In this paper,using cyclostationarity-based sensing method to detect the presence of Orthogonal Frequency Division Multiplexing(OFDM) signal over doubly-selective fading channels is studied.By approximating the channe...In this paper,using cyclostationarity-based sensing method to detect the presence of Orthogonal Frequency Division Multiplexing(OFDM) signal over doubly-selective fading channels is studied.By approximating the channel with Basis Expansion Model(BEM),we derive the second-order cyclostationary statistics of the received OFDM signal over doubly-selective fading channels.Theoretical analysis indicates that new cyclostationary signatures produced by Doppler spread and multipath delay can be further exploited in the detecting process.Simulation examples demonstrate that the sensing methods using channel-induced cyclostationary features provide substantial improvements on detection performance.展开更多
In this paper,we study the accuracy of delay-Doppler parameter estimation of targets in a passive radar using orthogonal frequency division multiplexing(OFDM)signal.A coarse-fine joint estimation method is proposed to...In this paper,we study the accuracy of delay-Doppler parameter estimation of targets in a passive radar using orthogonal frequency division multiplexing(OFDM)signal.A coarse-fine joint estimation method is proposed to achieve better estimation accuracy of target parameters without excessive computational burden.Firstly,the modulation symbol domain(MSD)method is used to roughly estimate the delay and Doppler of targets.Then,to obtain high-precision Doppler estimation,the atomic norm(AN)based on the multiple measurement vectors(MMV)model(MMV-AN)is used to manifest the signal sparsity in the continuous Doppler domain.At the same time,a reference signal compensation(RSC)method is presented to obtain highprecision delay estimation.Simulation results based on the OFDM signal show that the coarse-fine joint estimation method based on AN-RSC can obtain a more accurate estimation of target parameters compared with other algorithms.In addition,the proposed method also possesses computational advantages compared with the joint parameter estimation.展开更多
