Convergence analysis of a CM array for CDMA systems

The incorporation of directional sensitivity, provided by so-called adaptive antennas is useful in suppressing interfering signals that arise from spatially distinct mobile sources. The problem is that in a cellular radio environment where multipath exists, the standard adaptive antenna using reference signals may not properly lock on the desired signal. This is because the signal correlation matrix processed by the antenna may then be close to singular and standard algorithms fail. Also, most standard algorithms need to cooperate with the receiver for the spatial discrimination of signals. A smart antenna utilizing a blind algorithm is of interest since the antenna may not need to get any feedback from a receiver for the adjustment of weight coefficient for spatial processing and can stand alone to be plugged into any kind of receiver structure.In this paper, we address the convergence property of a Constant Modulus Algorithm which is a blind algorithm and, if employed, can provide no need for an antenna to cooperate with a receiver attached. By identifying a relationship between the weight coefficients and output signal amplitude, we also evaluate the performance of such a stand-alone antenna plus a CDMA matched filter reception. Our results show that for a three element CM array, the BER of a desired user with the other interfering users is much better than a conventional correlation receiver for a single user case since the array suppresses interferences and achieves array gain in SNR.     

Robust Least Squares Constant Modulus Algorithm to Signal Steering Vector Mismatches

The linearly constrained least squares constant modulus algorithm (LSCMA) may suffer significant performance degradation and lack robustness in the presence of the slight mismatches between the actual and assumed signal steering vectors, which can cause the serious problem of desired signal cancellation. To account for the mismatches, we propose a doubly constrained robust LSCMA based on explicit modeling of uncertainty in the desired signal array response and data covariance matrix, which provides robustness against pointing errors and random perturbations in detector parameters. Our algorithm optimizes the worst-case performance by minimizing the output SINR while maintaining a distortionless response for the worst-case signal steering vector. The weight vector can be optimized by the partial Taylor-series expansion and Lagrange multiplier method, and the optimal value of the Lagrange multiplier is iteratively derived based on the known level of uncertainty in the signal DOA. The proposed implementation based on iterative minimization eliminates the covariance matrix inversion estimation at a comparable cost with that of the existing LSCMA. We present a theoretical analysis of our proposed algorithm in terms of convergence, SINR performance, array beampattern gain, and complexity cost in the presence of random steering vector mismatches. In contrast to the linearly constrained LSCMA, the proposed algorithm provides excellent robustness against the signal steering vector mismatches, yields improved signal capture performance, has superior performance on SINR improvement, and enhances the array system performance under random perturbations in sensor parameters. The on-line implementation and significant SINR enhancement support the practicability of the proposed algorithm. The numerical experiments have been carried out to demonstrate the superiority of the proposed algorithm on beampattern control and output SINR enhancement compared with linearly constrained LSCMA.     

An adaptive array utilizing an adaptive spatial averaging technique for multipath environments

When the interference is coherent with the desired signal, the conventional adaptive arrays working under the guiding principle of output power minimization tend to cancel the desired signal by using the coherent interference. A technique is described which enables the adaptive array to function even under such an environment. The array is divided into subarrays, whose input correlation matrices are adaptively averaged so as to produce a Toeplitz matrix which would be obtained when the interference did not correlate with the desired signal. The averaged matrix is now free from correlation terms between the desired signal and interference, and therefore may be used to derive the optimum weight for the array element just as in the ordinary radio environment of incoherent interference. Numerical examples show that the new adaptive array is highly capable to suppress the coherent interferences as well as incoherent ones.     

一种正交空时分组码盲识别方法

由于正交空时分组码(OSTBC)编码矩阵具有正交特性,可得正交空时分组码的虚拟信道矩阵的相关矩阵为对角矩阵。根据此原理,文中提出相关矩阵的非主对角元素能量与主对角元素能量之比的特征参数和基于此特征参数的正交空时分组码识别方法;同时利用最小描述长度(MDL)准则估计出信号子空间维数,即每组发射信号所含符号数。仿真结果表明,在不同发射天线数和接收天线数下,所提出的识别方法都具有较好的识别性能;符号数的估计性能与正确识别概率具有相同的变化规律。     

Blind adaptive beamforming based on generalized sidelobe canceller

The generalized sidelobe canceller (GSC) is an efficient implementation of the direction constrained adaptive array. Conventional GSC is designed according to a quiescent weight vector and a blocking matrix. The quiescent weight vector provides the array with specified array response at some direction. The blocking matrix is designed based on a priori knowledge of the direction of arrival (DOA) of the desired signal. In this paper, we propose a new GSC-based adaptive array without a priori knowledge of the DOA of the desired signal. This paper utilizes eigensubspace decomposition and statistically cyclostationary properties of the signals to design the adaptive array. A method for constructing the most efficient blocking matrix for the GSC is developed. Simulation examples illustrate the effectiveness of the proposed method.     

