Resource allocation method for energy efficiency calculation by wireless charging edge computing network

Abstract

The invention discloses a resource allocation method for energy efficiency calculation by a wireless charging edge computing network, mainly solving the problem that the calculated energy efficiency cannot be guaranteed maximized in the prior art. The method comprises the steps of setting a power allocation parameter; initializing the power allocation parameter; calculating optimal local calculation frequency, optimal user sending power and optimal calculation loading time; calculating Lagrange multiplier; calculating iteration optimal local calculation frequency, iteration optimal user sending power, iteration optimal calculation loading time and iteration optimal energy collection time; calculating a Lagrange multiplier iteration error; determining whether the Lagrange multiplier meets atermination condition; calculating system calculated energy efficiency; determining whether the system calculated energy efficiency meets a termination condition; and obtaining system maximized calculated energy efficiency and corresponding optimal local calculation frequency, optimal user sending power, optimal calculation loading time and optimal energy collection time.

Description

technical field [0001] The present invention belongs to the field of wireless communication technology, and further relates to an optimal resource allocation method in the field of wireless charging edge computing network with calculation energy efficiency as the optimization goal. According to the user's energy consumption constraints, the user's transmission data volume constraints, and the energy collection time and calculation loading time constraints, the present invention can obtain the optimal resource allocation for maximum computing energy efficiency under the nonlinear energy collection model. Background technique [0002] With the rapid development of the Internet of Things business and the rapid growth of new broadband services, the remote computing task loading method based on cloud computing technology will face severe challenges due to long communication delays and high operating costs. In addition, there are a large number of mobile devices with limited energ...

AI Technical Summary

Problems solved by technology

The disadvantage of this resource allocation method is that it is only applicable to traditional edge computing networks and cannot be applied to actual wireless charging edge computing networks.

The disadvantage of this resource allocation method is that the proposed strategy is based on the linear energy harvesting model

However, due to the nonlinear end-to-end energy conversion in the actual energy harvesting circuit, the collected energy presents a more complex nonlinear form

Therefore, the resource allocation strategy proposed in the literature cannot be applied to the wireless charging edge computing network under the nonlinear energy harvesting model

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