Deep Learning Based Advanced Estimation of Wireless Channels State Information for 6G Communication

The last few decades have seen an exponential increase in the demand for wireless connectivity. A channel estimator is a successful strategy for transferring data over wireless channels and is remarkably proficient. There will be an enormous rise in the pilot overhead for channel estimation because the dimension of the channels to be estimated is much greater than in traditional systems. It is the main issue created in channel estimation. Therefore, the deep learning-based advanced estimation of wireless channel state information for 6G communication is proposed in this research. A proposed Deep Learning (DL)-based channel estimation algorithm using the Deep Neural Network (DNN). Deep Learning (DL) is widely recognized for its capacity to offer an extensive selection of regulation strategies for various protocol levels and comprehensive assessment for intricate wireless networks containing large volumes of data. This paper aims to address important problems and potential fixes in Channel State Information (CSI) feedback and DL-based wireless channel estimation, covering the choice of DL model, acquisition of training data, and 6G neural network structure. The DL-based channel estimator is used to refine the estimated channel output, which is then used to enrich the total effectiveness and dependability of the communication system. The suggested method is appropriate for real-world communication scenarios because it not only provides increased channel estimation accuracy but also depicts specificity in response to shifting channel issues.