一种基于天线阵列的预处理盲多用户检测算法

该文提出了一种基于天线阵列的预处理判决反馈最小二乘恒模盲多用户检测算法,称为P-DFB-LSCMA算法。它是对天线阵列接收信号经预处理,采用判决反馈最小二乘恒模算法进行多用户检测。仿真结果表明,P-DFB-LSCMA算法比仅采用判决反馈最小二乘恒模算法(DFB-LSCMA)和传统的LSCMA算法性能要好,特别在低信噪比下性能仍优于后二者。     

一种基于天线阵列预处理盲多用户检测算法及其对DOA估计误差的鲁棒性研究

本文提出了一种基于天线阵列的预处理判决反馈最小二乘恒模盲多用户检测算法,称为PDF-LSCMA算法.它是对天线阵列接收信号经预处理,采用判决反馈最小二乘恒模算法进行多用户检测,确保了算法收敛于期望信号.文中就预处理中所用到的波达方向估计误差对PDF-LSCMA算法的影响进行了理论分析,给出了波达方向估计的取值范围,并用计算机仿真对这种算法及分析结果进行了验证.     

线性受限最小二乘恒模波束形成算法

将线性受限条件运用于最小二乘，提出了一种线性受限最小二乘恒模波束形成算法，称为LC-LSCMA，并将它与传统的最小二乘恒模波束形成算法(LSCMA)及预解扩最小二乘恒模波束形成算法(P-LSCMA)进行了仿真比较。仿真结果表明，提出的LC-LSCMA算法比LSCMA及P-LSCMA的信干比性能和误码率性能要好，特别在低信噪比情况下性能仍优于LSCMA。     

线性受限最小二乘恒模盲多用户检测算法

本文将线性受限条件运用于最小二乘恒模算法,提出了一种线性受限最小二乘恒模盲多用户检测算法,称为LCLSCMA,并将它与传统的最小二乘恒模算法(LSCMA)进行了仿真比较。仿真结果表明,本文提出的LCLSCMA算法比传统的最小二乘恒模算法的信干比性能和误码率性能要好,特别在低信噪比强多址干扰情况下性能仍优于LSCMA。     

一种基于DFP方法的拟牛顿恒模算法跟踪信道变化能力的研究

本文根据信道变化快慢程度,把信道模型分成剧变信道和渐变信道.分析研究了恒模盲均衡算法(CMA)在不同的信道模型下跟踪信道变化的能力.针对于原有典型恒模盲均衡算法的性能上的劣势,结合最优化理论,提出了一种线性受限拟牛顿恒模算法,称之为LC-DFPCMA,并将它与传统的线性受限恒模算法(LC-CMA)及最小二乘恒模算法(LSCMA)进行了仿真比较.仿真结果表明,提出的LC-DFPCMA算法比LC-CMA算法能更快地跟踪信道变化且收敛效果较好;与LSCMA算法相比,算法复杂度有明显降低.     

最小二乘恒模算法在阵列天线技术中的应用

介绍一种用于阵列天线，利用信号恒模特性进行自适应波束形成的算法，即最小二乘恒模算法。它通过对阵列天线单元接收信号进行非线性处理，在保持期望目标信号的同时，可在多个干扰方向形成零陷，从而大幅度提高信干噪比(SINR)。文章通过对该算法的仿真对其性能进行了分析，最后阐述了基于该算法的阵列天线应用。     

异步CDMA系统中基于预处理递归最小二乘恒模算法的盲自适应接收

该文提出一种基于预处理递归最小二乘恒模算法(PP-RLSCMA)的多径异步CDMA系统盲自适应接收技术。首先对接收信号进行自适应预处理,并分析了预处理器的复杂性和稳定性。预处理的目的是通过对干扰和噪声的部分抑制以提高恒模接收的性能,所提出的预处理方法只与多径信道的最大长度有关。鉴于统计恒模算法收敛速度慢的缺点,提出一种快速递归最小二乘恒模算法的盲自适应接收。仿真表明,该文算法的误码率及收敛性能比LCMMV,LCCMA好。     

基于二次型约束的鲁棒最小二乘恒模算法

阵列指向性偏差会导致线性约束最小二乘恒模算法(LSCMA)的性能急剧下降,为此提出了一种基于二次型约束的鲁棒LSCMA算法。该算法通过对期望信号波达方向附近小区域内的方向向量的误差模值进行约束来构造一种新的代价函数,并在此函数下迭代更新权重向量,以提高算法的鲁棒性。该算法收敛速度快,稳态性能好,能够有效地解决干扰捕获问题,对阵列指向性偏差具有很强的鲁棒性,从而改善了阵列输出的信干噪比,使其更接近最优值。仿真结果表明:与线性约束最小二乘恒模算法相比,所提鲁棒算法提高了输出性能,降低了计算量,易于实时实现,且能适应实际复杂的通信环境。     

