Joint Channel Estimation and Signal Detection for FBMC based on Artificial Neural Network

Filter Bank MultiCarrier with Offset Quadrature Amplitude Modulation (FBMC-OQAM) has been intensively studied, and becomes a very potential candidate in future wireless communication system because of its numerous advantages. This paper presents a framework of Artifical Neural Network (ANN)-aided receiver design for the FBMC system. Specifically, two new joint channel estimation and equalization architectures are developed, which are based on two classical ANN algorithms, Multi-layer Perceptron (MLP) and Functinal Link Artificial Neural Network (FLANN). In addition, a powerful Loss Function (LF) is proposed by combining intrinsic characteristics of FBMC and is applied in the ANN-aided FBMC receiver. Numerical results validate the effectiveness of the proposed ANN-aided design and demonstrate its remarkable bit-error-ratio (BER) performance under multi-path channel environment. Furthermore, the performance advantage of the proposed LF is also confirmed by simulations.