Machine-to-Machine (M2M) Communications in Software-defined and Virtualized Cellular Networks

Machine-to-machine (M2M) communications have attracted great attention from both academia and industry. In this paper, with recent advances in wireless network virtualization and software-defined networking (SDN), we propose a novel framework for M2M communications in software-defined cellular networks with wireless network virtualization. In the proposed framework, according to different functions and quality of service (QoS) requirements of machine-type communication devices (MTCDs), a hypervisor enables the virtualization of the physical M2M network, which is abstracted and sliced into multiple virtual M2M networks. In addition, we develop a decision-theoretic approach to optimize the random access process of M2M communications. Furthermore, we develop a feedback and control loop to dynamically adjust the number of resource blocks (RBs) that are used in the random access phase in a virtual M2M network by the SDN controller. Extensive simulation results with different system parameters are presented to show the performance of the proposed scheme.

[1]  F. Richard Yu,et al.  Software-Defined Networking (SDN) and Distributed Denial of Service (DDoS) Attacks in Cloud Computing Environments: A Survey, Some Research Issues, and Challenges , 2016, IEEE Communications Surveys & Tutorials.

[2]  Xianfu Chen,et al.  Software defined mobile networks: concept, survey, and research directions , 2015, IEEE Communications Magazine.

[3]  Victor C. M. Leung,et al.  Cross-Layer Design for TCP Performance Improvement in Cognitive Radio Networks , 2010, IEEE Transactions on Vehicular Technology.

[4]  Anna Larmo,et al.  Analysis of PDCCH Performance for M2M Traffic in LTE , 2014, IEEE Transactions on Vehicular Technology.

[5]  F. Richard Yu,et al.  Distributed Optimal Relay Selection in Wireless Cooperative Networks With Finite-State Markov Channels , 2010, IEEE Transactions on Vehicular Technology.

[6]  Tarek F. Abdelzaher,et al.  Design, implementation, and evaluation of differentiated caching services , 2004, IEEE Transactions on Parallel and Distributed Systems.

[7]  Murat Uysal,et al.  Next generation M2M cellular networks: challenges and practical considerations , 2015, IEEE Communications Magazine.

[8]  Laizhong Cui,et al.  When big data meets software-defined networking: SDN for big data and big data for SDN , 2016, IEEE Network.

[9]  Abd-Elhamid M. Taha,et al.  A survey of access management techniques in machine type communications , 2014, IEEE Communications Magazine.

[10]  Jim Esch,et al.  Software-Defined Networking: A Comprehensive Survey , 2015, Proc. IEEE.

[11]  Hsiao-Hwa Chen,et al.  M2M Communications in 3GPP LTE/LTE-A Networks: Architectures, Service Requirements, Challenges, and Applications , 2015, IEEE Communications Surveys & Tutorials.

[12]  Antonella Molinaro,et al.  Toward 5G densenets: architectural advances for effective machine-type communications over femtocells , 2015, IEEE Communications Magazine.

[13]  Harish Viswanathan,et al.  Fundamentals of Throughput Maximization With Random Arrivals for M2M Communications , 2013, IEEE Transactions on Communications.

[14]  Petar Popovski,et al.  Reliable Reporting for Massive M2M Communications With Periodic Resource Pooling , 2014, IEEE Wireless Communications Letters.

[15]  F. Richard Yu,et al.  Wireless Network Virtualization: A Survey, Some Research Issues and Challenges , 2015, IEEE Communications Surveys & Tutorials.

[16]  Ray-Guang Cheng,et al.  RACH Collision Probability for Machine-Type Communications , 2012, 2012 IEEE 75th Vehicular Technology Conference (VTC Spring).

[17]  F. Richard Yu,et al.  Software-Defined Device-to-Device (D2D) Communications in Virtual Wireless Networks With Imperfect Network State Information (NSI) , 2016, IEEE Transactions on Vehicular Technology.

[18]  F. Richard Yu,et al.  Dynamic Resource Allocation for Heterogeneous Services in Cognitive Radio Networks With Imperfect Channel Sensing , 2012, IEEE Trans. Veh. Technol..

[19]  Dan Keun Sung,et al.  A Novel Random Access for Fixed-Location Machine-to-Machine Communications in OFDMA Based Systems , 2012, IEEE Communications Letters.

[20]  Xi Zhang,et al.  Information-centric network function virtualization over 5g mobile wireless networks , 2015, IEEE Network.

[21]  Jaeho Kim,et al.  M2M Service Platforms: Survey, Issues, and Enabling Technologies , 2014, IEEE Communications Surveys & Tutorials.

[22]  Ananthram Swami,et al.  Decentralized cognitive MAC for opportunistic spectrum access in ad hoc networks: A POMDP framework , 2007, IEEE Journal on Selected Areas in Communications.

[23]  Jesus Alonso-Zarate,et al.  Massive access in the Random Access Channel of LTE for M2M communications: An energy perspective , 2015, 2015 IEEE International Conference on Communication Workshop (ICCW).

[24]  Victor C. M. Leung,et al.  Distributed Virtual Resource Allocation in Small-Cell Networks With Full-Duplex Self-Backhauls and Virtualization , 2015, IEEE Transactions on Vehicular Technology.

[25]  Victor C. M. Leung,et al.  Joint Relay Scheduling, Channel Access, and Power Allocation for Green Cognitive Radio Communications , 2015, IEEE Journal on Selected Areas in Communications.

