Channel Mismatch Research Articles

Knowledge based adaptive processing for ground moving target indication

This paper presents a preliminary knowledge based approach to space-time adaptive processing (STAP) for ground moving target indication from an airborne platform. The KB-processor accounts for practical aspects of adaptive processing, including detection and processing of non-homogeneous data, appropriate selection of training data, and accounting for array effects such as mutual coupling and channel mismatch. In combining these hitherto separate STAP issues into a unified approach, this paper furthers the move of STAP from theory to practice. The KB-approach is tested using measured data from the multi-channel airborne radar measurements (MCARM) program.

Design of Sample Adaptive Product Quantizers for Noisy Channels

Channel-optimized vector quantization (COVQ) has proven to be an effective joint source-channel coding technique that makes the underlying quantizer robust to channel noise. Unfortunately, COVQ retains the high encoding complexity of the standard vector quantizer (VQ) for medium-to-high quantization dimensions and moderate-to-good channel conditions. A technique called sample adaptive product quantization (SAPQ) was recently introduced by Kim and Shroff to reduce the complexity of the VQ while achieving comparable distortions. In this letter, we generalize the design of SAPQ for the case of memoryless noisy channels by optimizing the quantizer with respect to both source and channel statistics. Numerical results demonstrate that the channel-optimized SAPQ (COSAPQ) achieves comparable performance to the COVQ (within 0.2 dB), while maintaining considerably lower encoding complexity (up to half of that of COVQ) and storage requirements. Robustness of the COSAPQ system against channel mismatch is also examined.

Source-Optimized Channel Coding for Digital Transmission Channels

We present a new class of nonlinear block codes called source-optimized channel codes (SOCCs), which are particularly designed for parametric source encoding of speech, audio, and video. In contrast to conventional channel codes, the new codes are not optimized for minimizing residual bit-error rate, but maximizing the signal-to-noise ratio of transmitted source codec parameters. The decoding of SOCCs is not based on bit-error correction, but on parameter estimation. We compare SOCCs with other approaches to joint source/channel coding such as channel-optimized vector quantization, channel-constrained vector quantization, unequal error protection, and source-controlled channel decoding. In terms of performance, SOCCs show better robustness if under channel mismatch conditions. For real-world applications, SOCCs are attractive, since the separation of source and channel codec is preserved.

Time-reversal imaging for classification of submerged elastic targets via Gibbs sampling and the Relevance Vector Machine

Time-reversal imaging (TRI) is analogous to matched-field processing, although TRI is typically very wideband and is appropriate for subsequent target classification (in addition to localization). Time-reversal techniques, as applied to acoustic target classification, are highly sensitive to channel mismatch. Hence, it is crucial to estimate the channel parameters before time-reversal imaging is performed. The channel-parameter statistics are estimated here by applying a geoacoustic inversion technique based on Gibbs sampling. The maximum a posteriori (MAP) estimate of the channel parameters are then used to perform time-reversal imaging. Time-reversal implementation requires a fast forward model, implemented here by a normal-mode framework. In addition to imaging, extraction of features from the time-reversed images is explored, with these applied to subsequent target classification. The classification of time-reversed signatures is performed by the relevance vector machine (RVM). The efficacy of the technique is analyzed on simulated in-channel data generated by a free-field finite element method (FEM) code, in conjunction with a channel propagation model, wherein the final classification performance is demonstrated to be relatively insensitive to the associated channel parameters. The underlying theory of Gibbs sampling and TRI are presented along with the feature extraction and target classification via the RVM.

Comprehensive digital correction of mismatch errors for a 400-msamples/s 80-dB SFDR time-interleaved analog-to-digital converter

Comprehensive digital calibration of a high-speed and high-resolution time-interleaved analog-to-digital converter (TIADC) is described. A channel transfer function, which incorporates all linear errors between analog input and digital output, is measured for each channel by applying a series of sinusoids. A set of finite-impulse response (FIR) filters designed by the weighted least squares principle provides frequency-dependent mismatch correction so that the spurious-free dynamic range (SFDR) is no longer limited by channel mismatches. A four-channel TIADC prototype with 14-bit resolution and 400-MHz aggregate sampling rate was built to verify the proposed correction method. Uncalibrated SFDR was below 50 dB. After mismatch correction with 61-tap FIR filters, 80 dB of SFDR was achieved up to /spl sim/175 MHz of input frequency.

