Electronics and Telecommunications Trends (전자통신동향분석)

Electronics and Telecommunications Research Institute (한국전자통신연구원)
권호 표지
DOI QR코드

DOI QR Code

Research Status on Machine Learning for Self-Organizing Network-II

Self-Organizing Network에서 기계학습 연구동향-II

  • 권동승 (지능형고밀집스몰셀연구실) ;
  • 나지현 (지능형고밀집스몰셀연구실)
  • Published : 2020.08.01
Download PDF
⟨ Previous Next ⟩

Abstract

Several studies on machine learning (ML) based self-organizing networks (SONs) have been conducted, specifically for LTE, since studies to apply ML to optimize mobile communication systems started with 2G. However, they are still in the infancy stage. Owing to the complicated KPIs and stringent user requirements of 5G, it is necessary to design the 5G SON engine with intelligence to enable users to seamlessly and unlimitedly achieve connectivity regardless of the state of the mobile communication network. Therefore, in this study, we analyze and summarize the current state of machine learning studies applied to SONs as solutions to the complicated optimization problems that are caused by the unpredictable context of mobile communication scenarios.

Keywords

Acknowledgement

이 논문은 2020년도 정부(과학기술정보통신부)의 재원으로 정보통신기획평가원의 지원을 받아 수행된 연구임[No. 2020-0-009454, 5G 스몰셀을 위한 인공지능 기반 자율구성 네트워크(SON 기술 개발)].

