A Cell Outage Management Framework for Dense Heterogeneous Networks
A Cell Outage Management Framework for Dense Heterogeneous Networks
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DOI：
10.1109/tvt.2015.2431371
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发表时间：
2016-04-01
影响因子：
6.8
通讯作者：
Abu-Dayya, Adnan
中科院分区：
文献类型：
--
作者：
Onireti, Oluwakayode;Zoha, Ahmed;Abu-Dayya, Adnan
在本文中，我们提出了一种用于具有分离控制和数据平面的异构网络的新型小区中断管理（COM）框架，这是一种满足未来容量、服务质量和能源效率需求的候选架构。在这样的架构中，控制和数据功能不一定由同一节点处理。控制基站（BS）管理控制信息的传输和用户设备（UE）移动性，而数据BS处理UE数据。这种分离架构的含义是，一个平面中的BS的中断必须由同一平面中的其他BS来补偿。我们的 COM 框架通过结合两种不同的单元中断检测 (COD) 算法来应对数据平面和控制平面的特性，从而解决了这一挑战。控制小区的 COD 算法利用控制小区中相对较多的 UE 来收集大规模最小化路测报告数据，并通过应用机器学习和异常检测技术来检测中断。为了提高断电检测的准确性，我们还研究并比较了控制 COD 内两种异常检测算法的性能，即基于 k 最近邻和局部离群因子的异常检测器。另一方面，对于数据小区COD，我们提出了一种基于启发式格雷预测的方法，该方法可以与数据小区中的少量UE一起工作，通过利用控制BS管理UE-数据BS连接的事实以及通过接收其覆盖范围内的UE和数据BS之间的接收信号参考功率统计的定期更新。通过利用灰色预测模型固有的残余误差的傅里叶级数，进一步提高了启发式数据 COD 算法的检测精度。我们的 COM 框架将这两种 COD 算法与可应用于两个平面的小区中断补偿 (COC) 算法集成在一起。我们的 COC 解决方案采用基于 actor-critic 的强化学习算法，通过调整该平面中周围基站的天线增益和传输功率，优化该平面中已识别停电区域的容量和覆盖范围。仿真结果表明，所提出的框架可以检测数据和控制单元中断，并以可靠的方式补偿检测到的中断。
In this paper, we present a novel cell outage management (COM) framework for heterogeneous networks with split control and data planes-a candidate architecture for meeting future capacity, quality-of-service, and energy efficiency demands. In such an architecture, the control and data functionalities are not necessarily handled by the same node. The control base stations (BSs) manage the transmission of control information and user equipment (UE) mobility, whereas the data BSs handle UE data. An implication of this split architecture is that an outage to a BS in one plane has to be compensated by other BSs in the same plane. Our COM framework addresses this challenge by incorporating two distinct cell outage detection (COD) algorithms to cope with the idiosyncrasies of both data and control planes. The COD algorithm for control cells leverages the relatively larger number of UEs in the control cell to gather large-scale minimization-of-drive-test report data and detects an outage by applying machine learning and anomaly detection techniques. To improve outage detection accuracy, we also investigate and compare the performance of two anomaly-detecting algorithms, i.e., k-nearest-neighbor-and local-outlier-factor-based anomaly detectors, within the control COD. On the other hand, for data cell COD, we propose a heuristic Grey-prediction-based approach, which can work with the small number of UE in the data cell, by exploiting the fact that the control BS manages UE-data BS connectivity and by receiving a periodic update of the received signal reference power statistic between the UEs and data BSs in its coverage. The detection accuracy of the heuristic data COD algorithm is further improved by exploiting the Fourier series of the residual error that is inherent to a Grey prediction model. Our COM framework integrates these two COD algorithms with a cell outage compensation (COC) algorithm that can be applied to both planes. Our COC solution utilizes an actor-critic-based reinforcement learning algorithm, which optimizes the capacity and coverage of the identified outage zone in a plane, by adjusting the antenna gain and transmission power of the surrounding BSs in that plane. The simulation results show that the proposed framework can detect both data and control cell outage and compensate for the detected outage in a reliable manner.