Algorithm

Engineering

MIMO

Real-time computing

Telecommunications

Computer science

MIMO-OFDM

Multiplexing

Channel state information

Electronic engineering

Orthogonal frequency-division multiplexing

Channel (broadcasting)

Wireless

IEEE Communications Letters

Channel estimation is very challenging for multiple-input and multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems in high mobility environments with non-stationarity channel characteristics. In order to handle this problem, we propose a deep learning (DL)-based MIMO-OFDM channel estimation algorithm. By performing offline training to the learning network, the channel state information (CSI) generated by the training samples can be effectively utilized to adapt the characteristics of fast time-varying channels in the high mobility scenarios. The simulation results show that the proposed DL-based algorithm is more robust for the scenarios of high mobility in MIMO-OFDM systems, compared to the conventional algorithms.

Deep Learning Based Channel Estimation Algorithm for Fast Time-Varying MIMO-OFDM Systems