A nonlinear recursive least-squares algorithm for the blind separation of finite-alphabet sources

We present an adaptive algorithm that blindly separates mixtures of finite-alphabet sources given knowledge of the source alphabet and distribution. The algorithm is a nonlinear recursive least-squares procedure that employs a simple and numerically-robust square root Householder update. Simulations verify that the algorithm can separate large-scale noisy mixtures of finite-alphabet sources without any knowledge of the number of sources in the mixture.