Advanced Estimation and Feedback of Wireless Channels State Information for 6G Communication via Recurrent Conditional Wasserstein Generative Adversarial Network

Abstract

In this manuscript, an Advanced Estimation and Feedback of Wireless Channels State Information for sixth generation (6G) Communication via Recurrent Conditional Wasserstein Generative Adversarial Network (AEF-WCSI-6G-RCWGAN) is proposed. Deep Learning (DL) based channel estimation algorithm using Recurrent Conditional Wasserstein Generative Adversarial Network (RCWGAN) is estimated the channel parameters in 6G, such as channel gains and delays from received signals, which is crucial for effective communication and resource allocation. The primary purpose of this paper is to discuss key issues and possible solutions in DL-based wireless channel estimation and channel state information (CSI) feedback including the DL model selection, training data acquisition and neural network design for 6G. The deep learning-dependent channel estimator refines the predicted channel output, which is subsequently used for increase the efficacy and dependability of the communication scheme. The proposed AEF-WCSI-6G-RCWGAN is implemented and the performance metrics, like Detection Success Probability, Mean Square Error (MSE), and Normalized Mean Square Error (NMSE) are analyzed. Finally, the performance of the proposed AEF-WCSI-6G-RCWGAN method achieves 30.73%, 28.35%, and 29.62% higher Detection Success Probability, 25.73%, 28.05%, and 24.62% lower MSE when compared with existing methods: towards DL-assisted wireless channel estimate and CSI feedback for sixth generation (WCE-CSI-6G-GAN), an effectual deep neural network channel state estimate for Orthogonal frequency-division multiplexing (OFDM)wireless systems (CSE-WS-BiLSTM), and distributed machine learning dependent downlink channel estimate for reconfigurable intelligent surfaces supported wireless communications (DCE-AWC-HDCENet) methods, respectively.