针对正交频分复用(Orthogonal Frequency Division Multiplexing,OFDM)接收机解调精度低和计算复杂度高的问题,采用深度学习方法构建了一种新的模型驱动的接收机模型,称为FBLTNet(Fully Connected,Bi-LSTM and Transformer-encoder Neur...针对正交频分复用(Orthogonal Frequency Division Multiplexing,OFDM)接收机解调精度低和计算复杂度高的问题,采用深度学习方法构建了一种新的模型驱动的接收机模型,称为FBLTNet(Fully Connected,Bi-LSTM and Transformer-encoder Neural Network)。该模型分为信道估计和信号检测两个部分,其中信道估计以全连接神经网络(Fully Connected Deep Neural Network,FCDNN)替代线性插值,信号检测则使用深度自注意力网络编码器Transformer-encoder和双向长短期记忆网络(Bidirectional Long-Short Term Memory,Bi-LSTM)的组合网络,实现信号的解调和比特流的恢复。在瑞利衰落信道下测试了不同调制方式的接收机性能,结果表明FBLTNet与基于深度学习的接收机以及传统接收机相比,误比特率性能得到了显著的改善;与数据驱动的无线接收机算法相比,线下训练模型收敛时间和测试时间分别减少了33.0%和25%,网络结构参数减少了29.5%。展开更多
随着第五代移动通信技术、低轨卫星互联网等通信系统的发展,正交频分复用(Orthogonal Frequency Division Multiplexing,OFDM)技术凭借良好的频谱效率得到日益广泛的应用。但OFDM系统信号参数复杂,对非合作条件下的盲信号分析带来了较...随着第五代移动通信技术、低轨卫星互联网等通信系统的发展,正交频分复用(Orthogonal Frequency Division Multiplexing,OFDM)技术凭借良好的频谱效率得到日益广泛的应用。但OFDM系统信号参数复杂,对非合作条件下的盲信号分析带来了较大困难,需要在尽可能少的先验信息下,判定信号参数。为此,提出了一种基于相位轨迹特征的OFDM信号盲分析方法,利用OFDM信号固有的循环周期性,粗估计子载波数及子载波间隔,在此基础上通过非线性变换等处理获取子载波间及符号间的相位变化轨迹,基于相位变化轨迹分析其反映的采样定时误差、载波频率偏差等参数信息并修正估计值,最终通过信号接收误码率进行估计参数正确性验证,实现了对OFDM信号盲分析。仿真分析结果表明,在信噪比(SNR)大于8 dB时,即可实现参数准确估计,优于利用先验信息的相关处理方法。展开更多
正交频分复用(Orthogonal Frequency Division Multiplexing,OFDM)技术在无线通信领域中拥有着重要地位,但OFDM系统中存在子载波间干扰和较高的峰均比的缺点,使得OFDM系统在信号检测方面的表现不太理想。针对OFDM系统中信号检测性能较...正交频分复用(Orthogonal Frequency Division Multiplexing,OFDM)技术在无线通信领域中拥有着重要地位,但OFDM系统中存在子载波间干扰和较高的峰均比的缺点,使得OFDM系统在信号检测方面的表现不太理想。针对OFDM系统中信号检测性能较差的问题,提出一种基于自归一化网络的索引调制(Index Modulation for Self Normalizing Network,IM-SNN)算法,并采用4QAM、8QAM、16QAM的调制方式验证系统的信号检测性能。结果表明,所提出的算法提高了接收端解调信号的性能,有效增强了信号检测的能力,并表现出优于传统技术中最大似然检测(Maximum Likelihood Detection,MLD)算法及现有技术中基于深度神经网络的索引调制(Index Modulation in Deep Neural Network,IM-DNN)算法的系统误码率及网络损失。在3种调制方式下,性能改善0.6~8 dB。展开更多
干扰识别是无线电监测和通信抗干扰的关键环节。针对正交频分复用(Orthogonal Frequency Division Multiplexing,OFDM)宽带传输系统中潜在的干扰问题,提出了一种基于目标检测网络的干扰识别方法。核心思想是将传输频带中的多干扰识别问...干扰识别是无线电监测和通信抗干扰的关键环节。针对正交频分复用(Orthogonal Frequency Division Multiplexing,OFDM)宽带传输系统中潜在的干扰问题,提出了一种基于目标检测网络的干扰识别方法。核心思想是将传输频带中的多干扰识别问题转化为时频谱图中的多目标检测问题,进而利用改进的目标检测算法进行识别。实验结果表明,该方法能有效识别传输频带内音调干扰、噪声干扰、扫频干扰、脉冲噪声干扰和锯齿波扫频干扰的类型、数量、干扰频率和时间范围,同时相比改进前的YOLOv3算法,平均精度提高了7.6%,权值文件、参数量和计算量分别降低了82.5%,82.6%,90%,对能耗受限场景下的实时检测具有潜在应用价值。展开更多
随着雷达系统和通信系统的快速发展,二者之间相互融合,能够凸显一体化系统设计和应用便利优势,如在通信系统方面,高数据速率传输能够更好地满足信号大带宽拓展需求,在一定程度上推动通信工作频段往高频段方向发展,有利于进行一体化的信...随着雷达系统和通信系统的快速发展,二者之间相互融合,能够凸显一体化系统设计和应用便利优势,如在通信系统方面,高数据速率传输能够更好地满足信号大带宽拓展需求,在一定程度上推动通信工作频段往高频段方向发展,有利于进行一体化的信号设计和应用。为此,从正交频分复用(Orthogonal Frequency Division Multiplexing,OFDM)信号的峰值平均功率比(Peak to Average Power Ratio,PAPR)性能问题出发,通过仿真分别研究和探讨不同子载波数目以及调制方式对于OFDM一体化信号的PAPR特性的影响,列举两种改善PAPR性能的技术方法,以期达到预设的研究目标。展开更多
