Genetic Algorithm Optimization of the Link Layer for Throughput Improvement in 5G NR Networks

With the proliferation of the fifth generation (5G) wireless communications, adaptive resource allocation in various deployment scenarios remains a significant research topic. In this paper we propose a genetic algorithm (GA) based optimization of network-link layer parameters to improve the throughput in 5G New Radio (NR) networks. For achieving optimal system throughput while maintaining stringent quality of service (QoS) requirements for the block error rates (BLER) and signal-to-noise ratio (SNR), this work develops a mathematical model that incorporates the SNR, modulation and coding schemes (MCS), and hybrid automatic repeat request (HARQ) processes. This solution provides a robust foundation for the implementation of 5G NR networks in dynamic environments and arbitrary channel conditions. As a result, throughput of up to 240 Mbps is achieved. Multi-objective optimization, including energy efficiency and latency parameters, may be considered as future directions for exploration.