Physical layer key generation(PKG)technology leverages the reciprocal channel randomness to generate the shared secret keys.The low secret key capacity of the existing PKG schemes is due to the reduction in degree-of-...Physical layer key generation(PKG)technology leverages the reciprocal channel randomness to generate the shared secret keys.The low secret key capacity of the existing PKG schemes is due to the reduction in degree-of-freedom from multipath fading channels to multipath combined channels.To improve the wireless key generation rate,we propose a multipath channel diversity-based PKG scheme.Assisted by dynamic metasurface antennas(DMA),a two-stage multipath channel parameter estimation algorithm is proposed to efficiently realize super-resolution multipath parameter estimation.The proposed algorithm first estimates the angle of arrival(AOA)based on the reconfigurable radiation pattern of DMA,and then utilizes the results to design the training beamforming and receive beamforming to improve the estimation accuracy of the path gain.After multipath separation and parameter estimation,multi-dimensional independent path gains are utilized for generating secret keys.Finally,we analyze the security and complexity of the proposed scheme and give an upper bound on the secret key capacity in the high signal-to-noise ratio(SNR)region.The simulation results demonstrate that the proposed scheme can greatly improve the secret key capacity compared with the existing schemes.展开更多
在室内可见光通信中符号间干扰和噪声会严重影响系统性能,K均值(K-means)均衡方法可以抑制光无线信道的影响,但其复杂度较高,且在聚类边界处易出现误判。提出了改进聚类中心点的K-means(Improved Center K-means,IC-Kmeans)算法,通过随...在室内可见光通信中符号间干扰和噪声会严重影响系统性能,K均值(K-means)均衡方法可以抑制光无线信道的影响,但其复杂度较高,且在聚类边界处易出现误判。提出了改进聚类中心点的K-means(Improved Center K-means,IC-Kmeans)算法,通过随机生成足够长的训练序列,然后将训练序列每一簇的均值作为K-means聚类中心,避免了传统K-means反复迭代寻找聚类中心。进一步,提出了基于神经网络的IC-Kmeans(Neural Network Based IC-Kmeans,NNIC-Kmeans)算法,使用反向传播神经网络将接收端二维数据映射至三维空间,以增加不同簇之间混合数据的距离,提高了分类准确性。蒙特卡罗误码率仿真表明,IC-Kmeans均衡和传统K-means算法的误码率性能相当,但可以显著降低复杂度,特别是在信噪比较小时。同时,在室内多径信道模型下,与IC-Kmeans和传统Kmeans均衡相比,NNIC-Kmeans均衡的光正交频分复用系统误码率性能最好。展开更多
In this paper, a time-varying channel prediction method based on conditional generative adversarial network(CPcGAN) is proposed for time division duplexing/frequency division duplexing(TDD/FDD) systems. CPc GAN utiliz...In this paper, a time-varying channel prediction method based on conditional generative adversarial network(CPcGAN) is proposed for time division duplexing/frequency division duplexing(TDD/FDD) systems. CPc GAN utilizes a discriminator to calculate the divergence between the predicted downlink channel state information(CSI) and the real sample distributions under a conditional constraint that is previous uplink CSI. The generator of CPcGAN learns the function relationship between the conditional constraint and the predicted downlink CSI and reduces the divergence between predicted CSI and real CSI.The capability of CPcGAN fitting data distribution can capture the time-varying and multipath characteristics of the channel well. Considering the propagation characteristics of real channel, we further develop a channel prediction error indicator to determine whether the generator reaches the best state. Simulations show that the CPcGAN can obtain higher prediction accuracy and lower system bit error rate than the existing methods under the same user speeds.展开更多
