A Traffic Load Balancing Framework for Software-Defined Radio Access Networks Powered by Hybrid Energy Sources PROJECT TITLE :A Traffic Load Balancing Framework for Software-Defined Radio Access Networks Powered by Hybrid Energy SourcesABSTRACT:Dramatic mobile data traffic growth has spurred a dense deployment of small cell base stations (SCBSs). Tiny cells enhance the spectrum potency and so enlarge the capacity of mobile networks. Although SCBSs consume abundant less power than macro BSs (MBSs) do, the general power consumption of a large number of SCBSs is phenomenal. Because the energy harvesting technology advances, base stations (BSs) can be powered by inexperienced energy to alleviate the on-grid power consumption. For mobile networks with high BS density, traffic load balancing is essential in order to use the capacity of SCBSs. To completely utilize harvested energy, it is fascinating to incorporate the green energy utilization as a performance metric in traffic load balancing methods. During this paper, we have proposed a traffic load balancing framework that strives a balance between network utilities, e.g., the average traffic delivery latency, and therefore the green energy utilization. Various properties of the proposed framework have been derived. Leveraging the software-outlined radio access network design, the proposed scheme is implemented as a nearly distributed algorithm, which significantly reduces the Communication overheads between users and BSs. The simulation results show that the proposed traffic load balancing framework permits an adjustable trade-off between the on-grid power consumption and the typical traffic delivery latency, and saves a considerable quantity of on-grid power, e.g., thirtypercent, at a price of only a tiny increase, e.g., 8p.c, of the common traffic delivery latency. Did you like this research project? To get this research project Guidelines, Training and Code... Click Here facebook twitter google+ linkedin stumble pinterest Recommender System and Web 2.0 Tools to Enhance a Blended Learning Model