Nonlinear effects in LMS adaptive equalizers

Reuter, M.; Zeidler, J.R.

doi:10.1109/78.765126

Cited by 49 publications

(30 citation statements)

References 20 publications

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“…One can see that including the quadratic term has a smaller effect on the prediction gain, as compared with the results reported in Figure 5b for forward scheme. This is mainly because of the nonlinear nature of the adaptive backward predictor, even when used with linear filtering [22]. Also it should be noted that results reported in Figure 7a,b are achieved when the residual signal remains unquantised.…”

Section: Results Using Apbmentioning

confidence: 81%

Employing Volterra filters in the ADPCM technique for speech coding: a comprehensive investigation

Alipoor

Savoji

2010

Trans Emerging Tel Tech

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Although linear filters are useful in a various applications in the context of speech processing, there are several evidences for existence of nonlinearity in speech signals. Our main aim is to launch a comprehensive investigation into the exploitation of nonlinear Volterra filters in the context of the ADPCM-based speech coding technique, using two methods of forward prediction, based on the LS criterion, and backward prediction, based on both LMS and RLS adaptation algorithms. In any case, after solving some innate problems, for example, ill-conditioning and instability, schemes for optimum exploitation of nonlinear prediction are developed and simulation results are provided, tested with several performance criteria. With forward prediction a scheme is developed to detect and flag those frames for which, after stabilizing, including the quadratic predictor is beneficial. Scalar and vector quantisation methods are used for quantising the residual signal and the filter parameters, respectively. The results show that using this scheme a negligible improvement (up to 0.62 dB in the SNR) can be achieved, in spite of the increase in bit rate and complexity. With backward prediction two frame-based schemes are developed in which for each frame, after examining a set of quadratic filters, the best filter in the sense of the best quality of the reconstructed speech is selected. The ultimate schemes result in an improvement of up to 1.5 dB in the overall SNR of the reconstructed speech at the cost of a slight increase in the bit-rate, a short delay and a demanding increase in the complexity.

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Section: Results Using Apbmentioning

confidence: 81%

Employing Volterra filters in the ADPCM technique for speech coding: a comprehensive investigation

Alipoor

Savoji

2010

Trans Emerging Tel Tech

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“…The NLMS (Normalized Least Mean Square) is originated from LMS (Least Mean Square) algorithm [13]. The problem with LMS was its instability of input signal power.…”

Section: B Normalized Step Size Lms (Nlms)mentioning

confidence: 99%

An approach to implement LMS and NLMS adaptive noise cancellation algorithm in frequency domain

Nagal¹,

Kumar

Bansal³

2014

2014 5th International Conference - Confluence the Next Generation Information Technology Summit (Confluence)

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This paper presents the implementation of adaptive algorithms like Least Mean Square (LMS) and Normalized Least Mean Square (NLMS) in the frequency domain and their comparison to that implemented in the time domain. Adaptive filtering using adaptive algorithm in frequency domain can be done by taking Fourier Transform of input signal and independent weigh coefficient. By frequency domain approach significant reduction in mathematical computation has been achieved. An expression for updating the weights is implemented in the frequency domain and statistical analysis has been performed. The SNR (Signal to Noise Ratio) is a parameter used to evaluate the performance with different step size. The signal power and noise power has also been calculated using MATLAB. The SNR of output signal rises about 8-9 times in frequency domain than the time domain.

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“…Furthermore, it has been reported that NLMS implemented adaptive filter can produce better performance than the corresponding Wiener filter [9]. Subsequent simulations have revealed with the proper choice of the step-size parameter, the nonlinear nature of the NLMS algorithm can be exploited to generate MSE that is less than the Wiener MSE.…”

Section: Introductionmentioning

confidence: 95%

A novel adaptive interference mitigation approach based on space time processing for global navigation system receiver arrays

Sun

2010

IEEE 10th INTERNATIONAL CONFERENCE ON SIGNAL PROCESSING PROCEEDINGS

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This paper proposes a novel adaptive interference mitigation approach based on space time processing for global navigation system receiver arrays. The novel approach combines two adaptive filters, Normalized Least Mean Square (NLMS) algorithm and reduced-rank Multistage Nested Wiener Filter (MNWF), for interference mitigation, and shares the information of different channels' characters under various interference environments. Furthermore, the proposed filter adaptive exchanges their weights according to acquisition results and MSE (Mean Square Error). It is demonstrated that the proposed mixed double adaptive filter provides both advantages at the aspects of higher convergence speed and lower complexity for navigation receiver arrays than the other traditional adaptive array algorithms via simulations.

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Nonlinear effects in LMS adaptive equalizers

Cited by 49 publications

References 20 publications

Employing Volterra filters in the ADPCM technique for speech coding: a comprehensive investigation

Employing Volterra filters in the ADPCM technique for speech coding: a comprehensive investigation

An approach to implement LMS and NLMS adaptive noise cancellation algorithm in frequency domain

A novel adaptive interference mitigation approach based on space time processing for global navigation system receiver arrays

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