Channel Estimation with Fully Connected Deep Neural Network

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.

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“…Thus, CNN solves the problem of getting the correct class (or context) q. From ( 22), (23), and ( 24)…”

Section: Training Phasementioning

confidence: 99%

“…Effect of variations in the hyper parameters of the DLN on its performance has been thoroughly expounded. In Reference 23, the authors have described Rayleigh channel estimation using a fully connected deep neural network for different sizes of pilot data. A DLN can be designed for regression or classification 24,25 .…”

Section: Introductionmentioning

confidence: 99%