Research On Energy Consumption Cost Minimization Algorithm In Heterogeneous Cellular Networks With Hybrid Energy Supplies

With the widespread use of mobile devices,the ever increasing data demand has led to the significant increase of energy consumption in wireless cellular networks.To cope with the energy crisis,many researchers and engineers have proposed various solutions.Among them,heterogeneous network technology and renewable energy supply strategy have attracted wide public concerns.Based on the two advancements,we study how to allocate the network resources to achieve energy cost saving in heterogeneous cellular networks with hybrid energy supplies,while ensuring the user rate requirement and quality of service.In this paper,our proposed energy cost minimization algorithms involve both temporal and spatial dimensional resource optimization in basic scene and complex scene of green heterogeneous networks.First,for the spatial dimensional traffic load balancing problem in the basic scene,we propose two cell breathing algorithms,intelligent cell breathing and intelligent biasing based cell breathing algorithm.Second,for the temporal and spatial dimensional resource allocation problem in the basic scene,we first decompose the target problem into four sub-problems from the temporal and spatial domains,then propose four sub-algorithms each corresponding to solve one sub-problem: energy consumption estimation,green energy allocation,user association,and green energy reallocation algorithm.Among them,the user association algorithm includes centralized and distributed algorithms.Third,we consider a complex network scene,that is,the green heterogeneous cellular networks with wireless backhauls.Here,we divide the power consumption of each BS into two parts: wireless access networks and backhaul links.Owing to the diversities of mobile traffic and renewable energy,we propose four heuristic sub-algorithms to solve the problem of spatial and temporal dimensional resource allocation in this scenario.Finally,for the above three kinds of problems and corresponding solutions,we have performed the simulations to validate the feasibility of our proposed algorithms.Simulation results show that compared with peer algorithms,our proposed solutions can significantly reduce the total energy cost.