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Distributed ADMM-based approach for total harvested power maximization in non-linear SWIPT system

  • Pham Viet TuanEmail author
  • Insoo Koo
Article
  • 30 Downloads

Abstract

In this paper, the distributed alternating direction method of multipliers (ADMM)-based approach is investigated for total harvested power maximization (THPM) in multiple-input single-output transceiver pairs with simultaneous wireless information and power transfer. The power-splitting architecture and practical non-linear energy harvesting are utilized at each receiver. The highly non-convex THPM problem is formulated under requirements for the data rate, the amount of harvested power, and with limited power at the transmitters. First, semidefinite relaxation (SDR) and sequential parametric convex approximation are exploited to convert the non-convex THPM to a series of convex subproblems. Then, slack variables replacing interference are introduced to allow decomposing into distributed subproblems via ADMM. The interesting point is that optimal precoding matrices of the local subproblems satisfy the rank-1 constraints of SDR. Finally, the numerical experiments present the convergence of the proposed algorithm, obtaining results similar to the centralized approach, as well as comparing it with some baseline schemes.

Keywords

Simultaneous wireless information and power transfer (SWIPT) Semidefinite relaxation (SDR) Non-linear energy harvesting model Sequential parametric convex approximation (SPCA) Alternating direction method of multipliers (ADMM) 

Notes

Acknowledgements

This work was supported by the National Research Foundation of Korea (NRF) grant through the Korean Government (MSIT) under Grant NRF-2018R1A2B6001714.

