Peak-to-average Power Ratio Reduction Algorithm of Artificial-noise-aided Secure Signal

HONG Tao; ZHANG Gengxin

doi:10.11999/JEIT170739

Volume 40 Issue 6

May 2018

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Article Navigation > Journal of Electronics & Information Technology > 2018 > 40(6): 1426-1432

HONG Tao, ZHANG Gengxin. Peak-to-average Power Ratio Reduction Algorithm of Artificial-noise-aided Secure Signal[J]. Journal of Electronics & Information Technology, 2018, 40(6): 1426-1432. doi: 10.11999/JEIT170739

Citation:

HONG Tao, ZHANG Gengxin. Peak-to-average Power Ratio Reduction Algorithm of Artificial-noise-aided Secure Signal[J]. Journal of Electronics & Information Technology, 2018, 40(6): 1426-1432. doi: 10.11999/JEIT170739

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Peak-to-average Power Ratio Reduction Algorithm of Artificial-noise-aided Secure Signal

doi: 10.11999/JEIT170739

HONG Tao¹,
ZHANG Gengxin¹

Funds:

The National Natural Science Foundation of China (91738201, 61302102), The Natural Science Foundation of the university of Jiangsu Province (13KJB510023)

Received Date: 2017-07-20
Rev Recd Date: 2018-03-19
Publish Date: 2018-06-19

Abstract

Abstract

The research of improving the Secrecy Capacity (SC) of wireless communication system using Artificial Noise (AN) is one of the classics models in the field of physical layer security communication. Considering the Peak-to-Average Power Ratio (PAPR) problem of transmit signal, a power allocation of AN subspaces algorithm is proposed to reduce the PAPR of transmit signal based on convex optimization. This algorithm utilizes a series of convex optimization problems to approach the nonconvex PAPR optimization problem based on fractional programming, Difference of Convex (DC) functions programming and nonconvex quadratic equality constraint transformation. Simulation results show that the proposed algorithm reduce the PAPR value of transmit signal to improve the communication performance of legitimate user compared with benchmark problems.
- Wireless communication,
- Physical layer security,
- Artificial Noise (AN) aided,
- Power allocation,
- Convex optimization

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References(19)

References

GE Xiaohu, TU Song, MAO Guoqiang, et al. 5G ultra-dense cellular networks[J]. IEEE Wireless Communications, 2016, 23(1): 72-79. doi: 10.1109/MWC.2016.7422408.

GE Xiaohu, ZI Ran, WANG Haichao, et al. Multi-user massive MIMO communication systems based on irregular antenna arrays[J]. IEEE Transactions on Wireless Communications, 2016, 15(8): 5287-5301. doi: 10.1109/TWC. 2016.2555911.

GOEL S and NEGI R. Guaranteeing secrecy using artificial noise[J]. IEEE Transactions on Wireless Communications, 2008, 7(6): 2180-2189. doi: 10.1109/TWC.2008.060848.

ZHOU Xiangyun and MCKAY M R. Secure transmission with artificial noise over fading channels: Achievable rate and optimal power allocation[J]. IEEE Transactions on Vehicular Technology, 2010, 59(8): 3831-3842. doi: 10.1109/TVT.2010. 2059057.

LIAO Weicheng, CHANG Tsunghui, MA Wingkin, et al. QoS-based transmit beamforming in the presence of eavesdroppers: An optimized artificial-noise-aided approach [J]. IEEE Transactions on Signal Processing, 2011, 59(3): 1202-1216. doi: 10.1109/TSP.2010.2094610.

LIU Shuiyin, HONG Yi, and VITERBO E. Artificial noise revisited[J]. IEEE Transactions on Information Theory, 2015, 61(7): 3901-3911. doi: 10.1109/TIT.2015.2437882.

TANG Yanqun, XIONG Jun, MA Dongtang, et al. Robust artificial noise aided transmit design for MISO wiretap channels with channel uncertainty[J]. IEEE Communications Letters, 2013, 17(11): 2096-2099. doi: 10.1109/LCOMM.2013. 100713.131673.

LI Na, TAO Xiaofeng, and XU Jin. Artificial noise assisted communication in the multiuser downlink: Optimal power allocation[J]. IEEE Communications Letters, 2015, 19(2): 295-298. doi: 10.1109/LCOMM.2014.2385779.

WANG Huiming, LIU Feng, and YANG Mengchen. Joint cooperative beamforming jamming and power allocation to secure AF relay systems[J]. IEEE Transactions on Vehicular Technology, 2015, 64(10): 4893-4898. doi: 10.1109/TVT.2014. 2370754.

KAPETANOVIC D, ZHENG G, and RUSEK F. Physical layer security for massive MIMO: An overview on passive eavesdropping and active attacks[J]. IEEE Communications Magazine, 2015, 53(6): 21-27. doi: 10.1109/MCOM.2015. 7120012.

ZHU Jun, SCHOBER R, and BHARGAVA V K. Secure transmission in multicell massive MIMO systems[J]. IEEE Transactions on Wireless Communications, 2014, 13(9): 4766-4781. doi: 10.1109/TWC.2014.2337308.

ZHU Jun, SCHOBER R, and BHARGAVA V K. Linear precoding of data and artificial noise in secure massive MIMO systems[J]. IEEE Transactions on Wireless Communications, 2016, 15(3): 2245-2261. doi: 10.1109/TWC. 2015.2500578.

LIU Xiaoran, MA Dongtang, XIONG Jun, et al. Power allocation for AN-aided beamforming design in MISO wiretap channels with finite-alphabet signaling[C]. 2016 IEEE 84th Vehicular Technology Conference (VTC-Fall), Montral, Canada, 2016: 1-6. doi: 10.1109/VTCFall.2016. 7881170.

ANOH K, TANRIOVER C, ADEBISI B, et al. A new approach to iterative clipping and filtering PAPR reduction scheme for OFDM systems[J]. IEEE Access, 2017. doi: 10.1109/ACCESS.2017.2751620.

DINKELBACH W. On nonlinear fractional programming [J]. Management Science, 1967, 13(7): 492-498.

AN L T H and TAO P D. The DC (difference of convex functions) programming and DCA revisited with DC models of real world nonconvex optimization problems[J]. Annals of Operations Research, 2005, 133(1/4): 23-46. doi: 10.1007/ s10479-004-5022-1.

FUCHS B, SKRIVERVIK A, and MOSIG J R. Shape beam synthesis of arrays via sequential convex optimizations[J]. IEEE Antennas and Wireless Propagation Letters, 2013, 12: 1049-1052. doi: 10.1109/LAWP.2013.2280043.

WANG Jiandong, ZHANG Qinghua, and LJUNG L. Revisiting the two-stage algorithm for hammerstein system identification[C]. Proceedings of the 48h IEEE Conference on Decision and Control Held Jointly with 2009 28th Chinese Control Conference, Shanghai, China, 2009: 3620-3625. doi: 10.1109/CDC.2009.5400243.

RICHTER S, JONES C N, and MORARI M. Computational complexity certification for real-time MPC with input constraints based on the fast gradient method[J]. IEEE Transactions on Automatic Control, 2012, 57(6): 1391-1403. doi: 10.1109/TAC.2011.2176389.

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