Chih-Chieh Cheng

Bio: Chih-Chieh Cheng is an academic researcher from SRI International. The author has contributed to research in topics: Anomaly detection & Cellular network. The author has an hindex of 3, co-authored 3 publications receiving 47 citations.

Anomaly Detection and Diagnosis for Automatic Radio Network Verification

Abstract: The concept known as Self-Organizing Networks (SON) has been developed for modern radio networks that deliver mobile broadband capabilities. In such highly complex and dynamic networks, changes to the configuration management (CM) parameters for network elements could have unintended effects on network performance and stability. To minimize unintended effects, the coordination of configuration changes before they are carried out and the verification of their effects in a timely manner are crucial. This paper focuses on the verification problem, proposing a novel framework that uses anomaly detection and diagnosis techniques that operate within a specified spatial scope. The aim is to detect any anomaly, which may indicate actual degradations due to any external or system-internal condition and also to characterize the state of the network and thereby determine whether the CM changes negatively impacted the network state. The results, generated using real cellular network data, suggest that the proposed verification framework automatically classifies the state of the network in the presence of CM changes, indicating the root cause for anomalous conditions.

Managing scope changes for cellular network-level anomaly detection

Abstract: The Self-Organizing Networks (SON) concept is increasingly being used as an approach for managing complex, dynamic mobile radio networks. In this paper we focus on the verification component of SON, which is the ability to automatically detect problems such as performance degradation or network instability stemming from configuration management changes. In previous work, we have shown how Key Performance Indicators (KPIs) that are continuously collected from network cells can be used in an anomaly detection framework to characterize the state of the network. In this study, we introduce new methods designed to handle scope changes. Such changes can include the addition of new KPIs or cells in the network, or even re-scoping the analysis from the level of a cell or group of cells to the network level. Our results, generated using real cellular network data, suggest that the proposed network-level anomaly detection can adapt to such changes in scope and accurately identify different network states based on all types of available KPIs.

Demo: SONVer: SON verification for operational cellular networks

Abstract: The Self-Organizing Networks (SON) concept includes the functional area known as self-healing, which aims to automate the detection and diagnosis of, and recovery from, network degradations and outages Changes to the configuration management (CM) parameters for network elements could be a cause for degraded network performance and stability; hence, the verification of their effects becomes crucial In this paper, we present SONVer, a tool that performs SON verification, using anomaly detection and diagnosis techniques that operate within a specified spatial scope larger than an individual cell [1] SONVer automatically classifies the state of the network in the presence of CM changes, indicating the root cause for anomalous conditions SONVer uses Key Performance Indicators (KPIs) and CM history from real cellular networks to determine the state of the network; visualize anomalies at a large scale; and identify the causes of anomalies and the group of cells that were affected

Self-Healing in Emerging Cellular Networks: Review, Challenges, and Research Directions

Abstract: Mobile cellular network operators spend nearly a quarter of their revenue on network management and maintenance. Incidentally, a significant proportion of that budget is spent on resolving outages that degrade or disrupt cellular services. Historically, operators mainly rely on human expertise to identify, diagnose, and resolve such outages. However, with growing cell density and diversifying cell types, this approach is becoming less and less viable, both technically and financially. To cope with this problem, research on self-healing solutions has gained significant momentum in recent years. Self-healing solutions either assist in resolving these outages or carry out the task autonomously without human intervention, thus reducing costs while improving mobile cellular network reliability. However, despite their growing popularity, to this date no survey has been undertaken for self-healing solutions in mobile cellular networks. This paper aims to bridge this gap by providing a comprehensive survey of self-healing solutions proposed in the domain of mobile cellular networks, along with an analysis of the techniques and methodologies employed in those solutions. This paper begins by providing a quantitative analysis to highlight why in emerging mobile cellular network self-healing will become a necessity instead of a luxury. Building on this motivation, this paper provides a review and taxonomy of existing literature on self-healing. Challenges and prospective research directions for developing self-healing solutions for emerging and future mobile cellular networks are also discussed in detail. Particularly, we identify that the most demanding challenges from self-healing perspective are the difficulty of meeting 5G low latency and the high quality of experience requirement.

CellPAD: Detecting Performance Anomalies in Cellular Networks via Regression Analysis

Abstract: How to accurately detect Key Performance Indicator (KPI) anomalies is a critical issue in cellular network management. We present CellPAD, a unified performance anomaly detection framework for KPI time-series data. CellPAD realizes simple statistical modeling and machine-learning-based regression for anomaly detection; in particular, it specifically takes into account seasonality and trend components as well as supports automated prediction model retraining based on prior detection results. We demonstrate how CellPAD detects two types of anomalies of practical interest, namely sudden drops and correlation changes, based on a large-scale real-world KPI dataset collected from a metropolitan LTE network. We explore various prediction algorithms and feature selection strategies, and provide insights into how regression analysis can make automated and accurate KPI anomaly detection viable.

Anomaly Detection and Diagnosis for Automatic Radio Network Verification

Abstract: The concept known as Self-Organizing Networks (SON) has been developed for modern radio networks that deliver mobile broadband capabilities. In such highly complex and dynamic networks, changes to the configuration management (CM) parameters for network elements could have unintended effects on network performance and stability. To minimize unintended effects, the coordination of configuration changes before they are carried out and the verification of their effects in a timely manner are crucial. This paper focuses on the verification problem, proposing a novel framework that uses anomaly detection and diagnosis techniques that operate within a specified spatial scope. The aim is to detect any anomaly, which may indicate actual degradations due to any external or system-internal condition and also to characterize the state of the network and thereby determine whether the CM changes negatively impacted the network state. The results, generated using real cellular network data, suggest that the proposed verification framework automatically classifies the state of the network in the presence of CM changes, indicating the root cause for anomalous conditions.

Cell Fault Management Using Machine Learning Techniques

Abstract: This paper surveys the literature relating to the application of machine learning to fault management in cellular networks from an operational perspective. We summarise the main issues as 5G networks evolve, and their implications for fault management. We describe the relevant machine learning techniques through to deep learning, and survey the progress which has been made in their application, based on the building blocks of a typical fault management system. We review recent work to develop the abilities of deep learning systems to explain and justify their recommendations to network operators. We discuss forthcoming changes in network architecture which are likely to impact fault management and offer a vision of how fault management systems can exploit deep learning in the future. We identify a series of research topics for further study in order to achieve this.

Sequence-based detection of sleeping cell failures in mobile networks

Abstract: This article presents an automatic malfunction detection framework based on data mining approach to analysis of network event sequences. The considered environment is long term evolution (LTE) of Universal Mobile Telecommunications System with sleeping cell caused by random access channel failure. Sleeping cell problem means unavailability of network service without triggered alarm. The proposed detection framework uses N-gram analysis for identification of abnormal behavior in sequences of network events. These events are collected with minimization of drive tests functionality standardized in LTE. Further processing applies dimensionality reduction, anomaly detection with K-Nearest Neighbors, cross-validation, postprocessing techniques and efficiency evaluation. Different anomaly detection approaches proposed in this paper are compared against each other with both classic data mining metrics, such as F-score and receiver operating characteristic curves, and a newly proposed heuristic approach. Achieved results demonstrate that the suggested method can be used in modern performance monitoring systems for reliable, timely and automatic detection of random access channel sleeping cells.