基于四阶循环平稳的STBC-OFDM信号盲识别

针对非理想信道环境下多输入单输出(MISO)通信系统中STBC-OFDM信号的盲识别问题,提出了一种基于四阶循环平稳的STBC-OFDM信号盲识别算法。该方法首先对MISO通信系统中STBC-OFDM信号进行建模;然后利用STBC-OFDM信号编码矩阵的相关性,结合四阶循环平稳特性,推导了不同时延向量下接收信号块的四阶循环矩;最后根据有无尖峰盲识别空时分组码的类型。该方法采用分层识别的思想,无需信道系数、噪声功率和调制方式等先验信息,适合非合作通信场所。仿真结果表明:算法适应于低信噪比的环境,且不受调制方式、相位噪声和多普勒频移影响,实用性很强。      

频率选择信道下的空时分组码盲识别

在单接收天线下,针对频率选择性衰落信道下空时分组码( STBC)的盲识别问题,提出了一种基于Kolmogorov-Smirnov( K-S)检测的有效算法。该算法以经验累积分布函数作为特征函数,通过K-S检测经验累积分布函数之间的距离,达到识别空时分组码的目的。在不同调制方式、采样因子和置信区间的条件下分别对算法进行仿真并讨论其性能,结果表明,该算法性能较好,在信噪比大于6 dB时可达到90%以上的正确识别概率,在非合作通信方面具有一定的实用价值。     

四阶时延矩STBC-OFDM信号调制方式识别

针对单接收天线的STBC-OFDM信号调制识别问题,提出了一种基于OFDM块四阶时延矩盲调制识别算法。首先计算了在不存在噪声的理想条件下不同星座符号的四阶时延矩的理论值和其方差,然后根据其理论值求取阈值大小,最后比较阈值与实验值判别发射端信号调制方式。该算法不需要预先知道STBC编码方式等先验信息,非常适合于非合作通信场合。仿真结果表明:所提出的算法即使在低信噪比下也能较好的识别单接收天线STBC-OFDM信号调制方式。      

基于高阶累积量的空时分组码盲识别算法研究

针对MISO通信系统的空时分组码盲识别问题,提出了一种基于高阶累积量的空时分组码盲识别算法。首先,给出了MISO接收信号模型,利用高阶累积量的性质分析得到接收信号的四阶累积量的表达式;然后,利用编码矩阵的特性,证明接收信号在不同时延向量下的四阶累积量呈现非零值,其非零值取决于STBC的类型;最后,采用四阶累积量的实验值与理论值的最小欧式距离盲识别空时分组码的类型。仿真结果表明,即使在低信噪比条件下,所提方法能够较好地识别空时分组码。     

基于FOLP的STBC-OFDM信号盲识别方法

针对STBC-OFDM信号盲识别中存在着识别所需样本数多、对频偏敏感和不适用于单接收天线等问题,提出一种基于FOLP（Fourth Order Lag Product）的识别方法.根据不同空时分组码元素的相关性,推导了接收信号的FOLP,构造了基于FOLP的峰值检测算法.推导和仿真结果表明,该算法能够在单接收天线下运行,且不需要知道信道信息、噪声信息、调制信息以及OFDM块的起始位置;且该算法不受调制方式的影响,对时延、相位噪声和频率偏移鲁棒性能好,能够应用于认知无线电、频谱监控等工程领域中.     

