Today, the growing development of the wireless devices and the expansion of the telecommunication networks have made the supply of these systems a major challenge. Therefore, energy consumption is considered as a key issue for the design of the wireless networks. Due to the limited lifetime of the wireless devices and the high costs needed to replace the battery, energy harvesting by eliminating the need for battery replacement and reducing costs is a promissing technique in the fifth generation of the wireless telecommunication networks. Unlike energy harvesting from energy sources such as wind and sun, which are of a random nature, energy harvesting from dedicated radio frequency signals provides a reliable source of energy for wireless devices. One of the challenges of energy harvesting from the radio signals is the attenuation of these signals due to propagation through space increased by distance, and as a result, a kind of unfairness is created among network users. since energy efficiency is one of the most important issues in the fifth generation of telecommunication, it is important to optimize energy efficiency in these systems. In this study, in order to solve these challenges, the problem of maximizing the minimum energy efficiency of the users in a multi-input multi-output (MIMO) system is investigated. Thus, the optimal energy and information transmission covariance matrices, as well as, the optimal time allocation between the downlink and uplink phases. Since the optimization problem is non-convex, we deal with it by an iterrative alternating optimization (AO) algorithm. First, we consider energy covariance matrix design problem assuming the other parameters are fixed and show that the problem is convex. Then, by considering the transmit covariance matrices as the only optimization variables, the objective function of the problem transforms to a non-linear fractional form. Thus, we deal with this problem by using Dinkelbach algorithm. Finally in the third step, we solve the problem with respect to the allocation time. Then, by repeating these steps, we will achieve convergence. We also consider, the quality of service (QoS) for the users and solve the resulting problem. We then deal with the problem of maximizing the total energy efficiency of the system and compare it with the previous problem. Finally, the simulatin results show the effectiveness of the proposed method in enhancing the users energy efficiency and providing fairness in the network. Key Words : Energy Harvesting, Energy Efficiency, Fairness, Wireless power communication network