Abstract
The well-known Analytic Constant Modulus Algorithm (ACMA) is a classic algebraic method for signal separation and equalization, which can achieve blind signal recovery and fast convergence. In this paper, we explore new means for incorporating the inherent statistical signal orthogonality in ACMA for improved signal separation and recovery. This statistical orthogonality constraint adds new restrictions to ACMA, leading to a novel approach we denote as Orthogonality Constrained Analytic Constant Modulus Algorithm (OC-ACMA). We further generalize OC-ACMA for applications in blind equalization. Results show that OC-ACMA can successfully separate and recover constant modulus signals at higher accuracy.
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