A complex pi-sigma network and its application to equalizaton of nonlinear satellite channels
Yoan Shin, Keun-Sik Jin, Byung Moon Yoon · Proceedings of International Conference on Neural Networks (ICNN'97) · 2002
Digital satellite communication channels have a nonlinearity with memory due to saturation characteristics of the high power amplifier in the satellite and transmitter/receiver linear filters used in the overall system. In this paper, we propose a network structure and a learning algorithm for complex pi-sigma network (CPSN) and exploit CPSN in the problem of equalization of nonlinear satellite channels. The proposed CPSN is a complex-valued extension of real-valued pi-sigma network (PSN) that is a higher-order feedforward network with fast learning while greatly reducing network complexity by utilizing efficient form of polynomials for many input variables. The performance of the proposed CPSN is demonstrated by computer simulation on the equalization of complex-valued QPSK input symbols distorted by a nonlinear channel modeled as a Volterra series and additive noise. The results indicate that the CPSN shows good equalization performance, fast convergence, and a lot less computations as compared to conventional higher-order neural networks such as Volterra filters.