3D Wireless Secure Transmission under Random Frequency Diversity  Array Assisted by Deep Learning

HU Jinsong; JIANG Wanling; CHEN Youjia; XU Yiwen; ZHAO Tiesong; SHU Feng

doi:10.11999/JEIT220457

Volume 45 Issue 6

Jun. 2023

Turn off MathJax

Article Contents

Article Navigation > Journal of Electronics & Information Technology > 2023 > 45(6): 2063-2070

HU Jinsong, JIANG Wanling, CHEN Youjia, XU Yiwen, ZHAO Tiesong, SHU Feng. 3D Wireless Secure Transmission under Random Frequency Diversity Array Assisted by Deep Learning[J]. Journal of Electronics & Information Technology, 2023, 45(6): 2063-2070. doi: 10.11999/JEIT220457

Citation:

HU Jinsong, JIANG Wanling, CHEN Youjia, XU Yiwen, ZHAO Tiesong, SHU Feng. 3D Wireless Secure Transmission under Random Frequency Diversity Array Assisted by Deep Learning[J]. Journal of Electronics & Information Technology, 2023, 45(6): 2063-2070. doi: 10.11999/JEIT220457

Citation:

HU Jinsong, JIANG Wanling, CHEN Youjia, XU Yiwen, ZHAO Tiesong, SHU Feng. 3D Wireless Secure Transmission under Random Frequency Diversity Array Assisted by Deep Learning[J]. Journal of Electronics & Information Technology, 2023, 45(6): 2063-2070. doi: 10.11999/JEIT220457

PDF( 2330 KB)

3D Wireless Secure Transmission under Random Frequency Diversity Array Assisted by Deep Learning

doi: 10.11999/JEIT220457

HU Jinsong^{1, 2},
JIANG Wanling¹,
CHEN Youjia^{1, 2
,
,},
XU Yiwen^{1, 2},
ZHAO Tiesong^{1, 2},
SHU Feng³

1.
College of Physics and Information Engineering, Fuzhou University, Fuzhou 350108, China
2.
Fujian Key Laboratory for Intelligent Processing and Wireless Transmission of Media Information, Fuzhou 350108, China
3.
School of Information and Communication Engineering, Hainan University, Haikou 570228, China

Funds: The National Natural Science Foundation of China (62001116, 62071234, 62171134), The Natural Science Foundation of Fujian Province (2020J05106)

Received Date: 2022-04-18
Rev Recd Date: 2022-06-17

Available Online: 2022-06-24

Publish Date: 2023-06-10

Abstract

Abstract

To solve the potential security issue caused by the fact that the transmitted beam in the phased array-assisted wireless communication systems only depend on angle characteristics and high computational complexity caused by the traditional iteration algorithms. A secure transmission scheme with 3D secure region assisted by Random Frequency Diverse Array (RFDA) and Deep Learning (DL) is proposed in this paper. Firstly, the requirements for the secure communication with the desired user within 3D secure zone are derived. Based on it, an optimization problem is formulated to maximize the lower bound of the secure rate of the considered system. Then, an optimization scheme based on deep learning is proposed to design the beamforming vector and Artificial Noise (AN) vector, so as to reduce the computational complexity. Simulation results show that even when the eavesdropper is located at the edge of the desired user’s secure region, the proposed scheme can achieve the 3D secure transmission, and ensure the received confidential information in secure region.
- Secure transmission,
- Random Frequency Diverse Array (RFDA),
- Deep Learning (DL)

FullText(HTML)

References(15)

