Muhammad Zeeshan Asghar Design and Evaluation of Self-Healing Solutions for Future Wireless Networks

Asghar, Muhammad Zeeshan Design and Evaluation of Self-Healing Solutions for Future Wireless Networks Jyväskylä: University of Jyväskylä, 2016, 52 p.(+included articles) (Jyväskylä Studies in Computing ISSN 1456-5390; 253) ISBN 978-951-39-6884-7 (nid.) ISBN 978-951-39-6885-4 (PDF) Finnish summary Diss. This doctoral dissertation is aimed at the creation of comprehensive and innovative Self-Organizing Networks (SON) solutions for the Network Management of future wireless networks. More specifically, the thesis focuses on the Self-Healing (SH) part of SON. Faults can appear at several functional areas of a complex cellular network. However, the most critical domain from a fault management viewpoint is the Radio Access Network (RAN). The fault management of network elements is not only difficult but also imposes high costs both in capital investment (CAPEX) and operational expenditures (OPEX). The SON concept has emerged with the goal to foster automation and to reduce human involvement in management tasks. SH is the part of SON that refers to autonomous fault management in wireless networks, including performance monitoring, detection of faults and their causes, triggering compensation and recovery actions, and evaluating the outcome. It improves business resiliency by eliminating disruptions that are discovered, analyzed and acted upon. With the advent of 5G technologies, the management of SON becomes more challenging. The traditional SH solutions are not sufficient for the future needs of the cellular network management because of their reactive nature, i.e., they start recovering from faults after detection instead of preparing for possible faults in a preemptive manner. The detection delays are especially problematic with regard to the zero latency requirements of 5G networks. In order to address this challenge, the existing SON enabled networks need to be upgraded with additional features. This situation pushes operators to upgrade their SONs from reactive to proactive response and opens doors for further reseach on SON and SH. This dissertation provides several contributions to this direction.

[1]  Slawomir Stanczak,et al.  Network State Awareness and Proactive Anomaly Detection in Self-Organizing Networks , 2015, 2015 IEEE Globecom Workshops (GC Wkshps).

[2]  Ulf Lindqvist,et al.  Anomaly Detection and Diagnosis for Automatic Radio Network Verification , 2014, MONAMI.

[3]  Christopher M. Bishop,et al.  Neural networks and machine learning , 1998 .

[4]  Tommi Kärkkäinen,et al.  MLP in Layer-Wise Form with Applications to Weight Decay , 2002, Neural Computation.

[5]  Juan Ramiro,et al.  Self-Organizing Networks (SON): Self-Planning, Self-Optimization and Self-Healing for GSM, UMTS and LTE , 2012 .

[6]  Muhammad Ali Imran,et al.  A Cell Outage Management Framework for Dense Heterogeneous Networks , 2016, IEEE Transactions on Vehicular Technology.

[7]  Muhammad Ali Imran,et al.  A Survey of Self Organisation in Future Cellular Networks , 2013, IEEE Communications Surveys & Tutorials.

[8]  Raquel Barco,et al.  Automatic root cause analysis based on traces for LTE self-organizing networks , 2016, IEEE Wireless Communications.

[9]  Sergio Fortes Rodriguez,et al.  Location-based distributed sleeping cell detection and root cause analysis for 5G ultra-dense networks , 2016, EURASIP J. Wirel. Commun. Netw..

[10]  Szabolcs Nováczki An improved anomaly detection and diagnosis framework for mobile network operators , 2013, 2013 9th International Conference on the Design of Reliable Communication Networks (DRCN).

[11]  Miroslaw Malek,et al.  A survey of online failure prediction methods , 2010, CSUR.

[12]  Nei Kato,et al.  Device-to-Device Communication in LTE-Advanced Networks: A Survey , 2015, IEEE Communications Surveys & Tutorials.

[13]  Tapani Ristaniemi,et al.  Self-healing framework for LTE networks , 2012, 2012 IEEE 17th International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD).

