Intelligent Channel State Estimation for 6G Communication Systems Using Deep Learning

Abstract

As the world moves towards the development of 6G communication systems, there is a growing need to overcome the challenges posed by the ever-increasing demand for higher data rates, massive connectivity, and ultra-low latency. So this project proposes an enhanced wireless channel state estimation through deep learning for 6g communication.The communication chain begins with a source encoder, which efficiently encodes the input data, followed by a channel encoder that introduces redundancy for error correction. The encoded data is then fed into a symbol mapper to modulate the information into symbols suitable for transmission over the communication channel. The innovative aspect of this work lies in the incorporation of a DL-based channel estimator. The channel estimator encompasses two key stages: channel data acquisition and data-driven learning. The acquisition stage gathers real-time channel data, providing the necessary input for the subsequent data-driven learning phase. Through DL techniques, the system learns the intricate characteristics of the communication channel, adapting to dynamic variations and optimizing the estimation process. The estimated channel output, refined through the DL-based channel estimator, is employed to enhance the overall communication system's reliability and performance. The proposed approach not only offers improved accuracy in channel estimation but also exhibits adaptability to changing channel conditions, making it suitable for real-world communication scenarios. This project is implemented by using MATLAB Software.