References

  1. T. Binzer and F. M. Landstorfer, "Radio Network Planning with Neural Networks," in Proc. IEEE-VTC Fall, Boston, MA, USA, Sept. 2000, pp. 811-817.
  2. S. S. Mwanje, N. Zia, and A. Mitschele-Thiel, "Self-Organized Handover Parameter Configuration for LTE," in Proc. Int. Symp. Wireless Comuun. Syst., Paris, France, Aug. 2012, pp. 26-30.
  3. H. Claussen et al., "Self-Optimization of Coverage for Femtocell Deployments," in Proc. Wireless Telecommun. Symp, Pomona, CA, USA, Apr. 2008, pp. 278-285.
  4. R. Razavi et al., "A Fuzzy Reinforcement Learning Approach for Self-Optimization of Coverage in LTE Networks," Bell Labs Tech. J., vol. 15, no. 3, Dec. 2010, pp. 153-175. https://doi.org/10.1002/bltj.20463
  5. F. J. Mullany et al., "Self-Deployment, Self-Configuration: Critical Future Paradigms for Wireless Access Networks," in Proc. Workshop Auton. Commun., Berlin, Germany, Oct. 2004, pp. 58-68.
  6. R. Joyce et al., "Self Organising Network Techniques to Maximize Traffic Offload Onto a 3G/WCDMA Small Cell Network Using MDT UE Measurement Reports," in Proc. IEEE Glob. Commun. Conf., Austin, TX, USA, Dec. 2014, pp. 2212-2217.
  7. A. Gerdenitsch et al., "A Rule-Based Algorithm for Common Pilot Channel and Antenna Tilt Optimization in UMTS FDD Networks," ETRI J., vol. 26, no. 5, 2004, pp. 437-442. https://doi.org/10.4218/etrij.04.0703.0007
  8. H. Eckhardt et al., "Vertical Antenna Tilt Optimization for LTE Base Stations," in Proc. IEEE 73rd VTC, Yokohama, Japan, May. 2011, pp. 1-5.
  9. J.-H. Yun et al., "CTRL: A Self-Organizing Femtocell Management Architecture for Co-channel Deployment," in Proc. 16th Annu. Int. Conf. Mobile Comput. Netw., Chicago, IL, USA, 2010, pp. 61-72.
  10. I. Karla, "Distributed Algorithm for Self Organizing LTE Interference Coordination," in Proc. Int. Conf. Mobile Netw. Manag., Athens, Greece, 2009, pp. 119-128.
  11. M. Mehta et al., "A Self-Organized Resource Allocation Scheme for Heterogeneous Macro-Femto Networks," Wireless Commun. Mobile Comput., vol. 16, no. 3, 2016, pp. 330-342. https://doi.org/10.1002/wcm.2518
  12. X. Zhao et al., "Improving UE SINR and Networks Energy Efficiency Based on Femtocell Self-Optimization Capability," in Proc. WCNC Workshop, Istanbul, Turkey, 2014, pp. 155-160.
  13. M. Bennis et al., "A Q-Learning Based Approach to Interference Avoidance in Self-Organized Femtocell Networks," in Proc. IEEE Globecom Workshops, Miami, FL, USA, Dec. 2010, pp. 706-710.
  14. M. Dirani et al., "A Cooperative Reinforcement Learning Approach for Inter-Cell Interference Coordination in OFDMA Cellular Networks," in Proc. Int. Symp. Model. Opt. Mobile Ad Hoc Wireless Netw., Avignon, France, May. 2010, pp. 170-176.
  15. X. Chen et al., "Predicting a User's Next Cell with Supervised Learning Based on Channel States," in Proc. IEEE Workshop SPAWC, Darmstadt, Germany, Jun. 2013, pp. 36-40.
  16. A. Mohamed et al., "Mobility Prediction for Handover Management in Cellular Networks with Control/Data Separation," in Proc. IEEE ICC, London, UK, June. 2015, pp. 3939-3944.
  17. H. Si et al., "Mobility Prediction in Cellular Network Using Hidden Markov Model," in Proc. IEEE Consum. Commun. Netw. Conf., Las Vegas, NV, USA, Jan. 2010, pp. 1-5.
  18. P. Fazio et al., "A Distributed Hand-Over Management and Pattern Prediction Algorithm for Wireless Networks with Mobile Hosts," in Proc. IWCMC, July. 2013, pp. 294-298.
  19. B. Sas et al., "A SON Function for Steering Users in Multi-Layer LTE Networks Based on Their Mobility Behaviour," in Proc. IEEE VTC, Glasgow, UK, May. 2015, pp. 1-7.
  20. C. Yu et al., "Modeling User Activity Patterns for Next-Place Prediction," IEEE Syst. J., vol. 11, no. 2, June. 2017, pp. 1060-1071. https://doi.org/10.1109/JSYST.2015.2445919
