Robust multiuser detection in non-Gaussian channels

In many wireless systems where multiuser detection techniques may be applied, the ambient channel noise is known through experimental measurements to be decidedly non-Gaussian, due largely to impulsive phenomena. The performance of many multiuser detectors can degrade substantially in the presence of such impulsive ambient noise. We develop robust multiuser detection techniques for combating multiple-access interference and impulsive noise in CDMA communication systems. These techniques are based on the M-estimation method for robust regression. Analytical and simulation results show that the proposed robust techniques offer significant performance gain over linear multiuser detectors in impulsive noise, with little attendant increase in computational complexity. We also develop a subspace-based technique for blind adaptive implementation of the robust multiuser detectors, which requires only the signature waveform and the timing of the desired user in order to demodulate that user's signal. The robust multiuser detection technique and its blind adaptive version can be applied to both synchronous and asynchronous CDMA channels     

Nonparametric multiuser detection in non-Gaussian channels

Existing multiuser detection techniques in wireless systems are based on the assumption that some information on the parameters of the probability density function (pdf) of ambient noise is available. Such information may not be available in all cases, particularly for non-Gaussian and impulsive noises, or may change depending on circumstances. In this paper, we present a technique for multiuser detection that does not require any a priori knowledge about the noise parameters. This method is based on using pseudo norms for linear nonparametric regression. Analytical and simulation results show that the proposed method offers an improved, or at least comparable, performance over existing robust techniques in the absence of any information on the nature of noise in the environment. The increased computational complexity is marginal compared to existing parametric detectors. In addition, the proposed nonparametric detector is portable in the sense that it does not need to be tuned for different noise models without any considerable degradation of performance. We also show that in non-Gaussian noise, the performance of blind adaptive nonparametric multiuser detectors is better than that of robust multiuser detectors.     

A detection statistic for random-valued impulse noise.

This paper proposes an image statistic for detecting random-valued impulse noise. By this statistic, we can identify most of the noisy pixels in the corrupted images. Combining it with an edge-preserving regularization, we obtain a powerful two-stage method for denoising random-valued impulse noise, even for noise levels as high as 60%. Simulation results show that our method is significantly better than a number of existing techniques in terms of image restoration and noise detection.     

Narrowband weak signal detection by higher order spectrum

A study of narrowband weak signal detection by higher order spectrum (HOS) using real signals and real noise, rather than just Gaussian noise, is presented. Noisy real signals are processed using various HOS techniques. We propose to look at the diagonal slices of the bispectrum and the trispectrum as possible substitutes for the power spectrum. The result of applying these slices to weak, real signals is surprisingly good. The dramatic improvements are presented for visual inspection. The performance of the various techniques are then compared quantitatively for different signal-to-noise ratios. The diagonal slices prove to be fast and robust techniques for weak signal detection     

Impulse noise detection and removal using fuzzy techniques

A new algorithm is presented which can remove impulse noise from corrupted images while preserving details. The algorithm is based on fuzzy impulse detection and fuzzy noise cancellation techniques. Experimental results show that the algorithm is capable of providing significant improvement over many published techniques in terms of both subjective and objective evaluations     

Decoding real-number convolutional codes: change detection, Kalmanestimation

Convolutional codes which employ real-number symbols are difficult to decode because of the size of the alphabet and the numerical and roundoff noise inherent in arithmetic operations. Such codes find applications in both channel coding for communication systems and in fault-tolerance support for signal processing subsystems. A new method for error correction based on optimum mean-square recursive Kalman estimation techniques incorporates time-varying models for the system and associated disruptive noise sources. The underlying common model for communications and fault tolerance applications assumes the system operates nominally with low levels of channel or numerical and roundoff noise, occasionally experiencing temporarily larger noise statistics. A time-varying Kalman estimation structure which uses single-step and fixed-lag smoothing predictors can correct errors to within the nominal low-noise levels. Correction actions may be activated only when larger activity is detected, so methods for detecting possible error situations are developed. However, misdetection is not a serious problem because the Kalman correction methods only track significant errors in the data. Two activity detection techniques are examined; one is based on likelihood ratio tests while another uses clipped samples and binary pattern matching. Several examples showing simulated mean-square error performance and decoded waveforms from error injection experiments are presented     

Turbo multiuser detection for coded DMT VDSL systems

In recent years, iterative processing techniques with soft-in/soft-out components have received considerable attention. Such techniques, based on the so-called turbo principle, are exemplified through turbo decoding, turbo equalization, and turbo multiuser detection. Turbo multiuser detection is applied to a discrete multitone (DMT) very-high-rate digital subscriber line system to combat crosstalk signals and to obtain substantial coding gain. The proposed iterative DMT receiver is shown to achieve an overall 7.0 dB gain over the uncoded optimum receiver at a bit error rate of 10^-7 for a channel with severe intersymbol interference and additive white Gaussian noise and with one dominant crosstalk signal. Impulse noise is detrimental to the proposed scheme but can be overcome through erasure decoding techniques, as is shown by example     

