NeuroRIS: Neuromorphic-Inspired Metasurfaces

Conventional reconfigurable intelligent surfaces (RIS) are controlled through high-latency field programmable gate array or micro-controller circuits usually implementing artificial neural networks (ANNs) for tuning the RIS phase array that exhibit very high energy requirements. Most importantly, conventional RIS are unable to function under realistic scenarios, i.e, high-mobility/low-end user equipment (UE). In this letter, we benefit from the advanced computing power of neuromorphic processors and design a new type of RIS named NeuroRIS, to supporting high mobility UEs through real time adaptation to the ever-changing wireless channel conditions. To this end, the neuromorphic processing unit tunes all the RIS meta-elements in the orders of ns for particular switching circuits, e.g., varactors while exhibiting significantly low energy requirements since it is based on event-driven processing through spiking neural networks for accurate and efficient phase-shift vector design. Simulations show that the NeuroRIS achieves very close rate performance to a conventional RIS-based on ANNs, while requiring significantly reduced energy consumption.