Adaptive blind equalisation using second and higher-order statistics

Although research into blind equalisation algorithms has been an on-going activity for more than a decade, the existing algorithms are often inefficient in combating the impairments introduced by rapidly changing communication channels. New and efficient algorithms are thus needed. In this paper, an efficient blind equalisation algorithm is proposed by modifying the existing super exponential algorithm. This method, based on fast Kalman filtering theory, is recursive in order and in time, and leads to a significant reduction in the number of arithmetic operations. Using the order update by partitioning the covariance matrix of the first hundred data in a specific form, fast initialisation is also implemented. The proposed algorithm requires computational complexity proportional to N but acquires the fast convergence characteristics of the super exponential algorithm. Simulation results indicate that this equaliser performs better with a soft decision device applied at the output of the equaliser for various modulation schemes and channel conditions.