Distributed Optimization for SWIPT-Enabled Hybrid-Powered Multicell Communication Networks With Energy Trading

This paper investigates a simultaneous wireless information and power transfer-enabled hybrid-powered multicell communication network with a nonlinear energy harvesting model. In this multicell environment, information interaction and energy trading are carried out among base stations (BSs), and each BS powered by hybrid sources simultaneously provides information/energy to its user equipments (UEs) over the downlinks. A novel global utility function is proposed by comprehensively considering the incomes from information and energy transmission, energy trading, and the costs from the electricity companies. To maximize this goal, a nonconvex problem that jointly optimizing energy procurement, power allocation, and power splitting ratios is formulated to deal with the imbalance between energy supply and demand at BSs. Considering the nonconvexity of the problem and the strong coupling among the optimization variables, the formulated problem is difficult to solve directly with conventional convex optimization methods. To overcome these obstacles, a two-step solution combining alternating optimization, distributed optimization, and successive convex approximation is designed, in which the BS layer scheme and the UE layer scheme are performed alternately. Different from the existing centralized schemes, the proposed distributed scheme makes local optimal decisions independently at each BS only resorting to the information of neighbor BSs, which brings great advantages in reducing signaling and computational overheads. Moreover, the convergence and advantages of the proposed distributed scheme are verified by rigorous analyses and simulations.