Adaptive Minimum Mean Square Research Articles

Improved Non-Negative Matrix Factorization-Based Noise Reduction of Leakage Acoustic Signals.

The detection of gas leaks using acoustic signals is often compromised by environmental noise, which significantly impacts the accuracy of subsequent leak identification. Current noise reduction algorithms based on non-negative matrix factorization (NMF) typically utilize the Euclidean distance as their objective function, which can exacerbate noise anomalies. Moreover, these algorithms predominantly rely on simple techniques like Wiener filtering to estimate the amplitude spectrum of pure signals. This approach, however, falls short in accurately estimating the amplitude spectrum of non-stationary signals. Consequently, this paper proposes an improved non-negative matrix factorization (INMF) noise reduction algorithm that enhances the traditional NMF by refining both the objective function and the amplitude spectrum estimation process for reconstructed signals. The improved algorithm replaces the conventional Euclidean distance with the Kullback-Leibler (KL) divergence and incorporates noise and sparse constraint terms into the objective function to mitigate the adverse effects of signal amplification. Unlike traditional methods such as Wiener filtering, the proposed algorithm employs an adaptive Minimum Mean-Square Error-Log Spectral Amplitude (MMSE-LSA) method to estimate the amplitude spectrum of non-stationary signals adaptively across varying signal-to-noise ratios. Comparative experiments demonstrate that the INMF algorithm significantly outperforms existing methods in denoising leakage acoustic signals.

Recursive Least Squares-Algorithm-Based Normalized Adaptive Minimum Symbol Error Rate Equalizer

The adaptive minimum symbol error rate (AMSER) equalizer is known to have better symbol error rate (SER) performance than the adaptive minimum mean square error equalizer. Furthermore, the normalized AMSER (NAMSER) equalizer outperforms the AMSER equalizer, which can be regarded as the improvement of the normalized least mean square (NLMS) equalizer by incorporating the minimum SER (MSER) criterion. Inspired by that, we propose an improved recursive least squares-based NAMSER equalizer (RLS-NAMSER) that takes the advantage of faster convergence of the RLS algorithm over the NLMS algorithm. The RLS algorithm is first reconsidered from the perspective of optimization problem and an approximate RLS (ARLS) algorithm is proposed which converges faster than the NLMS algorithm. The RLS-NAMSER equalizer is then proposed by combining the ARLS equalizer with the MSER criterion. Simulation results show that the RLS-NAMSER equalizer has better convergence performance than the NAMSER equalizer while having nearly the same steady state performance as the NAMSER equalizer.

Genetic Algorithm Based Optimum Finger Selection for Adaptive Minimum Mean Square Error Rake Receivers Discrete Sequence-CDMA Ultra-Wide Band Systems

Genetic Algorithm Based Optimum Finger Selection for Adaptive Minimum Mean Square Error Rake Receivers Discrete Sequence-CDMA Ultra-Wide Band Systems

Interference Mitigation by Adaptive Analog Spatial Filtering for MIMO Receivers

Exposed to strong cochannel/adjacent-channel interference, digital multiple-input–multiple-output (MIMO) receivers require high-dynamic-range analog-to-digital converters (ADCs). Hybrid beamforming featuring spatial filtering before the ADCs can adaptively mitigate interference in both the analog and digital domains; hence relaxing the required dynamic ranges of the ADCs. This article demonstrates the effectiveness of hybrid beamforming in this mitigation utilizing adaptive minimum mean square error (MMSE) and error vector magnitude (EVM) as an optimization criterion. Extensive EVM measurements are carried out with a conductive setup using a four-element 22-nm FD-SOI CMOS prototype MIMO receiver chip to verify the performance of the adaptive MMSE algorithm. Over-the-air (OTA) measurements with a linear four-element dipole antenna array with half-wavelength spacing in the 2.4-GHz industrial scientific and medical (ISM) band quantify the improvement for real-world scenarios, e.g., having a multipath propagation channel and mutual coupling between the antenna array elements. OTA results show that a rejection of 22.5 and 24.5 dB can be achieved on average in an in-door laboratory environment utilizing vector modulator (VM) constellations with 16 and 64 points, respectively.

Automated material identification with a Raman spectrometer based on the contribution enhancement of small differences and the adaptive target Raman peak subtraction.

