Effects of Heterogeneous Mobility on D2D- and Drone-Assisted Mission-Critical MTC in 5G

mcMTC is starting to play a central role in the industrial Internet of Things ecosystem and have the potential to create high-revenue businesses, including intelligent transportation systems, energy/ smart grid control, public safety services, and high-end wearable applications. Consequently, in the 5G of wireless networks, mcMTC have imposed a wide range of requirements on the enabling technology, such as low power, high reliability, and low latency connectivity. Recognizing these challenges, the recent and ongoing releases of LTE systems incorporate support for lowcost and enhanced coverage, reduced latency, and high reliability for devices at varying levels of mobility. In this article, we examine the effects of heterogeneous user and device mobility -- produced by a mixture of various mobility patterns -- on the performance of mcMTC across three representative scenarios within a multi-connectivity 5G network. We establish that the availability of alternative connectivity options, such as D2D links and drone-assisted access, helps meet the requirements of mcMTC applications in a wide range of scenarios, including industrial automation, vehicular connectivity, and urban communications. In particular, we confirm improvements of up to 40 percent in link availability and reliability with the use of proximate connections on top of the cellular-only baseline.

[1]  IMT Vision – Framework and overall objectives of the future development of IMT for 2020 and beyond M Series Mobile , radiodetermination , amateur and related satellite services , 2015 .

[2]  Olga Galinina,et al.  Understanding the IoT connectivity landscape: a contemporary M2M radio technology roadmap , 2015, IEEE Communications Magazine.

[3]  Sergey Andreev,et al.  Direct Connection on the Move: Characterization of User Mobility in Cellular-Assisted D2D Systems , 2016, IEEE Vehicular Technology Magazine.

[4]  David Tse,et al.  Mobility increases the capacity of ad hoc wireless networks , 2002, TNET.

[5]  Gerardo Rubino,et al.  Rare Event Simulation using Monte Carlo Methods , 2009 .

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

[7]  José Francisco Monserrat del Río,et al.  D1.1 Refined scenarios and requirements, consolidated use cases, and qualitative techno-economic feasibility assessment , 2016 .

[8]  Andrea Zanella,et al.  Context-Aware Handover Policies in HetNets , 2016, IEEE Transactions on Wireless Communications.

[9]  Y.-P. Eric Wang,et al.  Analysis of ultra-reliable and low-latency 5G communication for a factory automation use case , 2015, 2015 IEEE International Conference on Communication Workshop (ICCW).

[10]  Stefano Giordano,et al.  Rare event simulation , 2002, Eur. Trans. Telecommun..

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

[12]  Theodore S. Rappaport,et al.  Millimeter Wave Mobile Communications for 5G Cellular: It Will Work! , 2013, IEEE Access.

[13]  Zhu Han,et al.  Game-theoretic resource allocation methods for device-to-device communication , 2014, IEEE Wireless Communications.

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

[15]  Anders Eriksson,et al.  Providing quality of service in always best connected networks , 2003, IEEE Commun. Mag..

[16]  Stefan Parkvall,et al.  Device-to-Device Communications for National Security and Public Safety , 2014, IEEE Access.