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Confidence interval based model predictive control of transmit power with reliability constraint

  • Wei Sun
  • Hao Yu
  • Yangzhao Yang
  • Qiyue LiEmail author
  • Daoming Mu
  • Xiaobing Xu
Article
  • 3 Downloads

Abstract

Because the wireless signal, such as 5G, is propagated in the medium of air, it is easily been interfered by other devices or environmental factors. Adjusting the transmit power of wireless transceiver could control the signal-to-noise ratio (SNR) and accordingly improves the reliability of communication. However, the control of stochastic SNR with deterministic requirements is still a challenging problem. Hence we study the control of transmit power to satisfy the reliability requirements and, in the meanwhile, save battery energy. To deal with the stochastic character of the wireless link, we introduce the confidence interval bound and then propose confidence interval based model predictive control (CI-MPC), in which we creatively separated the SNR as the feedback signal and the confidence interval based compensating signal. To verify the performance of our CI-MPC, we compared it with the state-of-the-art methods by simulation. Besides, we also tested the proposed CI-MPC method in the real-world industrial environment on the test-bed to show its effectiveness.

Keywords

Transmit power control Confidence interval Model predictive control Wireless communication reliability Internet of things 

Notes

Acknowledgements

This work was supported by National Natural Science Foundation of China (51877060), the Fundamental Research Funds for the Central Universities of China (PA2019GDQT0006 and JZ2018HGTB0253), and the Science and Technology Project of State Grid “Research and application of key Technologies for operation and maintenance of smart substation based on the fusion of heterogeneous network and data”.