There is only a small difference in Raman peaks between two materials, but they also represent different molecular materials. Therefore, the accurate identification ability for similar materials with small differences among their Raman peaks plays a key role in Raman spectrometers for material identification. However, the noises, the baseline (i.e., fluorescence backgrounds), and the requirements, such as fast and automated detection, of excellent user experiences cause many difficulties. In this paper, the target Raman peak is directly subtracted from the detected Raman spectrum by the adaptive minimum root mean square error (RMSE) estimation for a residual spectrum. Unlike the usual methods in which the detected Raman peak needs to be first recovered by removing the baseline from its Raman spectrum and then to be compared with the target Raman peak, our method can effectively enhance the contribution of small differences between the detected and the target Raman peak on the residual spectrum so as to make the RMSE of the residual spectrum more sensitive with increasing differences. On the other hand, the obtained RMSE of the residual spectrum only has a small change for the detected Raman spectrum with various baselines. So the common criteria (i.e., the third-order polynomials describing RMSE) to identify the detected Raman spectrum with various baselines and the target Raman spectrum is presented. Simulation results show that the small difference, where there is only an additional small Raman peak as low as 1/25 of the maximum peak height, can also be accurately identified. Experiments also demonstrate that similar materials can be accurately identified, whereas some commercial Raman spectrometers fail to identify them. Our method effectively deals with the problem in which the error of the complex baseline correction causes erroneous judgement in Raman spectrometers for material identification.

Effective Transmission Method With Adaptive Nonstationary Channel Equalization for Hypersonic Reentry Communications

The reentry plasma sheath propagation environment is nonstationary and has high-dynamic characteristics. The communication signal suffers serious parasitic modulation effect since the time-varying degree of the channel is on the same order as the telemetry symbol rate. This paper proposes an effective transmission scheme to cancel the plasma sheath channel (PSC) distortion by utilizing the short spreading and adaptive nonstationary channel equalization technology. The proposed scheme spreads the telemetry signal and inserts a small number of pilot signals to determine whether the nonstationary state of the channel is changed by the channel amplitude attention value of the pilot signals. A novel adaptive nonstationary stochastic gradient recursive minimum mean square error (NSGR–MMSE) algorithm is then proposed to estimate the other channel response values in each current state. Finally, the channel equalization is performed to mitigate the damage from the parasitic modulation effect. The proposed method can accommodate the nonstationary characteristics of the channel and the small-scale channel distortion simultaneously with small bandwidth efficiency scarification and minimal algorithm complexity cost. The simulation results show that the proposed transmission scheme can ensure reliable communication under two typical reentry PSC models. Furthermore, the performance of the proposed adaptive NSGR–MMSE algorithm is improved by at least 3 dB versus the classic MMSE algorithm under the effective transmission scheme.

Adaptive Statistical Bayesian MMSE Channel Estimation for Visible Light Communication

Visible light communication (VLC) is considered to be one of the promising technologies for future wireless systems and has attracted an increasing number of research interests in recent years. Optical orthogonal frequency division multiplexing has been proposed for VLC systems to eliminate the multipath interference while also facilitating the frequency domain equalization. In comparison with the conventional radio frequency (RF) based wireless communications, there has been limited considerations on channel estimation (CE) for VLC, where the indoor optical wireless channel model differs from the traditional RF case. In this paper, we present a new CE algorithm for indoor downlink VLC systems, referred to as the adaptive statistical Bayesian minimum mean square error CE (AS-BMMSE-CE). Furthermore, a so-called variable statistic window (VSW) mechanism is designed for exploiting past channel information within a window of adaptively optimized size, such that the CE performance can be significantly improved. Detailed theoretical analysis is provided and verified by extensive numerical results, demonstrating the superior performance of the proposed AS-BMMSE-CE scheme.

Synthetic aperture radar image despeckling based on adaptive iterative risk estimator

Some classical filters, such as bilateral filter, nonlocal means (NLM) filter, and locally adaptive regression kernel, have proven to have a good performance in image denoising. However, there is one shortcoming, i.e., they cannot control the denoising strength very well. As for synthetic aperture radar (SAR) images, due to the special multiplicative noise, the denoising process becomes more complicated. A diffusion iterative filter can enhance the performance of a kernel in the denoising process but will lose some latent details and important targets from the underlying SAR image. In contrast, an iterative boosting filter can preserve these latent details of the SAR image well, although the improvement of the kernel performance is not very desired. By adopting the advantages of diffusion and boosting of the two iterative methods, an adaptive iterative risk estimator minimum mean square error (Min-MSE) method is proposed, which is mainly based on the Min-MSE to adaptively get the optimal iterative method and the corresponding optimal iterative number. The analysis of the experimental results and the comparison with some other state of the art methods demonstrate that our proposed method can improve the performance of an NLM filter and effectively suppress the SAR image speckle.

