Energy Efficiency Maximization in Green Energy Aided Heterogeneous Cloud Radio Access Networks

Abstract

As the number of cellular and multimedia users are increasing the current mobile networks are being overloaded and need to be upgraded to new architectural featured 5G networks. Heterogeneous Cloud Radio Access Networks (HCRAN) are one of the dominant candidates for future networks with high data rate, minimized interference and high Energy Efficiency (EE). Due to dense users and base stations placement, the power consumption of H-CRAN is much higher than today's cellular networks. Energy harvesting (EH) is the solution to mitigate the grid power consumption problem in which power is harvested from natural resources like wind, solar, etc. EE of the system can be improved using energy harvesting and efficient resource allocation. In the presented article EE of H-CRAN with energy harvesting aided radio remote heads is explored. Formulated system problem is a mixed-integer non-linear programming (MINLP) problem which has the objective to maximize the H-CRAN system's EE. To optimize the proposed optimization problem Mesh Adaptive Direct Search (MADS) algorithm is explored. EE of H-CRAN system is maximized by resource allocation and power allocation which is efficient in terms of energy consumption. Our results show the objective is achieved with the help of low complexity algorithm and lower consumption of grid energy.