References

  1. 1.
    Akhlaghi, S., Zhou, N., & Huang, Z. (2017). Adaptive adjustment of noise covariance in kalman filter for dynamic state estimation. In Power & energy society general meeting, 2017 (pp. 1–5). IEEE.Google Scholar
  2. 2.
    Arshad, R., Afify, L. H., ElSawy, H., Al-Naffouri, T. Y., & Alouini, M. S. (2019). On the effect of uplink power control on temporal retransmission diversity. IEEE Wireless Communications Letters, 8(1), 309–312.CrossRefGoogle Scholar
  3. 3.
    Chen, H., Ma, Y., Lin, Z., Li, Y., & Vucetic, B. (2016). Distributed power control in interference channels with qos constraints and rf energy harvesting: A game-theoretic approach. IEEE Transactions on Vehicular Technology, 65(12), 10063–10069.CrossRefGoogle Scholar
  4. 4.
    Gong, X., Plets, D., Tanghe, E., De Pessemier, T., Martens, L., & Joseph, W. (2018). An efficient genetic algorithm for large-scale transmit power control of dense and robust wireless networks in harsh industrial environments. Applied Soft Computing, 65, 243–259.CrossRefGoogle Scholar
  5. 5.
    Hu, H., Liu, Z., & An, J. (2019). Mining mobile intelligence for wireless systems: A deep neural network approach. IEEE Computational Intelligence Magazine, 2019, 1–1.Google Scholar
  6. 6.
    Lee, W., Kim, M., & Cho, D. H. (2018). Deep learning based transmit power control in underlaid device-to-device communication. IEEE Systems Journal, 13(3), 2551–2554.CrossRefGoogle Scholar
  7. 7.
    Li, J., Feng, R., Sun, W., Chen, L., Xu, X., & Li, Q. (2018). Joint mode selection and resource allocation for scalable video multicast in hybrid cellular and D2D network. IEEE Access, 6, 64350–64358.CrossRefGoogle Scholar
  8. 8.
    Li, J., Li, Q., Qu, Y., & Zhao, B. (2011). An energy-efficient mac protocol using dynamic queue management for delay-tolerant mobile sensor networks. Sensors, 11(2), 1847–1864.CrossRefGoogle Scholar
  9. 9.
    Lin, S., Miao, F., Zhang, J., Zhou, G., Gu, L., He, T., et al. (2016). ATPC: Adaptive transmission power control for wireless sensor networks. ACM Transactions on Sensor Networks (TOSN), 12(1), 6.CrossRefGoogle Scholar
  10. 10.
    Liu, Z., Tsuda, T., Watanabe, H., Ryuo, S., & Iwasawa, N. (2019). Data driven cyber-physical system for landslide detection. Mobile Networks and Applications, 24(3), 991–1002.CrossRefGoogle Scholar
  11. 11.
    Liu, Z., Zhang, C., Dong, M., Gu, B., Ji, Y., & Tanaka, Y. (2016). Markov-decision-process-assisted consumer scheduling in a networked smart grid. IEEE Access, 5, 2448–2458.CrossRefGoogle Scholar
  12. 12.
    Muntjir, M., Rahul, M., & Alhumyani, H. A. (2017). An analysis of internet of things (IoT): Novel architectures, modern applications, security aspects and future scope with latest case studies. International Journal of Engineering Research and Technology, 6(6), 422–447.Google Scholar
  13. 13.
    Phan, D. D., Moulay, E., Coirault, P., Poussard, A. M., & Vauzelle, R. (2015). Potential feedback control for the power control in wireless sensor networks. IET Control Theory & Applications, 9(13), 2022–2028.MathSciNetCrossRefGoogle Scholar
  14. 14.
    Soleymani, T., Zoppi, S., Vilgelm, M., Hirche, S., Kellerer, W., & Baras, J. S. (2018). Covariance-based transmission power control for estimation over wireless sensor networks. In 2018 European control conference (ECC) (pp. 857–862). IEEE.Google Scholar
  15. 15.
    Sun, P., Shin, K. G., Zhang, H., & He, L. (2016). Transmit power control for D2D-underlaid cellular networks based on statistical features. IEEE Transactions on Vehicular Technology, 66(5), 4110–4119.CrossRefGoogle Scholar
  16. 16.
    Sun, W., Li, Q., Wang, J., Chen, L., Mu, D., & Yuan, X. (2018). A radio link reliability prediction model for wireless sensor networks. International Journal of Sensor Networks, 27(4), 215–226.CrossRefGoogle Scholar
  17. 17.
    Sun, W., Yuan, X., Wang, J., Li, Q., Chen, L., & Mu, D. (2018). End-to-end data delivery reliability model for estimating and optimizing the link quality of industrial wsns. IEEE Transactions on Automation Science and Engineering, 15(3), 1127–1137.CrossRefGoogle Scholar
  18. 18.
    Suto, K., Nishiyama, H., Kato, N., & Kuri, T. (2018). Model predictive joint transmit power control for improving system availability in energy-harvesting wireless mesh networks. IEEE Communications Letters, 22(10), 2112–2115.CrossRefGoogle Scholar
  19. 19.
    Tan, Q., An, W., Han, Y., Liu, Y., Ci, S., Shao, F. M., et al. (2015). Energy harvesting aware topology control with power adaptation in wireless sensor networks. Ad Hoc Networks, 27, 44–56.CrossRefGoogle Scholar
  20. 20.
    Torkestani, J. A. (2015). An energy-efficient topology control mechanism for wireless sensor networks based on transmit power adjustment. Wireless Personal Communications, 82(4), 2537–2556.CrossRefGoogle Scholar
  21. 21.
    Wang, H., Ding, G., Gao, F., Chen, J., Wang, J., & Wang, L. (2018). Power control in uav-supported ultra dense networks: Communications, caching, and energy transfer. IEEE Communications Magazine, 56(6), 28–34.CrossRefGoogle Scholar
  22. 22.
    Wang, X., Liu, Z., Gao, Y., Zheng, X., Chen, X., & Wu, C. (2019). Near-optimal data structure for approximate range emptiness problem in information-centric internet of things. IEEE Access, 7, 21857–21869.CrossRefGoogle Scholar
  23. 23.
    Zahid, N., Sodhro, A. H., Zafar, R. F., Zahid, B., Khan, S. A., & Akhter, F.: Regression-based transmission power control for green healthcare. In 2019 2nd international conference on computing, mathematics and engineering technologies (iCoMET) (pp. 1–9). IEEE.Google Scholar
  24. 24.
    Zamalloa, M. Z., & Krishnamachari, B. (2007). An analysis of unreliability and asymmetry in low-power wireless links. ACM Transactions on Sensor Networks (TOSN), 3(2), 7.CrossRefGoogle Scholar

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 TechnologyHefeiChina
  2. 2.China Academy of Electronics and Information TechnologyBeijingChina

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