## 1 Introduction
In [35], the authors develop a robust mixed integer optimization model for minimizing the power consumption of network function virtualization (NFV) infrastructure with uncertain resource demands by the virtual network functions (VNFs). Since it takes a very long time to solve the robust optimization problem using an exact solver, the authors develop a fast three-phase heuristic, i.e., green and robust VNFs placement. In [36], the authors propose novel optimization models to minimize the energy consumption of computing and network infrastructure of 5G networks by using both exact and heuristic methods. The authors show that the proposed heuristic converges very fast, compared to the exact solver. In [37], the authors design and implement a video streaming service using mobile edge computing functionalities in 5G networks based on virtual functions. In [38], the authors design an efficient PSO-based algorithm to deliver a high definition video to the users in 5G networks based on RFBs, with the objective of either the maximization of user throughput or the minimization of the number of used 5G nodes. Wireless network design is considered as one of the important topics of wireless network areas and readers are referred to the works in [39; 40; 41; 42] for better understanding. In [39], optimization issues in telecommunication are covered extensively, and the work in [40] deals with optimization problems  
Figure 3: Separated data and control architecture. and models for the design of cellular networks. In [41], up-to-date challenges in the modern design of wireless networks are presented. In [42], an exhaustive introduction to the use of mathematical optimization models and algorithms for wireless network design is provided and also a hierarchy of design problems is discussed in detail. Works on the optimization models and algorithms for wireless network design have been covered extensively in [43; 44; 45; 46; 47; 48; 49; 50; 51; 52]. In [43], the authors investigate the use of accurate (non-finite precision) optimization solvers for linear and mixed-integer programs applied to signal-to-interference-based wireless coverage formulations. In [44], the authors present a mathematical formulation for wireless network design purely based on binary user-transmitter assignment variables to maximize network coverage. In [45], the authors propose a pure 0-1 formulation based on generalized upper bound cover inequalities for wireless network design for configuring a set of transmitters for coverage to a set of receivers based on a finite set of power values. In [46], the authors propose a genetic algorithm as a fast heuristic to solve large realistic instances of the power, frequency, and modulation assignment problem and show that the proposed genetic algorithm helps to improve the value of solutions found through a wider exploration of the power space. In [47], the authors propose a robust optimization model for tackling the uncertainty of wireless signal propagation in the design of fiber-based telecommunications access networks integrating wired and wireless technologies. Then, they propose a heuristic algorithm to find the solution to the proposed algorithm. In [48], the authors present a new model and solution for assigning users to mesh access points and for routing data within wireless mesh network to achieve max-min fair throughput by formulating a mixed-integer linear programming problem and propose a fast decomposition heuristic algorithm to solve the optimization problem. In [49], the authors propose effective ways to pursue interference minimization while maintaining the network coverage to improve cell capacity. In [50], the authors consider assignment of power level to access points and the assignment of user terminals to the opened access points in green wireless local area networks. Then they propose an effective branch-and-Benders-cut algorithm for power minimization in wireless networks.