Blockchain-Enabled Resource Trading and Deep Reinforcement Learning-Based Autonomous RAN Slicing in 5G

Abstract

The advent of radio access network (RAN) slicing is envisioned as a new paradigm for accommodating different virtualized networks on a single infrastructure in 5G and beyond. Consequently, infrastructure providers (InPs) desire virtualized networks to share their subleased resources for effective resource management. Nonetheless, security and privacy challenges in the wireless network deter operators from collaborating with one another for resource trading. Lately, blockchain technology has received overwhelming attention for secure resource trading thanks to its security features. This paper proposes a novel hierarchical framework for blockchain-based resource trading among peer-to-peer (P2P) mobile virtual network operators (MVNOs), for autonomous resource slicing in 5G RAN. Specifically, a consortium blockchain network that supports hyperledger smart contract (SC) is deployed to set up secure resource trading among seller and buyer MVNOs. With the aim of designing a fair incentive mechanism, we model the pricing and demand problem of the seller and buyers as a two-stage Stackelberg game, where the seller MVNO is the leader and buyer MVNOs are followers. To achieve a Stackelberg equilibrium (SE) for the formulated game, a dueling deep Q-network (Dueling DQN) scheme is designed to achieve optimal pricing and demand policies for autonomous resource allocation at negotiation interval. Comprehensive simulation results analysis prove that the proposed scheme reduces double spending attacks by 12% in resource trading settings, and maximizes the utilities of players. The proposed scheme also outperforms deep Q-Network (DQN), Q-learning (QL) and greedy algorithm (GA), in terms of slice and system level satisfaction and resource utilization.