Optimization in Cognitive Radio Networks with SWIPT-Based DF Relay

  • Jie ZhangEmail author
  • Weidang Lu
  • Hong Peng
  • Zhijiang Xu
  • Xin Liu
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 251)


Energy-constrained relay networks are normally powered by a fixed energy, which limits the runtime of networks. Energy harvesting (EH) with simultaneous wireless information and power transfer (SWIPT) is hopeful to increase the life of energy-limited relay networks. We investigate the optimization problem about power splitting ratio for SWIPT-based decode-and-forward (DF) relay in cognitive radio networks (CRNs). Secondary relaying node (SRN) harvests energy from secondary source node (SSN) then use the energy to assist forwarding SSN information to the secondary destination node (SDN). We maximize throughput of secondary users (SUs) if the interference caused by SU to the primary users (PUs) is under the threshold. Some opinions are provided through theory analysis and simulation results.


Energy harvesting SWIPT Decode-and-forward Power splitting Cognitive radio networks 


  1. 1.
    FCC Spectrum Policy Task Force: Report of the spectrum efficiency working group. Technical report ET Docket No. 02-135, Federal Communications Commission, Washington, D.C. (2002)Google Scholar
  2. 2.
    Lee, S., Zhang, R., Huang, K.: Opportunistic wireless energy harvesting in cognitive radio networks. IEEE Trans. Wirel. Commun. 12(9), 4788–4799 (2013)CrossRefGoogle Scholar
  3. 3.
    Zheng, M., Xu, C., Liang, W., Yu, H.: Harvesting-throughput tradeoff for RF-powered underlay cognitive radio networks. Electron. Lett. 52(10), 881–883 (2016)CrossRefGoogle Scholar
  4. 4.
    Lozano, A., Tulino, A.M., Verdú, S.: Optimum power allocation for parallel Gaussian channels with arbitrary input distributions. IEEE Trans. Inf. Theory 52(7), 3033–3051 (2006)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Zhang, R., Ho, C.K.: MIMO broadcasting for simultaneous wireless information and power transfer. IEEE Trans. Wireless Commun. 12(5), 1989–2001 (2013)CrossRefGoogle Scholar
  6. 6.
    Bi, S., Ho, C.K., Zhang, R.: Wireless powered communication: opportunities and challenges. IEEE Commun. Mag. 53, 117–125 (2015)CrossRefGoogle Scholar
  7. 7.
    Vu, Q.D., Tran, L.N., Farrel, R., Hong, E.K.: An efficiency maximization design for SWIPT. IEEE Signal Process. Lett. 22, 2189–2193 (2015)CrossRefGoogle Scholar
  8. 8.
    Mohjazi, L., Dianati, M., Karagiannidis, G.K., Muhaidat, S.: RF-powered cognitive radio networks: technical challenges and limitations. IEEE Commun. Mag. 53, 94–100 (2015)CrossRefGoogle Scholar
  9. 9.
    Zheng, G., Ho, Z., Jorswieck, E.A., Ottersten, B.: Information and energy cooperation in cognitive radio networks. IEEE Trans. Wirel. Commun. 62, 2290–2303 (2014)MathSciNetzbMATHGoogle Scholar
  10. 10.
    Xing, H., Kang, X., Wong, K.-K., Nallanathan, A.: Optimizing DF cognitive radio networks with full-duplex-enabled energy access points. IEEE Trans. Wireless Commun. 16, 4683–4697 (2017)CrossRefGoogle Scholar
  11. 11.
    Tuan, P.V., Koo, I.: Robust weighted sum harvested energy maximization for SWIPT cognitive radio networks based on particle swarm optimization. Sensors 17(10), 2275 (2017). Scholar
  12. 12.
    Nasir, A.A., Zhou, X., Durrani, S., Kennedy, R.A.: Throughput and ergodic capacity of wireless energy harvesting based DF relaying network. In: Proceedings of the IEEE ICC, pp. 4066–4071 (2014).

Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2018

Authors and Affiliations

  • Jie Zhang
    • 1
    Email author
  • Weidang Lu
    • 1
  • Hong Peng
    • 1
  • Zhijiang Xu
    • 1
  • Xin Liu
    • 2
  1. 1.College of Information EngineeringZhejiang University of TechnologyHangzhouChina
  2. 2.School of Information and Communication EngineeringDalian University of TechnologyDalianChina

Personalised recommendations