Channel Modeling using Deep Neural Network with RIS-powered Wireless Communication Systems

Abstract

This work deals with Reconfigurable Intelligent Surfaces or (also known as Intelligent Reflecting Surfaces) which provide an improvement of performance of wireless communications by utilizing software-controlled meta-surfaces for the reflected signals from the source to destination, especially when the direct path is weak and hence improving the antenna array.

The performance evaluation is done by one of the techniques that utilizes the very high rates and/or large meta-surfaces to outperform the classic method of decode and forward, both in terms of the total transmit power and the energy efficiency. The channel measurements used by classifying them as a function of the frequency band, and usage of deployment scenarios such as indoor/outdoor, and system configuration. So, the 5G and beyond which is 6G use several antennas including the algorithms to make use of signal processing. This improves the antenna array technology.

In the next steps, we work on machine learning-based performance prediction using a deep neural network (DNN) to evaluate the performance of the RIS-aided system in the low-frequency range. This would provide a greater influence of scatterers with a weaker signal attenuation allowing the neural network to pick up a larger number of features, e.g. accurately predicting the energy efficiency (EE), and outage probability (OP).