Least Mean Square Adaptive Filter Research Articles

Implementation of Massive Multiple-Input Multiple-Output (MIMO) 5G Communication System using Modified Least-Mean-Square (LMS) Adaptive Filters Algorithm

The massive MIMO systems are the more popular field in the present era for the 5G wireless communication system. The MIMO system is a demanding research topic for the last four decades. This topic is under implementation and observation from the last few years. These systems have many advantages and many research sub-areas but this paper investigates the modified model of the massive MIMO receiver system. The traditional receiver system model of massive MIMO system reduces the channel noise using a linear filter in the receive combiner bank (RCB) but the proposed model removes the channel noise before the RCB using an adaptive filter bank (AFB). The AFB is the combination of LMS adaptive filters. The analysis parameters are channel noise, signal-to-noise ratio (SNR) and bit-error-rate (BER) using the hybrid precoder and combiner computation algorithms – Quantized Sparse Hybrid Beamforming (QSHB) and Hybrid Beamforming Peak Search (HBPS). Therefore, the proposed massive MIMO system model gives the less channel noise in the received signal, higher SNR and lower BER as compared to the traditional massive MIMO receiver systems.

A 0.00179 mm2/Ch Chopper-Stabilized TDMA Neural Recording System With Dynamic EOV Cancellation and Predictive Mixed-Signal Impedance Boosting.

This article presents a digitally-assisted multi-channel neural recording system. The system uses a 16-channel chopper-stabilized Time Division Multiple Access (TDMA) scheme to record multiplexed neural signals into a single shared analog front end (AFE). The choppers reduce the total integrated noise across the modulated spectrum by 2.4 × and 4.3 × in Local Field Potential (LFP) and Action Potential (AP) bands, respectively. In addition, a novel impedance booster based on Sign-Sign least mean squares (LMS) adaptive filter (AF) predicts the input signal and pre-charges the AC-coupling capacitors. The impedance booster module increases the AFE input impedance by a factor of 39 × with a 7.1% increase in area. The proposed system obviates the need for on-chip digital demodulation, filtering, and remodulation normally required to extract Electrode Offset Voltages (EOV) from multiplexed neural signals, thereby achieving 3.6 × and 2.8 × savings in both area and power, respectively, in the EOV filter module. The Sign-Sign LMS AF is reused to determine the system loop gain, which relaxes the feedback DAC accuracy requirements and saves 10.1 × in power compared to conventional oversampled DAC truncation-error ΔΣ-modulator. The proposed SoC is designed and fabricated in 65 nm CMOS, and each channel occupies 0.00179 mm2 of active area. Each channel consumes 5.11 μW of power while achieving 2.19 μVrms and 2.4 μVrms of input referred noise (IRN) over AP and LFP bands. The resulting AP band noise efficiency factor (NEF) is 1.8. The proposed system is verified with acute in-vivo recordings in a Sprague-Dawley rat using parylene C based thin-film platinum nanorod microelectrodes.

The RFI Fast Mitigation Algorithm Based on Block LMS Filter

The radio telescope possesses high sensitivity and strong signal collection capabilities. While receiving celestial radiation signals, it also captures Radio Frequency Interferences (RFIs) introduced by human activities. RFI, as signals originating from sources other than the astronomical targets, significantly impacts the quality of astronomical data. This paper presents an RFI fast mitigation algorithm based on block Least Mean Square (LMS) algorithm. It enhances the traditional adaptive LMS filter by grouping L adjacent time-sampled points into one block and applying the same filter coefficients for filtering within each block. This transformation reduces multiplication calculations and enhances algorithm efficiency by leveraging the time-domain convolution theorem. The algorithm is tested using baseband data from the Parkes 64 m radio telescope’s pulsar observations and simulated data. The results confirm the algorithm’s effectiveness, as the pulsar profile after RFI mitigation closely matches the original pulsar profile.

