Analysis of Convergence of a Frequency-Domain LMS Adaptive Filter Implemented as a Multi-Stage Adaptive Filter

We present an analysis of the convergence of the frequency-domain LMS adaptive filter when the DFT is computed using the LMS steepest descent algorithm. In this case, the frequency-domain adaptive filter is implemented with a cascade of two sections, each updated using the LMS algorithm. The structure requires less computations compared to using the FFT and is modular suitable for VLSI implementations. Since the structure contains two adaptive algorithms updating in parallel, an analysis of the overall system convergence needs to consider the effect of the two adaptive algorithms on each other, in addition to their individual convergence. Analysis was based on the expected mean-square coefficient error for each of the two LMS adaptive algorithms, with some simplifying approximations for the second algorithm, to describe the convergence behavior of the overall system. Simulations were used to verify the results.     

A Modular Design of LMS Adaptive Filter Based on FPGA

针对LMS自适应滤波器在FPGA上实现结构灵活性的问题,提出了一种模块化设计方法。根据LMS算法结构特点,结合FPGA硬件语言特点进行模块化设计,分别阐述了各模块设计结构,对模块进行并行调用与综合。对模块化设计的自适应滤波器与纯串行及纯并行设计的自适应滤波器所占用的资源以及处理速率进行比较,8个并行模块结构比全串行结构处理速率快了近7.6倍,硬件资源占用比全并行结构减少了近50%;结果说明模块化LMS自适应滤波器设计具有更加灵活的结构特点。     

Fast LMS/Newton algorithms based on autoregressive modeling andtheir application to acoustic echo cancellation

We propose two new implementations of the LMS/Newton algorithm for efficient realization of long adaptive filters. We assume that the input sequence to the adaptive filter can be modeled as an autoregressive (AR) process whose order may be kept much lower than the adaptive filter length. The two algorithms differ in their structural complexity. The first algorithm, which will be an exact implementation of the LMS/Newton algorithm if the AR modeling assumption is accurate, is structurally complicated and fits best into a digital signal processing (DSP)-based implementation. On the other hand, the second algorithm is structurally simple and is tailored more toward very large-scale integrated (VLSI) custom chip design. Analyses of the proposed algorithms are given. It is found that for long filters, both algorithms perform about the same. However for short filters, a noticeable difference between the two may be observed. Simulation results that confirm our theoretical findings are given. Moreover, experiments with speech signals for modeling the acoustics of an office room show the superior convergence of the proposed algorithms when compared with the normalized LMS algorithm     

A Modular Analog NLMS Structure for Adaptive Filtering

This paper proposes a modular Analog Adaptive filter (AAF) algorithm, in which the coefficient adaptation is carried out by using a time varying step size analog normalized LMS (NLMS) algorithm, which is implemented as an external analog structure. The proposed time varying step size is estimated by using the first element of the crosscorrelation vector between the output error and reference signal, and the first element of the crosscorrelation vector between the output error and the adaptive filter output signal, respectively. Proposed algorithm reduces distortion when additive noise power increases or DC offsets are present, without significatively decreasing the convergence rate nor increasing the complexity of the conventional NLMS algorithms. Simulation results show that proposed algorithm improves the performance of AAF when DC offsets are present. The proposed VLSI structure for the time varying step size normalized NLMS algorithm has, potentially, a very small size and faster convergence rates than its digital counterparts. It is suitable for general purpose applications or oriented filtering solution such as echo cancellation and equalization in cellular telephony in which high performance, low power consumption, fast convergence rates and small size adaptive digital filters (ADF) are required. The convergence performance of analog adaptive filters using integrators like first order low pass filter is analyzed.     

扩频通信中干扰抑制的自适应非线性滤波技术

本文研究了自适应非线性滤波在直扩通信中抑制窄带干扰的应用，修正了Ｖｉｊａｙａｎ和Ｐｏｏｒ所采用的抽头更新算法，使非线性滤波的性能明显改善，同时把自适应非线性横向滤波结构，推广到Ｌａｔｔｉｃｅ结构，提高了收敛速度。     

