Minimum bit-error rate design for space-time equalization-based multiuser detection

A novel minimum bit-error rate (MBER) space-time-equalization (STE)-based multiuser detector (MUD) is proposed for multiple-receive-antenna-assisted space-division multiple-access systems. It is shown that the MBER-STE-aided MUD significantly outperforms the standard minimum mean-square error design in terms of the achievable bit-error rate (BER). Adaptive implementations of the MBER STE are considered, and both the block-data-based and sample-by-sample adaptive MBER algorithms are proposed. The latter, referred to as the least BER (LBER) algorithm, is compared with the most popular adaptive algorithm,known as the least mean square (LMS) algorithm. It is shown that in case of binary phase-shift keying, the computational complexity of the LBER-STE is about half of that required by the classic LMS-STE. Simulation results demonstrate that the LBER algorithm performs consistently better than the classic LM Salgorithm, both in terms of its convergence speed and steady-state BER performance.     

Total least mean squares algorithm

Widrow (1971) proposed the least mean squares (LMS) algorithm, which has been extensively applied in adaptive signal processing and adaptive control. The LMS algorithm is based on the minimum mean squares error. On the basis of the total least mean squares error or the minimum Raleigh quotient, we propose the total least mean squares (TLMS) algorithm. The paper gives the statistical analysis for this algorithm, studies the global asymptotic convergence of this algorithm by an equivalent energy function, and evaluates the performances of this algorithm via computer simulations     

MIMO系统中应用张量分解进行半盲信道估计的算法分析

针对多输入多输出系统半盲信道估计问题,提出一种基于张量分解的半盲联合信号检测和信道估计算法。其思想是利用张量分解的唯一性,对接收信号构造基于张量分解的平行因子模型,并利用正则交替最小二乘算法对信道和发送信号进行联合迭代估计。仿真结果表明:与传统基于导频信道估计方法相比,所提算法只需少量的导频序列即可获得较高的信道估计精度;与已有的交替最小二乘算法相比,所提算法消除了矩阵求伪逆时可能带来的病态问题,收敛速度较快。文章还详细的分析了正则系数和收敛条件等参数对正则交替最小二乘算法性能的影响。      

A hybrid frequency–time domain symmetric adaptive decorrelator

Symmetric adaptive decorrelation (SAD) is a semi-blind method of separating convolutely mixed signals. While it has restrictions on the physical layout of the demixing equipment, it is better suited for some applications (e.g., live sound mixing) as no post-processing is required to ascertain which output corresponds with which source. Since SAD is based on the least mean squares algorithm, it can be modified to perform the bulk of the processing in the frequency domain. This makes it more efficient for larger filter sizes and/or larger number of sources but renders it unsuitable for real-time applications as there is a lag between the output and the input. In this paper, we propose a hybrid approach that does not suffer from the lag of the frequency domain approach. While the proposed algorithm is slightly less computationally efferent than the pure frequency domain algorithm, it is significantly more efficient than the time domain approach. A comparison of the frequency domain and hybrid algorithms shows that both achieve separation equivalent to the time domain algorithm in a real-world environment.     

OFDM系统自适应数字预失真研究

研究了正交频分复用（OFDM）传输系统中高功率放大器（HPA）的自适应预失真方法。针对OFDM信号的高峰平比特性及HPA带来的非线性失真,提出一种基于训练序列的最小均方误差（LMS）算法和递归最小二乘（RLS）算法的组合算法,将其应用到基于记忆多项式模型的数字预失真系统中。用MATLAB构建一个基于该自适应算法的预失真系统。仿真结果表明：该算法能有效的改善放大器的非线性特性。     

Adaptive Semi-blind Immune Algorithm for Image Enhancement

The principle and steps of semi-blind immune algorithm are studied, and an adaptive image enhancement method using semi-blind immune algorithm is proposed. The non-linear transform of gray level is an efficient way of image enhancement. In classical image enhancement methods, the specific transform function is determined according to the gray level distribution in the processed image. Tubbs proposed a normalized incomplete Beta function to represent the four kinds of non-linear transform functions most commonly used. But how to adaptively define the coefficients of the Beta function is still a problem. We adopt an adaptive semi-blind immune algorithm that explicitly searches the optimal or suboptimal coefficients more quickly. Compared with the common image adjustment approach, our method is more efficient and powerful.     

A fast learning algorithm for Gabor transformation

An adaptive learning approach for the computation of the coefficients of the generalized nonorthogonal 2-D Gabor (1946) transform representation is introduced. The algorithm uses a recursive least squares (RLS) type algorithm. The aim is to achieve minimum mean squared error for the reconstructed image from the set of the Gabor coefficients. The proposed RLS learning offers better accuracy and faster convergence behavior when compared with the least mean squares (LMS)-based algorithms. Applications of this scheme in image data reduction are also demonstrated.     

