Mobility Management in Emerging Ultra-Dense Cellular Networks: A Survey, Outlook, and Future Research Directions

The exponential rise in mobile traffic originating from mobile devices highlights the need for making mobility management in future networks even more efficient and seamless than ever before. Ultra-Dense Cellular Network vision consisting of cells of varying sizes with conventional and mmWave bands is being perceived as the panacea for the eminent capacity crunch. However, mobility challenges in an ultra-dense heterogeneous network with motley of high frequency and mmWave band cells will be unprecedented due to plurality of handover instances, and the resulting signaling overhead and data interruptions for miscellany of devices. Similarly, issues like user tracking and cell discovery for mmWave with narrow beams need to be addressed before the ambitious gains of emerging mobile networks can be realized. Mobility challenges are further highlighted when considering the 5G deliverables of multi-Gbps wireless connectivity, <1ms latency and support for devices moving at maximum speed of 500km/h, to name a few. Despite its significance, few mobility surveys exist with the majority focused on adhoc networks. This paper is the first to provide a comprehensive survey on the panorama of mobility challenges in the emerging ultra-dense mobile networks. We not only present a detailed tutorial on 5G mobility approaches and highlight key mobility risks of legacy networks, but also review key findings from recent studies and highlight the technical challenges and potential opportunities related to mobility from the perspective of emerging ultra-dense cellular networks.

[1]  Jianxi Yang,et al.  A scheme of terminal mobility prediction of Ultra Dense Network based on SVM , 2017, 2017 IEEE 2nd International Conference on Big Data Analysis (ICBDA)(.

[2]  Il-Gyu Kim,et al.  From architecture to field trial: A millimeter wave based MHN system for HST Communications toward 5G , 2017, 2017 European Conference on Networks and Communications (EuCNC).

[3]  Sami Tabbane,et al.  A novel mobility-based COMP handover algorithm for LTE-A / 5G HetNets , 2015, 2015 23rd International Conference on Software, Telecommunications and Computer Networks (SoftCOM).

[4]  Aiko Pras,et al.  Mobility and bandwidth prediction as a service in virtualized LTE systems , 2015, 2015 IEEE 4th International Conference on Cloud Networking (CloudNet).

[5]  Muhammad Ali Imran,et al.  Memory-Full Context-Aware Predictive Mobility Management in Dual Connectivity 5G Networks , 2018, IEEE Access.

[6]  Nikos Pleros,et al.  A moving extended cell concept for seamless communication in 60 GHz radio-over-fiber networks , 2008, IEEE Communications Letters.

[7]  Muhammad Ali Imran,et al.  Predictive and Core-Network Efficient RRC Signalling for Active State Handover in RANs With Control/Data Separation , 2017, IEEE Transactions on Wireless Communications.

[9]  Bao-Shuh Paul Lin,et al.  Big data and machine learning driven handover management and forecasting , 2017, 2017 IEEE Conference on Standards for Communications and Networking (CSCN).

[10]  Hans D. Schotten,et al.  Framework to Support Mobility Context Awareness in Cellular Networks , 2016, 2016 IEEE 83rd Vehicular Technology Conference (VTC Spring).

[11]  Javier Lorca,et al.  Resource and Mobility Management in the Network Layer of 5G Cellular Ultra-Dense Networks , 2017, IEEE Communications Magazine.

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

[13]  N. B. Prajapati,et al.  Dynamic Location area planning in cellular network using Apriori algorithm , 2015, 2015 International Conference on Industrial Instrumentation and Control (ICIC).

[14]  Ismail Güvenç,et al.  Handover Count Based Velocity Estimation and Mobility State Detection in Dense HetNets , 2015, IEEE Transactions on Wireless Communications.

[15]  Suvadip Batabyal,et al.  Mobility Models, Traces and Impact of Mobility on Opportunistic Routing Algorithms: A Survey , 2015, IEEE Communications Surveys & Tutorials.

[16]  Guohua Zhou,et al.  Mobility performance and suitability of macro cell power-off in LTE dense small cell HetNets , 2013, 2013 IEEE 18th International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD).

[17]  Ali Imran,et al.  Mobility Prediction-Based Autonomous Proactive Energy Saving (AURORA) Framework for Emerging Ultra-Dense Networks , 2018, IEEE Transactions on Green Communications and Networking.

[18]  Michele Zorzi,et al.  Improved user tracking in 5G millimeter wave mobile networks via refinement operations , 2017, 2017 16th Annual Mediterranean Ad Hoc Networking Workshop (Med-Hoc-Net).

[19]  Ignas G. Niemegeers,et al.  Toward a Seamless Communication Architecture for In-building Networks at the 60 GHz band , 2006, Proceedings. 2006 31st IEEE Conference on Local Computer Networks.

