Cooperative resource allocation in cognitive wireless powered communication networks with energy accumulation and deadline requirements
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摘要
This study investigates a multi-carrier cognitive wireless powered communication network(CWPCN) 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 study investigates a multi-carrier cognitive wireless powered communication network(CWPCN) 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 study investigates a multi-carrier cognitive wireless powered communication network(CWPCN) 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.
引文
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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
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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
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17 L′opez O L A,Fern′andez E M G,Souza R D,et al.Wireless powered communications with finite battery and finite blocklength.IEEE Trans Commun,2018,66:1803-1816
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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
1)This assumption is valid when PTx is a low-power device. For example, suppose that PTx is a low-power wireless sensor node that supports energy harvesting from RF signals and that PRx is a powerful sink node connected to the power grid. When PTx has data to transmit to the sink node, it requires energy harvested from RF signals. Alternatively, STx and SRx can be more powerful devices than PTx, with information to be communicated on an ad-hoc basis but lacking a licensed spectrum. In this case, PTx can allow STx and SRx access to its licensed spectrum, enabling the transmission of information bearing RF signals. In return, PTx can harvest energy from the RF signals sent by STx.
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.IEEEWirel 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.IEEEWirel 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 L′opez O L A,Fern′andez 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
1)This assumption is valid when PTx is a low-power device. For example, suppose that PTx is a low-power wireless sensor node that supports energy harvesting from RF signals and that PRx is a powerful sink node connected to the power grid. When PTx has data to transmit to the sink node, it requires energy harvested from RF signals. Alternatively, STx and SRx can be more powerful devices than PTx, with information to be communicated on an ad-hoc basis but lacking a licensed spectrum. In this case, PTx can allow STx and SRx access to its licensed spectrum, enabling the transmission of information bearing RF signals. In return, PTx can harvest energy from the RF signals sent by STx.