Joint data detection and phase recovery for downlink MC-2D-CDMA systems

The performance of a downlink synchronous MCCDMA system with joint frequency-time domain spreading (MC-2D-CDMA) is investigated in this paper. We propose a two dimensional adaptive minimum mean square error (MMSE) receiver, which works in decision-directed mode after an initial training period. A subcarrier phase tracker, which comprises a bank of phase locked-loops (PLLs), is employed in the receiver to track the fading phase variability. Furthermore, a simplified phase tracker structure is proposed to reduce the system complexity. The performance of the data detector and the behavior of the phase tracker are analyzed theorectically in this paper and are shown to match the simulation results. Both analysis and simulation indicate that the proposed system outperforms the conventional MC-DS-CDMA systems by exploiting frequency diversity and facilitating subcarrier synchronization.

Adaptive DFE Multiuser Receiver for CDMA Systems using Two-Step LMS-Type Algorithm: An Equalization Approach

This paper presents an adaptive decision feedback equalizer (DFE) based multiuser receiver for code division multiple access (CDMA) systems over smoothly time-varying multipath fading channels using the two-step LMS-type algorithm. The frequency-selective fading channel is modeled as a tapped-delay-line filter with smoothly time-varying Rayleigh-distributed tap coefficients. The receiver uses an adaptive minimum mean square error (MMSE) multiuser channel estimator based on the reduced Kalman least mean square (RK-LMS) algorithm to predict these tap coefficients (Kohli and Mehra, Wireless Personal Communication 46:507---521, 2008). We propose the design of adaptive MMSE feedforward and feedback filters by using the estimated channel response. Unlike the previously available Kalman filtering algorithm based approach (Chen and Chen, IEEE Transactions on Signal Processing 49:1523---1532, 2001), the incorporation of RK-LMS algorithm reduces the computational complexity of multiuser receiver. The computer simulation results are presented to show the substantial improvement in its bit error rate performance over the conventional LMS algorithm based receiver. It can be inferred that the proposed multiuser receiver proves to be robust against the nonstationarity introduced due to channel variations, and it is also beneficial for the multiuser interference cancellation and data detection in CDMA systems.

Performance of Adaptive MMSE Receivers Using Aided Tentative Coefficients in Flat Fading Channels

The conventional matched filter (MF) receiver is considered the optimum filter to recover the CDMA signals. One of the problems of the MF is that its performance is significantly degraded due to the channel impairments and the increase of the multiple access interference (MAI). In this paper, an adaptive minimum mean square error-maximum likelihood (MMSE-ML) receiver is introduced to overcome this problem. This receiver uses two sets of adaptive coefficients to increase the ability of tracking the time variations of the channel. The performance of the receiver is measured in terms of bit error rate (BER) and compared with other receivers over flat fading channel. It is found that the performance of theadaptive MMSE-ML receiver is much better than the performance of the other receivers in the flat fading channel.

Performance Evaluation of Adaptive MMSE Receivers in Frequency Selective Fading Channels Using Aided Tentative Coefficients

Recently, the code division multiple access (CDMA) technique has become the most widely communication technique, because of its varity of applications. The well-known matched filter (MF) is considered as the optimum filter that recovers the CDMA signals. However; its performance recently becomes unsatisfying. This is due to increasing the multiple access interference (MAI), in addition to the other channel impairments such as the fading or the multi path interference (MPI). In this paper, an adaptive minimum mean square error-maximum likelihood (MMSE-ML) receiver is presented as an alternative solution to overcome the drawbacks of the MF receiver. This adaptive MMSE-ML receiver uses adaptive aided tentative coefficients in addition to the basic adaptive coefficients to increase the tracking ability of the channel impulse response variations. The performance of the adaptive MMSE-ML receiver is compared with the traditional adaptive MMSE receiver over a frequency selective fading channel. Moreover, the performances of both the presented and traditional adaptive receivers are compared with the performance of the MF under the same condition. It is found that the performance of the adaptive MMSE-ML receiver is much improved using the proposed technique.

Adaptive linear minimum mean square error restoration: influence on hyperspectral detection strategy

In this paper, we consider the problem of multichannel restoration. Current multichannel least squares restoration filters assume the separability of the signal covariance, which describes the between‐channel and within‐channel relationships. We propose a new solution for a multichannel restoration scheme, the Adaptive Linear Minimum Mean Square Error (ALMMSE), based on a local signal model, without the hypothesis of spectral and spatial separability. The proposed filter is developed to be used as a preprocessing step for detection in hyperspectral imagery. Tests on real data show that the proposed filter enables us to enhance detection performance in target detection and anomaly detection applications with the well‐known hyperspectral imagery detection algorithms AMF and RX. The comparison with detection results, after classical restoration methods, shows the superiority of the proposed approach for hyperspectral images.

