As an advanced technology, simultaneous wireless information and power transfer (SWIPT), combined with the internet of things (IoT) devices, can effectively extend the online cycle of the terminal. To cope with the fluctuation of energy harvesting by the hybrid access points (H-AP), the energy cooperation base station is introduced to realize the sharing of renewable energy. In this paper, we study the SWIPT-enabled IoT networks with cooperation. Our goal is to maximize the energy efficiency of the system, and at the same time, we need to meet the energy harvesting constraints, user quality of service (QoS) constraints and transmission power constraints. We jointly solve the power allocation, time switching and energy cooperation problems. Because this problem is a nonlinear programming problem, it is difficult to solve directly, so we use the alternating variable method, the iterative algorithm is used to solve the power allocation and time switching problem, and the matching algorithm is used to solve the energy cooperation problem. Simulation results show that the proposed algorithm has obvious advantages in energy efficiency performance compared with the comparison algorithm. At the same time, it is also proved that the introduction of energy cooperation technology can effectively reduce system energy consumption and improve system energy efficiency.

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.