  21. A. Chakraborty et al., "Network-Side Positioning of Cellular-Band Devices with Minimal Effort," in Proc. INFOCOM, Hong Kong, Apr. 2015, pp. 2767-2775.
  22. R. Narasimhan et al., "A Handoff Algorithm for Wireless Systems Using Pattern Recognition," in Proc. IEEE Int. Symp. Pers. Indoor Mobile Radio Commun., Boston, MA, USA, Sept. 1998, pp. 335-339.
  23. P. P. Bhattacharya et al., "An ANN Based Call Handoff Management Scheme for Mobile Cellular Network," Int. J. Wireless Mobile Netw. vol. 5, no. 6, Dec. 2013, pp. 125-135. https://doi.org/10.5121/ijwmn.2013.5610
  24. Z. Ali et al., "Machine Learning Based Handover Management for Improved QoE in LTE," in Proc. IEEE/IFIP NOMS, Istanbul, Turkey, Apr. 2016, pp. 794-798.
  25. N. Sinclair et al., "An Advanced SOM Algorithm Applied to Handover Management Within LTE" IEEE Trans. Veh. Technol., vol. 62, no. 5, June. 2013, pp. 1883-1894. https://doi.org/10.1109/TVT.2013.2251922
  26. M. Stoyanova and P. Mahonen, "Algorithmic Approaches for Vertical Handoff in Heterogeneous Wireless Environment" in Proc. IEEE Wireless Commun. Netw. Conf., Hong Kong, Mar. 2007, pp. 3780-3785.
  27. F. Bouali, K. Moessner, and M. Fitch, "A Context-Aware User-Driven Framework for Network Selection in 5G Multi-RAT Environments," in Proc. IEEE VTC, Montreal, Canada, Sept. 2016, pp. 1-7.
  28. S. S. Mwanje et al., "Cognitive Cellular Networks: A Q-Learning Framework for Self-Organizing Networks," IEEE Trans. Netw. Service Manag., vol. 13, no. 1, Mar. 2016, pp. 85-98. https://doi.org/10.1109/TNSM.2016.2522080
  29. V. Capdevielle, A. Feki, and A. Fakhreddine, "Self-Optimization of Handover Parameters in LTE Networks," in Proc. Int. Symp. Model. Opt. Mobile Ad Hoc Wireless Netw., Tsukuba, Japan, May. 2013, pp. 133-139.
  30. C. Dhahri and T. Ohtsuki, "Adaptive Q-Learning Cell Selection Method for Open-Access Femtocell Networks: Multi-User Case," IEICE Trans. Commun., vol. 97, no. 8, 2014, pp. 1679-1688.
  31. C. A. S. Franco and J. R. B. de Marca, "Load Balancing in Self-Organized Heterogeneous LTE Networks: A Statistical Learning Approach," in Proc. IEEE LATINCOM, Arequipa, Peru, 2015, pp. 1-5.
  32. I. Viering, M. Dottling, and A. Lobinger, "A Mathematical Perspective of Self-Optimizing Wireless Networks," in Proc. IEEE Int. Conf. Commun., Dresden, Germany, June. 2009, pp. 1-6.
  33. P. Munoz et al., "Optimization of a Fuzzy Logic Controller for Handover-Based Load Balancing," in Proc. IEEE VTC, Yokohama, Japan, 2011, pp. 1-5.
  34. J. Rodriguez et al., "Load Balancing in a Realistic Urban Scenario for LTE Networks," in Proc. IEEE VTC, Yokohama, Japan, 2011, pp. 1-5.
  35. P. Munoz et al., "Fuzzy Rule-Based Reinforcement Learning for Load Balancing Techniques in Enterprise LTE Femtocells," IEEE Trans. Veh. Technol., vol. 62, no. 5, June. 2013, pp. 1962-1973. https://doi.org/10.1109/TVT.2012.2234156
  36. T. Kudo and T. Ohtsuki, "Q-Learning Based Cell Selection for UE Outage Reduction in Heterogeneous Networks," in Proc. IEEE VTC, Vancouver, Canada, 2014, pp. 1-5.
  37. H. Hu et al., "Self-Configuration and Self-Optimization for LTE Networks," IEEE Commun. Mag., vol. 48, no. 2, Feb. 2010, pp. 94-100. https://doi.org/10.1109/MCOM.2010.5402670
  38. H.-M. Zimmermann, A. Seitz, and R. Halfmann, "Dynamic Cell Clustering in Cellular Multi-Hop Networks," in Proc. IEEE Singapore Int. Conf. Commun. Syst., 2006, pp. 1-5.
  39. M. Al-Rawi, "A Dynamic Approach for Cell Range Expansion in Interference Coordinated LTE-Advanced Heterogeneous Networks," in Proc. IEEE ICCS, Singapore, 2012, pp. 533-537.
  40. L. Du et al., "UUsing Dynamic Sector Antenna Tilting Control for Load Balancing in Cellular Mobile Communications," in Proc. ICT, vol. 2. 2002, pp. 344-348.
  41. S. Tomforde, A. Ostrovsky, and J. Hahner, "Load-Aware Reconfiguration of LTE-Antennas Dynamic Cell-Phone Network Adaptation Using Organic Network Control," in Proc. Int. Conf. Inform. Contr. Autom. Robot., Vienna, Austria, Sept. 2014.