Robust speech pulse detection using adaptive noise modelling

The problem of speech pulse detection with additive noise at a signal-to-noise ratio (SNR) as low as 0 and -6 dB is addressed. The noise is assumed to be reasonably stationary and correlated. Three techniques have been examined: the autoregressive analysis of noise; spectral density comparison; and the non-stationarity measure     

Near optimal detection of signals in impulsive noise modeled with asymmetric α-stable distribution

There has been great interest in symmetric α-stable distributions which have proved to be very good models for impulsive noise. However, most of the classical non-Gaussian receiver design techniques cannot be extended to the symmetric α-stable noise case since these techniques require an explicit compact analytical form for the probability density function (PDF) of the noise distribution which α-stable distributions do not possess. A new analytical representation has been suggested for the symmetric α-stable PDF which is based on scale mixtures of Gaussians. Based on this new analytical representation, this paper introduces a novel near-optimal receiver for the detection of signals in symmetric α-stable noise. The performance of the new receiver is very close to the locally optimum receiver and is significantly better than the performance of previously suggested sub-optimum receivers. The new technique has important potential in radar, sonar, and other applications     

Adaptive detection for DS-CDMA

A review of adaptive detection techniques for direct-sequence code division multiple access (CDMA) signals is given. The goal is to improve CDMA system performance and capacity by reducing interference between users. The techniques considered are implementations of multiuser receivers, for which background material is given. Adaptive algorithms improve the feasibility of such receivers. Three main forms of receivers are considered. The minimum mean square error (MMSE) receiver is described and its performance illustrated. Numerous adaptive algorithms can be used to implement the MMSE receiver, including blind techniques, which eliminate the need for training sequences. The adaptive decorrelator can be used to eliminate interference from known interferers, though it is prone to noise enhancement. Multistage and successive interference cancellation techniques reduce interference by cancellation of one detected signal from another. Practical problems and some open research topics are mentioned. These typically relate to the convergence rate and tracking performance of the adaptive algorithm     

Subspace methods for blind joint channel estimation and multiuser detection in CDMA systems

Recently developed subspace techniques for blind adaptive multiuser detection are briefly reviewed first. In particular, blind methods based on signal subspace tracking for adapting linear multiuser detectors in AWGN CDMA channels are considered, as well as extensions of these techniques to frequency selective fading channels, dispersive channels, and antenna array spatial processing. In addition, subspace‐based nonlinear adaptive techniques for robust blind multiuser detection in non‐Gaussian ambient noise channels are also described. Several new techniques are then developed within the subspace framework for blind joint channel estimation and multiuser detection, under some specific channel conditions. These include (1) an adaptive receiver structure for joint multiuser detection and equalization in dispersive CDMA channels, (2) a subspace method for joint multiuser detection and equalization in unknown correlated noise, and (3) a method for joint interference suppression and channel tracking in time‐varying fading channels. This revised version was published online in August 2006 with corrections to the Cover Date.     

On the near-equivalence of envelope and square-law detection of correlated Gaussian noise

Postdetection integration of radar signals in correlated noise background results in degraded detection performance which is analytically tractable for a square-law detector, but not for the linear envelope detector usually employed in practical radar receivers. It is shown that for stationary Gaussian input noise with arbitrary correlation, the "effective number of integrated independent noise samples" (Ne) satisfies: 1 ≤ Ne(envelope)/Ne(square-law) ≤ 4(4 - π)π ≈ 1.093, thus suggesting near-equivalence of the detection performance and justifying results previously obtained by simulation techniques.     

On the performance of stack filters and vector detection in image restoration

Two techniques for image restoration are compared in this paper. One is a technique based on the theory of optimal adaptive stack filtering; the other is a recently developed vector detection approach to image restoration. The primary difference between these two techniques is that the optimal detection technique exploits multilevela priori information, while the stack filter uses only single level zero crossing information.The design constraints for stack filters and vector detection are similar. Both approaches rely on the existence of a training sequence for the image source in order to obtain optimal processing. Adaptive stack filters do, however, require a training set of the noise while the optimal detection approach only needs a multivariate parametric representation.The image-restoration performance of these two methods is compared in a signal dependent noise environment characterizing imaging systems with speckle, film-grain, and Poisson shot noise. Comparisons are made using the mean absolute error measure as well as a subjective measure.This research was sponsored in part by the A. I. DuPont Institute.     

