A Statistic-Based Calibration Method for TIADC System

Time-interleaved technique is widely used to increase the sampling rate of analog-to-digital converter (ADC). However, the channel mismatches degrade the performance of time-interleaved ADC (TIADC). Therefore, a statistic-based calibration method for TIADC is proposed in this paper. The average value of sampling points is utilized to calculate offset error, and the summation of sampling points is used to calculate gain error. After offset and gain error are obtained, they are calibrated by offset and gain adjustment elements in ADC. Timing skew is calibrated by an iterative method. The product of sampling points of two adjacent subchannels is used as a metric for calibration. The proposed method is employed to calibrate mismatches in a four-channel 5 GS/s TIADC system. Simulation results show that the proposed method can estimate mismatches accurately in a wide frequency range. It is also proved that an accurate estimation can be obtained even if the signal noise ratio (SNR) of input signal is 20 dB. Furthermore, the results obtained from a real four-channel 5 GS/s TIADC system demonstrate the effectiveness of the proposed method. We can see that the spectra spurs due to mismatches have been effectively eliminated after calibration

Time-Interleaved Analog-to-Digital Converter (TIADC) Compensation Using Multichannel Filters

Published methods that employ a filter bank for compensating the timing and bandwidth mismatches of an M-channel time-interleaved analog-to-digital converter (TIADC) were developed based on the fact that each sub-ADC channel is a downsampled version of the analog input. The output of each sub-ADC is filtered in such a way that, when all the filter outputs are summed, the aliasing components are minimized. If each channel of the filter bank has N coefficients, the optimization of the coefficients requires computing the inverse of an MN times MN matrix if the weighted least squares (WLS) technique is used as the optimization tool. In this paper, we present a multichannel filtering approach for TIADC mismatch compensation. We apply the generalized sampling theorem to directly estimate the ideal output of each sub-ADC using the outputs of all the sub-ADCs. If the WLS technique is used as the optimization tool, the dimension of the matrix to be inversed is N times N. For the same number of coefficients (and also the same spurious component performance given sufficient arithmetic precision), our technique is computationally less complex and more robust than the filter-bank approach. If mixed integer linear programming is used as the optimization tool to produce filters with coefficient values that are integer powers of two, our technique produces a saving in computing resources by a factor of approximately (100.2N(M- 1)/(M-1) in the TIADC filter design.published_or_final_versio

Asymptotically Optimal Blind Calibration of Uniform Linear Sensor Arrays for Narrowband Gaussian Signals

An asymptotically optimal blind calibration scheme of uniform linear arrays for narrowband Gaussian signals is proposed. Rather than taking the direct Maximum Likelihood (ML) approach for joint estimation of all the unknown model parameters, which leads to a multi-dimensional optimization problem with no closed-form solution, we revisit Paulraj and Kailath's (P-K's) classical approach in exploiting the special (Toeplitz) structure of the observations' covariance. However, we offer a substantial improvement over P-K's ordinary Least Squares (LS) estimates by using asymptotic approximations in order to obtain simple, non-iterative, (quasi-)linear Optimally-Weighted LS (OWLS) estimates of the sensors gains and phases offsets with asymptotically optimal weighting, based only on the empirical covariance matrix of the measurements. Moreover, we prove that our resulting estimates are also asymptotically optimal w.r.t. the raw data, and can therefore be deemed equivalent to the ML Estimates (MLE), which are otherwise obtained by joint ML estimation of all the unknown model parameters. After deriving computationally convenient expressions of the respective Cram\'er-Rao lower bounds, we also show that our estimates offer improved performance when applied to non-Gaussian signals (and/or noise) as quasi-MLE in a similar setting. The optimal performance of our estimates is demonstrated in simulation experiments, with a considerable improvement (reaching an order of magnitude and more) in the resulting mean squared errors w.r.t. P-K's ordinary LS estimates. We also demonstrate the improved accuracy in a multiple-sources directions-of-arrivals estimation task.Comment: in IEEE Transactions on Signal Processin

Time-Interleaved Analog-to-Digital-Converters: Modeling, Blind Identification and Digital Correction of Frequency Response Mismatches

