Direct RF sampling receiver - a fully digital receiver architecture - undoubtedly becomes a favored choice for HF/VHF as this approach inherently bypasses the legacy nonlinearities caused by analog components. In DRF-...
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Direct RF sampling receiver - a fully digital receiver architecture - undoubtedly becomes a favored choice for HF/VHF as this approach inherently bypasses the legacy nonlinearities caused by analog components. In DRF-RF and wideband multichannel in general, LNA is still an indispensable component to ensure the receiver's sensitivity. However, with the presence of multiple channels, the total RF power often surpasses the linear threshold that LNA and the amplified signal become severely distorted. This paper proposed a method for mitigating the LNA distortion using the look-up table (LUT) approach. Specifically, our receiver is designed with two modes of operation. In training mode, a built-in signal circuit generates a training signal for extracting the LNA characteristic and eventually reconstructs the inverse LNA nonlinear model in the form of a LUT memory. During the receiving mode, a linearization circuit reverses the distortion impact by matching the RF power level with the inverse nonlinear model pre-stored in the LUT. The effectiveness of the proposed distortion compensation method first is evaluated by a MATLAB simulation with a multi-channel DRF-RF model. The simulation results show that the proposed approach significantly improved the SNDR for the channel of interest. Furthermore, the model has been practically verified, where the actual distorted signals are sampled from a commercial LNA (ZFL-500LN+) by a customized FPGA board. Results from measurements show an improvement of similar to 7 dB for SNDR and 27% for EVM in a strong distortion scenario of QPSK modulation signal. (C) 2021 Elsevier B.V. All rights reserved.
Least Mean Square (lms) filters are the most used adaptive filters with applications ranging from channel equalization to system identification and noise cancellation. An lms adaptive filter includes two main parts: a...
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ISBN:
(纸本)9781728160443
Least Mean Square (lms) filters are the most used adaptive filters with applications ranging from channel equalization to system identification and noise cancellation. An lms adaptive filter includes two main parts: a FIR filter and a block for coefficients updating that exploits the lms algorithm. The hardware implementation of lms filter requires a significant number of multipliers, adders and registers, resulting in power consumption issues. In this paper we propose a novel approximate, low-power implementation of the coefficients update block. In the proposed approach, the signal precision is dynamically scaled by using a time-variable rounding. The circuit can select between three levels of precision: no rounding, light rounding and strong rounding. An observation block decides at runtime the rounding level, based on the magnitude of the lms error signal. In this way, it is possible to minimize the convergence error while significantly reducing the switching activity when the algorithm is close to the convergence. VLSI implementation in TSMC 28nm CMOS technology shows that proposed approach results in a maximum power saving of 27% with respect to a standard lms, with negligible degradation of error performances and limited area overhead.
One of the research focuses on the least mean square (lms) algorithm is how to design the variable step-size rule to make the lms algorithm converge ratio and steady-state error. The step-size is relatively large in i...
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One of the research focuses on the least mean square (lms) algorithm is how to design the variable step-size rule to make the lms algorithm converge ratio and steady-state error. The step-size is relatively large in its early stages; that is, the algorithm at this stage converges quickly; when the algorithm tends to a steady-state, the step-size is relatively small, that is. It is at this stage that the steady-state estimation error is relatively tiny. Therefore, looking at it as a whole, the algorithm can converge quickly with lower steady-state errors. Therefore, this paper designs a new step-size rule based on the Sigmoid function and theoretically analyzes the convergence characteristics and steady-state performance. Experiments and simulation comparisons are carried out under the conditions of comparison. The theoretical analysis combined with experimental simulation verification: even if the linear system has a sudden change, this algorithm still has a faster convergence ratio and tracking speed and can obtain minor steady-state errors and steady-state offsets.
作者：
Lopes, Paulo A. C.Univ Lisbon
Inst Super Tecn Inst Enn Sistemas & Comp Invest & Desenvolvimento INESC ID IST UL Rua Alves Redol 9 P-1000029 Lisbon Portugal
Selecting the step of the least mean squares (lms) algorithm is an old problem. This study uses a new approach to address this problem resulting in a new algorithm with excellent system identification performance. The...
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Selecting the step of the least mean squares (lms) algorithm is an old problem. This study uses a new approach to address this problem resulting in a new algorithm with excellent system identification performance. The lms algorithm, with time-varying step, size can be shown to be equivalent to the Kalman filter in some conditions. This is as long as the state noise of the Kalman filter and the step size of the lms algorithm are chosen carefully. The Kalman filter is the optimum linear estimator (Bayesian) given the state and the measurement noise covariance matrices, but these matrices are not always known. This work considers the case where these matrices are not known, in the special cases that the Kalman filter reduces to the lms. This results in an algorithm to select the step-size of the lms algorithm with few priors. The optimum step size can be calculated using estimates of the probability density function (PDF) of the coefficient estimation error variance (q(w)) and measurement noise variance (q(v)). The PDFs can be estimated from the data using Bayes' rule and assuming Gaussian reference and measurement noise signals. The resulting algorithm to determineq(w)andq(v)is a second small Kalman filter, and the outputs of this filter (means and covariances) are used to determine the expected value of the step.
Ultra-compact electric vehicles (EVs) have been becoming increasingly popular recently. These vehicles have advanced characteristics such as compactness, light body, and low environmental impact. However, it is seriou...
