Estimation of Rayleigh flat channel coefficients using deep learning

Abstract

Channel estimation is a significant prerequisite in wireless communication, especially where the multipath radio propagation incurs significant fading in a noisy environment. In such a scenario, Rayleigh fading model is traditionally adopted to represent the communication channel. In this article, a new method of getting the Rayleigh flat channel coefficients using deep learning is presented.Here, we presume that the channel state and the corresponding channel coefficients remain constant in a given communication context which depends on the locations of transmitter/receiver, time of the day and the communication environment. When the context changes, the channel state also changes and the corresponding coefficients switch to the respective matching values. Thus the channel coefficients can have several possible realizations or classes. In our scheme, the deep learning network, after training, acts as a classifier to detect the class or the context of the channel state and based on that determines the corresponding channel coefficients. In our proposed method, the percentage reduction in the percentage error is about 26% of that of its nearest competitor when the channel SNR is 10 dB.

RNN-FL-based Channel Estimation Approach in mmWave Massive MIMO Systems

“…Thus, using extended pilot transmission frames to estimate the channels cannot be possible, and the channel estimation process should be performed with the shortest possible pilot transmission frame length. In order to tackle these challenges, in the literature, several data-driven approaches mainly investigated the advantages of utilizing machine learning-based schemes for solving the channel estimation problem with the purpose of better estimation performance and the shortest possible pilot transmission frame length 3,4,5 .…”

Recurrent neural network and federated learning based channel estimation approach in mmWave massive MIMO systems

“…Thus, using extended pilot transmission frames to estimate the channels cannot be possible, and the channel estimation process should be performed with the shortest possible pilot transmission frame length. In order to tackle these challenges, in the literature, several data‐driven approaches mainly investigated the advantages of utilizing machine learning‐based schemes for solving the channel estimation problem with the purpose of better estimation performance and the shortest possible pilot transmission frame length 3–5 …”

“…In order to tackle these challenges, in the literature, several data-driven approaches mainly investigated the advantages of utilizing machine learning-based schemes for solving the channel estimation problem with the purpose of better estimation performance and the shortest possible pilot transmission frame length. [3][4][5] Typically in some studies that use machine learning for solving channel estimation problems, the required dataset for training the desired network has been made by creating artificial data samples. For creating artificially labeled data, all…”