An efficient recursive total least squares algorithm for FIRadaptive filtering

An algorithm for recursively computing the total least squares (TLS) solution to the adaptive filtering problem is described. This algorithm requires O(N) multiplications per iteration to effectively track the N-dimensional eigenvector associated with the minimum eigenvalue of an augmented sample covariance matrix. It is shown that the recursive least squares (RLS) algorithm generates biased adaptive filter coefficients when the filter input vector contains additive noise. The TLS solution on the other hand, is seen to produce unbiased solutions. Examples of standard adaptive filtering applications that result in noise being added to the adaptive filter input vector are cited. Computer simulations comparing the relative performance of RLS and recursive TLS are described     

Low-complexity adaptive decision-feedback equalization of MIMO channels

A new adaptive MIMO channel equalizer is proposed based on adaptive generalized decision-feedback equalization and ordered-successive interference cancellation. The proposed equalizer comprises equal-length subequalizers, enabling any adaptive filtering algorithm to be employed for coefficient updates. A recently proposed computationally efficient recursive least squares algorithm based on dichotomous coordinate descents is utilized to solve the normal equations associated with the adaptation of the new equalizer. Convergence of the proposed algorithm is examined analytically and simulations show that the proposed equalizer is superior to the previously proposed adaptive MIMO channel equalizers by providing both enhanced bit error rate performance and reduced computational complexity. Furthermore, the proposed algorithm exhibits stable numerical behavior and can deliver a trade-off between performance and complexity.     

一种半盲异步快跳频多址系统多用户检测算法

针对异步用户到达时间的差异性,以及期望用户信号的一致性,该文提出了基于最大行的均方误差(MSE)判决准则的半盲异步跳频多址多用户检测算法。利用期望用户时频矩阵具有最大行和最小均方偏差的特点实现检测目的。算法可以在未知干扰用户地址时实现盲检测,或利用部分干扰用户地址信息通过迭代提高检测性能。仿真结果表明提出的算法具有较好的性能和较低的复杂度,较强的抗虚警和漏检能力。     

一种基于拟牛顿法的递归最小二乘方法

基于拟牛顿优化方法,提出了一种稳健的自适应FIR滤波算法。新算法用最小二乘误差(LSE)代替了均方误差(MSE)作为代价函数,它具有和常规递归最小二乘(CRLS)算法相近似的追踪能力,且不存在数值计算不稳定性的问题,在收敛速度以及稳态效果方面也要优于De Campos的拟牛顿(QN)算法。通过计算机仿真比较了有关算法的性能。     

HOS-Based Semi-Blind Spatial Equalization for MIMO Rayleigh Fading Channels

In this paper, we concentrate on the direct semi-blind spatial equalizer design for MIMO systems with Rayleigh fading channels. Our aim is to develop an algorithm which can outperform the classical training-based method with the same training information used and avoid the problems of low convergence speed and local minima due to pure blind methods. A general semi-blind cost function is first constructed which incorporates both the training information from the known data and some kind of higher order statistics (HOS) from the unknown sequence. Then, based on the developed cost function, we propose two semi-blind iterative and adaptive algorithms to find the desired spatial equalizer. To further improve the performance and convergence speed of the proposed adaptive method, we propose a technique to find the optimal choice of step size. Simulation results demonstrate the performance of the proposed algorithms and comparable schemes.     

Finite word-length effects in recursive least squares algorithms with application to adaptive equalization

In this paper we provide a summary of recent and new results on finite word length effects in recursive least squares adaptive algorithms. We define the numerical accuracy and numerical stability of adaptive recursive least squares algorithms and show that these two properties are related to each other, but are not equivalent. The numerical stability of adaptive recursive least squares algorithms is analyzed theoretically and the numerical accuracy with finite word length is investigated by computer simulation. It is shown that the conventional recursive least squares algorithm gives poor numerical accuracy when a short word length is used. A new form of a recursive least squares lattice algorithm is presented which is more robust to round-off errors compared to the conventional form. Optimum scaling of recursive least squares algorithms for fixedpoint implementation is also considered.     

An adaptive algorithm for differentially coherent detection in the presence of intersymbol interference

An adaptive equalization method is proposed for use with differentially coherent detection of M-ary differential phase-shift keying (DPSK) signals in the presence of unknown carrier frequency offset. A decision-feedback or a linear equalizer is employed, followed by the differentially coherent detector. The equalizer coefficients are adjusted to minimize the post-detection mean squared error. The error, which is a quadratic function of the equalizer vector, is used to design an adaptive algorithm of stochastic gradient type. The approach differs from those proposed previously, which linearize the post-detection error to enable the use of least mean squares (LMS) or recursive least squares (RLS) adaptive equalizers. The proposed quadratic-error (Q) algorithm has complexity comparable to that of LMS, and equal convergence speed. Simulation results demonstrate performance improvement over methods based on linearized-error (L) algorithm. The main advantages of the technique proposed are its simplicity of implementation and robustness to carrier frequency offset, which is maintained for varying modulation level.     