Robust adaptive beamforming for general-rank signal models

The performance of adaptive beamforming methods is known to degrade severely in the presence of even small mismatches between the actual and presumed array responses to the desired signal. Such mismatches may frequently occur in practical situations because of violation of underlying assumptions on the environment, sources, or sensor array. This is especially true when the desired signal components are present in the beamformer "training" data snapshots because in this case, the adaptive array performance is very sensitive to array and model imperfections. The similar phenomenon of performance degradation can occur even when the array response to the desired signal is known exactly, but the training sample size is small. We propose a new powerful approach to robust adaptive beamforming in the presence of unknown arbitrary-type mismatches of the desired signal array response. Our approach is developed for the most general case of an arbitrary dimension of the desired signal subspace and is applicable to both the rank-one (point source) and higher rank (scattered source/fluctuating wavefront) desired signal models. The proposed robust adaptive beamformers are based on explicit modeling of uncertainties in the desired signal array response and data covariance matrix as well as worst-case performance optimization. Simple closed-form solutions to the considered robust adaptive beamforming problems are derived. Our new beamformers have a computational complexity comparable with that of the traditional adaptive beamforming algorithms, while, at the same time, offer a significantly improved robustness and faster convergence rates.     

基于二阶锥优化方法的MIMO阵列发射方向图设计

由于MIMO 阵列发射方向图通常是由期望方向图优化出信号协方差矩阵来实现,其方法运算量大且算法性能依赖参数选取等。该文提出利用基于二阶锥优化(second-order cone programs)方法的主瓣宽度约束条件下最低旁瓣波束形成构造出一组较低旁瓣的基波束,在集中式MIMO阵列的任意发射方向图可以由一组基波束合成的基础上,应用线性规划方法快速求解出构成发射方向图的基波束的比例系数。该方法设计的方向图具有更低的旁瓣和更加平滑的主瓣指向,仿真结果证实了方法的有效性。     

Direction of arrival estimation for more correlated sources than active sensors

In this paper, a new direction of arrival (DOA) estimation method for more correlated sources than active receiving antennas is proposed. The trick to solve this problem using only second-order statistics is to consider a periodic scanning of an underlying uniform array, where a single scanning period contains several time slots and in different time slots different sets of antennas are activated leading to a dynamic non-uniform array with possibly less active antennas than sources in each time slot. We collect the spatial correlation matrices of the active antenna arrays for all time slots and are able to present them as a linear function of the spatial correlation matrix of the underlying array. We provide a necessary and sufficient condition for this system of equations to be full column-rank, which allows for a least squares (LS) reconstruction of the spatial correlation matrix of the underlying array. Some practical greedy algorithms are presented to design dynamic arrays satisfying this condition. In a second step, we use the resulting spatial correlation matrix of the underlying array to estimate the DOAs of the possibly correlated sources by spatial smoothing and MUSIC. Alternatively, we can express this matrix as a linear function of the correlation matrix of the sources (incoming signals) at a grid of investigated angles, and solve this system of equations using either LS or sparsity-regularized LS (possibly assisted by additional constraints), depending on the grid resolution compared to the number of antennas of the underlying array.     

基于随机天线阵的物理层安全通信系统峰均功率比问题研究

采用多天线发射方式保障通信信息的安全传输是物理层安全通信研究领域中常采用的手段之一,但这种方式会导致发射信号高峰均功率比的缺点,从而影响合法用户的通信性能。该文从多天线发射的加权系数出发,定义了基于多天线发射方式的物理层安全通信信号峰均功率比(PAPR)概念,并且针对随机天线阵物理层安全通信系统提出一种基于部分传输序列的物理层安全通信信号PAPR抑制算法。仿真结果表明该文提出的PAPR抑制算法能在不降低发射信号安全性能的条件下有效地降低发射信号的PAPR值。     

Realization of space-time adaptive filtering by employing electronically steerable passive array radiator antennas

Electronically steerable passive array radiator (ESPAR) antennas are considered to have the capability to form a beam spatially toward the desired signal with the lowest cost. We propose a method to realize space-time adaptive filtering (STAF) by employing ESPAR antennas for TDMA or CDMA signal waveforms. According to the method, the cochannel interference signals are spatially suppressed by the adaptive beamforming, and the intersymbol interference signals are suppressed by the temporal waveform-based adaptive equalization. Simulation results show the effectiveness of the proposed method in the signal environment of a local network communication system.     

