A Digital Twin Based Reconfigurable Intelligent Surface Phase Adaptation Using Spiking Reinforcement Learning Policy Optimization

This demo presents a digital twin of a reconfigurable intelligent surface-empowered wireless system that employs spiking reinforcement learning (SRL) optimization policy for phase adaptation in order to maximize the network coverage, while minimizing the energy consumption at both the microcontroller and transmission related processes. The demo assesses the efficiency of SRL against conventional deep reinforcement learning approaches in terms of (i) energy consumption, (ii) reduction of training latency, (iii) probability of outage, and (iv) bit error rate.