Blockchain Enabled Federated Slicing for 5G Networks with AI Accelerated Optimization

【Author】 Hu, Qiwei; Wang, Wei; Bai, Xiang; Jin, Shi; Jiang, Tao

【Source】IEEE NETWORK

【影响因子】10.294

【Abstract】Recent years have witnessed a blooming of new applications that demand different network services. Network slicing is advocated by the research community to simultaneously support multiple services on a common physical infrastructure. Federated network slicing, which involves multiple operators, further generalizes the concept to cover a broader range. Existing federated slicing systems advocate the master-slave architecture among untrusted operators, which brings some centralization concern, making operators hesitate to join the system. Recently, blockchain shows great power to build trust in decentralized environments. Besides, artificial intelligence (Ai), especially reinforcement learning, is envisioned with the potential to develop more efficient optimization algorithms. Motivated by innovations in blockchain, smart contract, and Ai, this article proposes a decentralized federated slicing architecture that is trustful and efficient. We systematically discuss the design principles and key challenges in realizing the blockchain-enabled architecture. With these principles and challenges in mind, we develop a general architecture for multiple operators and cloud providers, with a new proof of business consensus protocol to ensure incentive and fairness. To further enhance its efficiency, we utilize reinforcement learning to accelerate optimizations in the resource allocation. Benefits of the Ai accelerated optimizer are demonstrated in simulations.

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【发表时间】2020 NOV-DEC

【收录时间】2022-01-02

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