The 5G (fifth generation) network technology aimed to enhance Quality of Service (QoS). Massive multiple-input multiple-output (MIMO) enable the strong 5G experience. In order to deliver the high performance and uniform services to end user the optimization plays an essential role. In this paper, the role of optimization in power allocation of massive MIMO systems has been investigated. Therefore, first an understanding about the massive MIMO and resource allocation has been given, next the need of user scheduling and optimization criteria has been discussed. Further, the paper includes a review of the recent contributions in field of massive MIMO based power allocation problems. During this investigation it is observed that traditionally this problem has been considered as optimization problem and solved by using the improvement on pre-coding and optimization algorithms. On the other hand, recent approaches are recommending the utilization of Deep Learning technology to efficiently and optimally solve the optimization problem. Therefore, the power allocation problem of the massive MIMO has been formulated for solving it with the use of Deep Neural Network. Finally, a complete system architecture has been discussed which will utilized to improve the energy efficiency of massive MIMO operations. Additionally, the conclusion has been made and future extension plan of study has been discussed.

Resource Scheduling, Energy Efficiency, Power Allocation, Massive MIMO, Optimization, Deep Learning.