Energy consumption optimization for edge computing-supported cellular networks based on optimal transport theory

With advancements in mobile communication technologies, there have been multiple user device (UD) connections to cellular networks. Because of changes in the spatial distribution of UDs and application requirements, the traditional offloading mechanism based on the nearest distance will result in heavy loads for some base stations (BSs). Because there is a high-order relationship between the energy consumption of the BS processing tasks and the number of computing tasks, the traditional offloading mechanism will result in high energy consumption, and UD’s offloading decision must be dynamically adjusted. The offloading decision of UDs is optimized based on detailed information about various parameters associated with a network from the standpoint of distribution. This information includes details regarding the spatial distribution of UDs, application requirements, and the offloading period of computing tasks. Based on optimal transport theory, an energy consumption optimization algorithm is suggested to lower the total amount of energy consumed by the offloading process of UDs’ computing tasks by reasonably planning the offloading BSs of the UDs in the networks. The simulation results show that the proposed offloading mechanism based on energy consumption optimization reduces the total energy consumption of the offloading process by 28.09