[20]  Miroslav Voznák,et al.  Pattern Prediction and Passive Bandwidth Management for Hand-over Optimization in QoS Cellular Networks with Vehicular Mobility , 2016, IEEE Transactions on Mobile Computing.

[21]  Yuanyuan Qiao,et al.  Improving mobility prediction performance with state based prediction method when the user departs from routine , 2016, 2016 IEEE International Conference on Big Data Analysis (ICBDA).

[22]  Athanasios V. Vasilakos,et al.  A survey of millimeter wave communications (mmWave) for 5G: opportunities and challenges , 2015, Wireless Networks.

[23]  Hannu Flinck,et al.  Mobility management enhancements for 5G low latency services , 2016, 2016 IEEE International Conference on Communications Workshops (ICC).

[24]  Hyong S. Kim,et al.  Dynamic bandwidth reservation in cellular networks using road topology based mobility predictions , 2004, IEEE INFOCOM 2004.

[25]  Didier Colle,et al.  Radio-over-fiber based architecture to provide broadband internet access to train passengers , 2008 .

[26]  Satoshi Ikeda,et al.  Adaptive mobility management in cellular networks with multiple model-based prediction , 2013, 2013 9th International Wireless Communications and Mobile Computing Conference (IWCMC).

[27]  Guangyi Liu,et al.  5G: Vision and Requirements for Mobile Communication System towards Year 2020 , 2016 .

[28]  Yuanyuan Qiao,et al.  User location prediction with energy efficiency model in the Long Term‐Evolution network , 2016, Int. J. Commun. Syst..

[29]  Syed Muhammad Asad Zaidi,et al.  Where to Go Next?: A Realistic Evaluation of AI-Assisted Mobility Predictors for HetNets , 2020, 2020 IEEE 17th Annual Consumer Communications & Networking Conference (CCNC).

[30]  Petra Perner,et al.  Data Mining - Concepts and Techniques , 2002, Künstliche Intell..

[31]  Gerhard Fettweis,et al.  Impact of Mobility on the Reliability Performance of 5G Multi-Connectivity Architectures , 2017, 2017 IEEE Wireless Communications and Networking Conference (WCNC).

[32]  Tarik Taleb,et al.  Mobility and bandwidth prediction in virtualized LTE systems: Architecture and challenges , 2014, 2014 European Conference on Networks and Communications (EuCNC).

[33]  Ahmed S. Mubarak,et al.  Backhaul Overhead Traffic Reduction in Dense MmWave Heterogeneous Networks Towards 5G Cellular Systems , 2019, 2019 36th National Radio Science Conference (NRSC).

[34]  Chris G. Guy,et al.  A mobility prediction scheme of LTE/LTE-A femtocells under different velocity scenarios , 2015, 2015 IEEE 20th International Workshop on Computer Aided Modelling and Design of Communication Links and Networks (CAMAD).

[35]  Ashok Kumar Reddy Chavva,et al.  Deep Learning Based Link Failure Mitigation , 2017, 2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA).

[36]  Min Chen,et al.  Green and Mobility-Aware Caching in 5G Networks , 2017, IEEE Transactions on Wireless Communications.

[37]  Jedrzej Stanczak Mobility enhancements to reduce service interruption time for LTE and 5G , 2016, 2016 IEEE Conference on Standards for Communications and Networking (CSCN).

[38]  Janne Riihijärvi,et al.  Coverage and Robustness of mm-Wave Urban Cellular Networks: Multi-Frequency HetNets Are the 5G Future , 2017, 2017 14th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON).

[39]  Ali Imran,et al.  Spatiotemporal Mobility Prediction in Proactive Self-Organizing Cellular Networks , 2017, IEEE Communications Letters.

[40]  Gerhard Fettweis,et al.  Mobility Modeling and Performance Evaluation of Multi-Connectivity in 5G Intra-Frequency Networks , 2015, 2015 IEEE Globecom Workshops (GC Wkshps).

[41]  Li Yan,et al.  Handover Scheme for 5G C/U Plane Split Heterogeneous Network in High-Speed Railway , 2014, IEEE Transactions on Vehicular Technology.

[42]  Rami Langar,et al.  OHMP-CAC: Optimized handoff scheme based on Mobility Prediction and QoS constraints for femtocell networks , 2016, 2016 International Wireless Communications and Mobile Computing Conference (IWCMC).

[43]  Yuanyuan Qiao,et al.  An improved Markov method for prediction of user mobility , 2016, 2016 12th International Conference on Network and Service Management (CNSM).

[44]  Min Chen,et al.  Energy efficiency analysis of 5G Ultra-dense networks based on random way point mobility models , 2016, 2016 19th International Symposium on Wireless Personal Multimedia Communications (WPMC).

[45]  Dan Hu,et al.  Mobility prediction based seamless RAN-cache handover in HetNet , 2016, 2016 IEEE Wireless Communications and Networking Conference.