References

[1]	YERRAPRAGADA A K, EISMAN T, and KELLEY B. Physical layer security for beyond 5G: Ultra secure low latency communications[J]. IEEE Open Journal of the Communications Society, 2021, 2: 2232–2242. doi: 10.1109/OJCOMS.2021.3105185
[2]	ARFAOUI M A, SOLTANI D M, TAVAKKOLNIA I, et al. Physical layer security for visible light communication systems: A survey[J]. IEEE Communications Surveys & Tutorials, 2020, 22(3): 1887–1908. doi: 10.1109/COMST.2020.2988615
[3]	WEI Zhongxiang, MASOUROS C, and LIU Fan. Secure directional modulation with few-bit phase shifters: Optimal and iterative-closed-form designs[J]. IEEE Transactions on Communications, 2021, 69(1): 486–500. doi: 10.1109/TCOMM.2020.3032459
[4]	ZHANG Bo, LIU Wei, LI Qiang, et al. Directional modulation design under a given symbol-independent magnitude constraint for secure IoT networks[J]. IEEE Internet of Things Journal, 2021, 8(20): 15140–15147. doi: 10.1109/JIOT.2020.3040303
[5]	HU Jinsong, SHU Feng, and LI Jun. Robust synthesis method for secure directional modulation with imperfect direction angle[J]. IEEE Communications Letters, 2016, 20(6): 1084–1087. doi: 10.1109/LCOMM.2016.2550022
[6]	SHU Feng, TENG Yin, LI Jiayu, et al. Enhanced secrecy rate maximization for directional modulation networks via IRS[J]. IEEE Transactions on Communications, 2021, 69(12): 8388–8401. doi: 10.1109/TCOMM.2021.3110598
[7]	MA Yezi, WEI Ping, and ZHANG Huaguo. General focusing beamformer for FDA: Mathematical model and resolution analysis[J]. IEEE Transactions on Antennas and Propagation, 2019, 67(5): 3089–3100. doi: 10.1109/TAP.2019.2900400
[8]	HU Jinsong, YAN Shihao, SHU Feng, et al. Artificial-noise-aided secure transmission with directional modulation based on random frequency diverse arrays[J]. IEEE Access, 2017, 5: 1658–1667. doi: 10.1109/ACCESS.2017.2653182
[9]	SHU Feng, WU Xiaomin, HU Jinsong, et al. Secure and precise wireless transmission for random-subcarrier-selection-based directional modulation transmit antenna array[J]. IEEE Journal on Selected Areas in Communications, 2018, 36(4): 890–904. doi: 10.1109/JSAC.2018.2824231
[10]	WANG Shuaiyu, YAN Shihao, ZHANG Jia, et al. Secrecy zone achieved by directional modulation with random frequency diverse array[J]. IEEE Transactions on Vehicular Technology, 2021, 70(2): 2001–2006. doi: 10.1109/TVT.2021.3054803
[11]	XIE Ning, LI Zhuoyuan, and TAN Haijun. A survey of physical-layer authentication in wireless communications[J]. IEEE Communications Surveys & Tutorials, 2021, 23(1): 282–310. doi: 10.1109/COMST.2020.3042188
[12]	LIN Tian and ZHU Yu. Beamforming design for large-scale antenna arrays using deep learning[J]. IEEE Wireless Communications Letters, 2020, 9(1): 103–107. doi: 10.1109/LWC.2019.2943466
[13]	HUANG Hongji, SONG Yiwei, YANG Jie, et al. Deep-learning-based millimeter-wave massive MIMO for hybrid precoding[J]. IEEE Transactions on Vehicular Technology, 2019, 68(3): 3027–3032. doi: 10.1109/TVT.2019.2893928
[14]	ZENG Jun, HE Zhengran, SUN Jinlong, et al. Deep transfer learning for 5G massive MIMO downlink CSI feedback[C]. 2021 IEEE Wireless Communications and Networking Conference (WCNC), Nanjing, China, 2021.
[15]	HU Zhengyang, GUO Jianhua, LIU Guanzhang, et al. MRFNet: A deep learning-based CSI feedback approach of massive MIMO systems[J]. IEEE Communications Letters, 2021, 25(10): 3310–3314. doi: 10.1109/LCOMM.2021.3099841