A Novel Hybridized Optimization Mechanism for Efficient Channel Estimation Using Adaptive and Attention-Based Convolutional Autoencoder in MIMO-NOMA for mm Wave Systems

The channel estimation is crucial in the "millimeter Wave (mmWave) Massive Multiple-Input Multiple-Output (MIMO) and Non-Orthogonal Multiple Access (NOMA)" devices. Hybrid beamforming techniques are employed nowadays to minimize the complexity and equipment price. However, the absence of digital beam forming in mmWave affects the dynamic range and accuracy of the channel estimation. Previous research is concentrated mainly on predicting narrow-band mmWave channels using deep learning networks as the wideband channels of mmWave create a considerable amount of range and noise issues. Accurate channel estimation in the MIMO system is challenging because of the increased number of antennas and RadioFrequency (RF) chains. MIMO system communications using mmWave are frequently chosen because of their massive spectrum resources. Therefore, it is essential to tackle the obstacles obtained in the standard channel estimation framework by developing a MIMO-NOMA network with the help of deep learning methods. In this paper, an advanced tuning and prediction approach with a deep learning mechanism is designed to perform an accurate estimation of channels for the MIMO-NOMA system. Moreover, a hybridized optimization model called Wild Horse-Piranha Foraging Optimization Algorithm (WH-PFOA) is developed and utilized with Adaptive and Attention-based Convolutional Autoencoder (Ada-ACAE) for estimating the channels in mmWave-based MIMO-NOMA system. Furthermore, the complexity rate and the hardware cost of the MIMO-NOMA network are reduced by adapting the hybrid beam-forming mechanism. Initially, to perform channel estimation, the pilot symbols are tuned by the introduced WH-PFOA to enhance the channel estimation performance. Later, the channel estimation is carried out with the optimal pilot symbols and the channel coefficients are validated. Numerical results show that the proposed channel estimation and pilot estimation process outperforms the state-of-the-art approaches.