[46]  Muhammad Ali Imran,et al.  How Reliable is MDT-Based Autonomous Coverage Estimation in the Presence of User and BS Positioning Error? , 2016, IEEE Wireless Communications Letters.

[47]  Guy Pujolle,et al.  Optimized Handoff with Mobility Prediction Scheme Using HMM for femtocell networks , 2015, 2015 IEEE International Conference on Communications (ICC).

[48]  Mikko Säily,et al.  Hybrid paging and location tracking scheme for inactive 5G UEs , 2017, 2017 European Conference on Networks and Communications (EuCNC).

[49]  Daqiang Zhang,et al.  NextCell: Predicting Location Using Social Interplay from Cell Phone Traces , 2015, IEEE Transactions on Computers.

[50]  Didier Colle,et al.  Radio-over-fiber-based solution to provide broadband internet access to train passengers [Topics in Optical Communications] , 2007, IEEE Communications Magazine.

[51]  Hyong S. Kim,et al.  QoS provisioning in cellular networks based on mobility prediction techniques , 2003, IEEE Commun. Mag..

[52]  Georg Singer,et al.  Cell phone subscribers mobility prediction using enhanced Markov Chain algorithm , 2014, 2014 IEEE Intelligent Vehicles Symposium Proceedings.

[53]  Henrik Ryden,et al.  Predicting strongest cell on secondary carrier using primary carrier data , 2018, 2018 IEEE Wireless Communications and Networking Conference Workshops (WCNCW).

[54]  Wolfgang Kiess,et al.  SimpleCore: A connectionless, best effort, no-mobility-supporting 5G core architecture , 2016, 2016 IEEE International Conference on Communications Workshops (ICC).

[55]  Muhammad Ali Imran,et al.  On energy efficient inter-frequency small cell discovery in heterogeneous networks , 2015, 2015 IEEE International Conference on Communications (ICC).

[56]  Hui Xu,et al.  Localized Mobility Management for 5G Ultra Dense Network , 2017, IEEE Transactions on Vehicular Technology.

[57]  Osama A. Omer,et al.  Geometry Aware Scheme for Initial Access and Control of MmWave Communications in Dynamic Environments , 2019, AISI.

[58]  James Irvine,et al.  An Advanced SOM Algorithm Applied to Handover Management Within LTE , 2013, IEEE Transactions on Vehicular Technology.

[59]  Josep Mangues-Bafalluy,et al.  Machine learning based handover management for improved QoE in LTE , 2016, NOMS 2016 - 2016 IEEE/IFIP Network Operations and Management Symposium.

[60]  H.S. Kim,et al.  A Predictive Bandwidth Reservation Scheme Using Mobile Positioning and Road Topology Information , 2006, IEEE/ACM Transactions on Networking.

[61]  Shahid Mumtaz,et al.  Millimeter-Wave Massive MIMO Communication for Future Wireless Systems: A Survey , 2018, IEEE Communications Surveys & Tutorials.

[62]  Abolfazl Mehbodniya,et al.  Sojourn Time-Based Velocity Estimation in Small Cell Poisson Networks , 2016, IEEE Communications Letters.

[63]  Zaher Dawy,et al.  Planning Wireless Cellular Networks of Future: Outlook, Challenges and Opportunities , 2017, IEEE Access.

[64]  Gerhard Fettweis,et al.  Evaluation of adaptive active set management for multi-connectivity in intra-frequency 5G networks , 2016, 2016 IEEE Wireless Communications and Networking Conference.

[65]  Fatos Xhafa,et al.  VANET Simulators: A Survey on Mobility and Routing Protocols , 2011, 2011 International Conference on Broadband and Wireless Computing, Communication and Applications.

[66]  Hamada Esmaiel,et al.  Location-Based Millimeter Wave Multi-Level Beamforming Using Compressive Sensing , 2018, IEEE Communications Letters.

[67]  Min Young Chung,et al.  A multiple beam management scheme on 5G mobile communication systems for supporting high mobility , 2016, 2016 International Conference on Information Networking (ICOIN).

[68]  Hongtao Zhang,et al.  User mobility prediction based on Lagrange's interpolation in ultra-dense networks , 2016, 2016 IEEE 27th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC).

[69]  Delbert Dueck,et al.  Clustering by Passing Messages Between Data Points , 2007, Science.

[70]  Pingzhi Fan Advances in broadband wireless communications under high-mobility scenarios , 2014 .

[71]  Tarik Taleb,et al.  Efficient Tracking Area Management Framework for 5G Networks , 2016, IEEE Transactions on Wireless Communications.

[72]  Richard D. Gitlin,et al.  Base station prediction and proactive mobility management in virtual cells using recurrent neural networks , 2017, 2017 IEEE 18th Wireless and Microwave Technology Conference (WAMICON).