  42. S. Bassoy et al., "Load Aware Self-Organising User-Centric Dynamic CoMP Clustering for 5G Networks," IEEE Access, vol. 4, 2016, pp. 2895-2906. https://doi.org/10.1109/ACCESS.2016.2569824
  43. P. Sandhir and K Mitchell, "A Neural Network Demand Prediction Scheme for Resource Allocation in Cellular Wireless Systems," in Proc. IEEE Reg. 5 Conf., Kansas City, MO, USA, Apr. 2008, pp. 1-6.
  44. P. Fazio et al., "A Novel Passive Bandwidth Reservation Algorithm Based on Neural Networks Path Prediction in Wireless Environments," in Proc. Int. SPECTS, Ottawa, Canada, July. 2010, pp. 38-43.
  45. A. Adeel et al., "Critical Analysis of Learning Algorithms in Random Neural Network Based Cognitive Engine for LTE Systems," in Proc. IEEE VTC Spring, Glasgow, UK, 2015, pp. 1-5.
  46. Y. Zang et al., "Wavelet Transform Processing for Cellular Traffic Prediction in Machine Learning Networks," in Proc. IEEE China Summit Int. Conf. ChinaSIP, Chengdu, China, July. 2015, pp. 458-462.
  47. D. Kumar, N. kanagaraj, and R. Srilakshmi, "Harmonized Q-Learning for Radio Resource Management in LTE Based Networks," in Proc. ITU Kaleidoscope Build. Sustain. Communities (K), Kyoto, Japan, 2013, pp. 1-8.
  48. P. Savazzi and L. Facalli, "Dynamic Cell Sectorization Using Clustering Algorithms," in Proc. IEEE VTC, Dublin, Ireland, Apr. 2007, pp. 604-608.
  49. A. Galindo-Serrano et al., "Distributed Learning in Multiuser OFDMA Femtocell Networks," in Proc. IEEE VTC, Yokohama, Japan, May. 2011, pp. 1-6.
  50. B. Fan, S. leng, and K. Yang,, "A Dynamic Bandwidth Allocation Algorithm in Mobile Networks with Big Data of Users and Networks," IEEE Netw., vol. 30, no. 1, Jan./Feb. 2016, pp. 6-10. https://doi.org/10.1109/MNET.2016.7389824
  51. P. Kiran, M. G. Jibukumar, and C. V. Premkumar, "Resource Allocation Optimization in LTE-A/5G Networks Using Big Data Analytics," in Proc. ICOIN, Kota Kinabalu, Malaysia, 2016, pp. 254-259.
  52. A. Liakopoulos et al., "Applying Distributed Monitoring Techniques in Autonomic Networks," in Proc. IEEE Globecom Workshops, Miami, FL, USA, 2010, pp. 498-502.
  53. M. Dirani et al., "Self-Organizing Networks in Next Generation Radio Access Networks: Application to Fractional Power Control," Comput. Netw., vol. 55, no. 2, 2011, pp. 431-438. https://doi.org/10.1016/j.comnet.2010.08.012
  54. E. Alexandri and Z. Altman, "A distributed reinforcement learning approach to maximize resource utilization and control handover dropping in multimedia wireless networks," in Proc. 13th IEEE Int. Symp. PIMRC, vol. 5. 2002, pp. 2249-2253.
  55. L.-T. Lee et al., "A Cell-based Call Admission Control Policy with Time Series Prediction and Throttling Mechanism for Supporting QoS in Wireless Cellular Networks," in Proc. Int. Symp. Commun. Inf. Technol., Bangkok, Thailand, Oct. 2006, pp. 88-93.
  56. A. F. Santamaria and A. Lupia, "A New Call Admission Control Scheme Based on Pattern Prediction for Mobile Wireless Cellular Networks," in Proc. WTS, New York, NY, USA, Apr. 2015, pp. 1-6.
  57. D. Liu and Y. Zhang, "A Self-Learning Adaptive Critic Approach for Call Admission Control in Wireless Cellular Networks," in Proc. IEEE ICC, Anchorage, AK, USA, May. 2003, pp. 1853-1857.
  58. H. Y. Lateef, A. Imran, and A. Abu-dayya, "A Framework for Classification of Self-Organising Network Conflicts and Coordination Algorithms," in Proc. IEEE Annu. Int. Symp. PIMRC, London, UK, Sept. 2013, pp. 2898-2903.
  59. A. Tall et al., "Distributed Coordination of Self-Organizing Mechanisms in Communication Networks," IEEE Trans. Contr. Netw. Syst., vol. 1, no. 4, Dec. 2014, pp. 328-337. https://doi.org/10.1109/TCNS.2014.2357511
  60. H. Y. Lateef et al., "LTE-Advanced Self-Organizing Network Conflicts and Coordination Algorithms," IEEE Wireless Commun., vol. 22, no. 3, June. 2015, pp. 108-117. https://doi.org/10.1109/MWC.2015.7143333