用于MIMO-OFDM无线通信系统的一种新的自适应半盲波束形成算法

该文根据MIMO-OFDM物理层结构和无线移动矩阵信道的特性,从神经网络和信息论观点详细推导了一种新的多天线阵列自适应半盲波束形成器算法。与常规的盲估计和导频辅助信道估计算法不同的是,本文提出的新的半盲算法在权矢量自适应更新时,不断地用基于导频估计的权矢量进行修正。最后,用实际的HIPERLAN2协议进行了计算机仿真。结果表明这种算法不增加发射信号功率和不占用额外带宽,能提高天线增益性能和有效地提取出期望的发射信号,在收敛速度和BER性能方面优于常规的导频辅助信道估计算法。该波束形成器能自适应调整权矢量,其更新方式与TDMA和CDMA相似,所以本算法可直接用于有天线阵列的基于OFDM的第三代(3G)和三代后(3G beyond)的无线通信系统。     

A set of algorithms linking NLMS and block RLS algorithms

This paper describes a set of block processing algorithms which contains as extremal cases the normalized least mean squares (NLMS) and the block recursive least squares (BRLS) algorithms. All these algorithms use small block lengths, thus allowing easy implementation and small input-output delay. It is shown that these algorithms require a lower number of arithmetic operations than the classical least mean squares (LMS) algorithm, while converging much faster. A precise evaluation of the arithmetic complexity is provided, and the adaptive behavior of the algorithm is analyzed. Simulations illustrate that the tracking characteristics of the new algorithm are also improved compared to those of the NLMS algorithm. The conclusions of the theoretical analysis are checked by simulations, illustrating that, even in the case where noise is added to the reference signal, the proposed algorithm allows altogether a faster convergence and a lower residual error than the NLMS algorithm. Finally, a sample-by-sample version of this algorithm is outlined, which is the link between the NLMS and recursive least squares (RLS) algorithms     

一种抗飞行器衰落的快速均衡算法

对流层散射通信信道为时变多径信道,当飞行器飞越散射通信链路会导致飞行器衰落。针对飞行器衰落,提出了一种收敛速度快、跟踪能力强、数值稳定性高、复杂度低的快速自适应均衡算法——基于选择更新的累积误差递归最小二乘自适应均衡算法。根据指数加权最小二乘准则,推导出累积误差递归最小二乘算法,依据共轭斜量算法提出抽头系数选择更新准则。均衡算法的复杂度分析和仿真实验表明提出的快速自适应均衡算法不仅复杂度低,而且有效地提高了均衡器克服信道时间衰落的能力。     

Adaptive blind channel estimation by least squares smoothing

A least squares smoothing (LSS) approach is presented for the blind estimation of single-input multiple-output (SIMO) finite impulse response systems. By exploiting the isomorphic relation between the input and output subspaces, this geometrical approach identifies the channel from a specially formed least squares smoothing error of the channel output. LSS has the finite sample convergence property, i.e., in the absence of noise, the channel is estimated perfectly with only a finite number of data samples. Referred to as the adaptive least squares smoothing (A-LSS) algorithm, the adaptive implementation has a high convergence rate and low computation cost with no matrix operations. A-LSS is order recursive and is implemented in part using a lattice filter. It has the advantage that when the channel order varies, channel estimates can be obtained without structural change of the implementation. For uncorrelated input sequence, the proposed algorithm performs direct deconvolution as a by-product     

抗串扰QR格型自适应滤波算法

本文就抗串扰自适应噪声抵消器，研究了一种递归最小二乘(RLS)格型自适应算法。算法的导出仅利用数值稳健的Givens。旋转方法，因此具有优越的数值性能和较好的模块化结构性能。算法不含平方根运算，十分适合用通用数字信号处理器实现。文中还就算法的阵列实现进行了探讨，只需增加少量的时间，即可实现高性能的自适应噪声抵消器。模拟结果表明，算法的收敛速度明显优于现有的LMS算法。     

Convexly constrained linear inverse problems: iterativeleast-squares and regularization

We consider robust inversion of linear operators with convex constraints. We present an iteration that converges to the minimum norm least squares solution; a stopping rule is shown to regularize the constrained inversion. A constrained Laplace inversion is computed to illustrate the proposed algorithm