A Low-Complexity Joint Synchronization and Detection Algorithm for Single-Band DS-CDMA UWB Communications

The problem of asynchronous direct-sequence code-division multiple-access (DS-CDMA) detection over the ultra-wideband (UWB) multipath channel is considered. A joint synchronization, channel-estimation, and multiuser detection scheme based on the adaptive linear minimum mean square error (LMMSE) receiver is presented and evaluated. Further, a novel nonrecursive least-squares algorithm capable of reducing the complexity of the adaptation in the receiver while preserving the advantages of the recursive least-squares (RLS) algorithm is presented.

Space-Time Adaptive MMSE Multiuser Receiver Structures for Wideband CDMA Systems

In this paper we propose computationally efficient space-time multiuser adaptive minimum mean square error (MMSE) receivers using affine projection adaptive (APA) filtering algorithm for asynchronous wideband code division multiple access systems (W-CDMA) in multipath channels. It is found that APA algorithm outperforms normalized least mean square (NLMS) algorithm in convergence characteristics with significant reduction in computational complexity as compared to recursive least square (RLS) algorithm. Three adaptive multiuser receiver architectures are considered, all employ chip-matched filter (CMF) at their front end. In the first two architectures perfect estimation of time delay of individual user is assumed and one CMF is employed per user. The difference between two architectures is that, in the first the samples collected from CMFs are cross-coupled and in the second architecture samples collected for individual users are not combined. In the third architecture, there is no need to estimate the time delay, but fractional sampling is used. It is shown that first architecture outperforms the other two architectures by virtue of its diversity advantage. It is also shown that by exploiting the spatial diversity (using multiple antennas at the receiver) significant improvement can be achieved in the output signal-to-interference-and-noise ratio (SINR).

Linear MMSE receiver using hidden training sequence in DS∕CDMA

A novel linear adaptive minimum mean square error (MMSE) receiver using a hidden training sequence is proposed. The hidden training sequence, which uses a fraction of the informative sequence's transmitting power as a training sequence, is utilised for the adaptation of the receiver. An adaptive algorithm based on the modified canonical representation is also proposed to enhance the convergence rate in the near–far scenario. The performance of the proposed receiver is confirmed through computer simulations.

Multiuser detection for DS-CDMA UWB in the home environment

We demonstrate the effectiveness of multiuser detection for an ultra-wideband (UWB) pulse based direct sequence spread spectrum system using code division multiple access. Extensive simulations were run using channel soundings of the 2-8 GHz band collected in a residential setting and characterized by a high level of multipath fragmentation. We show that the adaptive minimum mean square error (MMSE) multiuser detection (MUD) receivers are able to gather multipath energy and reject intersymbol and interchip interference for these channels to a much greater extent than RAKE receivers with 4 and 8 arms. We also demonstrate the adaptive MMSE is able to reject a narrowband IEEE 802.11a OFDM interferer, even for signal-to-interference ratio as severe as -30 dB. We show the adaptive MMSE exhibits only a 6 dB penalty relative to the single user case for the heavy multi-access interference (number of asynchronous users equal to spreading code length). The practical RAKE receivers were incapable of effectively rejecting either the strong narrowband interference or the heavily loaded wideband interference. Even more moderate levels of interference caused significant degradation in the performance of the practical RAKE receivers.

Adaptive image noise filtering using transform domain local statistics

Image noise filtering has been widely perceived as an estimation problem in the spatial domain. We deal with it as an estimation problem in an uncorrelated transform domain. This idea leads to a generalization of the adaptive linear minimum mean square error (LMMSE) estimator for filtering noisy images. In our proposed method, the transform-domain local statistics obtained from the noisy image are exploited. Due to the fact that the transform-domain local statistics carry more information about the image than the spatial-domain local statistics do, improvement in noise filtering is gained overall and is particularly significant in the vicinity of edges.

Analysis of an adaptive SIC for near-far resistant DS-CDMA

A new multiuser detection scheme is proposed which employs adaptive minimum mean square error (MMSE) detection in combination with successive interference cancellation (SIC). Through theoretical analysis and numerical examples, it is shown that the proposed detector provides superior performance to existing ones in terms of asymptotic multiuser efficiency (AME) and bit error rate (BER).

The Stability of Adaptive Minimum Mean Square Error Equalizers Using Delayed Adjustment

The necessary and sufficient conditions for the stability and convergence of adaptive minimum mean square error equalizers are developed. The analysis includes the effect of delays in the adjustment path, as occur, for instance, when the decoder in a data transmission system delays its output decisions.