A Comprehensive Review and Deep Learning-Based Methodology for Channel Estimation in Next-Generation Wireless Communication Systems

Abstract:-The process of compressive review of channel estimation methods based on conventional and dynamic methods for next generation wireless communication. The neural network and machine learning based channel estimation enhance the capacity and performance of communication service and model. The deep learning-based channel estimator provides good accuracy and estimation but face a bottleneck problem of false alarm rate and false negative rate. The neural network-based channel estimation faces a problem of channel discontinue of estimation, this problem also finds in MLP based channel estimator The deep learning-based channel face a problem of pilot sequence training time is very high and decline the performance of communication model. , This paper describes the several approaches of channel estimation in communication systems. Also describes different types of deep learning algorithm and employed algorithm of machine learning for channel estimation ,This chapter describe as the proposed methodology of channel estimation in wireless communication. The proposed methodology design two algorithms based on deep learning and classification for channel estimation. .This paper includes the simulation tool parameters with its simulation analysis. Here we also consider the result analysis and performance analysis.. As wireless networks expand in size and complexity, particularly in mmWave-based 5G deployments, these legacy approaches become impractical. Therefore, scalable, efficient signal processing algorithms are essential to manage the vast amounts of real-time mobile data, ensuring performance is not compromised by network scale.

---