BER analysis of deep learning empowered wireless channel estimation techniques

This paper investigates the most commonly used Deep Learning (DL) based techniques for estimating the wireless channel at the User Equipment (UE). The generalized Nakagami-m fading distribution is considered for deep learning network. The motive of this work is to provide an experimental investigati...

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Bibliographic Details
Main Authors: Lakshmi, P. Dhivya, Chelian, T. Vetrivel, Kumaravelu, Vinoth Babu, Velmurugan, P. G. S., Thiruvengadam, S. J.
Format: Conference Proceeding
Language:English
Subjects:
Artificial neural networks
Bit error rate
Deep learning
Error analysis
Estimation
Machine learning
Online Access:Get full text
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Summary:This paper investigates the most commonly used Deep Learning (DL) based techniques for estimating the wireless channel at the User Equipment (UE). The generalized Nakagami-m fading distribution is considered for deep learning network. The motive of this work is to provide an experimental investigation on the wireless channels using three different deep-feed forward neural networks, namely Artificial Neural Networks (ANN), Long Short Term Memory (LSTM), and Convolution Neural Networks (CNN). The analysis includes the preprocessing approach for handling complex received signals as one of the inputs and actual channel coefficients as output. Bit Error Rate (BER) performance analysis is performed and is compared with the classical LS estimator. It is demonstrated that DL channel estimation outperforms the Least Square (LS) based estimation, and simulation results support that DL-based channel estimation is very efficient.
ISSN:0094-243X
1551-7616
DOI:10.1063/5.0199435