On the convergence of normalized constant modulus algorithms for blind equalization
Constantinos B. Papadias · 1993
One of the most known classes of algorithms for blind equalization is the so-called class of Constant Modulus Algorithms (CMA's) [1], [2]. These adaptive algorithms use an instantaneous gradient-search procedure similar to that of the LMS algorithm in order to minimize a stochastic criterion that penalizes the deviations of the received signal's modulus with respect to the known modulus of the emitted input signal. However, as has been recently reported [4], [5], [6], [7], these algorithms might ill-converge if they are not properly initialized, due to false minima of their corresponding cost function. This holds even in cases where the equalizer can match exactly the inverse of the transmitting channel [6]. Recently, a variant of CMA algorithms, the so-called Normalized CMA (NCMA) has been introduced in [8] and a more general class of normalized CMA algorithms containing NCMA as its first member has been introduced in [11]. These algorithms have a stable operation for any value of the...