Blind digital signal separation using successive interference cancellation iterative least squares

Blind separation of instantaneous linear mixtures of digital signals is a basic problem in communications. When little or nothing can be assumed about the mixing matrix, signal separation may be achieved by exploiting structural properties of the transmitted signals, e.g., finite alphabet or coding constraints. We propose a monotonically convergent and computationally efficient iterative least squares (ILS) blind separation algorithm based on an optimal scaling lemma. The signal estimation step of the proposed algorithm is reminiscent of successive interference cancellation (SIC) ideas. For well-conditioned data and moderate SNR, the proposed SIC-ILS algorithm provides a better performance/complexity tradeoff than competing ILS algorithms. Coupled with blind algebraic digital signal separation methods, SIC-ILS offers a computationally inexpensive true least squares refinement option. We also point out that a widely used ILS finite alphabet blind separation algorithm can exhibit limit cycle behavior