[26]  Liang Gong,et al.  An intelligent SDN framework for 5G heterogeneous networks , 2015, IEEE Communications Magazine.

[27]  Jing Wang,et al.  Resource Allocation in a New Random Access for M2M Communications , 2015, IEEE Communications Letters.

[28]  Ilsun You,et al.  A cross-layer protocol of spectrum mobility and handover in cognitive LTE networks , 2011, Simul. Model. Pract. Theory.

[29]  Yuguang Fang,et al.  Spectrum Management for Proactive Video Caching in Information-Centric Cognitive Radio Networks , 2016, IEEE Journal on Selected Areas in Communications.

[30]  Victor C. M. Leung,et al.  A new method to support UMTS/WLAN vertical handover using SCTP , 2004, IEEE Wirel. Commun..

[31]  Yuan Fei.  A NEW METHOD TO SUPPORT UMTS / WLAN VERTICAL HANDOVER USING SCTP , 2022 .

[32]  F. Richard Yu,et al.  Prediction-Based Topology Control and Routing in Cognitive Radio Mobile Ad Hoc Networks , 2010, 2010 INFOCOM IEEE Conference on Computer Communications Workshops.

[33]  Haipeng Yao,et al.  Dynamic Spectrum Management with Movement Prediction in Vehicular Ad Hoc Networks , 2016, Ad Hoc Sens. Wirel. Networks.

[34]  W. Lovejoy.  A survey of algorithmic methods for partially observed Markov decision processes , 1991 .

[35]  Zhiqiang Li,et al.  A Distributed Consensus-Based Cooperative Spectrum-Sensing Scheme in Cognitive Radios , 2010, IEEE Transactions on Vehicular Technology.

[36]  F. Richard Yu,et al.  Energy-Efficient Resource Allocation for Heterogeneous Cognitive Radio Networks with Femtocells , 2012, IEEE Transactions on Wireless Communications.

[37]  Victor C. M. Leung,et al.  Optimal Cooperative Internetwork Spectrum Sharing for Cognitive Radio Systems With Spectrum Pooling , 2010, IEEE Transactions on Vehicular Technology.

[38]  KrishnamachariBhaskar,et al.  Software-Defined Networking Paradigms in Wireless Networks: A Survey , 2014 .

[39]  F. Richard Yu,et al.  Optimal Joint Session Admission Control in Integrated WLAN and CDMA Cellular Networks with Vertical Handoff , 2007, IEEE Transactions on Mobile Computing.

[40]  Kim Chang,et al.  Global Wireless Machine-to-Machine Standardization , 2011, IEEE Internet Computing.

[41]  Tao Tang,et al.  Handoff Performance Improvements in MIMO-Enabled Communication-Based Train Control Systems , 2012, IEEE Transactions on Intelligent Transportation Systems.

[42]  Ali Hazmi,et al.  Feasibility study of IΕΕΕ 802.11ah radio technology for IoT and M2M use cases , 2012, 2012 IEEE Globecom Workshops.

[43]  Victor C. M. Leung,et al.  Mobility-based predictive call admission control and bandwidth reservation in wireless cellular networks , 2001, Proceedings IEEE INFOCOM 2001. Conference on Computer Communications. Twentieth Annual Joint Conference of the IEEE Computer and Communications Society (Cat. No.01CH37213).

[44]  Tae-Jin Lee,et al.  Joint Access Control and Resource Allocation for Concurrent and Massive Access of M2M Devices , 2015, IEEE Transactions on Wireless Communications.

[45]  Peter Xiaoping Liu,et al.  When the Smart Grid Meets Energy-Efficient Communications: Green Wireless Cellular Networks Powered by the Smart Grid , 2012, IEEE Transactions on Wireless Communications.

[46]  Bhaskar Krishnamachari,et al.  Software-Defined Networking Paradigms in Wireless Networks: A Survey , 2014, ACM Comput. Surv..

[47]  Richard D. Smallwood,et al.  Optimal Control of Partiality Observable Markov Processes over a Finite Horizon , 2012 .

[48]  Victor C. M. Leung,et al.  Mobility-based predictive call admission control and bandwidth reservation in wireless cellular networks , 2002, Comput. Networks.

[49]  F. Richard Yu,et al.  Wireless virtualization for next generation mobile cellular networks , 2015, IEEE Wireless Communications.

[50]  Tarik Taleb,et al.  Machine-type communications: current status and future perspectives toward 5G systems , 2015, IEEE Communications Magazine.

[51]  Dusit Niyato,et al.  Random access for machine-to-machine communication in LTE-advanced networks: issues and approaches , 2013, IEEE Communications Magazine.

[52]  Dan Keun Sung,et al.  Spatial Group Based Random Access for M2M Communications , 2014, IEEE Communications Letters.

[53]  Edward J. Sondik,et al.  The Optimal Control of Partially Observable Markov Processes over a Finite Horizon , 1973, Oper. Res..

[54]  Fernando M. V. Ramos,et al.  Software-Defined Networking: A Comprehensive Survey , 2014, Proceedings of the IEEE.

[55]  Zhu Han,et al.  Virtual Resource Allocation in Information-Centric Wireless Networks With Virtualization , 2016, IEEE Transactions on Vehicular Technology.