Quantizer Design for Channel Codes With Soft-Output Decoding

A new method of combined source-channel coding for the scalar quantization of a discrete memoryless source is presented, which takes advantage of the reliability information produced by a soft-output channel decoder. Numerical results are presented for a memoryless Gaussian source in conjunction with turbo code showing up to 1-dB improvement in the end-to-end distortion with respect to a traditional channel optimized scalar quantizer. The results include a Gaussian source designed using closed-form expression without the need for a training sequence, as well as image pixels using a training sequence. Furthermore, certain issues related to the effect of the channel mismatch and spectral efficiency of the system are studied. It is shown that the increase in distortion due to a channel mismatch can be substantially reduced by using an adaptive receiver.

The Impact of Combined Channel Mismatch Effects in Time-Interleaved ADCs

A time-interleaved multichannel analog-to-digital converter (ADC) achieves high sampling rates with the drawback of additional distortions caused by channel mismatches. In this paper, we consider the dependency of the signal-to-noise-and-distortion ratio (SINAD) on the combination of several different channel mismatch effects. By using either explicitly given mismatch parameters or given parameter distributions, we derive closed-form equations for calculating the explicit or the expected SINAD for an arbitrary number of channels. Furthermore, we extend the explicit SINAD by the impact of timing jitter. We clarify how channel mismatches interact and perform a worst case analysis of the explicit SINAD for individual mismatch errors. We also show that equations describing the expected SINAD of individual mismatch errors are special cases of our general formulation. We indicate how to use the expected SINAD for finding efficient optimization priorities and demonstrate the importance of worst case analyses.

Time-Interleaved Sigma–Delta Modulator Using Output Prediction Scheme

A time-interleaved sigma-delta modulator using the output prediction scheme is proposed. This approach uses only one integrator channel along with incomplete integrator output terms to eliminate the quantizer domino which is a key limit for practical circuit implementation of conventional time-interleaved sigma-delta modulators. In addition, channel mismatch effects due to mismatch within multiple integrator feedback paths can be reduced by optimizing the feedback path. An equivalent two-channel time-interleaved version of the conventional second-order sigma-delta modulator is realized to verify the proposed method.

A CMOS Oversampling Bandpass Cascaded D/A Converter With Digital FIR and Current-Mode Semi-Digital Filtering

An oversampling bandpass digital-to-analog converter has been designed so as to eliminate the carrier leak and in-band SNR degradation that accompany I and Q channel mismatch in wireless transmitters. The converter combines a cascaded noise-shaping sigma-delta (/spl Sigma//spl Delta/) modulator with digital finite impulse response (FIR) and mixed-signal semi-digital filters that attenuate out-of-band quantization noise. The performance of the converter in the presence of current source mismatch has been improved through the use of bandpass data weighted averaging. An experimental prototype of the converter, integrated in a 0.25-/spl mu/m CMOS technology, provides 83 dB of dynamic range for a 6.25-MHz signal band centered at 50 MHz, and suppresses out-of-band quantization noise by 38 dB.

A Comparative Analysis of Parallel Delta–Sigma ADC Architectures

Parallelism can be used to increase the conversion bandwidth of delta-sigma (/spl Delta//spl Sigma/) analog-to-digital converters (ADCs). Time-interleaved, parallel /spl Delta//spl Sigma/, and frequency-band-decomposition ADCs are three parallel architectures that are shown to be explained using the same underlying theory. This common structure is then used to explore the design tradeoffs among these architectures. It is shown that the frequency-band-decomposition ADC is insensitive to channel mismatches but it is the most complex to design. The Hadamard modulated parallel /spl Delta//spl Sigma/ ADC provides the best performance (without considering nonidealities) but requires large digital filters. Finally, a randomization technique is described that can be used with parallel /spl Delta//spl Sigma/ architectures to spread out the tonal energy due to channel mismatches over the frequency spectrum.

