On minimizing energy consumption cost in green heterogeneous wireless networks.

The Internet of Things has been adopted as an emerging service for future wireless networks, which, however, introduces new challenges for transmission bandwidth and energy guarantees. In this paper, we study the problem of energy cost minimization in heterogenous wireless networks with hybrid energy supplies from the perspective of resource allocation. Owing to the temporal and spatial diversities of user traffic and renewable energy, we propose both centralized and distributed heuristic algorithms to obtain approximate solutions by iteratively addressing the following sub-problems: the total energy minimization problem, green energy allocation problem, user association problem, and green energy reallocation problem. At first, based on the temporal traffic statistics, we obtain estimated average energy consumption profiles for all base stations; Second, we allocate the green energy in the temporal domain for each base station to minimize its energy cost based on its estimated energy consumption profile; Third, in each slot, we perform spatial resource allocation and propose a centralized and a distributed user association algorithm, given the allocated green energy and practical user distribution in each slot. Fourth, after user association and data transmission, we readjust the temporal green energy allocation for each BS to further improve green energy utilization. Simulation results show that compared with two peer algorithms, our proposed solution can significantly reduce the total energy cost.