On the Equalization of an OFDM-Based Radio-over-Fiber System Using Neural Networks

Abstract

In this study the impact of a Radio-over-Fiber (RoF) subsystem on the performance of Orthogonal Frequency Division Multiplexing (OFDM) system is evaluated. The study investigates the use of Multi-Layered Perceptron (MLP) and Radial Basis Function (RBF) neural networks to compensate for the optical subsystem nonlinearities in terms of bit error rate, error vector magnitude, and computational complexity. The Bit Error Rate (BER) and Error Vector Magnitude (EVM) results show that the performance of MLP neural network is superior to that of RBF neural network and time-multiplexed pilot-based equalizer especially in the case of highly nonlinear behavior of the RoF subsystem.