Resource allocation and virtual function placement in disaggregated radio access networks

• Gabriel Matheus Almeida UFG
• Leizer de Lima Pinto UFG
• Kleber Vieira Cardoso UFG

DOI: https://doi.org/10.5753/ctd.2024.2326

Abstract

The optimization of virtualized radio functions positioning in the radio access network is essential to ensure efficient resource management, being a relevant research topic in 5G and Post-5G. This study presents three approaches to solve this problem in the network planning scenario and two approaches in the network operational scenario. It includes a Mixed-Integer Linear Programming (MILP) model as well as methods based on artificial intelligence, machine learning, and meta-heuristics. Exact approaches serve as optimal upper bounds, albeit with low scalability, while non-exact methods are more efficient and can provide high-quality solutions. Deep reinforcement learning stands out for its fast convergence and generalization capability, while the genetic algorithm demonstrates more efficient processing time in the experiments conducted. However, the complexity of the problem increases considerably in the operational scenario with dynamic demand in the network varying over time.

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