FADING CHANNEL PARAMETER ESTIMATION USING DEEP LEARNING

Abstract

Wireless communications systems design and its performance depend on the wireless fading channels, which are often characterized using a Rician or Nakagami probability density function. A Rician or Nakagami parameter describes the fading severity in a fading channel and is used in the system design and performance evaluation. Therefore, the estimation of the fading parameter is important in wireless communications research and development. Traditionally, a fading parameter equation, maximum likelihood estimation, or Moment-based estimation is used to estimate the parameters. This work explores the use of deep learning for Rician K-factor and Nakagami-m parameter estimation. The signals are generated and used to train a convolutional neural network (CNN) to estimate the K-factor and m parameter from a waveform signal in a fading channel. In the first part, we generate random signals of different Rician K-factors with different Gaussian noise levels. Then, we use a convolutional neural network model to estimate the Rician K-factor. Several scenarios are considered, signals with different signal-to-noise ratios (SNR), dataset sizes, time window sizes, and LTE signal. In the Nakagami-m parameter estimation part, we generate different waveform signals with different parameters of Nakagami-m. Next, we classify the Nakagami-m parameters using the convolutional neural network. We estimate the Nakagami-m parameters with different SNR levels, dataset sizes, LTE signal, and time window sizes. In the final part, we generate signals of different levels of signal-to-noise ratio with the Rayleigh channel. Moreover, we identify the different levels of SNR using the CNN model. We study SNR levels with different dataset sizes, time window sizes, and LTE signal. Numerical results in all three parts demonstrate its good performance in estimating the Rician K-factors, Nakagami-m parameters, and different SNR levels.