Cognitive Radio Network Downlink Power Allocation and Beamforming Method with Imperfect Channel State Information

Zhongheng JI; Xinsheng JI; Kaizhi HUANG

doi:10.11999/JEIT171135

Volume 40 Issue 9

Aug. 2018

Turn off MathJax

Article Contents

Article Navigation > Journal of Electronics & Information Technology > 2018 > 40(9): 2072-2079

Zhongheng JI, Xinsheng JI, Kaizhi HUANG. Cognitive Radio Network Downlink Power Allocation and Beamforming Method with Imperfect Channel State Information[J]. Journal of Electronics & Information Technology, 2018, 40(9): 2072-2079. doi: 10.11999/JEIT171135

Citation:

Zhongheng JI, Xinsheng JI, Kaizhi HUANG. Cognitive Radio Network Downlink Power Allocation and Beamforming Method with Imperfect Channel State Information[J]. Journal of Electronics & Information Technology, 2018, 40(9): 2072-2079. doi: 10.11999/JEIT171135

Citation:

PDF( 1163 KB)

Cognitive Radio Network Downlink Power Allocation and Beamforming Method with Imperfect Channel State Information

doi: 10.11999/JEIT171135

National Digital Switching Systems Engineering & Technological Research Center, Zhengzhou 450002, China

Funds: The National 863 Program of China (SS2015AA011306), The National Natural Science Foundation of China (61379006, 61521003)

Received Date: 2017-12-04
Rev Recd Date: 2018-05-08

Available Online: 2018-07-12

Publish Date: 2018-09-01

Abstract

Abstract

Some problems of multi-user downlink power allocation and beamforming in a underlay Cognitive Radio Network (CRN) with imperfect Channel State Information (CSI) are addressed. They include ignoring the interferences of the Primary Network (PN) to the Secondary Users (SU), conventional SDR algorithm of convex optimization needing the constraint approximation, the high complexity of the algorithm, and implemented with difficulty, etc. Firstly the term of interference of the PN to the SU is added to the CRN model. The optimization problem is formulated with the worst-case imperfect CSI. Next the constraints of the problem are transformed by means of Lagrange duality. Then, based on the form of the problem, the simple, fast and practical iterative algorithm is obtained by utilizing the duality of uplink-downlink, introducing virtual power, and transforming the optimization problem into the problem of uplink power allocation and beamforming. Numerical simulation results show that it converges faster. It is also found that the errors of the imperfect CSI not only influence the downlink power but also change the feasibility region. The variation of transmitting power of the PN Base Station (PBS) could affect the feasibility region notably.
- Cognitive Radio Network (CRN),
- Imperfect Channel State Information (CSI),
- Iterative algorithm,
- Feasibility region

FullText(HTML)

References(18)

References

DENKOVSKI D, RAKOVIC V, ATANASOVSKI V, et al. Generic multiuser coordinated beamforming for underlay spectrum sharing[J]. IEEE Transactions on Communications, 2016, 64(6): 2285–2298 doi: 10.1109/TCOMM.2016.2561272

ZHANG Yu, DALLANESE E, and GIANNAKIS G B. Distributed optimal beamformers for cognitive radios robust to channel uncertainties[J]. IEEE Transactions on Signal Processing, 2012, 60(12): 6495–6508 doi: 10.1109/TSP.2012.2218240

WAJID I, PESAVENTO M, ELDAR Y C, et al. Robust downlink beamforming with partial channel state information for conventional and cognitive radio networks[J]. IEEE Transactions on Signal Processing, 2013, 61(14): 3656–3670 doi: 10.1109/TSP.2013.2265682

JEONG Y, QUEK T Q S, and SHIN H. Downlink beamforming optimization for cognitive underlay networks[C]. International Symposium On Information Theory & Its Applications, Taichung, 2010: 934–939. doi: 10.1109/ISITA.2010.5649545.

