Skip to main content
SpringerLink
Log in

Aggregation transmission scheme for machine type communications

  • Research Paper
  • Special Focus on Machine-Type Communications
  • Published:
Science China Information Sciences Aims and scope Submit manuscript

Abstract

Massive amount of small data generated by machine type communications (MTC) will pose a challenge to the future fifth generation (5G) wireless network. Since the information from or to the machine type users aggregating closely are highly correlated, the relevance of data can be excavated by big data analysis to help improve the spectral efficiency. In this paper we proposed an aggregation transmission scheme (ATS) for MTC downlink transmissions in which the transmission order of users’ data packets can be adjusted according to their relevance under the delay constraints. The users having relevance will temporally share the time slots and their data are transmitted in a multicast way so that much less timeslots are needed. We propose three different algorithms, conditional random search (CRS), standard-row algorithm (SRA), and genetic algorithm (GA) to tackle the problem of transmission order adjustment. Simulation results validate the good performance of ATS and demonstrate that SRA has the lowest complexity while GA may achieve a better performance. We also analyze the impact of different delay requirements. Our work sheds light on dealing with massive MTC data traffic for future wireless communications.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Chen K C. Machine-to-machine communications for healthcare. J Comput Inf Sci Eng, 2012, 6: 119–126

    Article  Google Scholar 

  2. Fadlullah Z M, Fouda M M, Kato N, et al. Toward intelligent machine-to-machine communications in smart grid. IEEE Commun Mag, 2011, 49: 60–65

    Article  Google Scholar 

  3. Wu G, Talwar S, Johnsson K, et al. M2M: from mobile to embedded Internet. IEEE Commun Mag, 2011, 49: 36–43

    Google Scholar 

  4. Cisco. Cisco Visual Networking Index: Global Mobile Data Traffic Forecast Update, 2016–2021. White Paper. 2017

  5. Ericsson L. More than 50 billion connected devices. White Paper. 2011

  6. Dawy Z, Saad W, Ghosh A, et al. Toward massive machine type cellular communications. IEEE Wirel Commun, 2017, 24: 120–128

    Article  Google Scholar 

  7. Shariatmadari H, Ratasuk R, Iraji S, et al. Machine-type communications: current status and future perspectives toward 5G systems. IEEE Commun Mag, 2015, 53: 10–17

    Article  Google Scholar 

  8. Ma Z, Zhang Z Q, Ding Z G, et al. Key techniques for 5G wireless communications: network architecture, physical layer, and MAC layer perspectives. Sci China Inf Sci, 2015, 58: 041301

    Google Scholar 

  9. Andrews J G, Buzzi S, Choi W, et al. What will 5G be? IEEE J Sel Areas Commun, 2014, 32: 1065–1082

    Article  Google Scholar 

  10. Chen X, Chen T Y, Guo D N. Capacity of Gaussian many-access channels. IEEE Trans Inf Theory, 2017, 63: 3516–3539

    Article  Google Scholar 

  11. Xie R G, Yin H R, Chen X H, et al. Many access for small packets based on precoding and sparsity-aware recovery. IEEE Trans Commun, 2016, 64: 4680–4694

    Article  Google Scholar 

  12. Zhang G P, Liu P, Yang K, et al. Orthogonal resource sharing scheme for device-to-device communication overlaying cellular networks: a cooperative relay based approach. Sci China Inf Sci, 2015, 58: 102301

    Google Scholar 

  13. Lien S Y, Chen K C, Lin Y. Toward ubiquitous massive accesses in 3GPP machine-to-machine communications. IEEE Commun Mag, 2011, 49: 66–74

    Article  Google Scholar 

  14. Lecompte D, Gabin F. Evolved multimedia broadcast/multicast service (eMBMS) in LTE-advanced: overview and Rel-11 enhancements. IEEE Commun Mag, 2012, 50: 68–74

    Article  Google Scholar 

  15. Valerio D, Ricciato F, Belanovic P, et al. UMTS on the road: Broadcasting intelligent road safety information via MBMS. In: Proceedings of the 2008 IEEE 67th Vehicular Technology Conference (VTC-Spring), Singapore, 2008. 3026–3030

    Chapter  Google Scholar 

  16. Chin W H, Fan Z, Haines R. Emerging technologies and research challenges for 5G wireless networks. IEEE Wirel Commun, 2014, 21: 106–112

    Article  Google Scholar 

  17. Lu X, Wetter E, Bharti N, et al. Approaching the limit of predictability in human mobility. Sci Rep, 2013, 3: 1–9

    Google Scholar 

  18. Song C M, Qu Z H, Blumm N, et al. Limits of predictability in human mobility. Science, 2010, 327: 1018–1021

    Article  MathSciNet  MATH  Google Scholar 

  19. Hung H N, Lin Y B, Lu M K, et al. A statistic approach for deriving the short message transmission delay distributions. IEEE Trans Wirel Commun, 2004, 3: 2345–2352

    Article  Google Scholar 

  20. Beasley D, Bull D R, Martin R R. An overview of genetic algorithms: part 1, fundamentals. Univ Comput, 1993, 15: 56–69

    Google Scholar 

  21. Sun Y H, Zhang S H, Zhu J K, et al. Aggregation postpone transmission scheme for machine type communications. In: Proceedings of the 2016 IEEE 19th Wireless Personal Multimedia Communications (WPMC), Shenzhen, 2016. 205–210

    Google Scholar 

Download references

Acknowledgements

This work was partially supported by Natural Science Foundation of China (Grant No. 61461136002), Key Program of National Natural Science Foundation of China (Grant No. 61631018), Fundamental Research Funds for the Central Universities, and Huawei Innovation Research Program.

Author information

Authors and Affiliations

  1. Key Laboratory of Wireless-Optical Communications, Chinese Academy of Sciences, University of Science and Technology of China, Hefei, 230027, China

    Yanhuan Sun, Ming Zhao, Sihai Zhang & Wuyang Zhou

  2. Laboratory of Personal Communication Network and Spread Spectrum, University of Science and Technology of China, Hefei, 230027, China

    Jinkang Zhu

  3. Department of Electrical and Computer Engineering, Storrs, CT, 06269, USA

    Shengli Zhou

Authors
  1. Yanhuan Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ming Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sihai Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jinkang Zhu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Wuyang Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Shengli Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ming Zhao.

Additional information

Conflict of interest The authors declare that they have no conflict of interest.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sun, Y., Zhao, M., Zhang, S. et al. Aggregation transmission scheme for machine type communications. Sci. China Inf. Sci. 60, 100305 (2017). https://doi.org/10.1007/s11432-017-9196-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11432-017-9196-0

Keywords

Search

Navigation