Performance of DMI and eigenspace-based beamformers

The performance of the direct matrix inversion (DMI) method for antenna arrays of arbitrary geometry is analyzed by asymptotic statistical techniques. The effects of eigenspace disturbance caused by finite samples on the output interference and noise powers are examined under the unit gain constraint in the direction of the desired signal. The results show that the performance of the DMI method is degraded mostly by the disturbed noise subspace. That suggests the use of an eigenspace-based beamformer in which the weight vector is computed by using the signal-plus-interference subspace component of the sample correlation matrix. Convergence properties of the eigenspace-based beamformer are evaluated for the cases in which the source number is known and in which it is overestimated. Theoretical analyses validated by computer simulations indicate that the eigenspace-based beamformer has faster convergence rate than the DMI method     

基于均圆阵的扩展循环 MUSIC 算法简

将循环平稳理论引入到均匀圆阵的波达方向估计中,提出了一种基于圆阵的扩展循环MUSIC算法。同时利用信号的循环相关阵和循环共轭相关阵的信息,有效抑制了同频带内干扰和噪声的影响,实现了感兴趣信号与干扰信号的有效分离。仿真实验结果表明,所提算法具有较高的波达方向估计精度和较好的多信号分辨能力,且突破了经典MUSIC算法关于信源数不能超过阵元数的限制。     

Robust Adaptive Beamforming Under Quadratic Constraint with Recursive Method Implementation

When adaptive arrays are applied to practical problems, the performances of the conventional adaptive beamforming algorithms are known to degrade substantially in the presence of even slight mismatches between the actual and presumed array responses to the desired signal. Similar types of performance degradation can occur because of data nonstationarity and small training sample size, when the signal steering vector is known exactly. In this paper, to account for mismatches, we propose robust adaptive beamforming algorithm for implementing a quadratic inequality constraint with recursive method updating, which is based on explicit modeling of uncertainties in the desired signal array response and data covariance matrix. We show that the proposed algorithm belongs to the class of diagonal loading approaches, but diagonal loading terms can be precisely calculated based on the given level of uncertainties in the signal array response and data covariance matrix. The variable diagonal loading term is added at each recursive step, which leads to a simpler closed-form algorithm. Our proposed robust recursive algorithm improves the overall robustness against the signal steering vector mismatches and small training sample size, enhances the array system performance under random perturbations in sensor parameters and makes the mean output array SINR consistently close to the optimal one. Moreover, the proposed robust adaptive beamforming can be efficiently computed at a low complexity cost compared with the conventional adaptive beamforming algorithms. Computer simulation results demonstrate excellent performance of our proposed algorithm as compared with the existing adaptive beamforming algorithms.     

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.     

Robust adaptive array for wireless communications

In the application of a receiver antenna array to wireless communications, a known signal preamble is used for estimating the propagation vector at the beginning of each data frame. The estimated propagation vector is then used in linear combining of array inputs for interference suppression and demodulation of a desired user's information data stream. Since the training preamble is usually very short, conventional training methods, which estimate the propagation vector based solely on the training preamble, may incur large estimation errors. In many wireless channels, the ambient noise is known to be decidedly non-Gaussian, due to impulsive phenomena. The conventional training methods may suffer further from such impulsive noise. Moreover, performance of linear combining techniques can degrade substantially in the presence of impulsive noise. We first propose a new technique for propagation vector estimation which exploits the whole frame of the received signal. It is shown that as the length of the signal frame tends to infinity, in the absence of noise, this method can recover the propagation vector of the desired user exactly, given a small number of training symbols for that user. We then develop robust techniques for propagation vector estimation and array combining in the presence of impulsive noise. These techniques are nonlinear in nature and are based on the M-estimation method. It is seen that the proposed robust methods offer performance improvement over linear techniques in non-Gaussian noise, with little attendant increase in computational complexity. Finally, we address the extension of the proposed techniques to dispersive channels with intersymbol interference     

