# Algorithms for Load Balancing in Next-Generation Mobile Networks: A Systematic Literature Review
## 4 Results
### Semi-Automatic
Improving Load Balancing Techniques [34]: In this paper, several indoor positioning and optimization methods in femtocell networks were discussed, focusing on improving user experience, load balancing and network performance. It introduced an algorithm that prioritizes voice traffic, adjusts cell transmit power and optimizes femtocell coverage areas based on user location and received signal strength. The effectiveness of these algorithms in terms of improving network performance and user satisfaction was also evaluated through field tests and simulations. A Novel, Efficient, Green and Real-Time Load Balancing Algorithm for 5G Network [35]: This study proposed a novel load balancing algorithm, called LPWLC (Load Prediction Weighted Least Connection), designed for 5G network measurement report-collecting clusters. The LPWLC algorithm leverages historical load data to predict server load and dynamically adjust server weights, aiming to enhance efficiency and scalability and reduce costs and power consumption compared to existing methods. Experimental results demonstrated significant improvements in transmission efficiency, load balancing and energy consumption. The algorithm was further evaluated in a simulation environment, showing reduced response times and power consumption savings. User-Driven Context-Aware Network Controlled RAT Selection for 5G Networks [36]: The main objective of this paper was to address the challenges posed by the increasing number of devices and data traffic in 5G networks. It focused on optimizing network performance through a context-aware RAT selection mechanism that integrates Wi-Fi with cellular networks, ensuring seamless handovers between different access technologies. The COmpAs5 algorithm focuses on context acquisition, performance evaluation and adherence to 3 GPP guidelines. It considers user preferences, triggers based on events and automatically adapts parameters based on network conditions to optimize QoS. The results indicated that COmpAs5 outperformed the baseline algorithm in terms of throughput and delay, showcasing its effectiveness in optimizing RAT selection and enhancing network performance. A New Fuzzy-Based Method for Energy-Aware Resource Allocation in Vehicular Cloud Computing [37]: The primary objective of this paper was to introduce a fuzzy-based approach for energy-conscious resource allocation in vehicular cloud computing, employing a nature-inspired algorithm. In particular, a fuzzy-based cuckoo search algorithm was employed for virtual cloud resource allocation, and its performance was compared against genetic algorithms, ant colony optimization and standard cuckoo search algorithms. The results indicated that the proposed method outperformed the other algorithms in terms of execution time, delay and makespan while exhibiting superior energy efficiency, SLA adherence and an average number of powered-off virtual machines. Application Association and Load Balancing to Enhance Energy Efficiency in Heterogeneous Wireless Networks [38]: The main objective of this paper was to enhance energy efficiency in heterogeneous wireless networks through the proposal of a game-based analysis for applications and network characteristics. The study focused on load balancing, resource allocation and network selection based on application requirements in order to achieve energy efficiency and fairness in resource allocation. The algorithms used in the study included a weight-based rotation game algorithm for load balancing and BLNF and TBNF algorithms for load balancing in heterogeneous wireless networks. The proposed algorithms outperformed existing methods in terms of power consumption, with the TBNF algorithm showing significant improvements in reducing power consumption for transmission- and intermittent-bound applications.