针对多输入单输出(MISO,multiple input single output)通信系统的STBC-OFDM信号盲识别问题,提出基于OFDM块的改进Kolmogorov-Smirnov(K-S)检测方法。该方法首先对MISO通信系统的STBC-OFDM信号建模;然后利用STBC-OFDM信号编码矩阵的相关...针对多输入单输出(MISO,multiple input single output)通信系统的STBC-OFDM信号盲识别问题,提出基于OFDM块的改进Kolmogorov-Smirnov(K-S)检测方法。该方法首先对MISO通信系统的STBC-OFDM信号建模;然后利用STBC-OFDM信号编码矩阵的相关性,构造不同时延向量下STBC-OFDM接收信号OFDM块的经验函数作为特征函数;最后通过改进K-S检测方法检验经验分布函数之间的距离盲识别STBC-OFDM信号。该方法不需要噪声信息、调制信息和信道系数,适合非合作通信场合。理论分析和实验验证了该方法的可行性。展开更多
基金Supported by the National Natural Science Foundation of China(No.61002017 and No.61072076)the STCSM and Shanghai Rising-Star Program(10JC1414400)
文摘In this paper,using cyclostationarity-based sensing method to detect the presence of Orthogonal Frequency Division Multiplexing(OFDM) signal over doubly-selective fading channels is studied.By approximating the channel with Basis Expansion Model(BEM),we derive the second-order cyclostationary statistics of the received OFDM signal over doubly-selective fading channels.Theoretical analysis indicates that new cyclostationary signatures produced by Doppler spread and multipath delay can be further exploited in the detecting process.Simulation examples demonstrate that the sensing methods using channel-induced cyclostationary features provide substantial improvements on detection performance.
基金supported by the National Natural Science Foundation of China(6193101562071335)+1 种基金the Technological Innovation Project of Hubei Province of China(2019AAA061)the Natural Science F oundation of Hubei Province of China(2021CFA002)。
文摘In this paper,we study the accuracy of delay-Doppler parameter estimation of targets in a passive radar using orthogonal frequency division multiplexing(OFDM)signal.A coarse-fine joint estimation method is proposed to achieve better estimation accuracy of target parameters without excessive computational burden.Firstly,the modulation symbol domain(MSD)method is used to roughly estimate the delay and Doppler of targets.Then,to obtain high-precision Doppler estimation,the atomic norm(AN)based on the multiple measurement vectors(MMV)model(MMV-AN)is used to manifest the signal sparsity in the continuous Doppler domain.At the same time,a reference signal compensation(RSC)method is presented to obtain highprecision delay estimation.Simulation results based on the OFDM signal show that the coarse-fine joint estimation method based on AN-RSC can obtain a more accurate estimation of target parameters compared with other algorithms.In addition,the proposed method also possesses computational advantages compared with the joint parameter estimation.
