A Novel Intelligent Channel Estimation Strategy for the 5G Wireless Communication Systems

Abstract Nowadays, the Multiple Input Multiple Output (MIMO) Orthogonal Frequency Division Multiplexing (OFDM) is an important method used in wireless communications, especially in 5G cellular communications. As in a wireless network, the input signals pass through a channel, and the input signal undergoes phase shift, attenuation, and interference. So, the password from the user side and the received signals are not the same. Thus, an effective channel estimator is essential to make cellular communication better. Hence, a novel hybrid technique called Chimp-based CatBoost channel estimation (CbCBCE) was proposed. This technique is the combination of the Chimp optimization algorithm and CatBoost algorithm. The channel parameters are estimated and then reduced using the Chimp optimization algorithm. Finally, the proposed model is validated with the case study. Then, the result of the proposed model was estimated, and it was compared with other existing techniques. It is observed that the outcome of the proposed design is more compared with the other conventional methods. The presented model is executed in the MATLAB platform, and it is proved that the proposed model has high throughput, high energy efficiency, less BER, and a high data transfer rate.