  61. I. Karla, "Resolving SON Interactions via Self-Learning Prediction in Cellular Wireless Networks," in Proc. Int. Conf. WiCOM, Shanghai, China, Sept. 2012, pp. 1-6.
  62. R. Barco, P. Lazaro, and P. Munoz, "A Unified Framework for Self-Healing in Wireless Networks," IEEE Commun. Mag., Dec. 2012, pp. 134-142.
  63. A. Coluccia, F. Ricciato, and P. Romirer-Maierhofer, "Bayesian Estimation of Network-Wide Mean Failure Probability in 3G Cellular Networks," in Performance Evaluation Comput. Commun. Syst. Milestones Future Challenges., Vienna, Austria, Oct. 2011, pp. 167-178.
  64. G. F. Ciocarlie et al., "Detecting Anomalies in Cellular Networks Using an Ensemble Method," in Proc. Int. CNSM, Zurich, Switzerland, Oct. 2013, pp. 171-174.
  65. K. Raivio et al., "Analysis of Mobile Radio Access Network Using the Self-Organizing Map," in Proc. IFIP/IEEE Int. Symp. Integr. Netw. Manag., Colorado Springs, CO, USA, Mar. 2003, pp. 439-451.
  66. P. Sukkhawatchani and W. Usaha, "Performance Evaluation of Anomaly Detection in Cellular Core Networks Using Self-Organizing Map," in Proc. Int. Conf. ECTI-CON, Krabi, Thailand, 2008, pp. 361-364.
  67. P. Szilagyi and S. Novaczki, "An Automatic Detection and Diagnosis Framework for Mobile Communication Systems," IEEE Trans. Netw. Service Manag., vol. 9, no. 2, June. 2012, pp. 184-197. https://doi.org/10.1109/TNSM.2012.031912.110155
  68. S. Novaczki, "An Improved Anomaly Detection and Diagnosis Framework for Mobile Network Operators," in Proc. Int. Conf. DRCN, Budapest, Hungary, 2013, pp. 234-241.
  69. A. D'Alconzo et al., "A Distribution-Based Approach to Anomaly Detection and Application to 3G Mobile Traffic," in Proc. GLOBECOM, Honolulu, HI, USA, 2009, pp. 1-8.
  70. N. Tcholtchev and R. Chaparadza, "Autonomic Fault-Management and Resilience from the Perspective of the Network Operation Personnel," in Proc. IEEE Globecom Workshops, Miami, FL, USA, 2010, pp. 469-474.
  71. Q. Liao and S. Stanczak, "Network State Awareness and Proactive Anomaly Detection in Self-Organizing Networks," in Proc. IEEE Globecom Workshops, San Diego, CA, USA, Dec. 2015, pp. 1-6.
  72. H. Farooq, Md. S. Parwez, and A. Imran, "Continuous Time Markov Chain Based Reliability Analysis for Future Cellular Networks," in Proc. IEEE GLOBECOM, San Diego, CA, USA, Dec. 2015, pp. 1-6.
  73. U. S. Hashmi et al., "Enabling Proactive Self Healing by Data Mining Network Failure Logs," in Proc. Int. ICNC, Santa Clara, CA, USA, Jan. 2017, pp. 511-517.
  74. A. Gomez-Andrades et al., "Data Analytics for Diagnosing the RF Condition in Self-Organizing Networks," IEEE Trans. Mobile Comput., vol. 16, no. 6, June. 2017, pp. 1587-1600. https://doi.org/10.1109/TMC.2016.2601919
  75. R. M. Khanafer et al., "Automated Diagnosis for UMTS Networks Using Bayesian Network Approach," IEEE Trans. Veh. Technol., vol. 57, no. 4, July. 2008, pp. 2451-2461. https://doi.org/10.1109/TVT.2007.912610
  76. J. Puttonen et al., "Coverage Optimization for Minimization of Drive Tests in LTE with Extended RLF Reporting," in Proc. Annu. IEEE Int. Symp. PIMRC, Instanbul, Turkey, 2010, pp. 1764-1768.
  77. W. Wang, J. Zhang, and Q. Zhang, "Transfer Learning Based Diagnosis for Configuration Troubleshooting in Self-Organizing Femtocell Networks," in Proc. IEEE GLOBECOM, Houston, TX, USA, 2011, pp. 1-5.
  78. C. M. Mueller et al., "A Cell Outage Detection Algorithm Using Neighbor Cell List Reports," in Proc. Int. Workshop Self Org. Syst., Vienna, Austria, 2008, pp. 218-229.
  79. W. Feng et al., "Cell Outage Detection Based on Improved BP Neural Network in LTE System," in Proc. Int. Conf. WiCOM, Shanghai, China, Sept. 2015, pp. 1-5.