Nonlinear techniques for interference suppression inspread-spectrum systems

The binary nature of direct-sequence signals is exploited to obtain nonlinear filters that outperform the linear filters hitherto used for this purpose. The case of a Gaussian interferer with known autoregressive parameters is considered. Using simulations, it is shown that an approximate conditional mean (ACM) filter of the Masreliez type performs significantly better than the optimum linear (Kalman-Bucy) filter. For the case of interferers with unknown parameters, the nature of the nonlinearity in the ACM filter is used to obtain an adaptive filtering algorithm that is identical to the linear transversal filter except that the previous prediction errors are transformed nonlinearly before being incorporated into the linear prediction. Two versions of this filter are considered: one in which the filter coefficients are updated using the Widrow LMS algorithm, and another in which the coefficients are updated using an approximate gradient algorithm. Simulations indicate that the nonlinear filter with LMS updates performs substantially better than the linear filter for both narrowband Gaussian and single-tone interferers, whereas the gradient algorithm gives slightly better performance for Gaussian interferers but is rather ineffective in suppressing a sinusoidal interferer     

应用于自适应格型RLS滤波器的变阶数算法

提出了一种新的变阶数(或抽头长度)算法,并将之应用于变阶数自适应格型递归最小二乘(RLS)滤波器的阶数更新中,讨论了格型滤波器阶数更新时相关参数的调整方法。新算法以分贝的形式比较短滤波器与长滤波器的时平均平方误差,采用自适应的抽头长度步长,能够在滤波器权值未收敛时同时快速更新滤波器长度与权值,且在不同大小噪声条件下都能收敛到最优阶数。理论分析与不同大小噪声条件下的自适应系统辨识仿真结果验证了新算法的有效性。      

一种改进的NLMS自适应滤波器的FPGA实现

针对当前广泛应用的自适应滤波器,提出了一种改进的变步长NLMS自适应算法,在不增加计算复杂度的条件下获得了更好的收敛速度。在硬件实现过程中,利用FPGA并行处理的特点,采用自上而下的设计方法和流水线设计技术,获得了较好的滤波效果和较快的处理速度,完全满足自适应信号处理领域中实时性的要求。     

用于低维混沌时间序列预测的一种非线性自适应预测滤波器

在二阶Volterra滤波器基础上,提出了一种用于低维混沌时间自适应预测的非线性自适应预测器。基于最小均方误差准则导出了一种NLMS类型的自适应算法来实时调整这种非线性滤波预测器的系数,仿真实验结果表明：这种线性化的非线性自适应滤波预测器能够有效地预测低维混时间序列,且它的模块化特征更易于VLSI电路实现,具有广泛的工程应用价值。     

Line search algorithms for adaptive filtering

Line search algorithms for adaptive filtering that choose the convergence parameter so that the updated filter vector minimizes the sum of squared errors on a linear manifold are described. A shift invariant property of the sample covariance matrix is exploited to produce an adaptive filter stochastic line search algorithm for exponentially weighted adaptive equalization requiring 3N+5 multiplications and divisions per iteration. This algorithm is found to have better numerical stability than fast transversal filter algorithms for an application requiring steady-state tracking capability similar to that of least-mean square (LMS) algorithms. The algorithm is shown to have faster initial convergence than the LMS algorithm and a well-known variable step size algorithm having similar computational complexity in an adaptive equalization experiment     

A Systolic, High Speed Architecture for an RSA Cryptosystem

An architecture based on the RSA public key cryptography algorithm is presented. The circuit includes two components, one for modular squaring and one for modular multiplication. Each component is based on the Montgomery algorithm and implements the modular operations using two modified serial-parallel multipliers. A full modular exponentiation is completed every n(n + 3) clock cycles. All circuits are systolic, operate with 100% efficiency and their maximum combinational delay is equal to one gated Full-Adder. Thus, high-speed performance is achieved while the low cell hardware complexity enables an efficient VLSI implementation.     

Rejection of CW Interference in QPSK Systems Using Decision-Feedback Filters

In this paper, the performance of QPSK systems using complex transversal filters with additional decision-feedback taps, in the presence of Gaussian noise and a single CW interferer, is analyzed. Both one-sided (with lagging taps) and two-sided transversal filters with additional decision-feedback taps are considered. Analytic expressions for the tap weights and the minimum mean square errors are obtained. The effect of error propagation on the error probability is discussed and an approximate solution for the error probability is obtained. The transient behavior of the filters using the LMS adaptation algorithm is analyzed. It is shown that if the filter is used for rejecting CW interference only, the one-sided decision-feedback filter is preferred.     

