Double Reconfigurable Intelligent Surface-aided Green Internet of Things Edge Computing for Research on Computation Capacity

In order to solve the micro battery energy problem of terminal Internet of Things user devices in computation-intensive applications, green renewable energy harvesting technology and double reconfigurable intelligent surfaces(RIS) technology enabling edge computing were studied and a green Internet of Things edge computing system assisted by double-RIS was constructed, effectively extending the computing life of terminal Internet of Things user devices and improving system computation capacity. Firstly, a multi-user cascade fading channel model assisted by double-RIS was established, and a multi-slot random arrival model of green renewable energy harvesting was established to model the causal constraints of energy supply and demand of Internet of Things terminal devices. Secondly, based on the maximization of system computation capacity, the joint optimization design problem of terminal local computing rate, edge computing offloading power and phase shift of RISs was modeled. This design problem belongs to a class of complex non-convex optimization problem. To this end, lightweight multi-stage optimization technology was adopted to rapidly and iteratively design variables such as local computation, computation offloading and phase shift of RISs, etc, to complete the design of green Internet of Things edge computing system. The experimental results show that the performance gains of the proposed scheme are better than the existing benchmark schemes, and the proposed scheme is equivalent to the scheme based on semidefinite relaxation algorithm under less system computing time.