Group-blind multiuser detection for uplink CDMA

Previously developed blind techniques for multiuser detection in code division multiple access (CDMA) systems lead to several near-far resistant adaptive receivers for demodulating a given user's data with the prior knowledge of only the spreading sequence of that user. In the CDMA uplink, however, typically the base station receiver has the knowledge of the spreading sequences of all the users within the cell, but not that of the users from other cells. In this paper, group-blind techniques are developed for multiuser detection in such scenarios. These new techniques make use of the spreading sequences and the estimated multipath channels of all known users to suppress the intracell interference, while blindly suppressing the intercell interference. Several forms of group-blind linear detectors are developed based on different criteria. Moreover, group-blind multiuser detection in the presence of correlated noise is also considered. In this case, two receiving antennas are needed for channel estimation and signal separation. Simulation results demonstrate that the proposed group-blind linear multiuser detection techniques offer substantial performance gains over the blind linear multiuser detection methods in a CDMA uplink environment     

Wiener filter realization for target detection using group delaystatistics

Several techniques are demonstrated for Wiener filter realization based on group delay statistics. The theoretical analysis shows that an inverse relationship exists between the frequency-domain signal-to-noise ratio and the group delay moving standard deviation and/or group delay moving entropy. Therefore, an adaptive Wiener filter can be realized without a priori knowledge of the signal and noise spectra. Group delay statistics estimation algorithms are proposed and evaluated by simulation. Experimental data from ultrasonic flaw detection are presented to support the effectiveness of the techniques     

Experimental detection of mobile satellite transmissions with cyclostationary features

One of the important functions of cognitive radio (CR) technology is spectrum sensing. The implementation of an efficient spectrum sensing function can be quite challenging because of various factors such as multi‐path fading, low signal‐to‐noise ratio of the radio communication services to be detected and the requirement to detect and analyze the signal in a short time. As a consequence, it is important to quantitatively assess the performance of spectrum sensing techniques in various scenarios. This paper investigates different digital signal processing techniques for spectrum sensing in the context of mobile satellite transmissions: power sensing, cyclostationary sensing, efficient cyclostationary sensing based on FFT accumulation method and strip spectral correlation algorithm. This paper presents experimental results on the cyclostationary properties of GSM Thuraya mobile satellite communications in various conditions both for the uplink and downlink channels. The receiver operating characteristics are computed, and the results are presented for different algorithms and different positions of the satellite terminals. The experimental results show that the cyclostationary‐feature‐based detection can be robust compared to energy‐based technique for low signal‐to‐noise ratio levels. Copyright © 2014 John Wiley & Sons, Ltd.     

Envelope detection of a unit-index amplitude-modulated cartier accompanied by noise

The facts concerning the relative merits of linear and square-law envelope detection of a sinusoidally modulated carrier accompanied by Gaussian noise are clarified in this paper. The comparison when the carrier is weakly modulated and has mean power substantially greater than that of the noise in the input circuit has been fully covered in prior technical literature. In this special case the signal-to-noise ratio (SNR) in the detected output is the same for the two methods, and the distortion of the signal is worse with square-law operation because of inherent second-harmonic production. Since noise in the detected output is typically measured in the absence of signal modulation, the fact that the relative performance changes when the index of modulation approaches 100 percent tends to be overlooked. It is shown in the present paper that the noise performance of the square-law detector suffers a penalty approaching 1.8 dB when the carrier is strongly modulated. Also, the distortion in the square-law case is characterized by a second harmonic only 12 dB down from the fundamental, while the second harmonic in the output of the linear detector is practically negligible. These results are important in the evaluation of techniques in diversity reception.     

Combining TDLAS and multi-fusion algorithms for methane gas concentration detection

High-precision methane gas detection is of great importance in industrial safety, energy production and environmental protection, etc. However, in the existing measurement techniques, the methane gas concentration information is susceptible to noise, which leads to its useful signal being drowned by noise. A fusion algorithm of variational modal decomposition (VMD) and improved wavelet threshold filtering is proposed, which is used in combination with tunable diode laser absorption spectroscopy (TDLAS) to implement a non-contact, high-resolution methane gas concentration detection. The fusion algorithm can perform noise reduction and further segmentation of the methane gas detection signal. And the simulation and experiment verify the effectiveness of the fusion algorithm, and the experimental results show that for the detection of air containing 10 ppm, 30 ppm, 60 ppm, 80 ppm, and 99 ppm methane, the errors are 12.75%, 8.18%, 3.37%, 2.46%, and 1.78%, respectively.     

Smart antenna‐assisted spectrum sensing for robust detection in cognitive radio networks

In this paper, we consider the problem of multiband spectrum sensing by employing smart antenna arrays at the cognitive receiver. Although energy detection is widely used for spectrum sensing in cognitive radio networks because of its simplicity and accuracy, it is severely deteriorated by the noise uncertainty. This paper introduces robust spectrum sensing techniques to circumvent this difficulty, which operate simultaneously over the total frequency channels rather than a single channel each time. To enhance the detection performance, the proposed schemes jointly utilize the information of eigenvalues and eigenvectors, signal and noise subspace components in conjunction with the likelihood functions and Gerschgorin radii. Neither subjective decision threshold setting nor the estimation of noise power is required in our schemes, making them robust to noise uncertainty. Simulations are presented to validate the performance of the proposed schemes, and the results show that our schemes can outperform other existing spectrum sensing methods. Copyright © 2014 John Wiley & Sons, Ltd.