An improved LMS adaptive algorithm for narrowband interference suppression in direct sequence spread spectrum
01 Jul 1995-IEEE Transactions on Aerospace and Electronic Systems (IEEE)-Vol. 31, Iss: 3, pp 1198-1201
TL;DR: In this article, a new adaptive least mean squares (LMS) algorithm to increase the slow convergence of their nonlinear adaptive filter is described. But the main drawback of their adaptive nonlinear filter is its slow convergence rate.
Abstract: In 1990 Vijayan and Poor proposed nonlinear predictive methods for suppressing narrowband interference in spread spectrum (SS) systems with a significant increase in signal-to-noise ratio (SNR) improvement. The main drawback of their adaptive nonlinear filter is its slow convergence rate. A new adaptive least mean squares (LMS) algorithm to increase the slow convergence of their nonlinear adaptive filter is described. Computer simulation results are presented to support the advantages of the new filter. >
Citations
More filters
01 Jan 2004
TL;DR: Joint iterative multiuser-detection (MUD), equalization and interference suppression are proposed to jointly combat co-channel interference (CCI), inter-symbol-interference (ISI) and unknown CCI (UCCI) in broadband single-carrier systems.
Abstract: The performance of conventional receivers for wireless communications may severely deteriorate in the presence of unaccounted interference. The effectiveness of methods for mitigating these effects greatly depends on the knowledge that is available about the interference and signal-of-interest (SOI), therefore making the design of robust receivers a great challenge. This thesis focuses on receiver structures for channel coded systems that exploit different levels of knowledge about the SOI and interference in an iterative fashion. This achieves both robustness and overall performance improvement compared to non-iterative receivers. Code division multiple access (CDMA) and spatial division multiple access (SDMA) systems are considered. The overlay of a turbo coded direct-sequence spread-spectrum (DS-SS) system and strong digitally modulated tone interference is studied. An iterative receiver, which is capable of blind cancellation of both wideband and narrowband interference is proposed based on the adaptive selfreconfigurable -filter scheme. Asymptotic performance analysis of the iterative receiver shows that significant iteration gains are possible if the signal-to-interference-plus-noise-ratio (SINR) is relatively large and the processing gain (PG) of the SOI is relatively small. Robust diversity detection in turbo-coded DS-SS system with statistically modeled interference is studied. A non-parametric type-based iterative receiver that estimates the probability density function (PDF) of interference-plus-noise is proposed. Its performance is shown to be rather robust to the number of interferers and their distances from the victim receiver and very similar to the performance of a clairvoyant receiver. Amazingly, this is achievable with no prior knowledge about the interference parameters. Furthermore, iteration gain is shown to significantly reduce the length of the pilot sequence needed for the PDF estimation. A family of iterative minimum-mean-squared-error (MMSE) and maximum-likelihood (ML) receivers for convolutionally and space-time coded SDMA systems is proposed. Joint iterative multiuser-detection (MUD), equalization and interference suppression are proposed to jointly combat co-channel interference (CCI), inter-symbol-interference (ISI) and unknown CCI (UCCI) in broadband single-carrier systems. It is shown that both in convolutional and space-time coded systems the ISI and CCI interference can be completely eliminated if UCCI is absent. This is achievable with a number of receive antennas equal to the number of users of interest and not to the total number of transmit antennas. In case UCCI is present, the effectiveness of CCI and ISI cancellation and UCCI suppression depends on the effective degrees of freedom of the receiver. Receiver robustness can be significantly preserved by using hybrid MMSE/ML detection for the signals of interest, or by using estimation of the PDF of the UCCI-plus-noise. A low complexity hybrid MMSE/ML iterative receiver for SDMA is proposed. It is shown that its performance is not significantly degraded compared to the optimal ML receiver. Its sensitivity to spatial correlation and a timing offset is assessed by using field measurement data. It was shown that the hybrid MMSE/ML receiver is robust against spatial correlation. The sensitivity to the timing offset is significantly reduced if the receiver performs UCCI suppression.
8 citations
Additional excerpts
...Improved adaptive and optimal time-domain linear predictive and interpolative techniques are proposed in [58, 59, 60, 61, 62, 63, 64]....
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TL;DR: Comparisons with the EM-derived HMM–EM scheme presented in Krishnamurthy and Logothetis (1999) demonstrate the improved performance of the proposed adaptive nonlinear filtering algorithm, both in relation to SNR improvement and properties of convergence.
6 citations
Cites methods from "An improved LMS adaptive algorithm ..."
...Methods for "ltering of the spread spectrum signal to suppress NBI have advanced from these timedomain linear methods to nonlinear methods (see [5,10] and references therein)....
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01 Jan 2013
TL;DR: In this article, a DOA estimation method using subarray and spatial smooth is proposed, which is then estimated based on the computation of the phase shift between the reference signal and its phase shifted signal.
Abstract: In this paper, a DOA estimation method using subarray and spatial smooth is proposed. The DOA is then estimated based on the computation of the phase shift between the reference signal and its phase shifted signal. To significantly improve the robustness of DOA estimation and of beamforming and to estimate azimuth angle in multipath mobile channel environment, we developed techniques for applying spatial smooth subarray method to arrays of linear geometry. We developed method for applying spatial smooth subarray to arrays of arbitrary geometry. We study the cause of ambiguities in a multiple signal environment and find the necessary and sufficient conditions for an array manifold to be ambiguity free. We show that can be the application of our method MUSIC and adaptive beamforming method. The estimation results are verified by simulations.
