Wireless Personal Communications

, Volume 111, Issue 1, pp 83–96 | Cite as

A Preamble Re-utilizing Access Scheme for Machine-Type Communications with Optimal Access Class Barring

  • Yue SunEmail author
  • Yu Zhu
  • Ying Li
  • Mingyu Zhang


The existing long term evolution networks originally designed for human-to-human communications are hard to tackle numerous and bursty random access requests from emerging machine-type communication devices (MTCDs). In this paper, we propose a novel preamble re-utilizing random access scheme for stationary MTCDs with optimal access class barring (ACB), which is based on joint ACB and timing advance (TA) model. By adopting load estimation algorithm, evolved node B (eNB) can detect the preamble collisions in the first step of random access procedure. Therefore, the eNB merely schedules physical uplink shared channel to the successful preambles according to standard random access procedure. The MTCDs that did not pass the first ACB check re-utilize the resources scheduled by the idle and collided preambles to access with a second optimal ACB. Analytical and simulation results manifest that our proposed scheme notably outperforms the conventional joint ACB and TA scheme in terms of four metrics: throughput, access delay, probability of preamble collision and resource efficiency.


Machine-type communications Random access Congestion control Resource allocation 



Funding was provided by National Natural Science Foundation of China (Grant No. 61671345 and 61971333).


  1. 1.
    Ko, K. S., Min, J. K., Bae, K. Y., Dan, K. S., Kim, J. H., & Ahn, J. Y. (2012). A novel random access for fixed-location machine-to-machine communications in OFDMA based systems. IEEE Communications Letters, 16(9), 1428–1431.CrossRefGoogle Scholar
  2. 2.
    Zhang, N., Kang, G., Wang, J., Guo, Y., & Labeau, F. (2015). Resource allocation in a new random access for M2M communications. IEEE Communications Letters, 19(5), 843–846.CrossRefGoogle Scholar
  3. 3.
    3rd Generation Partnership Project, Study on RAN improvements for machine-type communications. 3GPP TR 37.868 V11.0.0 (2011-09).Google Scholar
  4. 4.
    3rd Generation Partnership Project, Evolved universal terrestrial radio access(E-UTRA) physical channels and modulation. 3GPP TS 36.211 V11.3.0 (2013-06).Google Scholar
  5. 5.
    Wang, Z., & Wong, V. W. S. (2015). Optimal access class barring for stationary machine type communication devices with timing advance information. IEEE Transactions on Wireless Communications, 14(10), 5374–5387.CrossRefGoogle Scholar
  6. 6.
    Han, H., Guo, X., & Li, Y. (2016). A high throughput pilot allocation for M2M communication in crowded massive MIMO systems. IEEE Transactions on Vehicular Technology, 66(10), 9572–9576.CrossRefGoogle Scholar
  7. 7.
    Morvari, F., & Ghasemi, A. (2016). Two-stage resource allocation for random access M2M communications in LTE network. IEEE Communications Letters, 20(5), 982–985.CrossRefGoogle Scholar
  8. 8.
    Shirvanimoghaddam, M., Dohler, M., & Johnson, S. (2017). Massive multiple access based on superposition raptor codes for cellular M2M communications. IEEE Transactions on Wireless Communications, 16(1), 307–319.CrossRefGoogle Scholar
  9. 9.
    Wang, B., Wu, Y., Han, F., Yang, Y. H., & Liu, K. J. R. (2011). Green wireless communications: A time-reversal paradigm. IEEE Journal on Selected Areas in Communications, 29(8), 1698–1710.CrossRefGoogle Scholar
  10. 10.
    Duan, S., Shah-Mansouri, V., Wang, Z., & Wong, V. W. S. (2016). D-ACB: adaptive congestion control algorithm for bursty M2M traffic in lte networks. IEEE Transactions on Vehicular Technology, 65(12), 9847–9861.CrossRefGoogle Scholar
  11. 11.
    3rd Generation Partnership Project, Evolved universal terrestrial radio access (E-UTRA) physical layer procedures. 3GPP TS 36.213 V11.3.0 (2013-06).Google Scholar

Copyright information

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

Authors and Affiliations

  1. 1.State Key Laboratory of Integrated Service NetworksXidian UniversityXi’anChina

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