Stackelberg game-based dynamic resource trading for network slicing in 5G networks

Abstract

Network slicing (NS) has been envisioned as the main technology in 5G to accommodate different virtualized operators with versatile service requirements, on a common substrate infrastructure. NS has come to radically reform the interaction among different entities in the resource sharing market, where resource is exchanged for monetary gains as a way of ensuring efficient resource utilization. However, resource exchange is only attractive to entities if they are able to achieve their respective benefits in a win–win situation. In this paper, we design an economic model that analyzes the pricing and purchasing interaction between multiple mobile virtual network operators (MVNOs) and their respective users for resource trading in NS. We formulate the pricing and purchasing problem as a two-stage multi-leader multi-follower (MLMF) Stackelberg game where the MVNOs as leaders set their unit price first, and then users as followers respond by determining their purchasing volume. Then, we theoretically prove the existence and uniqueness of a Nash equilibrium (NE). To obtain an optimal dynamic pricing solution for the MVNOs, we transform the game-based optimization problem into a stochastic Markov decision process (MDP) problem and propose a method based on multi-agent dueling deep Q-Network (DQN) algorithm. Simulation results show that the proposed algorithm achieves convergence under competitive pricing scheme (CPS) and independent pricing scheme (IPS), while ensuring high MVNOs’ profit and users’ utility at acceptable levels. In terms of cumulative profits, the proposed dueling DQN-based pricing algorithm achieves percentage gains of 15.3%, 30.4%, and 71.4% against Q-learning-based pricing, premium pricing, and undercut pricing algorithms as benchmarks.