Transform domain adaptive linear phase filter

Describes a new adaptive linear-phase filter whose weights are updated by the normalized least-mean-square (LMS) algorithm in the transform domain. This algorithm provides a faster convergence rate compared with the time domain linear phase LMS algorithm. Various real-valued orthogonal transforms are investigated such as the discrete cosine transform (DCT), discrete Hartley transform (DHT), and power of two (PO2) transform, etc. By using the symmetry property of the transform matrix, an efficient implementation structure is proposed. A system identification example is presented to demonstrate its performance     

A 19.2 GOPS mixed-signal filter with floating-gate adaptation

We have built a 48-tap, mixed-signal adaptive FIR filter with 8-bit digital input and an analog output with 10 bits of resolution. The filter stores its tap weights in nonvolatile analog memory cells using synapse transistors, and adapts using the least mean square (LMS) algorithm. We run the input through a digital tapped delay line, multiply the digital words with the analog tap weights using mixed-signal multipliers, and adapt the tap coefficients using pulse-based feedback. The accuracy of the weight updates exceeds 13 bits. The total die area is 2.6 mm/sup 2/ in a 0.35-/spl mu/m CMOS process. The filter delivers a performance of 19.2 GOPS at 200 MHz, and consumes 20 mW providing a 6-mA differential output current.     

A Fully-Pipeline Linear Systolic Architecture for Modular Multiplier in Public-Key Crypto-Systems

In this paper, a fully-pipeline linear systolic array based on adjusted Montgomery's algorithm is presented to perform modular multiplication at extremely high speed. The processing element (PE) consists of only 4 full-adders and 14 flip-flops. Three-stage internal pipelined PE results in a very short critical path with only a one-bit full-adder delay. Thus, it can run at a very high cycle rate. The total execution time for an n-bit modular multiplication is 2n + 11 cycles with only (n/2 + 2) PEs. A modular exponentiation based on it takes (3n + 16.5)n cycles in average. Compared with most published VLSI modular multipliers, the hardware complexity is greatly reduced while keeping very high throughput. Therefore it is a good candidate of the arithmetic units used in the many public-key crypto-systems, e.g. RSA, Elliptic Curve and so on, especially for the embedded applications concerning information security.     

4模剩余数系统FIR数字滤波器设计

基于四模余数系统的FIR滤波器将一个滤波系统分为4个彼此独立,互不影响,并行运算的子滤波通道,消除了各个子运算通道之间的进位链,加快了计算的速度,提高了滤波精度。所有模都具有2n 和2n±1的形式,电路完全基于组合逻辑电路来实现。结果表明,无论在功耗,速度,实现复杂度等方面,采用余数系统构建的FIR滤波器都优于于传统二进制FIR滤波器。     

An SIMD Multiprocessor Ring Architecture for the LMS Adaptive Algorithm

A new architecture for a single instruction stream, multiple data stream (SIMD) implementation of the LMS adaptive algorithm is investigated. This is denoted as a ring architecture, due to its physical configuration, and it effectively solves the latency problem often associated with prediction error feedback in adaptive filters. The multiprocessor ring efficiently updates the filter input vector by operating as a pipeline structure, while behaving as a parallel structure in computing the filter output and applying the weight adaptation algorithm. Last, individual processor timing and capacity considerations are examined.     

Subband adaptive filtering with real-valued subband signals foracoustic echo cancellation

Adaptive filtering in subbands was originally proposed to overcome the limitations of conventional least-mean-square (LMS) algorithms. In general, subband adaptive filters offer computational savings, as well as faster convergence over the conventional LMS algorithm. However, improvements to current subband adaptive filters could be further enhanced by a more elegant choice of their design/structure. Classical subband adaptive filters employ DFT-based analysis and synthesis filter banks which results in subband signals that are complex-valued. The authors modify the structure of subband adaptive filters by using single-sideband (SSB) modulated analysis and synthesis filter banks, which result in subband signals that are real-valued. This simplifies the realisation of subband adaptive filters     

MIMO-OFDM系统中一种基于自适应滤波的信道估计方法

该文提出了一种适用于MIMO-OFDM系统的基于自适应滤波器的信道估计方法,此方法在不需要任何信道统计信息的前提下,通过自适应滤波的方法对时变信道状态参数进行即时跟踪与估计。仿真结果表明该文提出的基于自适应滤波的信道估计方法,相比于不考虑噪声的基于LS算法的信道估计方法,MSE和BER性能均有很大的提高。其中基于LMS滤波器的信道估计方法具有计算复杂度小的特点;而基于RLS的信道估计方法具有收敛速度快,MSE和BER性能均优于基于LMS方法的特点。