Conjugate-gradient-based decision feedback equalization with structured channel estimation for digital Television
M.D. Zoltowski, William J. Hillery, Serdar Özen, Mark J. Fimoff · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2002
In this paper, we show how the convergence time of equalizers for 8-VSB based on the conjugate gradient (CG) algorithm can be considerably improved through initialization based on a channel estimate. We derive real and complex minimum mean-square error (MMSE) equalizers and implement them adaptively using the conjugate gradient, recursive least squares (RLS), and least mean squares (LMS) algorithms. We show that both CG and RLS have similar convergence times --- both are much faster than LMS. Since the CG algorithm is easily initialized, we compare several methods of initialization to determine how each affects convergence and then apply the best methods to initialize equalizers using channel estimates. We find that initializing the correlation matrices and filling the feedback taps with training symbols greatly speeds convergence of the CG adaptive equalizer, potentially approaching the rate of convergence when running the algorithm on the matrix equations using the actual channel.