Analog-to-digital-conversion enables utilization of digital signal processing (DSP) in many applications today such as wireless communication, radar and electronic warfare. DSP is the favored choice for processing information over analog signal processing (ASP) because it can typically offer more flexibility, computational power, reproducibility, speed and accuracy when processing and extracting information. Software defined radio (SDR) receiver is one clear example of this, where radio frequency waveforms are converted into digital form as close to the antenna as possible and all the processing of the information contained in the received signal is extracted in a configurable manner using DSP. In order to achieve such goals, the information collected from the real world signals, which are commonly analog in their nature, must be converted into digital form before it can be processed using DSP in the respective systems. The common trend in these systems is to not only process ever larger bandwidths of data but also to process data in digital format at ever higher processing speeds with sufficient conversion accuracy. So the analog-to-digital-converter (ADC), which converts real world analog waveforms into digital form, is one of the most important cornerstones in these systems.The ADC must perform data conversion at higher and higher rates and digitize ever-increasing bandwidths of data. In accordance with the Nyquist-Shannon theorem, the conversion rate of the ADC must be suffcient to accomodate the BW of the signal to be digitized, in order to avoid aliasing. The conversion rate of the ADC can in general be increased by using parallel ADCs with each ADC performing the sampling at mutually different points in time. Interleaving the outputs of each of the individual ADCs provides then a higher digitization output rate. Such ADCs are referred to as TI-ADC. However, the mismatches between the ADCs cause unwanted spurious artifacts in the TI-ADC’s spectrum, ultimately leading to a loss in accuracy in the TI-ADC compared to the individual ADCs. Therefore, the removal or correction of these unwanted spurious artifacts is essential in having a high performance TI-ADC system.In order to remove the unwanted interleaving artifacts, a model that describes the behavior of the spurious distortion products is of the utmost importance as it can then facilitate the development of efficient digital post-processing schemes. One major contribution of this thesis consists of the novel and comprehensive modeling of the spurious interleaving mismatches in different TI-ADC scenarios. This novel and comprehensive modeling is then utilized in developing digital estimation and correction methods to remove the mismatch induced spurious artifacts in the TI-ADC’s spectrum and recovering its lost accuracy. Novel and first of its kind digital estimation and correction methods are developed and tested to suppress the frequency dependent mismatch spurs found in the TI-ADCs. The developed methods, in terms of the estimation of the unknown mismatches, build on statistical I/Q signal processing principles, applicable without specifically tailored calibration signals or waveforms. Techniques to increase the analog BW of the ADC are also analyzed and novel solutions are presented. The interesting combination of utilizing I/Q downconversion in conjunction with TI-ADC is examined, which not only extends the TI-ADC’s analog BW but also provides flexibility in accessing the radio spectrum. Unwanted spurious components created during the ADC’s bandwidth extension process are also analyzed and digital correction methods are developed to remove these spurs from the spectrum. The developed correction techniques for the removal of the undesired interleaving mismatch artifacts are validated and tested using various HW platforms, with up to 1 GHz instantaneous bandwidth. Comprehensive test scenarios are created using measurement data obtained from HW platforms, which are used to test and evaluate the performance of the developed interleaving mismatch estimation and correction schemes, evidencing excellent performance in all studied scenarios. The findings and results presented in this thesis contribute towards increasing the analog BW and conversion rate of ADC systems without losing conversion accuracy. Overall, these developments pave the way towards fulfilling the ever growing demands on the ADCs in terms of higher conversion BW, accuracy and speed

Estimation and Calibration Algorithms for Distributed Sampling Systems

Thesis Supervisor: Gregory W. Wornell Title: Professor of Electrical Engineering and Computer ScienceTraditionally, the sampling of a signal is performed using a single component such as an analog-to-digital converter. However, many new technologies are motivating the use of multiple sampling components to capture a signal. In some cases such as sensor networks, multiple components are naturally found in the physical layout; while in other cases like time-interleaved analog-to-digital converters, additional components are added to increase the sampling rate. Although distributing the sampling load across multiple channels can provide large benefits in terms of speed, power, and resolution, a variety mismatch errors arise that require calibration in order to prevent a degradation in system performance. In this thesis, we develop low-complexity, blind algorithms for the calibration of distributed sampling systems. In particular, we focus on recovery from timing skews that cause deviations from uniform timing. Methods for bandlimited input reconstruction from nonuniform recurrent samples are presented for both the small-mismatch and the low-SNR domains. Alternate iterative reconstruction methods are developed to give insight into the geometry of the problem. From these reconstruction methods, we develop time-skew estimation algorithms that have high performance and low complexity even for large numbers of components. We also extend these algorithms to compensate for gain mismatch between sampling components. To understand the feasibility of implementation, analysis is also presented for a sequential implementation of the estimation algorithm. In distributed sampling systems, the minimum input reconstruction error is dependent upon the number of sampling components as well as the sample times of the components. We develop bounds on the expected reconstruction error when the time-skews are distributed uniformly. Performance is compared to systems where input measurements are made via projections onto random bases, an alternative to the sinc basis of time-domain sampling. From these results, we provide a framework on which to compare the effectiveness of any calibration algorithm. Finally, we address the topic of extreme oversampling, which pertains to systems with large amounts of oversampling due to redundant sampling components. Calibration algorithms are developed for ordering the components and for estimating the input from ordered components. The algorithms exploit the extra samples in the system to increase estimation performance and decrease computational complexity

