Consortium Blockchain-Based Spectrum Trading for Network Slicing in 5G RAN: A Multi-Agent Deep Reinforcement Learning Approach

Abstract

Network slicing (NS) is envisioned as an emerging paradigm for accommodating different virtual networks on a common physical infrastructure. Considering the integration of blockchain and NS, a secure decentralized spectrum trading platform can be established for autonomous radio access network (RAN) slicing. Moreover, the realization of proper incentive mechanisms for fair spectrum trading is crucial for effective RAN slicing. This paper proposes a novel hierarchical framework for blockchain-empowered spectrum trading for NS in RAN. Specifically, we deploy a consortium blockchain platform for spectrum trading among spectrum providers and buyers for slice creation, and autonomous slice adjustment. For slice creation, the spectrum providers are infrastructure providers (InPs) and buyers are mobile virtual network operators (MVNOs). Then, underloaded MVNOs with extra spectrum to spare, trade with overloaded MVNOs, for slice spectrum adjustment. For proper incentive maximization, we propose a three-stage Stackelberg game framework among InPs, seller MVNOs, and buyer MVNOs, for joint optimal pricing and demand prediction strategies. Then, a multi-agent deep reinforcement learning (MADRL) method is designed to achieve a Stackelberg equilibrium (SE). Security assessment and extensive simulation results confirm the security and efficacy of our proposed method in terms of players’ utility maximization and fairness, compared with other baselines.