Title : ENHANCED WIRELESS CHANNEL STATE ESTIMATION THROUGH DEEP LEARNING FOR 6G COMMUNICATION
Author : Dr. K. Amit Bindaj, Mr. T. Gangadhar, Mr Ch. Gopala Rao
Abstract :
There is a pressing need to find solutions to the problems caused by the rising need for ultra-low latency, huge connection, and ever-increasing data rates as the globe advances towards the creation of 6G communication networks. In light of this, the authors of this study suggest using deep learning to improve 6G wireless channel state estimates.The input data is effectively encoded by a source encoder in the communication chain, and then redundancy is introduced for error correction by a channel encoder. In order to make the encoded data acceptable for transmission across the communication channel, it is passed into a symbol mapper. This work's novel part is a channel estimator that is based on DL. Two essential steps comprise the channel estimator: acquiring channel data and learning from data. In order to feed the data-driven learning phase that follows, the acquisition stage collects channel data in real-time. The system optimizes the estimating process by learning the complex prop