Sampling and quantization for optimal reconstruction

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2011.Cataloged from PDF version of thesis.Includes bibliographical references (p. 161-167).This thesis develops several approaches for signal sampling and reconstruction given different assumptions about the signal, the type of errors that occur, and the information available about the signal. The thesis first considers the effects of quantization in the environment of interleaved, oversampled multi-channel measurements with the potential of different quantization step size in each channel and varied timing offsets between channels. Considering sampling together with quantization in the digital representation of the continuous-time signal is shown to be advantageous. With uniform quantization and equal quantizer step size in each channel, the effective overall signal-to-noise ratio in the reconstructed output is shown to be maximized when the timing offsets between channels are identical, resulting in uniform sampling when the channels are interleaved. However, with different levels of accuracy in each channel, the choice of identical timing offsets between channels is in general not optimal, with better results often achievable with varied timing offsets corresponding to recurrent nonuniform sampling when the channels are interleaved. Similarly, it is shown that with varied timing offsets, equal quantization step size in each channel is in general not optimal, and a higher signal-to-quantization-noise ratio is often achievable with different levels of accuracy in the quantizers in different channels. Another aspect of this thesis considers nonuniform sampling in which the sampling grid is modeled as a perturbation of a uniform grid. Perfect reconstruction from these nonuniform samples is in general computationally difficult; as an alternative, this work presents a class of approximate reconstruction methods based on the use of time-invariant lowpass filtering, i.e., sinc interpolation. When the average sampling rate is less than the Nyquist rate, i.e., in sub-Nyquist sampling, the artifacts produced when these reconstruction methods are applied to the nonuniform samples can be preferable in certain applications to the aliasing artifacts, which occur in uniform sampling. The thesis also explores various approaches to avoiding aliasing in sampling. These approaches exploit additional information about the signal apart from its bandwidth and suggest using alternative pre-processing instead of the traditional linear time-invariant anti-aliasing filtering prior to sampling.by Shay Maymon.Ph.D

Research of algorithms for the reconstruction of non-uniform sampled discrete-time signals with unknown sampling locations

В монографии изложены результаты исследований алгоритмов восстановления дискретных сигналов (ДС), заданных в узлах временной сетки (ВС) с точно неизвестными значениями координат ее узлов. Проведен анализ состояния предметной области, включая: существующие виды неравномерной дискретизации сигналов по времени и причины ее возникновения. Предложена классификация видов неравномерной дискретизации сигналов во времени. Приведены примеры реальных измерительных систем, на выходе которых получают ДС, заданные в узлах неравномерной временной сетки (НВС), а также методы восстановления ДС данного типа. Приведена постановка задачи восстановления ДС, заданного в узлах НВС, с неизвестными точно значениями координат ее узлов. Изложены результаты исследования особенностей восстановления ДС, заданных в узлах НВС с неизвестными точно значениями координат ее узлов, с помощью интерполяционных методов, а также методов, основанных на уточнении значений координат узлов НВС, и оценки их точности. Предложены новые алгоритмы восстановления ДС, заданных на НВС с точно неизвестными значениями координат ее узлов, продемонстрирована их работоспособность и получены оценки точности восстановления ДС с помощью данных алгоритмов. Проведен анализ точности восстановления периодических ДС, получаемых на выходе реальных цифровых систем (высокоскоростного 8-ми битного АЦП на основе КМОП-технологии 0.18 мкм, системы, состоящей из 8 параллельных 5-ти битных АЦП на основе КМОП-тепхнологии 65 нм), с помощью разработанных алгоритмов восстановления.In this treatise are considered the results of research of algorithms for the reconstruction of non-uniform sampled band-limited discrete-time signal with unknown sampling location. The condition of problem domain is analyzed, including: applying signal sampling schemes and causes of origin of the non-uniform sampling signals. Also is proposed classification of signal sampling schemes. Examples of real measurement systems, on the output of which registers non-uniform sampled signals with unknown sampling location as well as methods to reconstruct this type signals are given. The problem of signal reconstruction from non-uniform samples is considered. Interpolation methods to reconstruct a band-limited discrete-time signal from non-uniform samples with unknown sampling location, and optimization methods by estimating unknown sampling locations values are analyzed. Estimates of accuracy of interpolation methods for recovery of irregularly sampled signals are given. Also are given estimates of accuracy of methods to reconstruct a signal from non-uniform samples with unknown sampling locations by estimating unknown sampling locations values and presenting a solution of certain multiparameter problem of global optimization. Are proposed the new algorithms to reconstruct band-limited discrete-time signal from non-uniform samples with unknown sampling location and is demonstrated their performance. Estimate of accuracy of algorithms is compared and reconstruction errors are given in the numerical examples. The results of the research on the accuracy of the periodic signals reconstruction registered at the high speed 8 parallel 5-bit ADC system output based on the 65 nm CMOS technology and 8-bit high-rate ADC output based on the 0.18µm CMOS technology by means of special algorithms to reconstruct a signal from non-uniform samples with unknown sampling locations are analyzed