Optimized CNN and Adaptive RBFNN for channel Estimation and Hybrid Precoding approaches for Multi User Millimeter wave Massive MIMO

The Millimetre Wave (mmWave) communication satisfy the demand for high data rates due to the characteristic of wide bandwidth. Using massive Multiple-Input Multiple-Output (MIMO) technology, significant propagation loss of mmWave communication is effectively compensated. However, it is challenging to provide a specialised Radio Frequency (RF) chain for each antenna due to constrained physical area with closely spaced antennas and prohibitive power consumption in mmWave massive MIMO systems. This paper presents novel approaches for effective channel estimation and hybrid precoding in mmWave communication systems. To address the challenges of channel estimation, a Convolutional Neural Network (CNN) is utilized, and network parameters are optimized using Enhanced Whale Optimization Algorithm (EWOA). The proposed CNN-based channel estimation method aims to accurately estimate the channel in mmWave systems with enhanced efficiency and reduced complexity. By training CNN using EWOA optimization algorithm, the network parameters are fine-tuned to improve accuracy and generalization capability of channel estimation process. Furthermore, hybrid precoding is achieved using Adaptive Radial Basis Function Neural Networks (Adaptive RBFNN) which enables efficient precoding while minimizing complexity. Moreover, the Adaptive RBFNN approach determines the optimal precoding weights based on Channel State Information (CSI), resulting in improved performance and reduced computational overhead. The performance analysis is validated using MATLAB/Simulink software and offers in providing effectual and reliable mmWave communication systems, facilitating the realization of high-speed and high-capacity wireless networks.