New adaptive blind equalization algorithms for constant modulus constellations

We present a new class of adaptive filtering algorithms for blind equalization of constant modulus signals. The algorithms are first derived in a classical system identification context by minimizing at each iteration a deterministic criterion and then their counterpart for blind equalization is derived by modifying this criterion taking into account the constant-modulus property of the transmitted signal. The algorithms impose more constraints than the classical constant modulus algorithm (CMA) and as a result achieve faster convergence. An asymptotic analysis has provided useful parameter bounds that guarantee the algorithms´ stability. A priori knowledge of these bounds helps the algorithms escape from undesirable local minima of their cost function thus giving them a potential advantage over the classical CMA. An efficient computational organization for the derived algorithms is also proposed and their behaviour has been tested by means of computer simulations