基于协方差矩阵重构和导向矢量估计的稳健自适应波束形成

实际应用中, 当假定的与真实的期望信号导向矢量之间存在一定误差时, 波束形成器的性能会急剧下降, 特别是当期望信号功率很强的时候.为解决这个问题, 提出了一种新的算法.当信源数小于阵元数时, 干扰加噪声协方差矩阵具有稀疏性.新方法首先利用该特性重构干扰加噪声协方差矩阵并由此得到与干扰导向矢量正交的子空间, 使接收的数据通过该子空间得到只含有期望信号和噪声的混合信号, 然后,对该混合信号基于最大化输出功率原理估计期望信号导向矢量, 最后,把得到的导向矢量和正交子空间来构造阵列加权值.仿真结果表明:该算法分别在假定的期望信号导向矢量存在误差、期望信号很强和低快拍数时仍然具有良好的性能.     

A spatial-temporal decorrelating receiver for CDMA systems withbase-station antenna arrays

We investigate multiuser signal detection with a base-station antenna array for a synchronous DS-CDMA uplink using nonorthogonal codes in Rayleigh fading channels. We have developed a new formulation for a spatial-temporal decorrelating detector using the maximum-likelihood criteria. The detector is shown to be near-far resistant. We propose to implement the spatial-temporal decorrelating receiver iteratively by applying the space-alternating generalized expectation-maximization (SAGE) algorithm. Simulation results show that the SAGE-based decorrelating receiver significantly outperforms the conventional single-user receiver and with performance close to that of a spatial-temporal decorrelating receiver with known channel parameters. We have observed that adding base-station antennas can actually improve convergence of the proposed iterative receiver     

基于拉伸式双偶极子天线Q-MUSIC算法的DOA估计

本文指出在四元数模型下,传统双偶极子天线组成的均匀线阵(ULA)在利用四元数多重信号分类算法(Q-MUSIC)估计极化信号波达角(DOA)时,存在“模型相干”问题,并提出了基于实同构矩阵变换的拉伸式双偶极子天线 MUSIC 算法,有效解决了上述问题,所提算法充分利用四元数模型的正交性,性能远优越于基于传统双偶极子天线的 MUSIC 算法,尤其是在低信噪比(SNR)条件下。最后,通过计算机仿真实验验证了所提算法的有效性。     

Robust cyclic adaptive beamforming using a compensation method

This paper deals with the problem of robust adaptive array beamforming using signal cyclostationarity. The constrained cyclic adaptive beamforming (C-CAB) algorithm presented by Wu and Wong (1996) [6] has been shown to be effective in performing adaptive beamforming without requiring the direction vector or the waveform of the desired signal. However, this algorithm suffers from severe performance degradation even if there is a small mismatch in the cycle frequency of the desired signal. In this paper, we first evaluate the performance degradation of the C-CAB algorithm in the presence of cycle frequency error (CFE). A novel compensation method in conjunction with the subspace projection is then proposed to tackle the problem due to CFE. We reconstruct the required cyclic conjugate correlation matrix by using a compensation matrix to cope with the deterioration of its dominant singular value when CFE exists. Finally, several simulation examples are provided to show the effectiveness of the proposed algorithm.     

基于互相关投影导航接收机欺骗干扰抑制方法

由于欺骗干扰模拟发送与导航卫星信号相同的C/A码,这使得卫星导航接收机极易被其误导和诱骗,从而给出错误的定位信息。考虑到导航信号和同结构欺骗干扰自身都具有强自相关性,且欺骗干扰功率稍大于真实信号,该文提出一种采用阵列天线抑制同结构欺骗干扰的盲干扰抑制方法。该方法从多天线接收数据与其自身延迟的参考数据做互相关入手,利用该互相关矩阵求解干扰的正交投影矩阵,最后将投影后信号互相关矩阵的最大特征值对应的特征矢量作为最优权。该方法处理时无需已知真实信号和干扰方向的来波方向,也无需对卫星C/A码序列进行遍历搜索从而解扩。理论分析和实验结果表明,该阵列波束能够有效抑制干扰,阵列输出SNR较高,干扰抑制后接收机捕获性能不受干扰影响。