Wireless networks have been generating a plethora of unstructured and highly-correlated big data with hidden anomalies. The anomalies may bring inaccurate predictions of network behaviors, which further lead to inefficient system designs such as proactive caching placement. Current Random Matrix Theory (RMT) approaches are unable to detect hidden anomalies with a satisfying tolerance of data correlation. We present a novel data Decomposition aided Random Matrix Theory (DC-RMT) framework, which enables a real-time anomaly detection of large scale multi-dimensional and highly-correlated data. The detection results show that the proposed DC-RMT methodology can detect anomalies with an accuracy of 28 times better than RMT applied without data decomposition. The prediction results present a 6 times higher accuracy than data with anomaly, which will facilitate the identification of regions of interests, and contribute to the improvement of resource allocation efficiency and user QoE.
Physical layer security (PLS) is a promising technology to enhance security performance of wireless communication systems, while the analyzing of PLS abnormal user detection is an important aspect of PLS research. Considering random matrix theory (RMT) is a time-efficient and theory-mature method, we will utilize RMT to analyze the abnormal user detection problem from the perspective of physical layer data analysis, where the carrier frequency offset (CFO) data is used as an indicator for abnormal user detection. Specially, the ring law theory and empirical spectral analysis of RMT are adopted for the analysis of CFO data, which is time-efficient and can be implanted in other detection methods. The proposed abnormal user detection method can provide a guidance for the appropriate choice of security enhancement technologies, so as to improve the utilization efficiency of different PLS technologies.INDEX TERMS Physical layer security, carrier frequency offset, abnormal user detection, random matrix theory, data analysis.
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