文摘针对正交频分复用(Orthogonal Frequency Division Multiplexing,OFDM)接收机解调精度低和计算复杂度高的问题,采用深度学习方法构建了一种新的模型驱动的接收机模型,称为FBLTNet(Fully Connected,Bi-LSTM and Transformer-encoder Neural Network)。该模型分为信道估计和信号检测两个部分,其中信道估计以全连接神经网络(Fully Connected Deep Neural Network,FCDNN)替代线性插值,信号检测则使用深度自注意力网络编码器Transformer-encoder和双向长短期记忆网络(Bidirectional Long-Short Term Memory,Bi-LSTM)的组合网络,实现信号的解调和比特流的恢复。在瑞利衰落信道下测试了不同调制方式的接收机性能,结果表明FBLTNet与基于深度学习的接收机以及传统接收机相比,误比特率性能得到了显著的改善;与数据驱动的无线接收机算法相比,线下训练模型收敛时间和测试时间分别减少了33.0%和25%,网络结构参数减少了29.5%。
文摘随着第五代移动通信技术、低轨卫星互联网等通信系统的发展,正交频分复用(Orthogonal Frequency Division Multiplexing,OFDM)技术凭借良好的频谱效率得到日益广泛的应用。但OFDM系统信号参数复杂,对非合作条件下的盲信号分析带来了较大困难,需要在尽可能少的先验信息下,判定信号参数。为此,提出了一种基于相位轨迹特征的OFDM信号盲分析方法,利用OFDM信号固有的循环周期性,粗估计子载波数及子载波间隔,在此基础上通过非线性变换等处理获取子载波间及符号间的相位变化轨迹,基于相位变化轨迹分析其反映的采样定时误差、载波频率偏差等参数信息并修正估计值,最终通过信号接收误码率进行估计参数正确性验证,实现了对OFDM信号盲分析。仿真分析结果表明,在信噪比(SNR)大于8 dB时,即可实现参数准确估计,优于利用先验信息的相关处理方法。
文摘正交频分复用(Orthogonal Frequency Division Multiplexing,OFDM)技术在无线通信领域中拥有着重要地位,但OFDM系统中存在子载波间干扰和较高的峰均比的缺点,使得OFDM系统在信号检测方面的表现不太理想。针对OFDM系统中信号检测性能较差的问题,提出一种基于自归一化网络的索引调制(Index Modulation for Self Normalizing Network,IM-SNN)算法,并采用4QAM、8QAM、16QAM的调制方式验证系统的信号检测性能。结果表明,所提出的算法提高了接收端解调信号的性能,有效增强了信号检测的能力,并表现出优于传统技术中最大似然检测(Maximum Likelihood Detection,MLD)算法及现有技术中基于深度神经网络的索引调制(Index Modulation in Deep Neural Network,IM-DNN)算法的系统误码率及网络损失。在3种调制方式下,性能改善0.6~8 dB。
文摘干扰识别是无线电监测和通信抗干扰的关键环节。针对正交频分复用(Orthogonal Frequency Division Multiplexing,OFDM)宽带传输系统中潜在的干扰问题,提出了一种基于目标检测网络的干扰识别方法。核心思想是将传输频带中的多干扰识别问题转化为时频谱图中的多目标检测问题,进而利用改进的目标检测算法进行识别。实验结果表明,该方法能有效识别传输频带内音调干扰、噪声干扰、扫频干扰、脉冲噪声干扰和锯齿波扫频干扰的类型、数量、干扰频率和时间范围,同时相比改进前的YOLOv3算法,平均精度提高了7.6%,权值文件、参数量和计算量分别降低了82.5%,82.6%,90%,对能耗受限场景下的实时检测具有潜在应用价值。
文摘随着雷达系统和通信系统的快速发展,二者之间相互融合,能够凸显一体化系统设计和应用便利优势,如在通信系统方面,高数据速率传输能够更好地满足信号大带宽拓展需求,在一定程度上推动通信工作频段往高频段方向发展,有利于进行一体化的信号设计和应用。为此,从正交频分复用(Orthogonal Frequency Division Multiplexing,OFDM)信号的峰值平均功率比(Peak to Average Power Ratio,PAPR)性能问题出发,通过仿真分别研究和探讨不同子载波数目以及调制方式对于OFDM一体化信号的PAPR特性的影响,列举两种改善PAPR性能的技术方法,以期达到预设的研究目标。
文摘针对多输入单输出(MISO,multiple input single output)通信系统的STBC-OFDM信号盲识别问题,提出基于OFDM块的改进Kolmogorov-Smirnov(K-S)检测方法。该方法首先对MISO通信系统的STBC-OFDM信号建模;然后利用STBC-OFDM信号编码矩阵的相关性,构造不同时延向量下STBC-OFDM接收信号OFDM块的经验函数作为特征函数;最后通过改进K-S检测方法检验经验分布函数之间的距离盲识别STBC-OFDM信号。该方法不需要噪声信息、调制信息和信道系数,适合非合作通信场合。理论分析和实验验证了该方法的可行性。