A Parallelized Input Matching LMS Adaptive Filter for the Rejection of Spatially Cyclic Disturbances

Spatially cyclic disturbances exist widely in rotating machines. They usually have fixed spatial cycles rather than constant time periods, which affect the stationarity of angular speed tracking. An input matching least mean square (LMS) adaptive filter (IMLMS-AF) is proposed to cope with the effects of spatially cyclic disturbances. The IMLMS-AF sets a part of the inputs as spatially cyclic signals for disturbance rejection and forms another part as a time-dependent function for reference tracking. Furthermore, an updated law and convergence of the pending weights are given. The system's stability is proved by combining the instantaneous gradient with Lyapunov theory. Moreover, the IMLMS-AFs are parallelized to reject disturbance with multiple components and reference tracking. The effectiveness and superiority of the proposed control method are verified and compared with other methods by simulations and experiments.

Impulsive noise mitigation based adaptive filtering and Reed Solomon coding for power line communication

In the realm of power line communication (PLC), impulsive noise is often regarded as the most difficult challenge to face in PLC. The impulsive noise effects are reduced by employing a combined adaptive least mean square (LMS) filtering and Reed Solomon (RS) coding. As a result, the bit error rate (BER) and throughput performance over this channel are enhanced. The results of a MATLAB computer simulation indicated that a significant improvement is achieved by utilizing the LMS adaptive filter in conjunction with the RS code. This is in contrast to a system that utilizes RS code alone or a system that utilizes the conventional time domain clipping approach.

Impulsive noise mitigation based adaptive filtering and Reed Solomon coding for power line communication

In the realm of power line communication (PLC), impulsive noise is often regarded as the most difficult challenge to face in PLC. The impulsive noise effects are reduced by employing a combined adaptive least mean square (LMS) filtering and Reed Solomon (RS) coding. As a result, the bit error rate (BER) and throughput performance over this channel are enhanced. The results of a MATLAB computer simulation indicated that a significant improvement is achieved by utilizing the LMS adaptive filter in conjunction with the RS code. This is in contrast to a system that utilizes RS code alone or a system that utilizes the conventional time domain clipping approach.

Time delay estimation using cascaded LMS filters fused by correlation coefficient for pipeline leak localization

Water leaks are commonly detected using acoustic/vibration sensors, which are sensitive to sounds and vibrations caused by leakage. In practical surveys, the degree of success for leak detection and localization depends on the operating conditions and construction materials of the pipeline in question. In particular, under the condition of low signal-to-noise ratios (SNRs) and small leaks, the traditional localization method based on time delay estimation (TDE) tends to give pseudo peaks, which may lead to appreciable errors in the estimate of the leak position. In this paper, TDE is carried out based on cascaded least mean square (LMS) filters fused by correlation coefficient. Combined with the propagation theory of leak signals in the pipeline, the TDE algorithms using the LMS adaptive filters are introduced for leak localization. It is found that the single LMS filter is replaced by cascaded architectures, further improving its performance for TDE in a low SNR environment. Numerical simulations are presented to demonstrate the best performance for TDE by using the cascaded LMS filters in comparison with the basic cross-correlation, the phase transform generalized cross-correlation and the single LMS filter. This is further verified using some measurements made in the laboratory and field tests.

A robust sliding window adaptive filtering technique for phonocardiogram signal denoising

AbstractObjectiveThe digital sound recording of several heart sounds (HSs) is named phonocardiogram (PCG) signal. Analysing these PCG signals is essential for diagnosing diverse sorts of heart disorders. Nevertheless, owing to troubling surrounding noise signals, PCG signal recording is challenging. So, before utilising PCG for advanced processing, de‐noising the PCG signal is executed. A new sliding window adaptive noise cancellers (SWANC)‐centred filter technique is proposed in this paper for de‐noising along with recovering the PCG signal effectively.MethodUtilising the least mean square (LMS), an SW optimum adaptive filter (AF) structure is introduced in this work for estimating a De‐noised signal (DS) with better accuracy. Here, via the SWAF stage, a noisy signal is processed. An SW of fixed duration slides over the signal; also, the signal is filtered utilising the AF in each window. Utilising this SWAF architecture, this method approximates the PCG signal's clean version with better accuracy.ResultsThe proposed robust SWAF is analogized to experimental PCG signals that were corrupted by Gaussian noise (GN) together with pink noise (PN) with distinct noise levels (NLs). From the physionet database, the experiential data are acquired. The outcomes exhibited that a remarkable performance was attained by the robust SWAF model.DiscussionA 2–10 times decrease in MSE values was attained by the proposed filter structure when analogized with conventional LMS filter configuration. Further, the SNR is improved by 3 times and comparatively, the PSNR enhancement was 4%–25%. The association betwixt the clean signal along with its estimate is greater than 0.92.ConclusionA cost‐efficient hardware installation of ANC with higher accuracy was offered by the SW LMS AF model. For obtaining desired convergence speed and accuracy, such models are tested for real‐time performances in the future.