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Ultra-compact electric vehicles (EVs) have been becoming increasingly popular recently. These vehicles have advanced characteristics such as compactness, light body, and low environmental impact. However, it is serious problem with interior noise which are from outside such as road noise and wind noise. Therefore, we have been studying an active noise control (ANC) system for ultra-compact EV by using the giant magnetostrictive actuator. In this paper, we propose a control system using an adaptive filter by least mean square (lms) algorithm which is updating a filter coefficient continuously and brings the reduce noise. Furthermore, we studied fundamental considerations on simulation and noise reduction experiments for the development new ANC system using the giant magnetostrictive actuator. We performed noise reduction simulation using lms algorithm and conducted low frequency noise simulated road noise reduction experiment by wall surface vibration using the giant magnetostrictive actuator. From the result of the simulation, the sound power became to close 0 Pa in the noise control system limitlessly by giving an appropriate step size parameter. Furthermore, in the noise reduction experiment, the sound pressure level of 200 Hz was reduced to 5.8 dB by controlling the noise. Comparing the results with the simulation, it was possible to reduce the noise level at the position of the driver's ears.
The requirement of a robust and adaptive control algorithm in active filters is inevitable nowadays. In this paper, an ADAptive LINEar neuron (ADALINE) based Least Mean Square (lms) control algorithm is proposed for a...
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The requirement of a robust and adaptive control algorithm in active filters is inevitable nowadays. In this paper, an ADAptive LINEar neuron (ADALINE) based Least Mean Square (lms) control algorithm is proposed for a three-phase-four-wire (3P4W) distribution power system under unbalanced loading conditions. The proposed controller has a unique inbuilt characteristic to extract the fundamental active positive sequence component from an unbalanced load current. Particularly, the control algorithm is intended to calculate an adaptive and suitable amplitude for the three-phase reference source currents which are used for the switching pulse generation in the subsequent steps. Moreover, the Distribution STATic COMpensator (DSTATCOM) is performed to eliminate harmonic contents of supply current and harmful excessive neutral current in the system. Further, the DC-link voltage is regulated in order to reduce the power loss across the DSTATCOM, and to attain a balanced, high-quality power availability at the Point of Common Coupling (PCC). The proposed system is accomplished in the MATLAB2014a/Simulink, and a low power rated 3P4WDSTATCOM prototype incorporating with the dSPACE1104 real-time environment to verify the proposed control algorithm.
In this paper, a new family of adaptive filtering algorithms is presented, which aims to combine the small misalignment resulting from the reuse of past weight vectors with the fast convergence arising from the propor...
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In this paper, a new family of adaptive filtering algorithms is presented, which aims to combine the small misalignment resulting from the reuse of past weight vectors with the fast convergence arising from the proportionate adaptation and logarithmic cost functions. This family of algorithms is obtained as a solution to a deterministic constrained optimization problem, by using the Lagrange multipliers technique, which differs from the traditionally employed stochastic gradient technique. Two special cases are proposed, namely the improved mu-law proportionate least mean logarithmic square with reuse of coefficients (IMPLMLS-RC) algorithm and the improved mu-law proportionate least logarithmic absolute difference with reuse of coefficients (IMPLLAD-RC) algorithm. An energy conservation relationship is established, which can be employed to perform stochastic transient analyses of the proposed algorithms. Simulations in system identification and active noise control applications show the advantages of the IMPLMLS-RC and IMPLLAD-RC algorithms over the traditional lms and LAD, and the recently proposed LMLS and LLAD, with respect to both steady-state performance and robustness against impulsive noise.
Sparsity property has long been exploited to improve the performance of least mean square (lms) based identification of sparse systems, in the form of l0-norm or l1-norm constraint. However, there is a lack of theoret...
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Sparsity property has long been exploited to improve the performance of least mean square (lms) based identification of sparse systems, in the form of l0-norm or l1-norm constraint. However, there is a lack of theoretical investigations regarding the optimum norm constraint for specific system with different sparsity. This paper presents an approach by seeking the tradeoff between the sparsity exploitation effect of norm constraint and the estimation bias it produces, from which a novel algorithm is derived to modify the cost function of classic lms algorithm with a non-uniform norm (p-norm like) penalty. This modification is equivalent to impose a sequence of l0-norm or l1-norm zero attraction elements on the iteration according to the relative value of each filter coefficient among all the entries. The superiorities of the proposed method including improved convergence rate as well as better tolerance upon different sparsity are demonstrated by numerical simulations.
The goal of this review is to introduce the concept of Active Noise Cancellation(ANC)technique and explain the core algorithms supporting this technology. Through showing the implementations and major features of thos...
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The goal of this review is to introduce the concept of Active Noise Cancellation(ANC)technique and explain the core algorithms supporting this technology. Through showing the implementations and major features of those algorithms, we were able to gain a better understanding of what're the dominating algorithms used by the ANC technology and both the traditional and nontraditional applications of ANC in today's market. Finally, the review also reveals the potential problem with the ANC technique and the opportunities for improvements.
In recent years, there is a growing effort in the learning algorithms area to propose new strategies to detect and exploit sparsity in the model parameters. In many situations, the sparsity is hidden in the relations ...
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ISBN:
(纸本)9781538646588
In recent years, there is a growing effort in the learning algorithms area to propose new strategies to detect and exploit sparsity in the model parameters. In many situations, the sparsity is hidden in the relations among these coefficients so that some suitable tools are required to reveal the potential sparsity. This work proposes a set of lms-type algorithms, collectively called Feature lms (F-lms) algorithms, setting forth a hidden feature of the unknown parameters, which ultimately would improve convergence speed and steady-state mean-squared error. The key idea is to apply linear transformations, by means of the so-called feature matrices, to reveal the sparsity hidden in the coefficient vector, followed by a sparsity-promoting penalty function to exploit such sparsity. Some F-lms algorithms for lowpass and highpass systems are also introduced by using simple feature matrices that require only trivial operations. Simulation results demonstrate that the proposed F-lms algorithms bring about several performance improvements whenever the hidden sparsity of the parameters is exposed.
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