Cluster-based channel model is the main stream of fifth generation mobile communications, thus the accuracy of clustering algorithm is important. Traditional Gaussian mixture model (GMM) does not consider the power in...Cluster-based channel model is the main stream of fifth generation mobile communications, thus the accuracy of clustering algorithm is important. Traditional Gaussian mixture model (GMM) does not consider the power information which is important for the channel multipath clustering. In this paper, a normalized power weighted GMM (PGMM) is introduced to model the channel multipath components (MPCs). With MPC power as a weighted factor, the PGMM can fit the MPCs in accordance with the cluster-based channel models. Firstly, expectation maximization (EM) algorithm is employed to optimize the PGMM parameters. Then, to further increase the searching ability of EM and choose the optimal number of components without resort to cross-validation, the variational Bayesian (VB) inference is employed. Finally, 28 GHz indoor channel measurement data is used to demonstrate the effectiveness of the PGMM clustering algorithm.展开更多
针对基于平面波声场模型的深海DOA(Direction of Arrival,DOA)估计存在误差的问题,从射线理论出发,建立了多途信道下深海DOA估计的阵列信号模型,推导出了深海多径声线传播时间和相邻阵元声线传播时延差的表征方法,运用解相干DOA估计算法...针对基于平面波声场模型的深海DOA(Direction of Arrival,DOA)估计存在误差的问题,从射线理论出发,建立了多途信道下深海DOA估计的阵列信号模型,推导出了深海多径声线传播时间和相邻阵元声线传播时延差的表征方法,运用解相干DOA估计算法,提高深海DOA估计性能,通过仿真验证了算法的有效性。研究表明,深海DOA估计问题实为多维参数优化问题;充分考虑海洋声场的声传播特性,可以从根本上解决深海DOA估计的误差问题。展开更多
通过对中波频段的特性分析,提出一种创新的中波应急广播覆盖网构建方案。基于信号传播理论和通信工程原理,结合地理信息系统(Geographic Information System,GIS)技术,优化中波发射台布局,提高应急广播信号的覆盖范围和稳定性。采用先...通过对中波频段的特性分析,提出一种创新的中波应急广播覆盖网构建方案。基于信号传播理论和通信工程原理,结合地理信息系统(Geographic Information System,GIS)技术,优化中波发射台布局,提高应急广播信号的覆盖范围和稳定性。采用先进的调制解调技术和功率控制策略,有效克服信道传播中的多径效应和信号衰减,提升应急广播系统的抗干扰性。通过在不同地理环境下的仿真实验,验证所提出构建方案的可行性和效果,为中波应急广播系统的建设提供有力的技术支持,为应对突发事件提供可靠的通信保障。展开更多
该文针对单路延迟对消系统不能有效解决多径信道的超短波无线电台共址干扰消除问题,给出了等间隔多路延迟正交合成的射频干扰对消方案,进而提出了新的衰减系数求解方法。在设定时间延迟范围和参考信号路数基础上,该方法通过迭代加权实...该文针对单路延迟对消系统不能有效解决多径信道的超短波无线电台共址干扰消除问题,给出了等间隔多路延迟正交合成的射频干扰对消方案,进而提出了新的衰减系数求解方法。在设定时间延迟范围和参考信号路数基础上,该方法通过迭代加权实时有效估计多路参考信号的相关矩阵,接收信号与参考信号的相关向量,进而求解维纳霍夫方程得到各路衰减系数,有效抑制多径信道的自干扰,克服了已有方法需同时调节幅度和相位,以及相关向量和相关矩阵估计精度低的不足。另外,理论分析了衰减系数的求解过程,并推导了自干扰对消比的闭合表达式。分析和仿真结果表明,该方法在一定延迟误差情况下,可获得90 d B以上的对消比,比已有方法提高了约9 d B,有效解决了多径信道的射频干扰对消问题。展开更多
基金supported in part by the National Natural Science Foundation of China(No.U22A2001)the National Key Research and Development Program of China(No.2022YFB2902202,No.2022YFB2902205)。
文摘Physical layer key generation(PKG)technology leverages the reciprocal channel randomness to generate the shared secret keys.The low secret key capacity of the existing PKG schemes is due to the reduction in degree-of-freedom from multipath fading channels to multipath combined channels.To improve the wireless key generation rate,we propose a multipath channel diversity-based PKG scheme.Assisted by dynamic metasurface antennas(DMA),a two-stage multipath channel parameter estimation algorithm is proposed to efficiently realize super-resolution multipath parameter estimation.The proposed algorithm first estimates the angle of arrival(AOA)based on the reconfigurable radiation pattern of DMA,and then utilizes the results to design the training beamforming and receive beamforming to improve the estimation accuracy of the path gain.After multipath separation and parameter estimation,multi-dimensional independent path gains are utilized for generating secret keys.Finally,we analyze the security and complexity of the proposed scheme and give an upper bound on the secret key capacity in the high signal-to-noise ratio(SNR)region.The simulation results demonstrate that the proposed scheme can greatly improve the secret key capacity compared with the existing schemes.