Direction-of-Arrival Estimation in Partly Calibrated Subarray-Based Sensor Arrays

The problem of direction-of-arrival (DOA) estimation in partly calibrated arrays is addressed. We assume that an array is composed of multiple well-calibrated subarrays of arbitrary known geometry, but there are imperfections between subarrays. We address the cases of unknown (or known with a certain error) intersubarray displacements, imperfect synchronization of subarrays in time, unknown propagation channel mismatches between subarrays, as well as combinations of these effects. A new subspace-based approach to DOA estimation is proposed, which is applicable to this general class of partly calibrated arrays. DOA identifiability issues for such arrays are discussed, and a relevant Cramer-Rao bound (CRB) is derived. Numerical examples illustrate the performance of the proposed estimators.

Impact of Random Channel Mismatch on the SNR and SFDR of Time-Interleaved ADCs

Using several analog-to-digital converters (ADCs) in parallel with convenient time offsets is considered an efficient way to push the speed limits of data acquisition systems. However, a serious drawback of this time-interleaving technique is that any mismatch between the channels will damage the precision. This paper gives a probabilistic description of the problem, studying the impact of time skews, gain, and offset mismatches. The probability density function of both signal-to-noise ratio (SNR) and spurious-free-dynamic range (SFDR) are explicitly calculated, giving access to important statistical parameters. It is shown that the SNR and SFDR dispersion should not be neglected in making practical considerations for design decisions.

Transmit diversity over quasi-static fading channels using multiple antennas and random signal mapping

We introduce a scheme that achieves a diversity gain for coded systems under static fading conditions by using multiple antennas and random signal mapping. In a two-antenna system, the bit-error rate performance of the proposed scheme approaches that of Alamouti's scheme when the channel is perfectly known. In the presence of channel mismatch, the proposed scheme outperforms Alamouti's scheme significantly. It is shown that, as the number of transmit antennas N goes to infinity, the effective channel for the introduced scheme behaves as if it were perfectly interleaved (i.e., as if the fading was independent). When N is small, further performance gain can be achieved by expanding the original signal constellation.

Quadrature sampling schemes with improved image rejection

A fundamental problem of analog quadrature sampling circuits is the in-phase/quadrature-phase (I/Q) mismatch, which adversely affects their image rejection performance. This brief proposes two new quadrature sampling circuits for the sampling of complex intermediate frequency signals with high image rejection performance. The first circuit reduces the I/Q mismatches by time-sharing of the sampling capacitors and by employing a special clocking scheme. The second circuit integrates a first-order complex notch filter to provide extra image rejection. Both circuits are very simple and they introduce little overhead compared with conventional circuits. Circuit simulations show that for 2% channel mismatch, the first circuit removes completely the image interferer, and the second one achieves over 70 dB of image rejection ratio in a narrow band around direct current, while the conventional circuits achieve only about 40 dB.

Memory efficient adaptation of vector quantizers to time-varying channels

Channel-optimized vector quantization (COVQ) is approximated by the novel channel-adaptive scaled vector quantization (CASVQ). This new method uses a reference codebook that is optimal for one specific channel condition. However, for a bit-error rate being different from the design assumption for the reference codebook, all codevectors are scaled by a common factor, which depends on the channel condition. It is shown by simulations that a performance close to that of COVQ can be achieved in many practically important situations. Without a significant increase in complexity, the new CASVQ-scheme can be adapted to time-varying channels by adjusting the scaling factor to the current bit-error probability. Another advantage is that only one codebook needs to be stored for all error probabilities, while for COVQ either the performance degrades significantly due to channel mismatch, or a large set of codebooks must be available at the encoder and the decoder.

Adaptive MLSE equalizers with parametric tracking for multipath fast-fading channels

We use the parametric channel identification algorithm proposed by Chen and Paulraj (see Proc. IEEE Vehicular Technology Conf., p.710-14, 1997) and by Chen, Kim and Liang (see IEEE Trans. Veh. Technol., p.1923-35, 1999) to adaptively track the fast-fading channels for the multichannel maximum likelihood sequence estimation (MLSE) equalizer using multiple antennas. Several commonly-used channel tracking schemes, decision-directed recursive least square (DD/RLS), per-survivor processing recursive least square (PSP/RLS) and other reduced-complexity MLSE algorithms are considered. An analytic lower bound for the multichannel MLSE equalizer with no channel mismatch in the time-varying specular multipath Rayleigh-fading channels is derived. Simulation results that illustrate the performance of the proposed algorithms working with various channel tracking schemes are presented, and then these results are compared with the analytic bit error rate (BER) lower bound and with the conventional MLSE equalizers directly tracking the finite impulse response (FIR) channel tap coefficients. We found that the proposed algorithm always performs better than the conventional adaptive MLSE algorithm, no matter what channel tracking scheme is used. However, which is the best tracking scheme to use depends on the scenario of the system.