基于子空间的线性约束最小二乘恒模算法

线性约束最小二乘恒模算法能够有效克服恒模算法中存在的干扰捕获问题,然而,在实际的系统中,由于受到来自噪声子空间分量的影响,从而导致性能下降。针对这一问题,该文提出了一种基于子空间方法的线性约束最小二乘恒模(SLSCM)多用户检测算法并对其性能进行了理论分析。该算法收敛速度快,信道跟踪能力强,有较好的输出信干噪比和误码率性能。理论分析和仿真实验验证了该文算法的有效性和优越性。     

一种结合HT算法和LSCMA的联合自适应算法

最小二乘恒模算法(LSCMA)是阵列信号处理中广泛使用的一种能全局收敛且稳定性强的算法,但是在低信噪比的情况下会发生干扰捕获问题.正交化(Hung-Turner)算法在低信噪比情况下有较好的收敛性能,且计算简单.本文提出了一种正交化算法和最小二乘恒模算法的联合算法.仿真结果表明这种算法能较好的解决干扰捕获问题,其收敛性能比LSCMA要好,尤其在低信噪比的情况下.     

Despread-respread multi-target constant modulus array for CDMAsystems

We apply the concept of the despread-respread (DR) algorithm to multi-target arrays for code-division multiple-access (CDMA) signals, the despread-respread algorithm exploits the knowledge of the pseudo-noise (PN) code used to generate the transmitted CDMA signal. We extend the DR algorithm to not only exploit the knowledge of the PN code but also to preserve the constant modulus property of the signal. This new algorithm is called the least squares despread respread multi-target constant modulus array (LS-DRMTCMA). LS-DRMTCMA performs better than the least squares despread respread multi-target array (LS-DTMTA), least squares multi-target constant modulus array (LS-MTCMA), and steepest descent multi-target decision directed array (SD-MTDDA)     

The Linearly Constrained LSCMA for Blind Multi-user Detection

The least square constant modulus algorithm (LSCMA) is a popular constant modulus algorithm (CMA) because of its global convergence and stability. But the performance will degrade when it is affected by the problem of interference capture in the MC-CDMA system that has several constant modulus signals. In order to overcome this shortage, a linearly constrained LSCMA multiuser detection algorithm is proposed by using the spreading code of the desired user to impose linear constraint on the LSCMA. The proposed algorithm ensures the algorithm convergence to the desired user. Thus the performance of the system is improved. The simulation results demonstrate that the proposed algorithm offers faster convergence rate and provides better output signal-to-interference-plus-noise-ratio (SINR) and bit error rate (BER) performance compared with the traditional LSCMA.     

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.     

MC-CDMA系统中一种预处理线性受限多用户检测算法

本文提出了MC-CDMA系统中一种预处理线性受限最小二乘恒模多用户检测算法(Pre-processed Linearly Con- strained Least Squares Constant Modulus Multi-user detection Algorithm),称为P-LCLSCMA。它是对MC-CDMA接收信号进行预处理,即在每次迭代中,用已估信道参数先消除信道在各个子载波上对期望用户信号的影响,然后对处理后的信号用P- LCLSCMA算法进行多用户检测,从而确保了算法收敛于期望信号,提高了系统的检测性能。我们对这种新算法分别在下行链路和上行链路进行了仿真,仿真结果表明,本文提出的P-LCLSCMA算法的误码率和信干比性能得到了提高。     

一种线性受限共轭梯度常模盲多用户检测算法

本文将线性受限条件和共轭梯度法应用于常模算法,提出了一种线性受限共轭梯度常模盲多用户检测算法,称之为LC-CGCMA,并将它与传统的线性受限常模算法(LC-CMA)及最小二乘常模算法(LSCMA)进行了仿真比较.仿真结果表明,提出的LC-CGCMA算法比LC-CMA算法的信干比性能和误码性能有较大改善;与LSCMA算法相比,算法复杂度有明显降低.     

Adaptive linearly constrained inverse QRD-RLS beamforming algorithm for moving jammers suppression

A general, linearly constrained (LC) recursive least squares (RLS) array-beamforming algorithm, based on an inverse QR decomposition, is developed for suppressing moving jammers efficiently. In fact, by using the inverse QR decomposition-recursive least squares (QRD-RLS) algorithm approach, the least-squares (LS) weight vector can be computed without back substitution and is suitable for implementation using a systolic array to achieve fast convergence and good numerical properties. The merits of this new constrained algorithm are verified by evaluating the performance, in terms of the learning curve, to investigate the convergence property and numerical efficiency, and the output signal-to-interference-and-noise ratio. We show that our proposed algorithm outperforms the conventional linearly constrained LMS (LCLMS) algorithm, and the one using the fast linear constrained RLS algorithm and its modified version.     

单通道最优恒模自适应干扰抑制方法

针对民航地空通信中常见干扰具有恒定包络的特点,该文提出了一种基于单通道最优恒模算法的自适应干扰抑制方法。该方法利用干扰信号的恒模特性,采用非线性最小二乘的方法对干扰信号的幅度与相位进行估计,并将其与接收到的混合信号相减,从而实现了恒模干扰信号的抑制。该方法与现有民航地空通信系统具有较好的兼容性,无需额外增加通道,且避免了一般自适应干扰抑制算法中的收敛问题。仿真和实测数据的处理结果表明该方法具有较好的干扰抑制效果。