Statistical QoS Provisioning Over Uncertain Shared Spectrums in Cognitive IoT Networks: A Distributionally Robust Data-Driven Approach
Statistical QoS Provisioning Over Uncertain Shared Spectrums in Cognitive IoT Networks: A Distributionally Robust Data-Driven Approach
复制标题
认知物联网网络中不确定共享频谱的统计 QoS 配置：一种分布式稳健的数据驱动方法
DOI：
10.1109/tvt.2019.2946834
复制
发表时间：
2019
影响因子：
6.8
通讯作者：
Yuguang Fang
中科院分区：
文献类型：
--
作者：
Xuanheng Li;Haichuan Ding;Miao Pan;Jie Wang;Haixia Zhang;Yuguang Fang
随着物联网（IoT）无线业务的飞速发展，频谱短缺问题日益严重，导致频谱使用模式从独占模式向共享模式转变。然而，由于共享频谱的可用性不确定，如何保证共享频谱的服务质量（QoS）并不简单。本文从基于会话的角度出发，提出了一种度量共享频带在会话期间可以传递多少数据的度量，称为概率链路容量（PLC），为QoS的统计保证提供了一种有效的方法。与大多数现有的假设分布信息完全已知的工作不同，我们开发了一种基于一阶和二阶统计量的分布鲁棒（DR）数据驱动方法来估计PLC的值。考虑两种情况，统计量是准确的或不确定的估计误差。对于每种情况，为了计算DR-PLC，我们将其化为基于条件风险值的最坏情况的半确定规划问题。根据提出的度量，我们进一步设计了基于服务的频谱感知数据传输方案，使我们能够有效地利用不同类型的频谱来满足不同的物联网服务需求。
With the soaring wireless traffic for Internet of Things (IoT), spectrum shortage becomes an extremely serious problem, leading to the paradigm shift in spectrum usage from an exclusive mode to a sharing mode. However, how to guarantee the quality of service (QoS) when using the shared spectrum is not straightforward due to its uncertain availability. In this paper, from a session-based view, we propose a metric to evaluate how much data can be delivered via a shared band during a session period, named probabilistic link capacity (PLC), which offers us an effective way to guarantee the QoS statistically. Different from most existing works where the distributional information is assumed exactly known, we develop a distributionally robust (DR) data-driven approach to estimate the value of the PLC based on the first and second order statistics. Two cases are considered that the statistics are exact or uncertain with estimation errors. For each case, to calculate the DR-PLC, we formulate it into a semidefinite programming problem based on the worst-case of conditional-value-at-risk. With the proposed metric, we further design a service-based spectrum-aware data transmission scheme, which allows us to efficiently use different kinds of spectrums to satisfy the diverse IoT service requirements.