Swarm intelligence‐based deep ensemble learning machine for efficient channel estimation in MIMO communication systems.

Summary: Multiple‐input multiple‐output (MIMO) technology is much significant for achieving high data rates while considering the multiuser communication. For this purpose, the estimation of Channel State Information (CSI) is performed by the pilot. But, the estimated CSI cannot be applied for downlinks while processing an extremely high speed mobile data as it gets outdated because of the rapid channel variation. Estimating the channel in the MIMO systems is highly difficult on using non‐stationary channel characteristics under the high mobility environments. Channel estimation with received signal‐to‐noise ratio (SNR) feedback ensures an effective performance in the practical wireless MIMO systems. This paper plans to develop an intelligent deep learning strategy to solve the channel estimation problem in MIMO system. The main goal of the proposed model is to estimate the MIMO channel coefficients at a transmitter based on the received SNR feedback information from a receiver using the swarm intelligence‐based deep ensemble learning machine (SI‐DELM). For both the scenarios, the SI‐DELM is developed, in which the convolution neural network (NN) with three classifiers is used. Here, the fully connected layers of CNN are replaced by the NN, recurrent neural network (RNN), and deep neural network (DNN). In the SI‐DEL‐based channel estimation, the hyper parameter tuning is performed by the adaptive iteration assisted mixture ratio‐based cat swarm optimization (AIMR‐CSO) for minimizing the mean square error (MSE) and bit error rate (BER) of the estimated channel. Finally, by performing numerical simulations, the efficiency and better performance of the suggested model is revealed by conducting the comparison between proposed and conventional models. [ABSTRACT FROM AUTHOR]