Non-cooperative signal modulation recognition algorithm based on joint feature parameter extraction

Aiming at the problem that in the current electromagnetic environment,the modulation method is complicated,the frequency-consuming equipment increases,the spectrum is congested,and the electromagnetic environment interference increases,the algorithm of OFDM signal detection and subcarrier identification in the background of non-cooperative communication were deeply studied.Using the different distribution states of OFDM signals and single carrier signals in the time domain,a joint characteristic parameter was proposed to solve the existence problem of OFDM in the received signal.For the phase shift and frequency offset problems caused by the channel transmission to the signal,by using the periodic stability the blind parameter estimation was performed to obtain the signal prior information.On the basis of the obtained signal prior information,a multi-level classification and recognition method for non-cooperative OFDM signal sub-carrier signals was proposed.Therefore,a model based on non-cooperative communication system OFDM signal detection and subcarrier modulation identification was designed,and finally modulation identification of unknown signals was completed.Simulation experiments show that in non-cooperative communication systems,OFDM signals and single-carrier signals can be accurately identified,and ideal modulation recognition effects can be achieved on empty subcarriers,QPSK,and 16QAM in the receiver OFDM signal subcarriers,overcoming the channel transmission band The problems of phase shift and frequency offset have improved the accuracy of modulation mode identification.     

一种单接收天线下的空时分组码识别方法

该文针对全盲条件下单接收天线的空时分组码(STBC)识别问题,提出一种基于四阶累积量识别方法。使用接收信号的四阶累积量作为特征参数,利用高阶累积量对零均值高斯噪声不敏感的特性,首先求取各种STBC的四阶累积量理论值,再对采样信号四阶累积量进行区间检测,从而实现STBC的识别。该算法不需要信道信息和噪声参数,仿真结果表明,该算法在单天线条件下性能较好。     

PARAFAC-based unified tensor modeling for wireless communication systems with application to blind multiuser equalization

In some antenna array-based wireless communication systems the received signal is multidimensional and can be treated as a tensor (3D array) instead of a matrix (2D array). In this paper, we make use of a generalized tensor decomposition known as constrained Block-PARAFAC and propose a tensor (3D) model for the signal received by three types of wireless communication systems. The considered wireless communication systems are multiuser systems subject to frequency-selective multipath and employing multiple receiver antennas together with (i) oversampling or (ii) direct-sequence spreading or (iii) multicarrier modulation. The proposed modeling approach aims at unifying the received signal model of these systems into a single PARAFAC model. We show that the proposed model has a constrained structure, where model constraints and associated dimensions depend on each particular system. The proposed constrained Block-PARAFAC model is demonstrated by expanding the tensor using Kronecker products of canonical vectors. As an application of this model to tensor signal processing, a new tensor-based receiver is proposed for blind multiuser equalization, which combines PARAFAC-based modeling with a subspace method. Simulation results are presented to illustrate the performance of the proposed blind receiver.     

基于K-S检验的BPSK信号盲处理结果可信性评估

本文提出了一种基于K-S(Kolmogorov-Smirnov)分布拟合检验的BPSK信号盲处理结果可信性评估算法.首先建立了BPSK信号盲处理结果可信性评估的假设检验模型,而后根据调制识别结果对应的信号模型构造参考信号,提取其与观测信号相关后的相位序列.基于K-S分布拟合检验,比较相位序列概率分布与零假设下特定概率分布是否一致,以判决某次处理结果的可信与否.计算机仿真结果表明,本算法可在较低信噪比条件下,实现对BPSK信号盲处理结果的可信性检验.     

Bayesian blind turbo receiver for coded OFDM systems with frequencyoffset and frequency-selective fading

The design of a blind receiver for coded orthogonal frequency-division multiplexing communication systems in the presence of frequency offset and frequency-selective fading is investigated. The proposed blind receiver iterates between a Bayesian demodulation stage and a maximum a posteriori channel decoding stage. The extrinsic a posteriori probabilities of data symbols are iteratively exchanged between these two stages to achieve successively improved performance. The Bayesian demodulator computes the a posteriori data symbol probabilities, based on the received signals (without knowing or explicitly estimating the frequency offset and the fading channel states), by using Markov chain Monte Carlo (MCMC) techniques. In particular, two MCMC methods-the Metropolis-Hastings algorithm and the Gibbs sampler-are studied for this purpose. Computer simulation results show that the proposed Bayesian blind turbo receiver can achieve good performance and is robust against modeling mismatch     

