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An Energy Efficient Uplink Scheduling and Resource Allocation for M2M Communications in SC-FDMA Based LTE-A Networks

  • Qiyue Li
  • Yuling Ge
  • Yangzhao Yang
  • Yadong Zhu
  • Wei SunEmail author
  • Jie Li
Article
  • 23 Downloads

Abstract

In future wireless communication, a large number of devices equipped with several different types of sensors will require access networks with diverse quality-of-service constraints. In cellular network evolution, the long term evolution advanced (LTE-A) networks has standardized Machine-to-Machine (M2M) features. Such M2M technology can provide a promising infrastructure for Internet of things (IoT) sensing applications, which usually require real-time data reporting. However, LTE-A is not designed for directly supporting such low-data-rate devices with optimized energy efficiency since it depends on core technologies of LTE that are originally designed for high-data-rate services. This paper investigate the maximum energy efficient data packets M2M transmission with uplink channels in LTE-A network. We formulate it into a joint problem of Modulation-and-Coding Scheme (MCS) assignment, resource allocation and power control, which can be expressed as a non-deterministic polynomial hard (NP-hard) mixed-integer linear fractional programming problem. Then we propose a global optimization scheme with Charnes-Cooper transformation and Glover linearization. The numerical experiment results show that with limited resource blocks, our algorithm can maintain low data packets dropping ratios while achieving optimal energy efficiency for a large number of M2M nodes, comparing with other typical counterparts.

Keywords

Energy efficiency Machine-to-Machine (M2M) communication Real-time data reporting Resource allocation and scheduling 

Notes

Acknowledgments

This work is supported in part by grants from the Fundamental Research Funds for the Central Universities (JZ2018HGTB0253, JZ2019HGTB0089, PA2019GDQT0006), National Natural Science Foundation of China (51877060), ANHUI Province Key Laboratory of Affective Computing & Advanced Intelligent Machine, Grant No. ACAIM180102, and State Grid Science and Technology Project (Research and application of key Technologies for integrated substation intelligent operation and maintenance based on the fusion of heterogeneous network and heterogeneous data).

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.School of Electrical Engineering and AutomationHefei University of TechnologyAnhuiChina
  2. 2.China Academy of Electronics and Information TechnologyBeijingChina
  3. 3.School of Computer and InformationHefei University of TechnologyAnhuiChina

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