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Aggregation transmission scheme for machine type communications

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.

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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

Correspondence to Ming Zhao.

Additional information

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

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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

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Keywords

  • machine type communications
  • aggregation transmission scheme
  • delay tolerance
  • genetic algorithm
  • big data