Abstract:
During the last decade, there has been remarkable development in cellular networks market due to the ubiquitous availability of internet access in worldwide. The number of users and corresponding cellular traffic has escalated astronomically. To cope with the tremendous growth of data demand across the globe, cellular networks are deploying an increasing number of base stations (BSs) which leads to a voluminous inflation in energy consumption. Cloud radio access network (C-RAN) is new born mobile network architecture has the potential to reduce the power consumption compared to the traditional RAN network architecture. But, network densification in C-RAN places an extensive burden on the electric grid system. Concerns about global warming and increasing number of base stations (BSs) leading to rising energy consumption have prompted extensive research effort focusing on energy efficiency (EE) issue for cellular networks. The integration of renewable energy harvesting (REH) technology is expected to be pervasively utilized by telecom operators aiming to reduce carbon foot-prints and gird energy consumption. However, the dynamic nature of RE generation could lead to energy outage and service quality deterioration. Thus utilization of commercial grid supply in conjunction with RE generators is a more realistic option for sustainable network operations.
In this thesis we propose hybrid powered C-RAN architectures and required energy usage algorithms to enhance EE. Each RRH is equipped with renewable energy generators, such as solar panel along with a set of batteries as an energy storage device and also connected to grid energy supply. Afterward, dynamic user association policies are integrated with the proposed model for further improving EE. The prime goal is to quantify the EE of various user association schemes, namely distance-based, SINR-based, green energy availability-based and traffic aware-based under the proposed network model. An extensive simulation-based study is carried out for evaluating the EE performance of the proposed framework varying different system parameters. Numerical results validate the proposed network models compared to other counterparts.
