Meta-Heuristic Optimization Algorithms for Resource Allocation in 5G New Radio Networks

The objective of this research paper is to evaluate effectiveness of various resource allocation algorithms currently used in 5G new radio networks. Due to these complications, the network is experiencing operational difficulties. Incorporating the development trend of 5G into efficient resource management is not only imperative but also requires hardware requirements and improvements to the current network architecture. In order to effectively tackle issue of resource allocation (RA) in a 5G network, primary purpose is to present a proposed scheme for RA that employs learning-based as well as optimization resource allocation methodologies. To ensure effective management of network traffic and operations, resource allocation has emerged as a problematic issue due to the concomitant increase in cellular service demand and the constrained resources at our disposal to provide it. In order to attain the desired level of quality of service (QoS), one of the most critical issues that must be resolved is the reduction of interference activity within the network. This study investigates the subject of resource allocation and optimization and the inspiration for the hunting behavior of meta-heuristic algorithms. This paper evaluates the current 5G NR network resource allocation technique. We formulate the issue of resource allocation as a stochastic optimization problem. Furthermore, throughput and path loss, SNR, and SINR are considered when performing this optimization. The comparison study shows that COA performs best in SNR optimization and FMNS in SINR optimization in resource allocation. Lower standard deviations suggest stability in algorithms like KOA. For effective wireless communication system resource management, the best method relies on network criteria such signal quality and consistency.