Transform domain quasi-Newton algorithms for adaptive equalization in burst transmission systems

Two new adaptive equalizers are proposed which belong to the quasi-Newton (QN) algorithmic family. The first algorithm. is a linear equalizer (LE) and the second one is a decision feedback equalizer (DFE). In the LE case, the involved inverse Hessian matrix is approximated by a proper expansion consisting of powers of a Toeplitz matrix. Due to this formulation, the algorithm can be efficiently implemented in the transform domain (TD) using FFT. The same idea is applied to the feedforward part of the DFE. The derived algorithms enjoy the advantages of QN algorithms, that is, they exhibit faster convergence than their stochastic gradient counterparts and less computational complexity as compared to other Newton-type algorithms. These advantages are further enhanced due to TD implementation