In the communication systems that use a linear modulation scheme for transmission, the fractionally-spaced (FS) samples of the received signal constitute a wide-sense cyclostationary time series. Hence, the standard F...
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In the communication systems that use a linear modulation scheme for transmission, the fractionally-spaced (FS) samples of the received signal constitute a wide-sense cyclostationary time series. Hence, the standard Fourier transform techniques cannot be used to study the spectral characteristics of the received FS samples or to derive the transfer function (TF) of the corresponding digital minimum mean-square error (MMSE) receiver. In this paper, an analytical expression for the TF of the FS MMSE equalizer is derived, which includes the effects of the continuous-time to discrete-time (C/D) converter used at the receiver front end. Using this TF, the sources of instability of the FS least-mean-square (lms) algorithm and the effects of the equalizer length and sampling phase on convergence of the lmsalgorithm are explained. For stabilization of the FS lmsalgorithm, conditions on the front-end C/D converter are provided, such that, when satisfied the lmsalgorithm becomes more stable and the learning characteristics of the modified receiver are better than the leaky-FS lmsalgorithm. Theoretical results are corroborated by simulations.
In this paper we have implemented an adaptive filter to compensate for the non-linearity in a speaker. An attempt has been made to minimize the Mean Square Error (MSE) and convergence time using the lms adaptive algor...
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ISBN:
(纸本)9788132222088;9788132222071
In this paper we have implemented an adaptive filter to compensate for the non-linearity in a speaker. An attempt has been made to minimize the Mean Square Error (MSE) and convergence time using the lms adaptive algorithm. Two adaptations of the lms have been considered, the general adaptive lmsalgorithm and the leaky lms algorithm. The leakylms adaptation is observed to be more efficient with almost a 40 % decrease in convergence time. The filter coefficients for the above objective function are obtained using MATLAB. The target processor for implementing the two algorithms is Tensilica/Xtensa SDK toolkit using 'C' language which enables the codes to be directly dumped on to hardware.
A simple adaptive least mean square (lms) type algorithm for channel estimation is developed based on certain modifications to finite-impulse response (FIR) Wiener filtering. The proposed algorithm is nearly blind sin...
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A simple adaptive least mean square (lms) type algorithm for channel estimation is developed based on certain modifications to finite-impulse response (FIR) Wiener filtering. The proposed algorithm is nearly blind since it does not require any training sequence or channel statistics, and it can be implemented using only noise variance knowledge. A condition guaranteeing the convergence of the algorithm and theoretical mean square error (MSE) values are also derived. Computer simulation results demonstrate that the proposed algorithm can yield a smaller MSE than existing techniques, and that its performance is close to that of optimal Wiener filtering.
We have implemented a four-tap adaptive filter in a continuous-time analog VLSI circuit. Since an ideal delay is impossible to implement in continuous-time hardware, we implemented the delay line as a cascade of lowpa...
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We have implemented a four-tap adaptive filter in a continuous-time analog VLSI circuit. Since an ideal delay is impossible to implement in continuous-time hardware, we implemented the delay line as a cascade of lowpass filters (called the gamma filter). Since many years of research in our lab has shown that the gamma filter outperforms the ideal delay line for a wide range of applications, the gamma filter should not be considered merely a crude approximation of the ideal delay line. We show measured results from an analog chip that solves the problem of system identification-identifying an unknown linear circuit from its input/output relationship. Furthermore, we believe that a cascade of all-pass filters (called the Laguerre filter) will potentially outperform the gamma. We have built an adaptive Laguerre filter and show that its measured convergence rate is superior to that of the gamma. Finally, rather than perform gradient descent on a multimodal error function to determine a single optimal time constant, we propose multi-scale realizations of these delay line structures.
One reason that recursive least-squares (RLS) algorithms are not used as widely as lmsalgorithms is an instability problem resulting from finite precision errors in the calculations;leaky lms algorithms that are nume...
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One reason that recursive least-squares (RLS) algorithms are not used as widely as lmsalgorithms is an instability problem resulting from finite precision errors in the calculations;leaky lms algorithms that are numerically stable have been proposed in the lms literature. in this paper, we offer a new RLS criterion that includes a strong parameter energy factor. Experiments show that adaptive algorithms that meet this criterion are numerically more stable than conventional RLS algorithms. (C) 1999 Scripta Technica, Electron Comm Jpn Pt 3, 82(4): 26-37, 1999.
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