Abstract
A new adaptive signal processing technique is described that does not require a training or reference signal for adaptation. The approach taken is to minimize variations in the complex envelope of an adaptive processor output while subjecting the processor coefficients to a set of linear constraints. This Linearly-Constrained Constant-Modulus (LCCM) method is motivated by the drawbacks associated with two existing untrained algorithms in certain signal environments. The problems specifically addressed by LCCM are the signal cancellation problem of Linearly-Constrained Power Minimization (LCPM) and the signal ambiguity problem of the Constant-Modulus Algorithm (CMA). A complete adaptive implementation of the LCCM algorithm is presented including its stability and convergence properties. In addition, adaptive performance of LCCM is demonstrated in a comparison with CMA and LCPM.
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