The design and implementation of folded adaptive lattice filter structures in FPGA for ECG signals

An adaptive filter is the utmost essential filter castoff in statistical signal dealing. The fine-tuning of the filter factor in relation to the response signal is the adaptive filter's key feature due to fewer calculations, Least Mean Square (LMS) adaptive filters are widely used to remove noise from Electrocardiograms (ECG). The adaptive filters are realized as signal processing algorithms in Digital Signal Processors (DSPs) or in VLSI Signal Processors (VSPs). The technique provides a way to create a folded adaptive lattice LMS filter, which requires less hardware than an adaptive lattice filter. Folding is an algorithm that uses a time scheduling technique that combines arithmetic operations into one operation which reduces Register and silicon chip areas. The design and implementation of a folded lattice adaptive filter remove Power Line Interference (PLI) noise from ECG signals. The MATLAB Xilinx System Generator tool is used to design the Adaptive Lattice LMS Filter and Folded Adaptive Lattice LMS Filter with Folding Order K = 2 and K = 4 and realized in the Virtex 5 FPGA KIT. The results of the folded architecture show that the area is reduced for K = 2 and K = 4 by 82.60% and 91.05%, respectively compared with a normal adaptive lattice filter.

The research on data acquisition and processing method of photovoltaic special metering protection unit based on LMS adaptive filtering

In order to solve the problem of synchronous acquisition of multi-parameter data of photovoltaic special metering protection unit and reduce the noise pollution of sensor data, this paper designs a multi-parameter data acquisition system of photovoltaic special metering protection unit; The system uses RN7326 and SWM32F103R chips as the main and auxiliary microprocessors, and designs a scheme of multi-sensor signal filtering and interface resource acquisition; The standardized data communication protocol is studied and designed to realize the synchronous data acquisition of multiple sensors; Finally, the LMS (Least mean square) based on adaptive filtering method is used to reduce the noise of multi-parameter data. In this paper, the temperature sensor pt100 is taken as an example to conduct temperature acquisition and filtering processing experiments. After data filtering, the noise above 1Hz is basically eliminated, and the noise pollution is effectively reduced when the noise amplitude is less than 1Hz, and the accuracy of sensor data is improved.

Control Study for Compensating Rotor Vibration of Four-DOF Six-Pole Hybrid Magnetic Bearings Based on Variable Step Size LMS Algorithm

In order to solve the problem that the unbalance vibration caused by rotor mass eccentricity of the four-degree-of-freedom (four-DOF) six-pole hybrid magnetic bearings (HMBs), a feed-forward compensation control strategy based on variable step size least-mean-square (LMS) adaptive filter is proposed. First, the structure, operation principle and mathematical model of the four-DOF six-pole HMBs are introduced, the motion law of rotor vibration is analyzed, and the dynamic equation is derived. Second, the variable step size LMS adaptive filter is improved by using the Versoria function. The step size of LMS algorithm is adjusted adaptively by using Versoria function model and Takagi–Sugeno (TS) fuzzy inference machine. The LMS adaptive filter can generate a signal with the same frequency, amplitude, and phase as the rotating speed to offset the vibration signal, so as to realize the vibration compensation control of the four-DOF six-pole HMB rotor. Finally, the simulations and experiments not only prove the effectiveness of this method but also show that the method has good compensation accuracy. The result shows that the Versoria function used in variable step size LMS adaptive filter for vibration compensation is better than the traditional variable step size filtering algorithm based on Sigmoid function.

