A normalized constant-modulus algorithm

The constant-modulus algorithm (CMA), while the most commonly used blind equalization technique, converges very slowly. We propose a "normalized" constant-modulus algorithm (analogous to the widely used normalized LMS algorithm) with an adjustable step size that greatly increases the convergence rate for noise colorings with large eigenvalue spreads. The normalized step size is proportional to that required to achieve the desired modulus with the current data vector. Only a few extra operations per update are required. Many applications now using the constant modulus algorithm should achieve greatly improved convergence rates at almost negligible computational increase by adopting the new normalized CMA algorithm.