文摘在室内可见光通信中符号间干扰和噪声会严重影响系统性能,K均值(K-means)均衡方法可以抑制光无线信道的影响,但其复杂度较高,且在聚类边界处易出现误判。提出了改进聚类中心点的K-means(Improved Center K-means,IC-Kmeans)算法,通过随机生成足够长的训练序列,然后将训练序列每一簇的均值作为K-means聚类中心,避免了传统K-means反复迭代寻找聚类中心。进一步,提出了基于神经网络的IC-Kmeans(Neural Network Based IC-Kmeans,NNIC-Kmeans)算法,使用反向传播神经网络将接收端二维数据映射至三维空间,以增加不同簇之间混合数据的距离,提高了分类准确性。蒙特卡罗误码率仿真表明,IC-Kmeans均衡和传统K-means算法的误码率性能相当,但可以显著降低复杂度,特别是在信噪比较小时。同时,在室内多径信道模型下,与IC-Kmeans和传统Kmeans均衡相比,NNIC-Kmeans均衡的光正交频分复用系统误码率性能最好。
基金supported in part by the National Science Fund for Distinguished Young Scholars under Grant 61925102in part by the National Natural Science Foundation of China(62201087&92167202&62101069&62201086)in part by the Beijing University of Posts and Telecommunications-China Mobile Research Institute Joint Innovation Center。
文摘In this paper, a time-varying channel prediction method based on conditional generative adversarial network(CPcGAN) is proposed for time division duplexing/frequency division duplexing(TDD/FDD) systems. CPc GAN utilizes a discriminator to calculate the divergence between the predicted downlink channel state information(CSI) and the real sample distributions under a conditional constraint that is previous uplink CSI. The generator of CPcGAN learns the function relationship between the conditional constraint and the predicted downlink CSI and reduces the divergence between predicted CSI and real CSI.The capability of CPcGAN fitting data distribution can capture the time-varying and multipath characteristics of the channel well. Considering the propagation characteristics of real channel, we further develop a channel prediction error indicator to determine whether the generator reaches the best state. Simulations show that the CPcGAN can obtain higher prediction accuracy and lower system bit error rate than the existing methods under the same user speeds.
基金supported by National Science and Technology Major Program of the Ministry of Science and Technology (No.2018ZX03001031)Key program of Beijing Municipal Natural Science Foundation (No. L172030)+2 种基金Beijing Municipal Science & Technology Commission Project (No. Z171100005217001)Key Project of State Key Lab of Networking and Switching Technology (NST20170205)National Key Technology Research and Development Program of the Ministry of Science and Technology of China (NO. 2012BAF14B01)
文摘Cluster-based channel model is the main stream of fifth generation mobile communications, thus the accuracy of clustering algorithm is important. Traditional Gaussian mixture model (GMM) does not consider the power information which is important for the channel multipath clustering. In this paper, a normalized power weighted GMM (PGMM) is introduced to model the channel multipath components (MPCs). With MPC power as a weighted factor, the PGMM can fit the MPCs in accordance with the cluster-based channel models. Firstly, expectation maximization (EM) algorithm is employed to optimize the PGMM parameters. Then, to further increase the searching ability of EM and choose the optimal number of components without resort to cross-validation, the variational Bayesian (VB) inference is employed. Finally, 28 GHz indoor channel measurement data is used to demonstrate the effectiveness of the PGMM clustering algorithm.
文摘针对基于平面波声场模型的深海DOA(Direction of Arrival,DOA)估计存在误差的问题,从射线理论出发,建立了多途信道下深海DOA估计的阵列信号模型,推导出了深海多径声线传播时间和相邻阵元声线传播时延差的表征方法,运用解相干DOA估计算法,提高深海DOA估计性能,通过仿真验证了算法的有效性。研究表明,深海DOA估计问题实为多维参数优化问题;充分考虑海洋声场的声传播特性,可以从根本上解决深海DOA估计的误差问题。
文摘通过对中波频段的特性分析,提出一种创新的中波应急广播覆盖网构建方案。基于信号传播理论和通信工程原理,结合地理信息系统(Geographic Information System,GIS)技术,优化中波发射台布局,提高应急广播信号的覆盖范围和稳定性。采用先进的调制解调技术和功率控制策略,有效克服信道传播中的多径效应和信号衰减,提升应急广播系统的抗干扰性。通过在不同地理环境下的仿真实验,验证所提出构建方案的可行性和效果,为中波应急广播系统的建设提供有力的技术支持,为应对突发事件提供可靠的通信保障。
文摘该文针对单路延迟对消系统不能有效解决多径信道的超短波无线电台共址干扰消除问题,给出了等间隔多路延迟正交合成的射频干扰对消方案,进而提出了新的衰减系数求解方法。在设定时间延迟范围和参考信号路数基础上,该方法通过迭代加权实时有效估计多路参考信号的相关矩阵,接收信号与参考信号的相关向量,进而求解维纳霍夫方程得到各路衰减系数,有效抑制多径信道的自干扰,克服了已有方法需同时调节幅度和相位,以及相关向量和相关矩阵估计精度低的不足。另外,理论分析了衰减系数的求解过程,并推导了自干扰对消比的闭合表达式。分析和仿真结果表明,该方法在一定延迟误差情况下,可获得90 d B以上的对消比,比已有方法提高了约9 d B,有效解决了多径信道的射频干扰对消问题。