Explicit analysis of channel mismatch effects in time-interleaved ADC systems

A time-interleaved A-D converter (ADC) system is an effective way to implement a high-sampling-rate ADC with relatively slow circuits. In the system, several channel ADCs operate at interleaved sampling times as if they were effectively a single ADC operating at a much higher sampling rate. However, mismatches such as offset, gain mismatches among channel ADCs as well as timing skew of the clocks distributed to them degrade S/N of the ADC system as a whole. This paper analyzes the channel mismatch effects in the time-interleaved ADC system. Previous analysis showed the effect for each mismatch individually, however in this paper we derive explicit formulas for the mismatch effects when all of offset, gain and timing mismatches exist together. We have clarified that the gain and timing mismatch effects interact with each other but the offset mismatch effect is independent from them, and this can be seen clearly in frequency domain. We also discuss the bandwidth mismatch effect. The derived formulas can be used for calibration algorithms to compensate for the channel mismatch effects.

Compensation of channel and noise distortions combining normalization and speech enhancement techniques

This paper introduces two techniques to obtain robust speech recognition devices in mismatch conditions (additive noise mismatch and channel mismatch). The first algorithm, adaptive Gaussian attenuation algorithm (AGA), is a speech enhancement technique developed to reduce the effects of additive background noise in a wide range of signal noise ratio (SNR) and noise conditions. The technique is closely related to the classical noise spectral subtraction (SS) scheme, but in the proposed model the mean and variance of noise are used to better attenuate the noise. Information of the SNR is also introduced to provide adaptability at different SNR conditions. The second algorithm, cepstral mean normalization and variance-scaling technique (CMNVS), is an extension of the cepstral mean normalization (CMN) technique to provide robust features to convolutive and additive noise distortions. The requirements of the techniques are also analyzed in the paper. Combining both techniques the relative channel distortion effects were reduced to 90% on the HTIMIT task and the relative additive noise effects were reduced to 77% using the TIMIT database mixed with car noises at different SNR conditions.

Transformation-based Bayesian prediction for adaptation of HMMs

Due to inaccuracies in the modeling procedure, estimation errors, and poor data to parameter ratios, adaptation techniques can perform poorly when only a limited amount of data is available. Modeling inflexibility, on the other hand, limits their potential when large amounts of data are present. In this paper, we present a transformation-based Bayesian predictive approach to hidden Markov model (HMM) adaptation that addresses the above problems. The new technique, called Bayesian predictive adaptation (BPA), treats adaptation as model evolution arising from attempted transformation of the model parameters. The transformation is a structural representation of the assumed mismatch between the trained models and the adaptation data. Instead of estimating the transformation parameters directly, and blindly treating the estimates as if they are the true values, BPA averages over the variation of the parameters to generate a new model that can be used in the decoding process. By combining the power of Bayesian prediction to take into consideration the errors in estimation and modeling, with the power of transformation based techniques to use fewer parameters for adaptation, the proposed approach creates a new family of techniques that tend to be robust to estimation and modeling errors when only limited data are available, and to modeling inflexibility when large amounts of data are present. We present adaptation results under channel and speaker mismatches, and compare the performance of BPA to other adaptation techniques to demonstrate its effectiveness.

Multiuser detectors with disjoint Kalman channel estimators for synchronous CDMA mobile radio channels

We compare performance of several multiuser detectors for differentially encoded data combined with simple, disjoint, decision-directed Kalman channel estimators over flat Rayleigh fading channels. Simpler detectors with noncoherent differential detection are also compared. Different performance trends relative to the case of perfect channel estimation are observed. We find that in the presence of channel mismatch, the linear decorrelator is the most robust detector in terms of the bit-error rate and the near-far resistance. Parameter adjustment for fading channel modeling and estimation in the decision-directed mode are also discussed.