Abstract
This study investigates a multi-carrier cognitive wireless powered communication network (CW-PCN) with a wirelessly powered primary user (PU). A two-stage cooperative protocol between the PU and the secondary user (SU) is adopted so that the PU can harvest energy from the SU while the SU gains transmission opportunities. It is assumed that the energy harvested by the PU can be accumulated for future usage, and the quality of service of the PU is guaranteed by satisfying the required minimum number of data bits for a given deadline. Herein, we maximize the SU rate by considering the time allocation, subcarrier allocation, and power allocation in both an offline setting (in which the future channel gains are known a priori) and an online setting (in which only the current channel gains are known). In the offline and online schemes, the maximization problem is solved using the block-coordinate descent method and the Lagrange duality method. The effectiveness of the proposed schemes is evaluated and verified via simulation experiments against benchmark schemes.
This is a preview of subscription content, access via your institution.
References
- 1
Zaidi SAR, Afzal A, Hafeez M, et al. Solar energy empowered 5G cognitive metro-cellular networks. IEEE Commun Mag, 2015, 53: 70–77
- 2
Xu D, Li Q. Price-based time and energy allocation in cognitive radio multiple access networks with energy harvesting. Sci China Inf Sci, 2017, 60: 108302
- 3
Mohjazi L, Dianati M, Karagiannidis G K, et al. RF-powered cognitive radio networks: technical challenges and limitations. IEEE Commun Mag, 2015, 53: 94–100
- 4
Xu D, Li Q. Joint power control and time allocation for wireless powered underlay cognitive radio networks. IEEE Wirel Commun Lett, 2017, 6: 294–297
- 5
Wang D, Ren P, Wang Y, et al. Energy cooperation for reciprocally-benefited spectrum access in cognitive radio networks. In: Proceedings of IEEE Global Communications Conference, Washington, 2014. 1320–1324
- 6
Shafie A El, Dhahir N Al, Hamila R. Cooperative access schemes for efficient SWIPT transmissions in cognitive radio networks. In: Proceedings of IEEE Global Communications Conference Workshops, San Diego, 2015. 1–6
- 7
Zhai C, Liu J, Zheng L. Cooperative spectrum sharing with wireless energy harvesting in cognitive radio networks. IEEE Trans Veh Technol, 2016, 65: 5303–5316
- 8
Zhai C, Chen H, Wang X, et al. Opportunistic spectrum sharing with wireless energy transfer in stochastic networks. IEEE Trans Commun, 2018, 66: 1296–1308
- 9
Xu D, Li Q. Cooperative resource allocation in cognitive radio networks with wireless powered primary users. IEEE Wirel Commun Lett, 2017, 6: 658–661
- 10
Xu D, Li Q. Resource allocation in cognitive wireless powered communication networks with wirelessly powered secondary users and primary users. Sci China Inf Sci, 2019, 62: 029303
- 11
Yang J, Yang Q, Shen Z, et al. Suboptimal online resource allocation in hybrid energy supplied OFDMA cellular networks. IEEE Commun Lett, 2016, 20: 1639–1642
- 12
Yousaf R, Ahmad R, Ahmed W, et al. A unified approach of energy and data cooperation in energy harvesting WSNs. Sci China Inf Sci, 2018, 61: 082303
- 13
Wang Z, Wang X, Aggarwal V. Transmission with energy harvesting nodes in frequency-selective fading channels. IEEE Trans Wirel Commun, 2016, 15: 1642–1656
- 14
Zhang B, Dong C, El-Hajjar M, et al. Outage analysis and optimization in single- and multiuser wireless energy harvesting networks. IEEE Trans Veh Technol, 2016, 65: 1464–1476
- 15
Yao Q, Huang A, Shan H, et al. Delay-aware wireless powered communication networks-energy balancing and optimization. IEEE Trans Wirel Commun, 2016, 15: 5272–5286
- 16
Morsi R, Michalopoulos D S, Schober R. Performance analysis of near-optimal energy buffer aided wireless powered communication. IEEE Trans Wirel Commun, 2018, 17: 863–881
- 17
López O L A, Fernández E M G, Souza R D, et al. Wireless powered communications with finite battery and finite blocklength. IEEE Trans Commun, 2018, 66: 1803–1816
- 18
Zhang R, Chen H, Yeoh P L, et al. Full-duplex cooperative cognitive radio networks with wireless energy harvesting. In: Proceedings of IEEE International Conference on Communications, Paris, 2017. 1–6
- 19
Hoang D T, Niyato D, Wang P, et al. Opportunistic channel access and RF energy harvesting in cognitive radio networks. IEEE J Sel Areas Commun, 2014, 32: 2039–2052
- 20
Hoang D T, Niyato D, Wang P, et al. Performance optimization for cooperative multiuser cognitive radio networks with RF energy harvesting capability. IEEE Trans Wirel Commun, 2015, 14: 3614–3629
- 21
Boyd S, Vandenberghe L. Convex Optimization. Cambridge: Cambridge University Press, 2004
- 22
Bland R G, Goldfarb D, Todd M J. The ellipsoid method: a survey. Oper Res, 1981, 29: 1039–1091
- 23
Bertsekas D P. Nonlinear Programming. Belmont: Athena Scientific Press, 1999
- 24
Potra F A, Wright S J. Interior-point methods. J Comput Appl Math, 2000, 124: 281–302
- 25
Ju H, Zhang R. Throughput maximization in wireless powered communication networks. IEEE Trans Wirel Commun, 2014, 13: 418–428
Acknowledgements
This work was supported by National Science and Technology Major Project of China (Grant No. 2017ZX03001008), Postdoctoral Research Plan of Jiangsu Province (Grant No. 1701167B), Postdoctoral Science Foundation of China (Grant No. 2017M621795), and NUPTSF (Grant Nos. NY218007, NY218026).
Author information
Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Xu, D., Li, Q. Cooperative resource allocation in cognitive wireless powered communication networks with energy accumulation and deadline requirements. Sci. China Inf. Sci. 62, 82302 (2019). https://doi.org/10.1007/s11432-018-9813-9
Received:
Revised:
Accepted:
Published:
Keywords
- wireless powered communication networks
- energy harvesting
- cognitive radio
- deadline constraint
- energy accumulation