3 citations
Cites methods from "An improved LMS adaptive algorithm ..."
...The array output covariance matrix can be written as follow [16-17] [ ( ) ( )] (16)...
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07 Jun 1998
TL;DR: In this article, a nonlinear estimation algorithm based on hidden Markov models for narrowband interference suppression in CDMA spread spectrum systems is proposed, which combines a recursive hidden markov model estimator, a Kalman filter and a recursive prediction error parameter estimation algorithm.
Abstract: This paper presents a novel nonlinear estimation algorithm based on hidden Markov models for narrowband interference suppression in CDMA spread spectrum systems. The proposed algorithm combines a recursive hidden Markov model estimator, a Kalman filter and a recursive prediction error parameter estimation algorithm. It is shown that the proposed algorithm not only outperforms the nonlinear filtering techniques for narrowband interference suppression presented in Rusch and Poor (1994), but that it outperforms and has faster, more robust convergence properties than the cross-coupled expectation maximization based algorithm presented in Logothetis and Krishnamurthy.
3 citations
15 Jul 2013
TL;DR: In this paper, an improved Variable Step-Size Affine Projection Algorithm (VS-APA) is used in adaptive filters for narrowband interference suppression of direct sequence spectrum spreading (DSSS).
Abstract: In this paper, an improved Variable Step-Size Affine Projection Algorithm (VS-APA) is used in adaptive filters for narrowband interference suppression of direct sequence spectrum spreading (DSSS). Compared with traditional VS-APA and NLMS, this algorithm uses exponential function to vary the step-size, which can estimate NBI more accurately and a better tradeoff between performance and complexity. Computer simulations are shown to verify the analyses and compare the performance of the proposed Narrow-band Interference (NBI) suppression scheme with traditional VS-APA and NLMS adaptive algorithm.
2 citations
Cites background or methods from "An improved LMS adaptive algorithm ..."
...Although spread spectrum itself possesses certain ability to reject NBI, yet the gain is low due to bandwidth restriction [1, 2]....
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...At the present time, the adaptive algorithms based on the method of least-meansquare (LMS) are used to reject NBI in DSSS widely because of its computational simplicity and ease of implementation [1]....
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References
More filters
TL;DR: A least-mean-square adaptive filter with a variable step size, allowing the adaptive filter to track changes in the system as well as produce a small steady state error, is introduced.
Abstract: A least-mean-square (LMS) adaptive filter with a variable step size is introduced. The step size increases or decreases as the mean-square error increases or decreases, allowing the adaptive filter to track changes in the system as well as produce a small steady state error. The convergence and steady-state behavior of the algorithm are analyzed. The results reduce to well-known results when specialized to the constant-step-size case. Simulation results are presented to support the analysis and to compare the performance of the algorithm with the usual LMS algorithm and another variable-step-size algorithm. They show that its performance compares favorably with these existing algorithms. >
966 citations
TL;DR: In this article, two approaches to the non-Gaussian filtering problem are presented, which retain the computationally attractive recursive structure of the Kalman filter and approximate well the exact minimum variance filter in cases where either the state noise is Gaussian or its variance small in comparison to the observation noise variance, or the system is one step observable.
Abstract: Two approaches to the non-Gaussian filtering problem are presented. The proposed filters retain the computationally attractive recursive structure of the Kalman filter and they approximate well the exact minimum variance filter in cases where either 1) the state noise is Gaussian or its variance small in comparison to the observation noise variance, or 2) the observation noise is Gaussian and the system is one step observable. In both cases, the state estimate is formed as a linear prediction corrected by a nonlinear function of past and present observations. Some simulation results are presented.
373 citations
TL;DR: 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.
Abstract: 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. >
189 citations
TL;DR: It is shown that the most common LLSE filter design can lead to performance inferior to that of various other filter designs, but results are also presented demonstrating that an LLSEfilter design motivated by the structure of the maximum-likelihood receiver leads to consistently superior performance.
Abstract: Linear least squares estimation (LLSE) techniques can provide an effective means of suppressing narrow-band interference in direct sequence (DS) spread-spectrum systems. In the results presented here, analytical expressions for bit error rate are derived for two DS spread-spectrum systems under the conditions of either tone or narrowband Gaussian interference. It is shown that the most common LLSE filter design can lead to performance inferior to that of various other filter designs. However, results are also presented demonstrating that an LLSE filter design motivated by the structure of the maximum-likelihood receiver leads to consistently superior performance. The performance of a system using this new design criterion is compared with that of an approximation to the maximum-likelihood (ML) receiver for the tone interference model and with that of the exact ML receiver for the Gaussian interference. Finally, it is shown that the bit error rate estimate obtained from application of a Gaussian approximation for the test statistic is overly pessimistic for the systems studied here.
132 citations
TL;DR: The performance of PN spread-spectrum communication systems in the presence of narrow-band interference is studied when linear interpolation filters are employed for the estimation and subsequent suppression of the interference.
Abstract: The performance of PN spread-spectrum communication systems in the presence of narrow-band interference is studied when linear interpolation filters are employed for the estimation and subsequent suppression of the interference. Closed-form analytical expressions for the system's performance are established for a broad class of interference processes. The advantages of linear interpolation filters over predictive filters with identical number of taps are examined analytically and some unexpected results are obtained. The analytical results are illustrated by examples.
96 citations