CLUSTER-BASED BLIND NONLINEAR-CHANNEL ESTIMATION

Recently, a cluster-based maximum-likelihood sequence estimator (MLSE) for nonlinear channels was described, which consists of a clustering network and an MLSE implemented by the Viterbi algorithm [6]. The clus ter-based MLSE can be used for digital communication through nonlinear finite-length channels because channel mapping estimation is used ins tead of channel estimation in the conventional MLSE. The clustering ne twork of the cluster-based MLSE, which estimates the channel mapping b etween the signal input vectors and the noiseless channel outputs, is a supervised network and requires a training sequence. In this paper, we propose a blind channel mapping estimator to estimate the channel m apping without using the training sequence, The blind channel mapping estimator has a clustering block and a mapping block, The clustering b lock estimates the channel outputs, which represent the channel mappin g, subject to an unknown permutation operation because no training seq uence is utilized, That permutation operation is resolved by the mappi ng block, and therefore, the channel mapping is obtained, Introducing the blind channel mapping estimator into the cluster-based MLSE, a bli nd cluster-based MLSE for nonlinear channels can be done. Computer sim ulations of the blind channel mapping estimator and the blind MLSE for nonlinear channels are presented.