[14]  Thomas G. Dietterich What is machine learning? , 2020, Archives of Disease in Childhood.

[15]  Hermann Wietgrefe Investigation and practical assessment of alarm correlation methods for the use in GSM access networks , 2002, NOMS 2002. IEEE/IFIP Network Operations and Management Symposium. ' Management Solutions for the New Communications World'(Cat. No.02CH37327).

[16]  Muhammad Ali Imran,et al.  Challenges in 5G: how to empower SON with big data for enabling 5G , 2014, IEEE Network.

[17]  Yongbin Wei,et al.  A survey on 3GPP heterogeneous networks , 2011, IEEE Wireless Communications.

[18]  Sergio Fortes Rodriguez,et al.  Contextualized indicators for online failure diagnosis in cellular networks , 2015, Comput. Networks.

[19]  Henning Sanneck,et al.  LTE Self-Organising Networks (SON): Network Management Automation for Operational Efficiency , 2012 .

[20]  Ulf Lindqvist,et al.  Managing scope changes for cellular network-level anomaly detection , 2014, 2014 11th International Symposium on Wireless Communications Systems (ISWCS).

[21]  Charalabos Skianis,et al.  A Survey on Context-Aware Mobile and Wireless Networking: On Networking and Computing Environments' Integration , 2013, IEEE Communications Surveys & Tutorials.

[22]  Sergio Fortes Rodriguez,et al.  Location-aware self-organizing methods in femtocell networks , 2015, Comput. Networks.

[23]  William Noah Schilit,et al.  A system architecture for context-aware mobile computing , 1995 .

[24]  Tapani Ristaniemi,et al.  Correlation-Based Cell Degradation Detection for Operational Fault Detection in Cellular Wireless Base-Stations , 2013, MONAMI.

[25]  Tommi Kärkkäinen,et al.  Neural Prediction of Product Quality Based on Pilot Paper Machine Process Measurements , 2011, ICANNGA.

[26]  Ulf Lindqvist,et al.  On the feasibility of deploying cell anomaly detection in operational cellular networks , 2014, 2014 IEEE Network Operations and Management Symposium (NOMS).

[27]  Alejandro Cadenas,et al.  Framework for intelligent service adaptation to user's context in next generation networks , 2012, IEEE Communications Magazine.

[28]  Péter Szilágyi,et al.  Radio Channel Degradation Detection and Diagnosis Based on Statistical Analysis , 2011, 2011 IEEE 73rd Vehicular Technology Conference (VTC Spring).

[29]  Junyi Li,et al.  Network densification: the dominant theme for wireless evolution into 5G , 2014, IEEE Communications Magazine.

[30]  Sergio Fortes Rodriguez,et al.  Context-Aware Self-Healing: User Equipment as the Main Source of Information for Small-Cell Indoor Networks , 2016, IEEE Vehicular Technology Magazine.

[31]  Ulf Lindqvist,et al.  Demo: SONVer: SON verification for operational cellular networks , 2014, 2014 11th International Symposium on Wireless Communications Systems (ISWCS).

[32]  Leo Breiman,et al.  Classification and Regression Trees , 1984 .

[33]  Seppo Hämäläinen,et al.  Experimental system for self-optimization of LTE networks , 2012, PM2HW2N '12.

[34]  Ulf Lindqvist,et al.  Detecting anomalies in cellular networks using an ensemble method , 2013, Proceedings of the 9th International Conference on Network and Service Management (CNSM 2013).

[35]  Paolo Bellavista,et al.  A Unifying Perspective on Context-Aware Evaluation and Management of Heterogeneous Wireless Connectivity , 2011, IEEE Communications Surveys & Tutorials.

[36]  Muhammad Ali Imran,et al.  Control-Data Separation Architecture for Cellular Radio Access Networks: A Survey and Outlook , 2016, IEEE Communications Surveys & Tutorials.