Intelligent Energy and Traffic Coordination for Green Cellular Networks With Hybrid Energy Supply
Abstract: In energy-harvesting-enabled enabled networks, the intermittent and randomly distributed renewable energy imposes severe challenges in reliably supplying the time-varying varying mobile traffic. To tackle this issue, we reshape the spatial renewable energy and mobile traffic fic by exploiting the approach of energy sharing and load shifting, with the objective of minimizing the grid energy expenditure of cellular networks powered by both grid and renewable energy. We formulate this problem as a mixed-integer integer nonlinear programm programming, ing, which is proved to be NPNP hard. For centralized networks, we first devise a cost cost-efficient efficient centralized algorithm leveraging the univariate search technique, which can find the nearnear optimal solutions with the advantages of low complexity and fast convergence. conver Specifically, by jointly optimizing the spatial distribution of renewable energy and mobile traffic, the centralized algorithm achieves a good match between the renewable energy supply and the total energy demand at each base station (BS), such that the grid energy expenditure of the whole network is greatly reduced. For distributed networks, we further propose a three three-phase phase distributed control policy in which BSs and mobile users adjust their strategies independently only with their local informatio information. n. Finally, we present extensive simulations to investigate the convergence and effectiveness of our proposed algorithms and