A family of predictive constant modulus algorithms for blind equalization

In this work we present a family of predictive constant modulus algorithms that performs blind equalization with some advantages over the CMA/FIR conventional technique. The LMS-like algorithm of the family (PCMA) was originally presented by Cavalcanti and Mota (1997). In this paper we present two new algorithms based in the same approach: the normalized PCMA (NPCMA) and the recursive PCMA (RPCMA). The choice of one of these algorithms depends on a compromise between performance and complexity, as usual. Simulation results confirm that PCMA-based algorithms perform better than conventional FIR equalizers in terms of steady-state error. Moreover, the proposed NPCMA and RPCMA provides increased convergence speed when compared to the original PCMA.