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     

Combined RLS-LMS initialization for per tone equalizers in DMT-receivers

In discrete multitone receivers, the classical equalizer structure consists of a (real) time domain equalizer (TEQ) combined with complex one-tap frequency domain equalizers. An alternative receiver is based on a per tone equalization (PTEQ), which optimizes the signal-to-noise ratio (SNR) on each tone separately and, hence, the total bitrate. In this paper, a new initialization scheme for the PTEQ is introduced, based on a combination of least mean squares (LMS) and recursive least squares (RLS) adaptive filtering. It is shown that the proposed method has only slightly slower convergence than full square-root RLS (SR-RLS) while complexity as well as memory cost are reduced considerably. Hence, in terms of complexity and convergence speed, the proposed algorithm is in between LMS and RLS.     

利用自适应均衡滤波器改善大气湍流信道OWC性能

光无线通信是近年来无线通信领域的研究热点之一。大气湍流是影响光无线通信的重要因素,特别是在高速数字通信中会产生严重的码间干扰。本文分析了大气湍流信道的特性,提出采用自适应LMS均衡技术改善大气湍流信道的性能,对采用OOK调制方式的OWC系统均衡前后的误码率性能进行了分析比较。仿真结果表明自适应LMS均衡技术可以将系统的性能提高约10dB。     

Orthogonal-transformed variable-gain least mean squares (OVLMS)algorithm for fractional tap-spaced adaptive MLSE equalizers

A fast channel-estimation scheme for adaptive maximum-likelihood sequence-estimation (MLSE) equalizers called the orthogonal-transformed variable-gain least mean squares (OVLMS) algorithm is proposed. This algorithm requires only as many operations as the least mean squares algorithm in spite of its excellent performance. Furthermore, an operational complexity reduction method is proposed in which the orthogonal matrix is reconfigured as eigenvectors with valid eigenvalues. The OVLMS algorithm is theoretically analyzed and is shown to have both a fast acquisition and a good tracking performance. An equalizer using OVLMS (OVLMS-MLSE) experimentally attains a 5-dB improvement in bit-error rate (BER) performance at BER of 1.0×10 ^-4 over coherent detection. The OVLMS-MLSE is found to be free of the degradation caused by sampling phase error. Finally, the OVLMS-MLSE equalizer is experimentally verified to synchronize within five symbols     

直接检测的光正交频分复用传输系统最小均方自适应算法

为了减少直接检测的光正交频分复用(DD-OOFDM)传输系统中色散对系统的影响,传输系统使用了基于频域的最小均方(LMS)自适应均衡技术,由于基于频域的LMS估计方法计算复杂度低且便于信号块处理,相比最小平方(LS)估计方法,可更有效地追踪信道变化,减小相位噪声对传输系统的影响。实验结果表明,经背靠背(BTB)和100km标准单模光纤(SSMF)传输后,使用频域LMS估计方法的信号比使用频域LS估计方法的信号系统接收功率代价在误码率为10×10-2.5和10×10-2.0时分别降低了2dB及2.5dB,频域LMS估计方法比频域LS估计方法对传输系统具有更好的色散补偿效果。     

Noncoherent adaptive channel identification algorithms fornoncoherent sequence estimation

In this letter, two novel noncoherent adaptive algorithms for channel identification are introduced. The proposed noncoherent least-mean-square (LMS) and noncoherent recursive least squares (RLS) algorithms can be combined easily with noncoherent sequence estimation (NSE) for M-ary differential phase-shift keying signals transmitted over intersymbol interference (ISI) channels. It is shown that the resulting adaptive noncoherent receivers are very robust against carrier phase variations. For zero frequency offset, the convergence speed and the steady-state error of the noncoherent adaptive algorithms are similar to those of conventional LMS and RLS algorithms. However, the conventional algorithms diverge even for relatively small frequency offsets, whereas the proposed noncoherent algorithms converge for relatively large frequency offsets. Simulations confirm the good performance of NSE combined with noncoherent adaptive channel estimation in time-variant (fading) ISI channels     

应用于10 Gbit/s光通信及背板传输的自适应均衡器设计

描述了一种既可用于背板传输也可用于光纤通信的高速串行收发器前端均衡器的设计。为适应光信号在传播中的色散效应,使用前馈均衡器(FFE)加判决反馈均衡器(DFE)的组合,取代了背板通信中常用的连续时间线性均衡器(CTLE)和DFE的组合。设计使用3 pre-tap、3 post-tap和1个main tap的抽头组合方式,兼顾pre-cursor和post-cursor的信号失真,有效补偿范围为15 dB。补偿系数采用完全自适应算法调整,对FFE采用模拟MSE算法调整,DFE引擎采用1/16速率数字sign-sign最小均方差(LMS)算法实现。芯片使用UMC 28 nm工艺流片,输入信号频率为10 Gbit/s。     

