# Power Optimization in Multi-Tier Heterogeneous Networks Using Genetic Algorithm
## 2 Related Work  
Wireless communication networks are bound to have a robust, reliable, and efficient spectrum power management mechanism. Similarly, 5G communication is no exception. All expected enhanced QoS parameters for 5G, such as higher bandwidth and lower power consumption, depend on improved energy efficiency (EE) and spectrum efficiency (SE) techniques. In the recent past, several research efforts have been made to propose optimum techniques and frameworks for power minimization in multi-tier 5G heterogeneous networks. The study in [14] suggested a hybrid model containing both on-grid and off-grid small cell base stations (SBS) in a heterogeneous network. Energy optimization has been achieved by controlling the on- or off-grid ratio. Similarly, the authors in [15] proposed an improved wakeup/sleep technique in D2D communication to counter the coverage gaps caused by closely located BSs. They suggested a joint implementation of fuzzy control and Q-learning to achieve a fuzzy interference system in a dynamic environment with varying BS availability. Likewise, the authors in [16] suggested the utilization of dynamic time-division duplexing and full-duplexing for optimum adaptive transmissions according to network services and traffic matching. Moreover, the authors in [17] provided a detailed analysis and discussion of the limitations and various available solutions to accommodate resource-constrained devices in 5G networks. It highlighted several requirements for spectrum control to provide an optimum power management solution for a 5G multi-tier heterogeneous network. The proliferation of wireless networks has resulted in increased interference among them, making the use of interference mitigation techniques necessary. A statistical framework for throughput analysis [18] has been proposed with successive interference cancellation to mitigate interference in 5G networks. Similarly, the study [19] highlighted the challenges associated with the expected tremendous expansion of smart infrastructures related to traffic chocking the bandwidthin next-generation mobile communication. They proposed a joint resource optimization approach by combining D2D communication with the 5G edge network architecture. The computations performed through modular approaches in this study involved local nodes, edge nodes, and fog nodes linked to a joint energy consumption model, latency model, and task scheduling model. The proposed solution was evaluated through a simulated environment of 5G mobile networks. The study in [20] highlighted the challenges related to spectrum shortage due to the massive deployment of Internet-enabled devices and services linked to Big Data. They suggested a time-switching method enabled by a power-splitting technique for minimum extra power transmission and optimum resource utilization. The study dealt with the joint optimization through two separate approaches, including rate control according to the transmissions from user end devices and maximizing the device transmissions under the spectrum-constrained condition of 5G. The simulation results of the proposed solution indicated substantial enhancement in power optimization. The authors in [21] presented a solution for the optimum control of the data rate and transmissions from IoT devices. The solution contained three distinct approaches, a fixed data rate, a fixed power level, and the optimal combination of both. The solution was evaluated through simulations to establish the significance of the energy savings in the overall communication. The authors in [4] exploited SIC to enhance the network performance through improved throughput, success probability, outage probability, coverage, and ergodic probability. The study indicated the high potential of SIC techniques for the control and optimization of spectrum use. Therefore, as an extension of the above-mentioned study, this paper presents an approach for utilizing the SIC technique for power optimization in a multi-tier heterogeneous network.