Optimization of 5G RAN network slicing based on auction models

We propose a novel two-level hierarchical auction model for 5G RAN network slicing that enables financially-aware resource allocation among Mobile Network Operators (MNOs), Mobile Virtual Network Operators (MVNOs), and end users. Compared to prior models that primarily emphasize technical resource utilization, our approach integrates a realistic financial cost framework, including electricity consumption and resale bidding fees into the optimization process. Each level solves a Winner Determination Problem (WDP) using Integer Linear Programming (ILP) and scalable heuristic algorithms. Vickrey–Clarke–Groves (VCG)-based pricing is adopted to ensure incentive compatibility and procedural fairness among bidders. The optimization objective is to maximize the social welfare, defined in this work as the total valuation of accepted requests minus the electricity cost incurred by CU–DU placement. Power consumption is therefore internalized as a cost component in the objective function rather than treated as an independent optimization target. Through extensive simulations over different network topologies and dynamic traffic conditions, we show that the proposed approach achieves higher social welfare compared to baseline methods, while maintaining scalability and satisfying the latency and capacity constraints of heterogeneous network slices.