases exponentially with time. A recursive approximate MAP estimator of fixed complexity is obtained by, at each time update, only keeping the K most probable input sequences. This method is evaluated on a Rayleigh fading communication channel.