基于循环频率特征的单信道混合通信信号的调制识别

单信道多个时频重叠的混合通信信号盲分离具有广泛的应用价值,是近年来通信信号处理研究领域的一个热点和难点,而单信道多个混叠通信信号的调制识别和信号个数的估计是实现单信道多个混叠通信信号盲分离的基础。该文通过分析数字调制信号的二阶和四阶循环累积量的循环频率特性和结构特征,提出一种基于循环频率特征的单信道混合通信信号调制识别的方法和实现算法,其主要特点是不需要已知混合信号的个数、功率、载频、符号率及定时等先验信息,对一些典型的数字调制信号(如BPSK, QPSK, OQPSK, MSK等)的随机混合,能够有效地辨识接收信号中所包含的信号个数以及每个调制信号的调制方式。利用计算机仿真实验,考察了不同条件下算法的性能,表明了其可行性和有效性。     

Kalman滤波下的多信号单通道盲源分离

窄带物联网环境中,接收机收到的信号通常为多路混合信号,对单通道接收来说,利用常规盲源分离方法很难实现混合信号的分离和源信号提取。针对这一问题,本文提出了一种利用Kalman滤波算法进行信号估计,解决单通道盲源分离的方法。该方法利用信号间的时序结构,通过Kalman滤波算法对多信号混合中的源信号不断估计并迭代更新,最终得到分离信号。仿真实验结果表明,该方法能有效估计并分离出源信号。      

基于独立分量分析的PCMA信号盲分离算法

针对非协作通信中成对载波多址(Paired Carrier Multiple Acess,PCMA)信号的盲分离问题,提出了一种基于独立分量分析(Independent component analysis,ICA)的单通道盲分离算法。首先对接收到的单路PCMA信号进行参数估计得到其残余载波频率,再对其处理得到两路基带混合信号,最后利用ICA算法分离出源基带信号。该算法在未知两个卫星地面站发送信号的情况下,从接收到的PCMA信号中恢复出两路源基带信号。仿真实验表明,本文算法在信噪比为-10dB时仍具有良好的分离效果,两路基带信号的波形相似系数可分别达到0.94与0.86以上。     

Automatic classification of multiple signals using 2D matching of magnitude–frequency density features

Signal classification is an important function of modern communication systems in software defined radio applications. The ability to quickly recognize the type of received signals allows a system to automatically adapt the processor to properly decode the signals. Many classification techniques assume that the received signal space is occupied by only one signal, and that the frequency of operation is known. However, in some systems, the receiver may be completely blind to the number and characteristics of signals within the bandwidth of interest. The technique introduced in this study proposes the collapsing of localized magnitude peaks from consecutive short time discrete fourier transform bins into magnitude histograms to create a two dimensional image of the frequency?Cmagnitude density of the received signal space. This image can be a useful visualization tool in the characterization of the signal space in user assisted modes of classification. Alternatively, the process could be automated by utilizing pattern recognition and image processing algorithms. Automatic signal classification is explored in this study.     

多天线超宽带通信中的一种盲接收机

针对多天线冲激无线电超宽带通信系统,提出了一种盲的线性接收机。在不需要信道信息的情况下,可以利用经过预编码的空时分组码给接收信号带来的特定结构,直接得到接收机系数,从而恢复出发送符号。仿真表明,随着用于估计的数据块的增加,盲接收机的性能逐渐接近于相关接收。     

基于时间反演的多用户下行安全传输技术

无线通信是开放的自由空间，窃听用户可能在通信过程中对信息进行窃取.针对多用户下行链路的信息泄露问题，提出一种基于时间反演（time reversal，TR）且采用波束赋形（beam forming，BF）技术优化的安全传输方案.该方案以最大化安全容量为多用户通信准则，建立多输入单输出（multiple-input single-output，MISO）窃听信道模型.一方面基站采用BF技术使信号在指定方向上增强，针对期望用户发送需求信号；另一方面由于TR技术对环境是自适应的，基站的信号在发射之前都要经过TR腔，其空时聚焦性使能量聚焦在合法接收方，保密性能更优.理论分析和仿真结果表明:与已有物理层安全方案相比，所提方案安全容量更高，误比特率更低.     