A robust low-cost adaptive filtering technique for phonocardiogram signal denoising

ObjectivePhonocardiogram (PCG) represents the recordings of various heart sounds. To diagnose the different ailments of the heart, it is required to analyze these PCG signals. However, recording PCG signals is challenging since it is prone to surrounding noise signals. Therefore, there is a need to denoise the PCG signal before being used for advanced processing. This paper proposes an Adaptive Noise Cancellers-based filter model for effectively denoising and recovering the PCG signal. MethodThis work introduces an optimum adaptive filter structure for estimating a noise-free signal with high accuracy using Least Mean Square (LMS) algorithm. A noisy signal is processed through multiple adaptive filter stages connected in series in the proposed work. Multiple stages are automatically added, and each stage filter’s step size process is dynamically changed. The estimate of clean PCG signal approximated using this multistage cascaded adaptive filter architecture is subsequently used in the next module to recover the clean PCG signal with high accuracy. ResultsThe proposed robust multistage adaptive filter is evaluated for denoising synthetic and experimental PCG signals corrupted by Gaussian and pink noise of various input Signal to noise (SNR) levels. The experimental data are taken from the physionet database (Classification of Heart Sound Recordings: The PhysioNet/Computing in Cardiology Challenge 2016). The results demonstrate that the robust multistage filter model performs remarkably well. DiscussionCompared with various filter configurations, the proposed filter structure achieves an 8-50% reduction in MAE values and the 45–87% reduction in MSE values. Further, there is an improved SNR of 15–60%, ANR of 15–65%, and PSNR improvement by 7–25% comparatively. The correlation between the clean signal and its estimate obtained using the proposed filter model is more than 0.99. ConclusionUsing an LMS adaptive filter in the proposed filter model offers a cost-effective hardware implementation of Adaptive Noise Canceller with high accuracy. In the future, the suggested robust multistage adaptive filter model can be tested for real-time performance when improved convergence speed and accuracy are desired.

New approach to eliminate noise attendants ECG signal corrupted

ECG signals have been largely utilized in cardiac pathology to detect heart ailments. ECG signals are commonly corrupted by disparate artifacts such as power line obtrusion exterior electromagnetic domain intervention, noise due to random object motions, breathing motions, and electromyography noise and these should be eliminated before diagnosis.This article explains the creation and performance of three types of band-stop filters based on FPAA technology to accurately estimate an ECG corrupted signal. The characteristics of these filters are improved and their parameters are calculated to minimize the corrupted ECG signals with noise. The Butterworth filter of the fourth order, Notch filters, and least-mean-square adaptive filter (LMSAF) stop-band are used to reduce noise interference from ECG signals. The performance of filters was analyzed by comparing signal power at 62 Hz before and after filtration and distortion to the ECG waveform. It is found that digital filters work satisfactorily. LMSAF gives the best performance as the QRST segment of the ECG signal is less modified compared to others

RETRACTED: Phased array ultrasonic test signal enhancement and classification using Empirical Wavelet Transform and Deep Convolution Neural Network

In the recent past, Non-Destructive Testing (NDT) has become the most popular technique due to its efficiency and accuracy without destroying the object and maintaining its original structure and gathering while examining external and internal welding defects. Generally, the NDT environment is harmful which is distinguished by huge volatile fields of electromagnetic, elevated radiation emission instability, and elevated heat. Therefore, a suitable NDT approach could be recognized and practiced. In this paper, a novel algorithm is proposed based on a Phased array ultrasonic test (PAUT) for NDT to attain the proper test attributes. In the proposed methodology, the carbon steel welding section is synthetically produced with various defects and tested using the PAUT method. The signals which are acquired from the PAUT device are having noise. The Adaptive Least Mean Square (ALMS) filter is proposed to filter PAUT signal to eliminate random noise and Gaussian noise. The ALMS filter is the combination of low pass filter (LPF), high pass filter (HPF), and bandpass filter (BPF). The time-domain PAUT signal is converted into a frequency-domain signal to extract more features by applying the Empirical Wavelet Transform (EWT) algorithm. In the frequency domain signal, first order and second order features extraction techniques are applied to extract various features for further classification. The Deep Learning methodology is proposed for the classification of PAUT signals. Based on the PAUT signal features, the Deep Convolution Neural Network (DCNN) is applied for further classification. The DCNN will classify the welding signal as to whether it is defective or non-defective. The Confusion Matrix (CM) is used for the estimation of measurement of performance of classification as calculating accuracy, sensitivity, and specificity. The experiments prove that the proposed methodology for PAUT testing for welding defect classification is obtained more accurately and efficiently across existing methodologies by providing numerical and graphical results.

