5G cascaded channel estimation using convolutional neural networks

Abstract

Cascaded channels have been considered in several physical multipath propagation scenarios. However they are subject to phenomena such as multipath scattering, time dispersion and Doppler shift between the different links, which impose great challenges in relation to the channel estimation processing function in the receiver. In this paper we propose to tackle the problem of cascaded channels estimation in the fifth-generation and beyond (5G+) systems using convolutional neural networks (CNNs), without forward error correction (FEC) codes. The results show that the CNN-based framework reaches very close to perfect (theoretical) channel estimation levels, in terms of bit error rate (BER) values, and outperforms the least square (LS) practical estimation, measured in mean squared error (MSE). The benefits of CNN-based wireless cascaded channels estimation are particularly relevant for increasing number of links and modulation order. These findings are further confirmed at the CNN implementation stage on a field programmable gate array (FPGA) platform for a number of realistic quantization scenarios.