PROJECT TITLE :

A Traffic Load Balancing Framework for Software-Defined Radio Access Networks Powered by Hybrid Energy Sources

ABSTRACT:

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


PROJECT TITLE : Prediction of Traffic Flow via Connnected Vehicles ABSTRACT: We propose a framework for short-term traffic flow prediction (STP) so that transportation authorities can take early actions to control flow and prevent
PROJECT TITLE : Graph Attention Spatial-Temporal Network with Collaborative Global-Local Learning for Citywide Mobile Traffic Prediction ABSTRACT: It is becoming increasingly important for proactive network service provisioning
PROJECT TITLE : Spatio-Temporal Meta Learning for Urban Traffic Prediction ABSTRACT: It is very difficult to predict urban traffic because of three factors: 1) the complex spatio-temporal correlations of urban traffic, which include
PROJECT TITLE : Learning Dynamics and Heterogeneity of Spatial-Temporal Graph Data for Traffic Forecasting ABSTRACT: It is essential to have accurate traffic forecasting in order to improve the safety, stability, and overall effectiveness
PROJECT TITLE : Foresee Urban Sparse Traffic Accidents: A Spatiotemporal Multi-Granularity Perspective ABSTRACT: Due to the rapid pace of urbanization, car accidents have evolved into a significant threat to both health and development.

Ready to Complete Your Academic MTech Project Work In Affordable Price ?

Project Enquiry