Two Distributed Arithmetic Based High Throughput Architectures of Non-Pipelined LMS Adaptive Filters

Distributed arithmetic (DA) is an efficient look-up table (LUT) based approach. The throughput of DA based implementation is limited by the LUT size. This paper presents two high-throughput architectures (Type I and II) of non-pipelined DA based least-mean-square (LMS) adaptive filters (ADFs) using two’s complement (TC) and offset-binary coding (OBC) respectively. We formulate the LMS algorithm using the steepest descent approach with possible extension to its power-normalized LMS version and followed by its convergence properties. The coefficient update equation of LMS algorithm is then transformed via TC DA and OBC DA to design and develop non-pipelined architectures of ADFs. The proposed structures employ the LUT pre-decomposition technique to increase the throughput performance. It enables the same mapping scheme for concurrent update of the decomposed LUTs. An efficient fixed-point quantization model for the evaluation of proposed structures from a realistic point-of-view is also presented. It is found that Type II structure provides higher throughput than Type I structure at the expense of slow convergence rate with almost the same steady-state mean square error. Unlike existing non-pipelined LMS ADFs, the proposed structures offer very high throughput performance, especially with large order DA base units. Furthermore, they are capable of performing less number of additions in every filter cycle. Based on the simulation results, it is found that 256^th order filter with 8^th order DA base unit using Type I structure provides 9.41× higher throughput while Type II structure provides 16.68× higher throughput as compared to the best existing design. Synthesis results show that 32^nd order filter with 8^th order DA base unit using Type I structure achieves 38.76% less minimum sampling period (MSP), occupies 28.62% more area, consumes 67.18% more power, utilizes 49.06% more slice LUTs and 3.31% more flip-flops (FFs), whereas Type II structure achieves 51.25% less MSP, occupies 21.42% more area, consumes 47.84% more power, utilizes 29.10% more slice LUTs and 1.47% fewer FFs as compared to the best existing design.

Feedback cancellation in digital hearing aids using convex combination of proportionate adaptive algorithms

The least mean square (LMS) based linear adaptive algorithms are commonly used for acoustic feedback cancellation (AFC) in digital hearing aids, given the simplicity, tracking capability, and robustness. Simultaneously, the LMS adaptive filter’s major limitation is slow convergence due to a compromised step-size selection. In recent times, the convex combination of LMS adaptive filter has depicted an improved trade-off between the convergence rate and steady-state while employed for adaptive feedback cancellation. However, there exists a scope for improving the convergence further for a time-varying feedback path under different noise conditions and input signals. The convex combination investigated with the mixing parameter δn is confined to the (0,1) interval. Here we observed that the convex combination of two adaptive filters estimates the undesired feedback path dependently. Therefore, the optimal convex combination coefficients occur as a sequence that mitigates the mean square error (MSE). The proposed adaptive feedback cancellation framework has been analysed statistically for convex optimization cost-function. Simulation and results present the improvements of the proposed framework in comparison to the existing state-of-the-art.

A Novel Multiplier-Less LMS Adaptive Filter Design Based on Offset Binary Coded Distributed Arithmetic

Multiply-Accumulate (MAC) operation is the backbone of Least Mean Squares (LMS) digital adaptive filters. Implementing LMS on hardware platform as a Fully Dedicated Architecture (FDA) multiplier becomes bottleneck for higher order filters, prompting high area, cost and power requirements and hence renders the design unsuited for practical implementation. In this paper, we have proposed a composite design that makes use of Distributed Arithmetic (DA) to replace the bottleneck multiplier with memory units that store Partial Products (PPs) to emulate multiplication. The depth of these memory units tends to exponentially grow as the filter order rises. To manage that, we have used Half Memory algorithm (HM) and Offset Binary Coding (OBC) to refine the structure of PPs such that the memory size is reduced at least by a factor of 4 for the same filter order. The proposed design improves system's Throughput, Critical Path Delay, Power Consumption and FPGA Resource Utilization. However, it introduces Latency in both the output and update segments of the LMS algorithm. To provide an option between resource utilization and latency, we have suggested a mechanism to halve the originally produced latency by the Parallel Processing of input bit steam w.r.t even and odd bits. Moreover, we have also proposed a method that reduces the latency of update module at the slight expense of other design attributes. The fundamental structure of the proposed design is flexible owing to the dynamic memory structure as well as the option to choose between latency and resource minimization. Simulations have been carried out in Xilinx Vivado and conclusions have been drawn by comparing both FDA and DA based designs. Results for a 16-tap filter indicate a remarkable improvement in Throughput, Area Utilization and Power Consumption by 18%, 5% and 3.5% respectively at the expense of 4× escalated latency. The Half Latency method allowed the latency to drop 2× but with slightly elevated power and area attributes.

