Volterra equalizers based on MBER and restarted BFGS algorithm for nonlinear channels
Renxiang Zhu, Lenan Wu, Zhengyi Wu · 2009
A Volterra equalizer based on MBER (Minimum Bit Error Rate) and restarted BFGS method is proposed in this paper for equalization of nonlinear channels. Restarted BFGS could quicken convergence speed in MBER equalizer trainings, and the updated matrix of BFGS is restarted conditionally and it follows that the new method becomes much more robust. By canceling line search, it is convenient to implement the new method online. In simulations, Volterra equalizers based on minimum mean square error principle degenerate rapidly in nonlinear channels, but that based on MBER provide very low bit error rate. MBER equalizers are trained online by restarted BFGS algorithm, and the results show that its convergence rate is much faster than that of stochastic gradient algorithm.