Deep learning-based time-varying channel estimation with basis expansion model for MIMO-OFDM system

For high-speed mobile MIMO-OFDM system, a low-complexity deep learning (DL) based time-varying channel estimation scheme is proposed. To reduce the number of estimated parameters, the basis expansion model (BEM) is employed to model the time-varying channel, which converts the channel estimation into the estimation of the basis coefficient. Specifically, the initial basis coefficients are firstly used to train the neural network in an offline manner, and then the high-precision channel estimation can be obtained by small number of inputs. Moreover, the linear minimum mean square error (LMMSE) estimated channel is considered for the loss function in training phase, which makes the proposed method more practical. Simulation results show that the proposed method has a better performance and lower computational complexity compared with the available schemes, and it is robust to the fast time-varying channel in the high-speed mobile scenarios. © 2022 Inst. of Scientific and Technical Information of China. All rights reserved.