DOI QR코드

DOI QR Code

Power Allocation and Splitting Algorithm with Low-complexity for SWIPT in Energy Harvesting Networks

에너지 하베스팅 네트워크에서 SWIPT를 위한 저복잡도를 갖는 파워 할당 및 분할 알고리즘

  • Lee, Kisong (Department of Information and Telecommunication Engineering, Kunsan National University) ;
  • Ko, JeongGil (Department of Software and Computer Engineering, Ajou University)
  • Received : 2016.04.05
  • Accepted : 2016.04.18
  • Published : 2016.05.31

Abstract

Recently, energy harvesting, in which energy is collected from RF signals, has been regarded as a promising technology to improve the lifetime of sensors by alleviating the lack of power supply problem. In this paper, we try to propose an efficient algorithm for simultaneous wireless information and power transfer. At first, we find the lower bound of water-level using the probability density function of channel, and derive the solution of power allocation in energy harvesting networks. In addition, we derive an efficient power splitting method for satisfying the minimum required harvested energy constraint. The simulation results confirm that the proposed scheme improves the average data rate while guaranteeing the minimum required harvested energy constraint, compared with the conventional scheme. In addition, the proposed algorithm can reduce the computational complexity remarkably with insignificant performance degradation less than 10%, compared to the optimal solution.

RF신호로부터 전력을 수집하는 에너지 하베스팅 기술은 센서의 전원 공급 문제를 해결함으로써 네트워크의 수명을 향상시킬 수 있는 방안으로 최근 큰 관심을 받고 있다. 본 논문에서는 무선 정보 및 전력 동시 전송을 위한 효율적인 알고리즘을 제안하고자 한다. 먼저, 에너지 하베스팅 네트워크에서 채널의 probability density function을 이용하여 water-level의 하계값을 찾은 후, 이를 기반으로 파워 할당 해를 도출한다. 또한, 최소 필요 획득 에너지 조건을 효율적으로 만족시켜줄 수 있는 파워 분할 방안을 제안하였다. 시뮬레이션을 통해 제안 방안은 기존 방안에 비해 최소 필요 획득 에너지 조건을 보장하면서 평균 데이터 전송률을 향상시키고, 최적해에 비해서는 10% 미만의 미미한 성능 저하가 있었지만 계산 복잡도를 현저히 줄일 수 있음을 보인다.

Keywords

References

  1. Study on enhancements for MTC, 3GPP TR Std. TR 22.888, v.0.4.0, 2011.
  2. M. Pinuela, P. Mitcheson, and S. Lucyszyn, "Ambient RF energy harvesting in urban and semi-urban environments," IEEE Trans. Microwave Theory Tech., vol. 61, no. 7, pp. 2715-2726, July 2013. https://doi.org/10.1109/TMTT.2013.2262687
  3. L. R. Varshney, "Transporting information and energy simultaneously," in Proc. IEEE Int. Symp. Inf. Theory (ISIT), pp. 1612-1616, July 2008.
  4. L. Liu, R. Zhang, and K. Chua, "Wireless information transfer with opportunistic energy harvesting," IEEE Trans. Wireless Commun., vol. 12, no. 1, pp. 288-300, Jan. 2013. https://doi.org/10.1109/TWC.2012.113012.120500
  5. L. Liu, R. Zhang, and K. Chua, "Wireless information and power transfer: a dynamic power splitting approach," IEEE Trans. Commun., vol. 61, no. 9, pp. 3990-4001, Sep. 2013. https://doi.org/10.1109/TCOMM.2013.071813.130105
  6. K. Lee and J.-P. Hong, "Energy efficient resource allocation for simultaneous information and energy transfer with imperfect channel estimation," IEEE Trans. Veh. Technol., vol. 65, no. 4, pp. 2775-2780, Apr. 2016. https://doi.org/10.1109/TVT.2015.2416754
  7. R. Berry and R. Gallager, "Communication over fading channels with delay constraints," IEEE Trans. Inf. Theory, vol. 48, no. 5, pp. 1135-1149, May 2002. https://doi.org/10.1109/18.995554
  8. B. Hassibi and B. M. Hochwald, "How much training is needed in multiple-antenna wireless links?," IEEE. Trans. Inf. Theory, vol. 49, no. 4, pp. 951-963, Apr. 2003. https://doi.org/10.1109/TIT.2003.809594
  9. W. Yu and R. Lui, "Dual methods for nonconvex spectrum optimization of multicarrier systems," IEEE Trans. Commun., vol. 54, no. 7, pp. 1310-1322, July 2006. https://doi.org/10.1109/TCOMM.2006.877962

Cited by

  1. 에너지 하베스팅 네트워크에서 최소 요구 보안 용량을 최대화하기 위한 시간 전환 기반의 아날로그 네트워크 코딩 vol.21, pp.11, 2016, https://doi.org/10.6109/jkiice.2017.21.11.2022