WFRFT认知通信系统控制参数联合优化方法研究

doi:10.19678/j.issn.1000-3428.0059768

摘要/Abstract

摘要： 针对WFRFT系统难以适应复杂电磁环境及最优控制参数难以选取的问题，提出一种新型WFRFT认知通信系统构建方法。在传统WFRFT通信系统的基础上引入信号认知模块，完成电磁信号的采集及电磁环境中目标信号调制特征的识别。根据WFRFT信号调制特征裂变的特性，分析控制参数间的耦合作用机理，并设定优化控制参数的目标。将目标信号调制特征参数引入到WFRFT控制参数优化模型中，提出基于目标特征的WFRFT认知通信系统控制参数联合优化方法，并通过粒子群算法的迭代计算，选取最优控制参数集，针对最优参数的WFRFT认知通信系统，分别仿真计算高斯、莱斯、瑞利等典型信道条件下的误码率性能。实验结果表明，该方法可伪装信号调制特征，能有效提高通信信号的安全性，且在典型信道条件下的误码率性能较好，具有较好的抗干扰能力。

关键词: 变换域通信, 加权分数阶傅里叶变换, 认知通信, 粒子群算法, 安全通信

Abstract: To deal with the selection of the optimal control parameters in Weighted Fractional Fourier Transform(WFRFT), this paper constructs a novel WFRFT cognitive communication system.On the basis of the traditional WFRFT communication system, a signal recognition module is introduced to realize the recognition of the modulation characteristics of target signals in the electromagnetic environment.At the same time, according to the fission characteristics of the modulation characteristics of WFRFT signals, the coupling mechanism between the control parameters is deeply analyzed, and the goal of control parameter optimization is set.On this basis, the parameters of target signal modulation characteristics are introduced into the optimization model for WFRFT control parameters, so as to form a joint optimization method of control parameters in a WFRFT cognitive communication system based on target characteristics.In addition, the optimal control parameter set is selected through the iterative calculation of the particle swarm algorithm.For the WFRFT cognitive communication system with optimal parameters, the Bit Error Ratio(BER) performance under typical channel conditions such as Gaussian, Rice and Rayleigh are simulated.The experimental results show that the proposed method can realize the camouflage of signal modulation characteristics, and effectively improve the security of communication signals.At the same time, the WFRFT system displays good anti-interference ability, and provides excellent BER performance under typical channel conditions, so it can effectively meet the requirements of engineering applications.

Key words: transform domain communication, Weighted Fractional Fourier Transform(WFRFT), cognitive communication, particle swarm algorithm, secure communication

中图分类号:

TP309

杜欣军, 刘鹏飞. WFRFT认知通信系统控制参数联合优化方法研究[J]. 计算机工程, 2021, 47(12): 177-184.

DU Xinjun, LIU Pengfei. Research on Joint Optimization Method of Control Parameters in WFRFT Cognitive Communication System[J]. Computer Engineering, 2021, 47(12): 177-184.

