Wireless Network Virtualization by Leveraging Blockchain Technology and Machine Learning

Ashish Adhikari, D. Rawat, Min Song
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引用次数: 9

Abstract

Wireless Virtualization (WiVi) is emerging as a new paradigm to provide high speed communications and meet Quality-of-Service (QoS) requirements of users while reducing the deployment cost of wireless infrastructure for future wireless networks. In WiVi, Wireless Infrastructure Providers (WIPs) sublease their RF channels through slicing to Mobile Virtual Network Operators (MVNOs) based on their Service Level Agreements (SLAs) and the MVNOs independently provide wireless services to their end users. This paper investigates the wireless network virtualization by leveraging both Blockchain technology and machine learning to optimally allocate wireless resources. To eliminate double spending (aka over-committing) of WIPs' wireless resources such as RF channels, Blockchain - a distributed ledger - technology is used where a reputation is used to penalize WIPs with past double spending habit. The proposed reputation based approach helps to minimize extra delay caused by double spending attempts and Blockchain operations. To optimally predict the QoS requirements of MVNOs for their users, linear regression - a machine learning approach - is used that helps to minimize the latency introduced due to (multiple wrong) negotiations for SLAs. The performance evaluation of the proposed approach is carried out by using numerical results obtained from simulations. Results have shown that the joint Blockchain and machine learning based approach outperforms the other approaches.
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利用区块链技术和机器学习实现无线网络虚拟化
无线虚拟化(WiVi)作为一种提供高速通信和满足用户服务质量(QoS)要求的新范式正在兴起,同时为未来的无线网络降低无线基础设施的部署成本。在WiVi中,无线基础设施提供商(wip)根据其服务水平协议(sla)通过切片将其RF信道转租给移动虚拟网络运营商(mvno), mvno独立地向其最终用户提供无线服务。本文研究了利用区块链技术和机器学习来优化无线资源分配的无线网络虚拟化。为了消除wip无线资源(如RF信道)的双重支出(也称为过度承诺),使用区块链(一种分布式账本)技术,其中使用声誉来惩罚过去有双重支出习惯的wip。提出的基于声誉的方法有助于最大限度地减少由双重支出尝试和区块链操作造成的额外延迟。为了最优地预测mvno对其用户的QoS需求,使用线性回归(一种机器学习方法)来帮助最大限度地减少由于sla协商(多次错误)而引入的延迟。利用仿真得到的数值结果对该方法进行了性能评价。结果表明,基于区块链和机器学习的联合方法优于其他方法。
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