Coherent LMS algorithms

Pilot symbol-assisted adaptive algorithms provide coherent detection for communication systems when the filtering coefficients, such as beamforming weights or equalizer coefficients, are converged. This property can be exploited to speed up the convergence of adaptive algorithms used. In this letter, two new adaptive algorithms, coherent least mean square (C-LMS) and coherent normalized LMS (CN-LMS), are proposed by constraining the desired signal component of the filtering output to be always coherent in phase with the reference signal at each iteration. For same adaption step size, these new algorithms provide faster convergence, while yield same steady-state excess mean-square error as compared with their standard LMS counterparts     

应用于流水线ADC的LMS自适应校准算法与FPGA实现

研究了应用于流水线模数转换器(ADC)的LMS自适应数字校准算法及其FPGA实现。该校准算法可用于校准大多数已知的误差,包括非线性运算放大器的有限增益、电容失配,以及比较器的失调等。通过Simulink软件,对一个12位160 MS/s的流水线ADC进行建模。采用LMS自适应校准算法对该流水线ADC进行校准,并将算法在Virtex-5上实现了硬件设计。实验结果表明, 输入信号频率为58.63 MHz时,流水线ADC的无杂散动态范围(SFDR)和有效位(ENOB)分别由校准前的46.31 dB和7.32位提高到校准后的82.03 dB和11.12位。     

Adaptive noncoherent DFE for MDPSK signals transmitted over ISIchannels

A novel noncoherent decision-feedback equalization (NDFE) scheme for M-ary differential phase shift-keying signals transmitted over intersymbol interference channels is presented. A suboptimum version with lower computational complexity and a noncoherent linear equalizer (NLE) are derived from the original NDFE scheme. Furthermore, the relation of the novel NLE to a previously proposed NLE is investigated. In contrast to known NDFE schemes, the novel scheme can approach the performance of coherent minimum mean-squared error decision-feedback equalization. For adaptation of the feedforward and feedback filters, efficient novel modified least mean-square and recursive least squares algorithms are presented. Finally, it is shown that the proposed adaptive NDFE scheme is robust against frequency offset     

Block sequential least squares decision feedback equalizationalgorithm with application to terrestrial HDTV transmission

Due to the very high symbol rate of terrestrial HDTV systems, up to now there exist no equalization solutions with sufficiently low hardware complexity and satisfactory performance for commercial applications. We present a block sequential least squares decision feedback equalization algorithm with application to over-the-air HDTV channels. The proposed adaptive algorithm is derived on the basis of minimization of the least squares criterion, thereby achieving faster convergent and tracking rate relative to the recommended LMS algorithm. Meanwhile, good numerical stability is guaranteed because it successfully eliminates time updates of the filtering coefficients, which is the main cause of instability of FTF-like algorithms. Also of great significance is its drastic reduction in computational complexity by means of block operation, taking advantage of the slowly time-varying nature of terrestrial broadcasting channels. Simulation results show that the equalizer achieves an almost 3.5 dB SNR improvement at a bit error rate of 3×10^-6 without significant increase in computational complexity, as compared to the conventional LMS decision feedback equalizer (DFE) when applied to the equalization of over-the-air HDTV channels     

一种用于水声相干通信系统的自适应均衡算法

多通道判决反馈均衡器(MC-DFE)是水声相干通信克服信道多径效应、消除码间干扰(ISI)的主要手段。为了减少多通道数据处理的复杂性、优化算法、提高算法精度,该文提出了自适应自最优预合并多通道判决反馈均衡算法。该算法将快速自优化LMS分集合并(FOLMSDC)算法、快速自优化LMS(FOLMS)算法和快速自优化LMS相位补偿(FOLMSPC)算法有机地结合在一起,使用统一的误差信号,按照最小化均方误差(MMSE)准则调节各部分的系数,从而实现均衡器性能的全局最优。仿真试验和湖上试验对该算法的性能进行了分析。实验结果表明,该文提出的算法可以进一步减少运算量,提高通信系统的接收性能,算法性能优于已有算法。     

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 chip-rate equalization of downlink multirate wideband CDMA

