Blockchain-Assisted Trustworthy Resource Management for AI-Driven 5G Network Slicing Architectures
Keywords:
5G network slicing, blockchain, artificial intelligence, resource management, trust, smart contracts, deep reinforcement learning, governanceAbstract
Fifth-generation mobile networks are engineered around the concept of network slicing, which enables multiple logical networks to coexist on a shared physical infrastructure, each tailored to distinct service requirements. The dynamic complexity of resource allocation across slices has motivated the integration of artificial intelligence techniques, particularly deep reinforcement learning, to automate and optimize decision-making in real time. However, the opacity of AI-driven control loops and the multi-stakeholder nature of modern network ecosystems introduce significant trust deficits, including concerns about fairness, verifiability, and adversarial robustness. This paper presents a system-level analysis of how blockchain technology can be harnessed to establish a trustworthy resource management framework for AI-driven 5G network slicing architectures. We examine the structural trade-offs inherent in combining distributed ledger mechanisms with machine learning pipelines at the orchestration layer, focusing on governance models, auditability of slice lifecycle events, and decentralized enforcement of service level agreements. The discussion covers the integration of smart contracts for automated compliance, the role of consensus protocols in maintaining resource state integrity, and the challenges of scaling blockchain components to meet carrier-grade transaction volumes. Architectural considerations are explored through the lens of infrastructure sustainability, cross-domain federation, and long-term policy evolution. The paper argues that blockchain-assisted architectures, when carefully designed to complement rather than duplicate AI functions, can provide a verifiable trust substrate that aligns economic incentives, mitigates resource contention inequities, and strengthens the resilience of next-generation network slicing platforms.
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This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