References

  1. 1.
    Zhang, R., & Ho, C. K. (2013). MIMO broadcasting for simultaneous wireless information and power transfer. IEEE Transactions on Wireless Communications, 12(5), 1989–2001.CrossRefGoogle Scholar
  2. 2.
    Valenta, C., & Durgin, G. (2014). Harvesting wireless power: Survey of energyharvester conversion efficiency in far-field, wireless power transfer systems. IEEE Microwave Magazine, 15(4), 108–120.CrossRefGoogle Scholar
  3. 3.
    Lu, X., Wang, P., Niyato, D., Kim, D. I., & Han, Z. (2015). Wireless networks with RF energy harvesting: A contemporary survey. IEEE Communications Surveys, 17(2), 757–789.CrossRefGoogle Scholar
  4. 4.
    Bi, S., Ho, C. K., & Zhang, R. (2015). Wireless powered communication: Opportunities and challenges. IEEE Communications Magazine, 53(4), 117–125.CrossRefGoogle Scholar
  5. 5.
    Clerckx, B., Zhang, R., Schober, R., Ng, D. W. K., Kim, D. I., & Poor, H. V. (2019). Fundamentals of wireless information and power transfer: From RF energy harvester models to signal and system designs. IEEE Journal on Selected Areas in Communications, 37(1), 4–33.CrossRefGoogle Scholar
  6. 6.
    Ding, Z., Zhong, C., Ng, D. W. K., Peng, M., Suraweera, H. A., Schober, R., et al. (2015). Application of smart antenna technologies in simultaneous wireless information and power transfer. IEEE Communications Magazine, 53(4), 86–93.CrossRefGoogle Scholar
  7. 7.
    Boshkovska, E., Ng, D. W. K., Zlatanov, N., & Schober, R. (2015). Practical nonlinear energy harvesting model and resource allocation for SWIPT systems. IEEE Communications Letters, 19(12), 2082–2085.CrossRefGoogle Scholar
  8. 8.
    Xiong, K., Wang, B., & Liu, K. J. R. (2017). Rate-energy region of SWIPT for MIMO broadcasting under nonlinear energy harvesting model. IEEE Transactions on Wireless Communications, 16(8), 5147–5161.CrossRefGoogle Scholar
  9. 9.
    Shi, Q., Liu, L., Xu, W., & Zhang, R. (2014). Joint transmit beamforming and receive power splitting for MISO SWIPT systems. IEEE Transactions on Wireless Communications, 13(6), 3269–3280.CrossRefGoogle Scholar
  10. 10.
    Tuan, P. V., & Koo, I. (2017). Optimal multiuser MISO beamforming for power-splitting SWIPT cognitive radio networks. IEEE Access, 5, 14141–14153.CrossRefGoogle Scholar
  11. 11.
    Shen, C., Li, W.-C., & Chang, T. H. (2014). Wireless information and energy transfer in multi-antenna interference channel. IEEE Transactions on Signal Processing, 62(23), 6249–6264.MathSciNetCrossRefGoogle Scholar
  12. 12.
    Timotheou, S., Krikidis, I., Zheng, G., & Ottersten, B. (2014). Beamforming for MISO interference channels with QoS and RF energy transfer. IEEE Transactions on Wireless Communications, 13(5), 2646–2658.CrossRefGoogle Scholar
  13. 13.
    Zhao, M. M., Cai, Y., Shi, Q., Champagne, B., & Zhao, M. J. (2016). Robust transceiver design for MISO interference channel with energy harvesting. IEEE Transactions on Signal Processing, 64(17), 4618–4633.MathSciNetCrossRefGoogle Scholar
  14. 14.
    Lu, Y., Xiong, K., Fan, P., Zhong, Z., & Letaief, K. B. (2018). Coordinated beamforming with artificial noise for secure SWIPT under non-linear EH model: Centralized and distributed designs. The IEEE Journal on Selected Areas in Communications (early access), 1–20.Google Scholar
  15. 15.
    Jang, S., Lee, H., Kang, S., Oh, T., & Lee, I. (2018). Energy efficient SWIPT systems in multi-cell MISO networks. IEEE Transactions on Wireless Communications, 17(12), 8180–8194.CrossRefGoogle Scholar
  16. 16.
    Joshi, S. K., Codreanu, M., & Latva-aho, M. (2014). Distributed resource allocation for MISO downlink systems via the alternating direction method of multipliers. EURASIP Journal on Wireless Communications and Networking, 2014, 1–19.CrossRefGoogle Scholar
  17. 17.
    Shen, C., Chang, T.-H., Wang, K.-Y., Qiu, Z., & Chi, C.-Y. (2012). Distributed robust multicell coordinated beamforming with imperfect CSI: An ADMM approach. IEEE Transactions on Signal Processing, 60, 2988–3003.MathSciNetCrossRefGoogle Scholar
  18. 18.
    Palomar, D. P., & Chiang, M. (2006). A tutorial on decomposition methods for network utility maximization. IEEE Journal on Selected Areas in Communications, 24(8), 1439–1451.CrossRefGoogle Scholar
  19. 19.
    Boyd, S., Parikh, N., Chu, E., Peleato, B., & Eckstein, J. (2010). Distributed optimization and statistical learning via the alternating direction method of multipliers. Foundation and Trends in Machine Learning, 3(1), 1–122.CrossRefGoogle Scholar
  20. 20.
    Huang, K., & Sidiropoulos, N. D. (2016). Consensus-ADMM for general quadratically constrained quadratic programming. IEEE Transactions on Signal Processing, 64(20), 5297–5310.MathSciNetCrossRefGoogle Scholar
  21. 21.
    Chen, E., & Tao, M. (2017). ADMM-based fast algorithm for multigroup multicast beamforming in large-scale wireless systems. IEEE Transactions on Communications, 65(6), 2685–2698.MathSciNetCrossRefGoogle Scholar
  22. 22.
    Wang, F., Peng, T., & Huang, Y. (2018). Decentralized robust transceiver designs for MISO SWIPT interference channel. IEEE Access, 6, 4537–4546.CrossRefGoogle Scholar
  23. 23.
    Tuan, P. V., & Koo, I. (2017). Robust weighted sum harvested energy maximization for SWIPT cognitive radio networks based on particle swarm optimization. SENSORS, 17(10), 1–22.CrossRefGoogle Scholar
  24. 24.
    Zhou, F., Chu, Z., Sun, H., Hu, R., & Hanzo, L. (2018). Artificial noise aided secure cognitive beamforming for cooperative MISO-NOMA using SWIPT. IEEE Journal on Selected Areas in Communications, 36, 918–931.CrossRefGoogle Scholar
  25. 25.
    Tuan, P. V., & Koo, I. (2019). Optimizing Efficient Energy transmission in SWIPT interference channel under non-linear/linear EH model. IEEE System Journal.  https://doi.org/10.1109/JSYST.2019.2924265.
  26. 26.
    Luo, Z., Ma, W., So, A., Ye, Y., & Zhang, S. (2010). Semidefinite relaxation of quadratic optimization problems. IEEE Signal Processing Magazine, 27(3), 20–34.CrossRefGoogle Scholar
  27. 27.
    Beck, A., Ben-Tal, A., & Tetruashvili, L. (2009). A sequential parametric convex approximation method with applications to nonconvex truss topology design problems. Journal of Global Optimization, 47(1), 29–51.MathSciNetCrossRefGoogle Scholar
  28. 28.
    Grant, M. C., & Boyd, S. P. (2017). The CVX User’s Guide, Release 2.1 [Online]. http://web.cvxr.com/cvx/doc/CVX.pdf.
  29. 29.
    Karipidis, E., Sidiropoulos, N. D., & Luo, Z. Q. (2008). Quality of service and max-min fair transmit beamforming to multiple cochannel multicast groups. IEEE Transactions on Signal Processing, 56(3), 1268–1279.MathSciNetCrossRefGoogle Scholar
  30. 30.
    Khandaker, M. R. A., & Wong, K. K. (2015). Robust secrecy beamforming with energy-harvesting eavesdroppers. IEEE Wireless Commun. Lett., 4(1), 10–13.CrossRefGoogle Scholar
  31. 31.
    Horn, R. A., & Johnson, C. R. (1985). Matrix analysis. Cambridge: Cambridge Univ Press.CrossRefGoogle Scholar

Copyright information

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

Authors and Affiliations

  1. 1.Division of Computational Mechatronics, Institute for Computational ScienceTon Duc Thang UniversityHo Chi Minh CityVietnam
  2. 2.Faculty of Electrical and Electronics EngineeringTon Duc Thang UniversityHo Chi Minh CityVietnam
  3. 3.School of Electrical and Computer EngineeringUniversity of UlsanUlsanKorea

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