CARRAS: Combined AOA and RBFNN for Resource Allocation in Single Site 5G Network

5G and future networks must manage flows with varying Quality of Service (QoS) requirements, even under unpredictable traffic conditions. As user requirements for network capacity evolve over time, it is crucial to allocate resources appropriately to maximize the efficiency of their application. Consequently, these demands are driving the creation of new resource management policies, as conventional methods are no longer sufficient to meet them effectively. Thus, we propose a framework for resource allocation at the radio access network (RAN) level, while taking into consideration throughput and delay probability. To solve the formulated problem and to make it more tractable, the archimedes optimization algorithm (AOA) combined with an artificial neural network (ANN) is introduced to explore the search space and find optimal solutions. Nevertheless, in a 5G environment, the interactions between users, services, and network resources are inherently complex and non-linear. To this end, the radial basis function (RBF) is then used to predict user needs and reallocate resources according to expected results. The simulation results show that the proposed approach has a significant advantage over traditional approaches such as Particle Swarm Optimization (PSO). To the best of our knowledge, this paper is the first attempt to study 5G resource allocation using a combination of AOA and RBFNN algorithms and adequately describes the approach.