A Moment-Based Estimation Strategy for Underdetermined Single-Sensor Blind Source Separation

We investigate the blind identification and separation of underdetermined linear instantaneous mixtures with a single sensor and an arbitrary known number of sources with finite known support and uniform distribution. We propose channel estimators based on the high-order statistics of the received signal and on the rotational symmetries of the source constellations. Explicit expressions for distinct and equal rotation orders are derived. The proposed estimators are used as initializers for the iterative least squares with enumeration algorithm to enhance its convergence properties. Simulation results on several high interference two-user wireless communication scenarios show that our initialization strategy achieves a substantial performance gain compared to other traditional initializers.