Resource Allocation in 5G NR Networks via Hybrid Lyrebird-Red Panda Optimization Model

Radio resource management in a dynamic 5G HWAN, which includes several RATs like LTE and 5G NR, is examined in this work. A unique resource allocation strategy for 5G NR networks is proposed in this paper to overcome the practical drawbacks of centralized systems, such as signaling overhead and computing complexity. The challenge of resource allocation is tackled by framing it as a stochastic optimization dilemma. Addressing this challenge involves optimizing resource allocation using a novel hybrid optimization algorithm termed CPF-RPLO. It adopts a chaotic probability factor for decision-making in the CPF-RPLO that allows for adaptability in choosing between modified RPO and modified LOA. Each population in CPF-RPLO either chooses modified RPO or modified LOA for optimal resource allocation. This optimization process considers parameters such as path loss and throughput. To achieve the goal of maximizing average throughput utility while maintaining network stability, restrictions on these characteristics are imposed. The effectiveness of our proposed approach is demonstrated by theoretical analysis and simulation results, taking into account assumptions about network stability.