Deep Neural Network with RIS-Powered Wireless Communication Systems for Channel Modeling

Deep Neural Network with RIS-Powered Wireless Communication Systems for Channel Modeling

연구 분야: Networking

학회: International Conference on Pattern Recognition

This research work discusses the Deep Neural Network (DNN)-based usage to calculate the Achievable rate and the Outage probability. The channel prediction is obtained by machine learning-based performance estimation. We 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). The RIS or Reconfigurable Intelligent Surfaces or (also known as Intelligent Reflecting Surfaces) which will 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 a weak and hence improving the antenna array technology for 5G and 6G. One must note that LIS (large Intelligent Surfaces) is another name for RIS. The main methodology is to design a method to evaluate the performance of the digital wireless communication system. Hence, it will accurately predict the Outage probability by increasing the number of reflecting elements for higher energy efficiency.