Hybrid wideband multipath self-interference cancellation with an LMS pre-adaptive filter for in-band full-duplex OFDM signal transmission.

A wideband multipath self-interference cancellation (SIC) system employing both dual-drive Mach-Zehnder modulator-based analog SIC and least mean square (LMS) algorithm-based pre-adaptive filter digital SIC is proposed and demonstrated for the cancellation of multipath self-interference (SI) and facilitation of in-band full-duplex (IBFD) orthogonal frequency-division multiplexing (OFDM) signal transmission. The multipath effect is an unavoidable challenge in SIC due to the dynamic and unpredictable properties in each path, as well as the need for separate matching components for compensating for each path. In this Letter, an LMS algorithm-based adaptive filter is used as a pre-equalizer to adapt and generate the matching signal to the closest approximate of the multipath SI signal. The adaptation is based on the minimization of the error signal generated from the matching signal and multipath SI signal in the LMS algorithm. With the introduction of the LMS adaptive filter to the analog SIC, an additional 9 dB cancellation improvement is obtained, resulting in a total of 32 dB cancellation depth over a cancellation bandwidth of 2.7 GHz at a center frequency of 1.65 GHz. To the best of our knowledge, the achieved performance is by far the widest cancellation bandwidth in a multipath SIC system, which is essential in a large bandwidth and high data rate transmission system. With the help of the proposed LMS adaptive filter digital SIC assisted analog SIC located at the remote node, power-efficient IBFD transmission of an OFDM signal through a 25 km fiber is experimentally demonstrated with a 6 dB bit error rate and 8% error vector magnitude improvements.

Design of New Reconfigurable Architecture for Implementing a Least Mean Square Finite Impulse Response Filter Using Borrow Select Subtraction (BSLS)

A design of reconfigurable architecture of FIR filter has been implemented using a Least Mean Square (LMS) adaptive filter. LMS adaptive filter is mainly sued for reducing the coefficients of the filter. Generally, a LMS filter contains normal adder, subtractor, mixer and a delay part. Most of the concepts deal with an adder namely Full Adder (FA), Ripple Carry Adder (RCA), Carry Select Adder (CSLA), etc., Instead of using CSLA; Borrow Select Subtractor (BSLS) is used in LMS filter architecture. By using BSLA LMS adaptive filter in a reconfigurable FIR filter architecture in the proposed scheme, the area, power and delay will be reduced. The proposed scheme achieves better performance when compared to an existing scheme. The proposed method is implemented in ModelSim tool and efficiency has been calculated by using the device Virtex 6 Low Power in Xilinx ISE Design Suite 12.4.

An Eight-parallel Hierarchical Constant Modulus Algorithm for Sixty-four Quadrature Amplitude Modulation

One derivative type of the constant modulus algorithm (CMA) termed hierarchical CMA (HCMA) is investigated for its parallel implementation with 8 lanes. Taking the essence of CMA, HCMA is also the steepest descent under the least mean-square (LMS) criterion theoretically. To update the tap coefficients of the equalizer, HCMA utilizes adjustable modulus in accordance with the dynamical amplitude of the baseband signals. The above mechanism of HCMA can make the expectation of the error estimation converge to 0. The 8-parallel HCMA adopts the framework of the pipelined LMS adaptive filter. On the basis of the iterated short convolution and the relaxed look-ahead approximation, the implementation of 8-parallel HCMA can reduce much multiplication. The numerical simulation indicates that HCMA performs better than CMA with evident gain for the equalization of 64 quadrature amplitude modulation (QAM).