Environment Twin Based Deep Learning Model Using Reconfigurable Holographic Surface for User Location Prediction

Reconfigurable Holographic Surface (RHS) is one of the meta material radiation elements which are integrated with transceivers to generate electromagnetic waves, empowering an ultrathin edifice. RHS exploits the meta material radiation elements to hypothesis a holographic strategy based on the holographic interference principle. Each component has electrical control over the radiation amplitude of the occurrence electromagnetic surfs to produce anticipated guiding beams. A digital twin is a representation of a physical object made from sensor data in the digital realm. A digital twin can combine intangible sensor data with physical object data, such as the shape or position of the real device, to create a final dynamic digital twin. Digital twin includes both stationary and active information. In this chapter, we present a novel digital-twin framework for RHS-assisted wireless networks which is called as Environment-Twin (Env-Twin). The objective of the Env-Twin framework is to empower mechanization of optimal control at various coarseness. Deep learning techniques such as Convolution Neural Network (CNN) and long short-term memory architecture (LSTM) are used to build our model and studied its performance and sturdiness. In this chapter, we also inspect the nascent for a digital twin deep learning model is used to find the reflection co-efficient of reconfigurable holographic surface for the receiver locations without the need for channel estimation and beamforming algorithms.