A Denoising Autoencoder based wireless channel transfer function estimator for OFDM communication system

Abstract

This paper proposes a channel estimation method for Orthogonal Frequency Division Multiple Access (OFDM) communication system by utilizing a Neural Network (NN) based a Machine Learning (ML). Especially, Autoencoder is utilized to estimate Channel Transfer Function (CTF) and to reduce a noise on the estimate. Japanese Digital TV broadcast system is assumed as target system. Then 8k FFT/IFFT is used and number of sub-carriers are 5617 such as mode3 in Integrated Services Digital Broadcasting-Terrestrial (ISDB-T) spec. 5617 complex CTF points must be estimated by limited number of scattered pilot sub-carriers. Assumed channel condition is 2 wave multipath channel with Additive White Gaussian Noise (AWGN). The multipath parameters are randomly generated. To train the autoencoder, 5000 CTFs are generated and pre-training was performed. System performance was evaluated by measuring Bit Error Rate (BER). The system with conventional frequency-domain interpolator and the system with autoencoder based were compared. According to BER simulation results, the autoencoder based system has shown lower BER than the conventional. At BER=10$^{-5}$, autoencoder system shows roughly 2dB gain than conventional system.