  80. O. Onireti et al., "A Cell Outage Management Framework for Dense Heterogeneous Networks," IEEE Trans. Veh. Technol., vol. 65, no. 4, Apr. 2016, pp. 2097-2113. https://doi.org/10.1109/TVT.2015.2431371
  81. W. Xue et al., "Classification-Based Approach for Cell Outage Detection in Self-Healing Heterogeneous Networks," in Proc. IEEE WCNC, Istanbul, Turkey, Apr. 2014, pp. 2822-2826.
  82. A. Zoha et al., "Data-Driven Analytics for Automated Cell Outage Detection in Self Organizing Networks," in Proc. Int. Conf. DRCN, Kansas City, MO, USA, Mar. 2015, pp. 203-210.
  83. W. Wang, Q. Liao, and Q. Zhangm "COD: A Cooperative Cell Outage Detection Architecture for Self-Organizing Femtocell Networks," IEEE Trans. Wireless Commun., vol. 13, no. 11, Nov. 2014, pp. 6007-6014. https://doi.org/10.1109/TWC.2014.2360865
  84. I. de-la Bandera et al., "Cell outage detection based on handover statistics," IEEE Commun. Lett., vol. 19, no. 7, Jul. 2015, pp. 1189-1192. https://doi.org/10.1109/LCOMM.2015.2426187
  85. P. Munoz et al., "Correlation-Based Time-Series Analysis for Cell Degradation Detection in SON," IEEE Commun. Lett., vol. 20, no. 2, Feb. 2016, pp. 396-399. https://doi.org/10.1109/LCOMM.2016.2516004
  86. Q. Liao, M. Wiczanowski, and S. Stanczak, "Toward Cell Outage Detection with Composite Hypothesis Testing," in Proc. IEEE ICC, Ottawa, Canada, 2012, pp. 4883-4887.
  87. M. N. U. Islam and A. Mitschele-Thiel, "Reinforcement Learning Strategies for Self-Organized Coverage and Capacity Optimization," in Proc. IEEE WCNC, Shanghai, China, Apr. 2012, pp. 2818-2823.
  88. A. Zoha et al., "A Learning-Based Approach for Autonomous Outage Detection and Coverage Optimization," Trans. Emerg. Telecom. Technol., vol. 27, no. 3, 2016, pp. 439-450. https://doi.org/10.1002/ett.2971
  89. A. Saeed et al., "Controlling Self Healing Cellular Networks Using Fuzzy Logic," in Proc. IEEE WCNC, Shanghai, China, Apr. 2012, pp. 3080-3084.
  90. J. Moysen et al., "A Reinforcement Learning Based Solution for Self-Healing in LTE Networks," in Proc. IEEE 80th Veh. Technol. Conf. (VTC Fall), Vancouver, Canada, Sept. 2014, pp. 1-6.
  91. M. Alias et al., "Efficient Cell Outage Detection in 5g Hetnets Using Hidden Markov Model," IEEE Commun. Lett., vol. 20, no. 3, 2016, pp. 562-565. https://doi.org/10.1109/LCOMM.2016.2517070
  92. Z. Jiang et al., "A Cell Outage Compensation Scheme Based on Immune Algorithm in LTE Networks," in Proc. APNOMS, Hiroshima, Japan, Sept. 2013, pp. 1-6.
  93. W. Li et al., "A Distributed Cell Outage Compensation Mechanism Based on RS Power Adjustment in LTE Networks," China Commun., vol. 11, no. 13, 2014, pp. 40-47. https://doi.org/10.1109/CC.2014.7022524
  94. I. de-la Bandera et al., "Improving Cell Outage Management Through Data Analysis," IEEE Wireless Commun., vol. 24, Aug. 2017, pp. 115-119.
  95. S. Chernov et al., "Data Mining Framework for Random Access Failure Detection in LTE Networks," in Proc. IEEE Annu. Int. Symp. PIMRC, Washington, DC, USA, 2014, pp. 1321-1326.
  96. A. Imran et al., "Challenges in 5G: How to Empower SON with BIG DAta for Enabling 5G," IEEE Netw., vol. 28, no. 6, Nov./Dec. 2014, pp. 27-33. https://doi.org/10.1109/MNET.2014.6963801
  97. J. Turkka et al., "An Approach for Network Outage Detection From Drive Testing Databases," J. Comput. Netw. Commun., vol. 2012, 2012, pp. 1-13. https://doi.org/10.1155/2012/163184
  98. S. Chernov et al., "Location Accuracy Impact on Cell Outage Detection in LTE-A Networks," in Proc. IWCMC, Dubrovnik, Croatia, Aug. 2015, pp. 1162-1167.
  99. A. Zoha et al., "A SON Solution for Sleeping Cell Detection Using Low-Dimensional Embedding of MDT Measurements," in Proc. IEEE Annu. Int. Symp. PIMRC, Washington, DC, USA, 2014, pp. 1626-1630.
  100. F. Chernogorov et al., "Detection of Sleeping Cells in LTE Networks Using Diffusion Maps," in Proc. IEEE VTC Spring, Yokohama, Japan, 2011, pp. 1-5.