MA Shuai and SUN Dechun. Chance constrained robust beamforming in cognitive radio networks[J]. IEEE Communications Letters, 2013, 17(1): 67–70 doi: 10.1109/LCOMM.2012.112812.121829

HUANG Yongwei and PALOMAR D P. Rank-constrained separable semidefinite programming with applications to optimal beamforming[J]. IEEE Transactions on Signal Processing, 2010, 58(2): 664–678 doi: 10.1109/TSP.2009.2031732

NOH J H and OH S J. Beamforming in a multi-user cognitive radio system with partial channel state information[J]. IEEE Transactions on Wireless Communications, 2013, 12(2): 616–625 doi: 10.1109/TWC.2012.121812.111897

TSAKMALIS A, CHATZINOTAS S, and OTTERSTEN B. Centralized power control in cognitive radio networks using modulation and coding classification feedback[J]. IEEE Transactions on Cognitive Communications and Networking, 2016, 2(3): 223–237 doi: 10.1109/TCCN.2016.2613562

XU Lei, WANG Ping, LI Qianmu, et al. Call admission control with inter-network cooperation for cognitive heterogeneous networks[J]. IEEE Transactions on Wireless Communications, 2017, 16(3): 1963–1973 doi: 10.1109/TWC.2017.2657757

TSAKMALIS A, CHATZINOTAS S, and OTTERSTEN B. Interference constraint active learning with uncertain feedback for cognitive radio networks[J]. IEEE Transactions on Wireless Communications, 2017, 16(7): 4654–4668 doi: 10.1109/TWC.2017.2701361

XU Yongjun, ZHAO Xiaohui, and LIANG Y C. Robust power control and beamforming in cognitive radio networks: a survey[J]. IEEE Communications Surveys&Tutorials, 2015, 17(4): 1834–1857 doi: 10.1109/COMST.2015.2452414

DHIFALLAH O, DAHROUJ H, AL-NAFFOURI T, et al. Decentralized SINR balancing in cognitive radio networks[J]. IEEE Transactions on Vehicular Technology, 2017, 66(4): 3491–3496 doi: 10.1109/TVT.2016.2587753

TONG Xue, DONG Xiaodai, and SHI Yi. Resource allocation strategy for multi-user cognitive radio systems: Iocation-aware spectrum access[J].IEEE Transactions on Vehicular Technology, 2017, 66(1): 884–889 doi: 10.1109/TVT.2016.2531738

PESAVENTO M, CIOCHINA D, and GERSHMAN A B. Iterative dual downlink beamforming for cognitive radio networks[C]. Fifth International Conference on Cognitive Radio Oriented Wireless Networks & Communications, Cannes, France, 2010: 1–5. doi: 10.4108/ICST.CROWNCOM2010.9247.

BOYD S and VANDENBERGHE L. Convex Optimization [M]. New York, USA: Cambridge University Press, 2004: 136–138, 234–236. doi: 10.1017/CBO9780511804441.

张贤达. 矩阵分析与应用[M]. 北京: 清华大学出版社, 2004: 491–492.

ZHANG Xianda. Matrix Analysis and Applications[M]. Beijing: Tsinghua University Press, 2004: 491–492.

GRANT M, BOYD S, and YE Y. CVX: MATLAB software for disciplined convex programming[OL]. http://www.stanford.edu/boyd/cvx/V.1.0RC3, 2009.

曹杰, 廖勇, 沈轩帆. 基于QoS的多小区下行TDD大规模MIMO系统顽健波束成形[J]. 通信学报, 2017, 38(11): 44–53 doi: 10.11959/j.issn.1000-436x.2017218

CAO Jie, LIAO Yong, and SHENG Xuanfan. Robust beamforming for multicell downlink TDD massive MIMO system based on QoS[J]. Journal on Communications, 2017, 38(11): 44–53 doi: 10.11959/j.issn.1000-436x.2017218

Relative Articles

Supplements(0)

Cited By

Proportional views