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Energy Efficiency Optimization-Based Joint Resource Allocation and Clustering Algorithm for M2M Communication Networks (Workshop)

  • Changzhu LiuEmail author
  • Ahmad Zubair
  • Rong Chai
  • Qianbin Chen
Conference paper
  • 122 Downloads
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 313)

Abstract

In recent years, machine-to-machine (M2M) communications have attracted great attentions from both academia and industry. In M2M communication networks, machine type communication devices (MTCDs) are capable of communicating with each other intelligently under highly reduced human interventions. In this paper, we address the problem of joint resource allocation and clustering for M2M communications. By defining the system energy efficiency (EE) as the sum of the EE of MTCDs, the joint resource allocation and clustering problem is formulated as a system EE maximization problem. As the original optimization problem is a nonlinear fractional programming problem, which cannot be solved conveniently, we transform it into two subproblems, i,e., power allocation subproblem and clustering subproblem, and solve the two subproblems by means of Lagrange dual method and modified K-means algorithm, respectively. Numerical results demonstrate the effectiveness of the proposed algorithm.

Keywords

Machine to machine (M2M) communications Clustering Resource allocation Energy efficiency (EE) 

References

  1. 1.
    Dawy, Z., Saad, W., Ghosh, A., Andrews, J.G., Yaacoub, E.: Toward massive machine type cellular communications. IEEE Wirel. Commun. 24(1), 120–128 (2017)CrossRefGoogle Scholar
  2. 2.
    Sun, W., Liu, J., Zhang, H.: When smart wearables meet intelligent vehicles: challenges and future directions. IEEE Wirel. Commun. 24(3), 58–65 (2017)CrossRefGoogle Scholar
  3. 3.
    Han, H., Guo, X., Li, Y.: A high throughput pilot allocation for M2M communication in crowded massive MIMO systems. IEEE Trans. Veh. Technol. 66(10), 9572–9576 (2017)CrossRefGoogle Scholar
  4. 4.
    Ghavimi, F., Lu, Y., Chen, H.: Uplink scheduling and power allocation for M2M communications in SC-FDMA-based LTE-a networks with QoS guarantees. IEEE Trans. Veh. Technol. 66(7), 6160–6170 (2017)CrossRefGoogle Scholar
  5. 5.
    Zhang, D., Chen, Z., Awad, M.K., Zhang, N., Zhou, H., Shen, X.S.: Utility-optimal resource management and allocation algorithm for energy harvesting cognitive radio sensor networks. IEEE J. Sel. Areas Commun. 34(12), 3552–3565 (2016)CrossRefGoogle Scholar
  6. 6.
    Miao, G., Azari, A., Hwang, T.: \(E^{2}\)-MAC: energy efficiency medium access for massive M2M communications. IEEE Trans. Commun. 64(11), 4720–4735 (2016)CrossRefGoogle Scholar
  7. 7.
    Yang, Z., Xu, W., Pan, Y., Pan, C., Chen, M.: Energy efficiency resource allocation in machine-to-machine communications with multiple access and energy harvesting for IoT. IEEE Internet Things J. 5(1), 229–245 (2018)CrossRefGoogle Scholar
  8. 8.
    Tefek, U., Lim, T.J.: Relaying and radio resource partitioning for machine-type communications in cellular networks. IEEE Trans. Wirel. Commun. 16(2), 1344–1356 (2017)CrossRefGoogle Scholar
  9. 9.
    Boyd, S., Vandenberghe, L.: Convex Optimization. Cambridge University Press, Cambridge (2004)CrossRefGoogle Scholar
  10. 10.
    Kaufman, L., Rousseeuw, P.J.: Finding groups in data: an introduction to cluster analysis. In: DBLP (2009)Google Scholar

Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2020

Authors and Affiliations

  • Changzhu Liu
    • 1
    Email author
  • Ahmad Zubair
    • 1
  • Rong Chai
    • 1
  • Qianbin Chen
    • 1
  1. 1.Key Lab of Mobile Communication TechnologyChongqing University of Posts and TelecommunicationsChongqingChina

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