Anomaly detection and array diagnosis in wireless networks are both important technologies and have many applications ranging from discovering malicious traffic and identifying abnormal nodes, to detecting faulty antennas and so on. In general, anomaly detection mainly depends on relational data, which denotes the links between nodes of the networks, to decide whether abnormal networks caused by intentional attack or array failure are embedded in large wireless networks. Additionally, the typical scheme of array diagnosis is to measure signals radiating from the array antennas under test to detect the faulty elements by using a centralized method. However, in largescale wireless networks, a centralized strategy results in a communication bottleneck because of transmitting all signals to a center node. Moreover, since faulty elements are only a tiny proportion for the whole networks, the method that all antennas are under test is unnecessary and also causes huge computational complexity to identify the failure of elements. Aiming to mitigate these problems, this article provides a novel framework to monitor networks and detect faulty antennas by fusing relational data and measured signals. In this article, we first review the algorithms related to anomaly detection and survey the array diagnosis problem. In particular, we discuss the relationship between anomaly detection and array diagnosis in the new framework and highlight the importance of data fusion. Finally, the main challenges are presented and mathematical tools are introduced to solve the corresponding problems.

中文翻译：

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多天线无线网络中的异常检测和阵列诊断：框架、挑战和工具


无线网络中的异常检测和阵列诊断都是重要的技术，具有许多应用，从发现恶意流量、识别异常节点到检测故障天线等。一般来说，异常检测主要依靠关系数据（表示网络节点之间的链接）来判断大型无线网络中是否嵌入了由故意攻击或阵列故障引起的异常网络。另外，阵列诊断的典型方案是测量被测阵列天线的辐射信号，采用集中式方法检测故障阵元。然而，在大规模无线网络中，集中式策略由于将所有信号传输到中心节点而导致通信瓶颈。而且，由于故障元件在整个网络中只占很小的比例，因此不需要对所有天线进行测试的方法，并且还导致识别元件故障的巨大计算复杂度。为了缓解这些问题，本文提供了一种新颖的框架，通过融合关系数据和测量信号来监控网络和检测故障天线。在本文中，我们首先回顾与异常检测相关的算法并调查阵列诊断问题。我们特别讨论了新框架中异常检测和阵列诊断之间的关系，并强调了数据融合的重要性。最后，提出了主要挑战，并引入了数学工具来解决相应的问题。