Energy Efficiency Maximization for Hybrid-Powered 5G Networks with Energy Cooperation

Abstract

The extensive deployment of 5G cellular networks causes increased energy consumption and interference in systems, and to address this problem, this paper investigates the optimization problem of joint energy harvesting and energy cooperation to maximize energy efficiency (EE). First, considering user equipment (UE) quality of service (QoS) constraints, cellular base station power constraints, and renewable energy harvesting constraints, we construct a mixed-integer nonlinear programming problem for joint resource allocation. This problem is difficult to solve directly, thus we combine the fixed-variable method to solve the complex original problem in three less difficult subproblems of user association, power allocation, and energy cooperation by solving them separately using Lagrangian method, improved particle swarm optimization algorithm, and matching theory, respectively. Finally, the final solution to the original problem is obtained by combining the above three algorithms through convergent iterative algorithms. The simulation results show that the joint algorithm proposed in this paper has a better performance in throughput and energy efficiency compared with the comparison algorithms.