基于MWC压缩采样宽带接收机的雷达信号脉内调制识别

针对传统宽带数字接收机在接收宽带雷达信号时产生的跨信道问题,以及低截获概率(LPI)雷达信号脉内调制盲识别问题,该文提出一种基于调制宽带转换器(MWC)离散压缩采样的新型宽带数字接收机结构对宽带雷达信号进行截获和识别。该结构采用伪随机序列将接收信号混频至基带和其他子带内,经低通滤波、降速采样获得基带压缩采样信号,解决了跨信道信号问题;又提出一种基于短时傅里叶变换(STFT)和频谱能量聚焦率检验的识别算法。首先检验STFT频谱带宽并进行调相和调频信号粗识别,然后检验压缩采样信号频谱能量聚焦率并进行具体的信号脉内调制识别。仿真结果证明了该新型接收机结构和该识别算法在低信噪比下的有效性。     

Full-Diversity Codes for MISO Systems Equipped With Linear or ML Detectors

In this paper, a general criterion for space-time block codes (STBC) to achieve full diversity with a linear receiver is proposed for a wireless communication system having multiple transmitter and single receiver antennas [multiple-input–single-output (MISO)]. Particularly, the STBC with Toeplitz structure satisfies this criterion, and therefore, enables full diversity. Further examination of this Toeplitz STBC reveals the following important properties: 1) the symbol transmission rate can be made to approach unity; 2) applying the Toeplitz code to any signalling scheme having nonzero distance between the nearest constellation points results in a nonvanishing determinant. In addition, if quadratic-amplitude modulation (QAM) is used as the signalling scheme, then for independent MISO flat-fading channels, the Toeplitz codes is proved to approach the optimal diversity-versus-multiplexing tradeoff with a zero-forcing (ZF) receiver when the number of channel uses is large. This is, so far, the first nonorthogonal STBC shown to achieve the optimal tradeoff for such a receiver. On the other hand, when maximum-likelihood (ML) detection is employed in a MISO system, the Toeplitz STBC achieves the maximum coding gain for independent channels. When the channel fading coefficients are correlated, the inherent transmission matrix in the Toeplitz STBC can be designed to minimize the average worst case pairwise error probability.      

PARAFAC-based channel estimation and data recovery in nonlinear MIMO spread spectrum communication systems

In this paper, a new tensorial modeling is first proposed for nonlinear multiple-input multiple-output (MIMO) direct sequence spread spectrum communication systems. The channel is modeled as an instantaneous MIMO Volterra system. Then, a direct data approach for joint blind channel estimation and data recovery is developed using the parallel factor (PARAFAC) decomposition of a third-order tensor composed of received signals, exploiting space, time and code diversities. A blind channel estimation method based on the PARAFAC decomposition of a fifth-order tensor composed of covariances of the received signals is also proposed, considering phase shift keying (PSK) modulated transmitted signals. The proposed estimation algorithms are evaluated by simulating a nonlinear uplink MIMO radio over fiber (ROF) communication system.     

基于SIC的单通道同频混合信号低复杂度盲分离算法

针对非协作通信中单通道同频混合信号盲分离算法复杂度高的问题,本文在连续干扰抵消（Serial Interference Cancellation, SIC）算法基础上,构建了单通道同频混合信号的信道接收模型,提出了基于SIC的低复杂度盲分离算法。并对信道重构中的信道截断影响进行了处理,避免了分离性能的损失。仿真结果表明,QPSK调制方式下分离性能与已有算法性能相当,但复杂度降低了2/3,且算法对频偏误差不敏感。