# Power Optimization in Multi-Tier Heterogeneous Networks Using Genetic Algorithm
## 2 Related Work
### Power Optimization in Wireless Networks  
In 5G technology, a major objective is to enhance the energy efficiency (EE) and spectrum efficiency (SE) to meet the increasing demand for high-quality service (QoS) while utilizing energy and spectrum resources more efficiently to achieve higher data rates. To examine the challenges related to spectrum efficiency and energy efficiency in 5G networks, a fuzzy-based approach [24] was implemented, aided by a look-up table. This approach achieved an efficient balanced trade-off between the two, thereby improving the system's overall performance. To fulfill the demands of increased capacity, faster data rates, and better quality of service in next-generation networks, the adoption of energy-efficient architectures is necessary [25]. Additionally, reducing the power consumption in wireless networks is important with respect to environmental and social responsibility, as reducing the carbon footprint is a pressing issue. Thus, green communication has become a critical need.  
In situations where congestion is not complete, the power optimization for low interference and throughput enhancement (POLITE) approach can significantly reduce the transmission power of active cells. Furthermore, the design of POLITE allows compatibility with other MAC components, such as scheduling and beam selection techniques, making its implementation in real-world systems both practical and possible. The experiment showcased the impact of various traffic loads managed by base link adaptation (BLA) and POLITE [26], emphasizing the latter's benefits in terms of reduced power consumption, decreased interference, and enhanced capacity in densely populated cells.  
The rapid growth of connected devices and mobile terminals in IoT networks is putting a significant strain on energy consumption, leading to concerns about energy sustainability. To address this challenge, a dynamic energy management model was presented in the paper [27] that aimed to improve the energy efficiency. Two new user scheduling strategies combined with power optimization resulted in enhanced network performance. The network efficiency was evaluated for a number of APs and by examining the performance of various precoding techniques. The study compared the results of various power allocation techniques, and it was proposed that the APs can utilize these algorithms to regulate the power distribution to user nodes.  
Cooperative relay (CR) is a cost-effective solution for improving spectral efficiency and expanding cell coverage. However, its widespread use can result in high power consumption due to the large number of relay nodes (RNs) used in the network. Furthermore, full channel state information (CSI) is not always available in dynamic environments, leading to increased overhead delay and power consumption if the RNs cannot quickly adjust to the channel changes. To tackle these challenges, the modified genetic algorithm (modified GA) described in [28] integrated the probability-based selection rules and fitness evaluations. The performance was measured using the symbol error rate (SER), network capacity, energy consumption, and power improvement metrics.