[73]  Xu Chen,et al.  Predicting a user's next cell with supervised learning based on channel states , 2013, 2013 IEEE 14th Workshop on Signal Processing Advances in Wireless Communications (SPAWC).

[74]  Keattisak Sripimanwat,et al.  Cell selection schemes for femtocell-to-femtocell handover deploying mobility prediction and downlink capacity monitoring in cognitive femtocell networks , 2014, TENCON 2014 - 2014 IEEE Region 10 Conference.

[75]  Ahmed S. Mubarak,et al.  LTE/Wi-Fi/mmWave RAN-Level Interworking Using 2C/U Plane Splitting for Future 5G Networks , 2018, IEEE Access.

[76]  Mohsen Guizani,et al.  Mobility Management for Intro/Inter Domain Handover in Software-Defined Networks , 2019, IEEE Journal on Selected Areas in Communications.

[77]  Mourad Khanfouci,et al.  Distributed mobility management based on centrality for dense 5G networks , 2017, 2017 European Conference on Networks and Communications (EuCNC).

[78]  Branka Vucetic,et al.  Managing Vertical Handovers in Millimeter Wave Heterogeneous Networks , 2019, IEEE Transactions on Communications.

[79]  E. Kirubakaran,et al.  A Survey on Cross-Layer Based Approach for Improving TCP Performance in Multi Hop Mobile Adhoc Networks , 2009, 2009 International Conference on Education Technology and Computer.

[80]  Pingzhi Fan,et al.  A Survey on High Mobility Wireless Communications: Challenges, Opportunities and Solutions , 2016, IEEE Access.

[81]  Raja Sattiraju,et al.  Achievable Performance Gains Using Movement Prediction and Advanced 3D System Modeling , 2014, 2014 IEEE 79th Vehicular Technology Conference (VTC Spring).

[82]  Ravi Jain,et al.  Evaluating Next-Cell Predictors with Extensive Wi-Fi Mobility Data , 2006, IEEE Transactions on Mobile Computing.

[83]  Nararat Ruangchaijatupon,et al.  Enhancing indoor positioning based on partitioning cascade machine learning models , 2014, 2014 11th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON).

[84]  Arshad Chowdhury,et al.  User mobility prediction based handoff scheme for 60 GHz radio over fiber network , 2014, 2014 IEEE 11th Consumer Communications and Networking Conference (CCNC).

[85]  Melody Moh,et al.  Location and Mobility Aware Resource Management for 5G Cloud Radio Access Networks , 2017, 2017 International Conference on High Performance Computing & Simulation (HPCS).

[86]  Muhammad Ali Imran,et al.  Mobility prediction for handover management in cellular networks with control/data separation , 2015, 2015 IEEE International Conference on Communications (ICC).

[87]  Jussi Turkka,et al.  Supporting mobility in 5G: A comparison between massive MIMO and continuous ultra dense networks , 2016, 2016 IEEE International Conference on Communications (ICC).

[88]  Naveed Ur Rehman Junejo,et al.  Sparse detection with orthogonal matching pursuit in multiuser uplink quadrature spatial modulation MIMO system , 2019, IET Commun..

[89]  Gerhard Fettweis,et al.  Achieving high availability in wireless networks by inter-frequency multi-connectivity , 2016, 2016 IEEE International Conference on Communications (ICC).

[90]  Chen He,et al.  A Novel Fuzzy Logic Vertical Handoff Algorithm with Aid of Differential Prediction and Pre-Decision Method , 2007, 2007 IEEE International Conference on Communications.

[91]  Gerhard Fettweis,et al.  Fast cell select for mobility robustness in intra-frequency 5G ultra dense networks , 2016, 2016 IEEE 27th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC).

[92]  Klaus I. Pedersen,et al.  Synchronized RACH-less handover solution for LTE heterogeneous networks , 2015, 2015 International Symposium on Wireless Communication Systems (ISWCS).

[93]  Preben E. Mogensen,et al.  From LTE to 5G for Connected Mobility , 2017, IEEE Communications Magazine.

[94]  Sami Tabbane,et al.  A novel green handover self-optimization algorithm for LTE-A / 5G HetNets , 2015, 2015 International Wireless Communications and Mobile Computing Conference (IWCMC).

[95]  Albert-László Barabási,et al.  Limits of Predictability in Human Mobility , 2010, Science.

[96]  Qing Wang,et al.  A contextual awareness-learning approach to multi-objective mobility management , 2017, 2017 12th International Conference on Computer Science and Education (ICCSE).

[97]  Lazaros F. Merakos,et al.  Mobility Management for Femtocells in LTE-Advanced: Key Aspects and Survey of Handover Decision Algorithms , 2014, IEEE Communications Surveys & Tutorials.