We consider a downlink direct sequence-code division multiple access (DS-CDMA) system in which multirate user signals are transmitted via synchronous orthogonal short codes overlaid with a common scrambling sequence. The transmitted signal is subjected to significant time- and frequency-selective multipath fading. In response to this scenario, a novel two-mode receiver is proposed that accomplishes chip-rate adaptive equalization aided by filtering and/or cancellation of multiaccess interference (MAI). In the acquisition mode, a code-multiplexed pilot is used to adapt the equalizer from cold start or loss-of-lock. The use of MAI filtering results in a third-order least mean squares (LMS) algorithm, which has significant advantages over standard (i.e., first-order) LMS in nonstationary environments. In the tracking mode, decision-direction facilitates MAI-cancellation in the equalizer update, which enhances performance. The receiver monitors pilot decision quality as a means of switching between the two modes. The performance of the adaptive receiver is studied through analysis and simulation.     

A channel estimation using sliding window approach and tuningalgorithm for MLSE

A simultaneous sliding window channel estimation and timing adjustment method is proposed for adaptive maximum-likelihood sequence equalizer (MLSE) in the global system for mobile communication (GSM) system, and also a tuning scheme based on least mean squared (LMS) algorithm is presented in order to improve the performance of equalizer. Simulation results show that the proposed channel estimation technique is effective for channel estimation of the adaptive equalizer     

An Equalizer Adaptation Algorithm to Reduce Jitter in Binary Receivers

This brief describes a hardware-efficient technique for adaptive equalization that minimizes jitter in baseband binary nonreturn-to-zero serial links. Whereas traditional least mean square (LMS) adaptation minimizes the mean squared error only at the center of the received eye pattern, this algorithm explicitly includes low jitter as part of its optimization criteria. The resulting equalizer coefficients provide a compromise between maximizing noise margin and minimizing jitter at the equalizer output. The adaptation algorithm operates on 1-bit (signed) signals and can be efficiently combined with binary phase detection in an integrated receiver. Behavioral simulations for a 10-Gb/s optical fiber link show that the proposed approach improves timing margin compared with sign–sign LMS adaptation.     

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.     

Synchronized per survivor MLSD receiver using a differential Kalmanfilter

A synchronized per survivor processing (PSP) maximum likelihood sequence detection (MLSD) receiver is presented for a fast frequency-selective Rayleigh fading channel and large carrier frequency offset. It is shown that, by incorporating the synchronization parameters (the timing error, carrier frequency and phase offsets) into the channel-impulse response (CIR) and modifying the resulting state-space model by a differential approach, the time-varying carrier frequency offset can be effectively tracked using a simple least mean square (LMS) estimator. New criteria are proposed based on the smoothness of the estimated channel derivatives. New criteria are proposed based on this fact that, for smoothly time-varying CIR and frequency offset, the magnitude of the CIR time derivative compared with the magnitude of CIR should be very small. It is illustrated that the new criteria and the conventional mean square error (MSE) are complementary     

Optimal tracking of time-varying channels: a frequency domainapproach for known and new algorithms

In this paper, we developed a systematic frequency domain approach to analyze adaptive tracking algorithms for fast time-varying channels. The analysis is performed with the help of two new concepts, a tracking filter and a tracking error filter, which are used to calculate the mean square identification error (MSIE). First, we analyze existing algorithms, the least mean squares (LMS) algorithm, the exponential windowed recursive least squares (EW-RLS) algorithm and the rectangular windowed recursive least squares (RW-RLS) algorithm. The equivalence of the three algorithms is demonstrated by employing the frequency domain method. A unified expression for the MSIE of all three algorithms is derived. Secondly, we use the frequency domain analysis method to develop an optimal windowed recursive least squares (OW-RLS) algorithm. We derive the expression for the MSIE of an arbitrary windowed RLS algorithm and optimize the window shape to minimize the MSIE. Compared with an exponential window having an optimized forgetting factor, an optimal window results in a significant improvement in the h MSIE. Thirdly, we propose two types of robust windows, the average robust window and the minimax robust window. The RLS algorithms designed with these windows have near-optimal performance, but do not require detailed statistics of the channel     

一种新的变步长LMS自适应算法

研究了自适应最小均方误差滤波算法的步长选取问题。在分析现有变步长LMS算法的基础上,给出一种以双曲正切函数的改进形式为变步长的LMS算法。在相同收敛速度的前提下,该算法具有更小的超量均方误差;而在相同超量均方误差的前提下,该算法具有更快的收敛速度。经实验,仿真结果与理论分析相一致,证实了该算法的优越性。