Title :
Volterra equalizers based on MBER and restarted BFGS algorithm for nonlinear channels
Author :
Zhu, Renxiang ; Wu, Lenan ; Wu, Zhengyi
Author_Institution :
Sch. of Electron. & Inf. Eng., Ningbo Univ. of Technol., Ningbo, China
Abstract :
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.
Keywords :
Volterra equations; error statistics; mean square error methods; telecommunication channels; MBER; Volterra equalizers; minimum bit error rate; minimum mean square error; nonlinear channels; restarted BFGS algorithm; Bit error rate; Convergence; Equalizers; Information science; Mean square error methods; Newton method; Physics; Power system modeling; Robustness; Stochastic processes; learning algorithm; minimum bit error rate; nonlinear equalization;
Conference_Titel :
Communications Technology and Applications, 2009. ICCTA '09. IEEE International Conference on
Conference_Location :
Beijing
Print_ISBN :
978-1-4244-4816-6
Electronic_ISBN :
978-1-4244-4817-3
DOI :
10.1109/ICCOMTA.2009.5349232