http://www.ecice06.com/CN/Y2021/V47/I12/177

图/表 16

20211214181504

20211214181507

20211214181511

20211214181515

20211214181518

20211214181521

20211214181524

20211214181528

20211214181531

20211214181535

20211214181539

20211214181542

20211214181545

20211214181549

20211214181553

20211214181557

参考文献

[1] 桑建华, 陈益临.发展中的飞行器射频隐身技术[J].航空制造技术, 2011, 23(11):48-50. SANG J H, CHEN Y L.Air vehicle RF stealth technology in evolution[J].Aeronautical Manufacturing Technology, 2011, 23(11):48-50.(in Chinese)
[2] DAVID L J.Introduction to RF stealth[M].Raleigh, North Carolina:Science Technology Publishing Incorporation, 2004:8-12.
[3] 杨宇晓, 汪飞, 周建江, 等.跳频周期和跳频间隔的最大条件熵射频隐身设计方法[J].电子与信息学报, 2015, 37(4):841-847. YANG Y X, WANG F, ZHOU J J, et al.RF stealth design method for hopping cycle and hopping interval based on conditional maximum entropy[J].Journal of Electronics and Information Technology, 2015, 37(4):841-847.(in Chinese)
[4] 达新宇, 廉晨.一种加权傅里叶变换域通信方法[J].系统工程与电子技术, 2015, 37(12):2853-2859. DA X Y, LIAN C.Method of weighted fourier transform domain communication[J].Systems Engineering and Electronics, 2015, 37(12):2853-2859.(in Chinese)
[5] 李婧, 沙学军, 梅林, 等.基于加权分数傅里叶变换域的2天线发射方法[J].哈尔滨工业大学学报, 2017, 49(5):10-15. LI J, SHA X J, MEI L, et al.Two-branch transmit method based on weighted-type fractional fourier transform[J].Journal of Harbin Institute of Technology, 2017, 49(5):10-15.(in Chinese)
[6] 房宵杰.基于加权分数阶傅里叶变换的物理层安全传输方法研究[D].哈尔滨:哈尔滨工业大学, 2018. FANG X J.Research on WFRFT-based physical layer security methodologies for wireless communications[D].Harbin:Harbin Institute of Technology, 2018.(in Chinese)
[7] LIU F, FENG Y.Anti-scanning research for MPWFRFT communication signals[J].Wireless Networks, 2020, 26(1):723-737.
[8] XIAOJIE F, XUEJUN S, YUE L.MP-WFRFT and constellation scrambling based physical layer security system[J].China Communications, 2016, 13(2):138-145.
[9] LUO Z, WANG H, ZHOU K, et al.Combined constellation rotation with weighted FRFT for secure transmission in polarization modulation based dual-polarized satellite communications[J].IEEE Access, 2017, 12:27061-27073.
[10] KHARBECH S, SIMON E P, BELAZI A, et al.Denoising higher-order moments for blind digital modulation identification in multiple-antenna systems[J].IEEE Wireless Communication Letters, 2020, 6(9):765-769.
[11] PAJIC M S, VEINOVIC M, PERIC M, et al.Modulation order reduction method for improving the performance of AMC algorithm based on sixth-order cumulants[J].IEEE Access, 2020, 10:86-94.
[12] GHASEMZADEH P, BANERJEE S, HEMPEL M, et al.A novel deep learning and polar transformation framework for an adaptive automatic modulation classification[J].IEEE Transactions on Vehicular Technology, 2020, 11(69):43-58.
[13] 达新宇, 王浩波, 罗章凯, 等.基于双层多参数加权类分数阶傅里叶变换的双极化卫星安全传输方案[J].电子与信息学报, 2019, 41(8):1974-1982. DA X Y, WANG H B, LUO Z K, et al.Dual-polarized satellite security transmission scheme based on double layer multi-parameter weighted-type fractional fourier transform[J].Journal of Electronics and Information Technology, 2019, 41(8):1974-1982.(in Chinese)
[14] XIA X, GUI L, YU F, et al.Triple archives particle swarm optimization[J].IEEE Transactions on Cybernetics, 2019, 34(9):1-14.
[15] MASOOD M, FOUAD M M, KAMAL R, et al.An improved particle swarm algorithm for multi-objectives based optimization in MPLS/GMPLS networks[J].IEEE Access, 2019, 7:147-162.
[16] SINGH P.A novel hybrid time series forecasting model based on neutrosophic-PSO approach[J].International Journal of Machine Learning and Cybernetics, 2020, 11(8):1643-1658.
[17] DYTSO A, YAGLI S, POOR H V, et al.The capacity achieving distribution for the amplitude constrained additive gaussian channel:an upper bound on the number of mass points[J].IEEE Transactions on Information Theory, 2020, 66(4):2006-2022.
[18] NOORAIEPOUR A, AGHDAM S R, DUMAN T M.On secure communications over Gaussian wiretap channels via Finite-Length Codes[J].IEEE Communications Letters, 2020, 24(9):1904-1908.
[19] SHAO J, LI X.Generalized zero-shot learning with multi-channel Gaussian mixture VAE[J].IEEE Signal Processing Letters, 2020, 27:456-460.
[20] OZDOGAN O, BJORNSON E, LARSSON E G.Massive MIMO with spatially correlated rician fading channels[J].IEEE Transactions on Communications, 2019, 67(5):3234-3250.
[21] VU M, TRAN N H, WIJERATNE D G, et al.Optimal signaling schemes and capacity of non-coherent rician fading channels with low-resolution output quantization[J].IEEE Transactions on Wireless Communications, 2019, 31(6):2989-3004.
[22] KUMAR S, SINGH A, KUMAR M.Covert communication integrates into wavelet packet transform OFDM system over Rayleigh fading channel[J].Wireless Networks, 2020, 26(1):81-89.
[23] TAHMASBI M, SAVARD A, BLOCH M R.Covert capacity of non-coherent Rayleigh-fading channels[J].IEEE Transactions on Information Theory, 2020, 66(4):1979-2005.

选择文件类型/文献管理软件名称

选择包含的内容