A Deep Learning-Based Channel Estimation for High-Speed Train Environments

Abstract

A communication system in the railway track environment provides constant channel coefficient property as trains travel on a predetermined route and speed. This channel characteristic provides advantages in designing a channel estimation. This paper proposes a channel estimation algorithm based on a deep learning network called the Convolutional Neural Network (CNN). The CNN has trained with an average Channel Frequency Response (CFR) dataset on railway track environments with different multi-path fading and noises. The CFR can be estimated using a known pilot symbol as conventional methods. The estimated CFR is also used to select the right CFR for multi-path fading compensation. The CNN will then classify the estimated CFR by recognizing estimated channel characteristics and determining the most matched estimated channel for equalization of received signals in a data channel. The simulation results show that the performance of the deep learning algorithms outperforms that of the conventional algorithms. Furthermore, the proposed method delivers better Bit Error Rate (BER) performance since the deep learning-based channel estimation can categorize the features of the channel characteristics with different multi-path and doppler shifts.