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

A Novel Iterative Structure for Online Calibration of M-Channel Time-Interleaved ADCs

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New iterative framework for frequency response mismatch correction in time-interleaved ADCs: Design and performance analysis

This paper proposes a new iterative framework for the correction of frequency response mismatch in time-interleaved analog-to-digital converters. Based on a general time-varying linear system model for the mismatch, we treat the reconstruction problem as a linear inverse problem and establish a flexible iterative framework for practical implementation. It encumbrances a number of efficient iterative correction algorithms and simplifies their design, implementation, and performance analysis. In particular, an efficient Gauss-Seidel iteration is studied in detail to illustrate how the correction problem can be solved iteratively and how the proposed structure can be efficiently implemented using Farrow-based variable digital filters with few general-purpose multipliers. We also study important issues, such as the sufficient convergence condition and reconstructed signal spectrum, derive new lower bound of signal-to-distortion-and-noise ratio in order to ensure stable operation, and predict the performance of the proposed structure. Furthermore, we propose an extended iterative structure, which is able to cope with systems involving more than one type of mismatches. Finally, the theoretical results and the effectiveness of the proposed approach are validated by means of computer simulations. © 2011 IEEE.published_or_final_versio

Estimation of Time-Limited Channel Spectra From Nonuniform Samples

This paper deals with the estimation of a time-invariant channel spectrum from its own nonuniform samples, assuming there is a bound on the channel’s delay spread. Except for this last assumption, this is the basic estimation problem in systems providing channel spectral samples. However, as shown in the paper, the delay spread bound leads us to view the spectrum as a band-limited signal, rather than the Fourier transform of a tapped delay line (TDL). Using this alternative model, a linear estimator is presented that approximately minimizes the expected root-mean-square (RMS) error for a deterministic channel. Its main advantage over the TDL is that it takes into account the spectrum’s smoothness (time width), thus providing a performance improvement. The proposed estimator is compared numerically with the maximum likelihood (ML) estimator based on a TDL model in pilot-assisted channel estimation (PACE) for OFDM.This work was supported by the Spanish Ministry of Economy and Competitiveness (MINECO) under Project TEC2011-28201-C02-02

Multirate digital filters, filter banks, polyphase networks, and applications: a tutorial

Multirate digital filters and filter banks find application in communications, speech processing, image compression, antenna systems, analog voice privacy systems, and in the digital audio industry. During the last several years there has been substantial progress in multirate system research. This includes design of decimation and interpolation filters, analysis/synthesis filter banks (also called quadrature mirror filters, or QMFJ, and the development of new sampling theorems. First, the basic concepts and building blocks in multirate digital signal processing (DSPJ, including the digital polyphase representation, are reviewed. Next, recent progress as reported by several authors in this area is discussed. Several applications are described, including the following: subband coding of waveforms, voice privacy systems, integral and fractional sampling rate conversion (such as in digital audio), digital crossover networks, and multirate coding of narrow-band filter coefficients. The M-band QMF bank is discussed in considerable detail, including an analysis of various errors and imperfections. Recent techniques for perfect signal reconstruction in such systems are reviewed. The connection between QMF banks and other related topics, such as block digital filtering and periodically time-varying systems, based on a pseudo-circulant matrix framework, is covered. Unconventional applications of the polyphase concept are discussed

Digital Filters and Signal Processing

Digital filters, together with signal processing, are being employed in the new technologies and information systems, and are implemented in different areas and applications. Digital filters and signal processing are used with no costs and they can be adapted to different cases with great flexibility and reliability. This book presents advanced developments in digital filters and signal process methods covering different cases studies. They present the main essence of the subject, with the principal approaches to the most recent mathematical models that are being employed worldwide

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

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