Channel Mismatch Research Articles

Impact on Antijamming Performance of Channel Mismatch in GNSS Antenna Arrays Receivers

The performance of antijamming is limited by channel mismatch in global navigation satellite system (GNSS) antenna arrays receivers. Only when the amplitude and phase characteristics of each array channels are the same is the interference likely to be completely suppressed. This paper analyzes the impact on antijamming performance of channel mismatch. We built the model of channel mismatch and derived the impact on transfer function with space-time adaptive processor (STAP) of channel mismatch in theory. The impact factor of channel mismatch is proposed by the fuzzy transfer function, which could directly reflect the antijamming performance under channel mismatch. In addition, every characteristic in the channel mismatch model is analyzed. The analysis results show that the greatest impacts on antijamming performance are the range of amplitude wave and the group delay bias, while the influence of the number of amplitudes is next. As for the effect of group delay fluctuation is the smallest.

Open-Loop Precoder Design for Spatial Multiplexing Systems in Transmit Correlated MIMO Channels

The performance of spatial multiplexing (SM) systems is significantly degraded in transmit correlated multiple-input multiple-output (MIMO) channels. In communication systems that are available for feedback, precoding can enhance performance by exploiting channel state information (CSI) at the transmitter. However, broadcasting systems have no reverse channel nor any CSI. Furthermore, channel mismatches in communication systems severely affect performance. For these systems, open-loop precoders that require no feedback CSI were proposed. However, an analytical precoder design method was not presented in the previous works. In this paper, we present an open-loop precoder design method for SM systems with a maximum-likelihood receiver in transmit correlated MIMO channels. We first describe how to design an open-loop precoder using a rotational matrix over the amplitude of the correlation. Our precoder design method is derived by observing the effect of correlation mismatch between the feedback CSI and the actual channel condition on a minimum Euclidean distance-based precoder. Next, we present a method to design an open-loop precoder that takes into account the impact of the phase of the correlation. For this precoder, a phase rotation matrix is added on the rotational matrix, and optimum parameters are obtained by maximizing the worst-case minimum distance for the phase of the correlation. The simulation results illustrate the validity of proposed open-loop precoder in transmit correlated MIMO channels.

Timing Skew Calibration Method for TIADC-Based 20 GSPS Digital Storage Oscilloscope

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). When input signal frequency is very high, timing skews have significant effect on distortion. Therefore, a new timing skew calibration method is proposed in this paper. This method is based on the truth that timing skews are related to the product of the outputs of sub-ADCs. After timing skews are estimated, the digital controlled delay elements (DCDE) in ADC and phase locked loop (PLL) are utilized to calibrate timing skews. No auxiliary circuit and digital filter are needed for this calibration method. Simulation results show that the proposed method can estimate timing skew accurately. It is also proved that an accurate estimation can be obtained even the signal to noise ratio (SNR) of input signal is 20[Formula: see text]dB. The proposed method is employed to calibrate timing skews in a 16-channel TIADC-based 20[Formula: see text]GSPS digital storage oscilloscope (DSO). The experiment results demonstrate the usefulness of the proposed method. We can see that after timing skews are calibrated, the spectrum spurs have been effectively eliminated.

Bandwidth-efficient calibration method for nonlinear errors in M-channel time-interleaved ADCs

In order to enhance the effective resolution of time-interleaved analog-to-digital converters (TI-ADCs), both linear and nonlinear channel mismatches should be carefully calibrated. This paper concentrates on a bandwidth-efficient background calibration method for nonlinear errors in M-channel TI-ADCs. It utilizes the least-mean square algorithm as well as a certain degree of oversampling to achieve adaptive mismatch tracking. The calibration performance and computational complexity are investigated and evaluated through behavioral-level simulations. Furthermore, a calibration strategy for narrow-band input signals is proposed and verified as an improvement of the basic calibration structure for such signals.

Photonic-assisted time-interleaved ADC based on optical delay line

An approach to implement photonic-assisted time-interleaved analog-to-digital conversion and its calibration method are presented. The analog modulated optical signal is divided into M channels, suffering different time delay induced by optical delay lines which provide great flexibility in producing time intervals and is then sampled by electronic analog-to-digital converters (ADCs). The channel mismatches resulting in performance degradation are estimated by a modified sine wave fitting method. The time mismatch and other mismatches are corrected by fine optical delay adjustment and digital processing, respectively. A four-channel photonic-assisted time-interleaved analog-to-digital converter (TIADC) system operating at 40 GSa s−1 was demonstrated experimentally. The photonic-assisted TIADC system was tested with a 6.31 GHz sine wave signal, exhibiting 40.3 dB signal-to-noise and distortion ratio (SINAD) and 57.6 dBc spurious-free dynamic range (SFDR). It is shown that the SINAD is dominated by the signal-to-noise ratio (SNR) of the analog optical link and the SFDR of the proposed system is limited by the linearity of the link.

Efficient Multi-Channel Signal Strength Based Localization via Matrix Completion and Bayesian Sparse Learning

Fingerprint-based location sensing technologies play an increasingly important role in pervasive computing applications due to their accuracy and minimal hardware requirements. However, typical fingerprint-based schemes implicitly assume that communication occurs over the same channel (frequency) during the training and the runtime phases. When this assumption is violated, the mismatches between training and runtime fingerprints can significantly deteriorate the localization performance. Additionally, the exhaustive calibration procedure required during training limits the scalability of this class of methods. In this work, we propose a novel, scalable, multi-channel fingerprint-based indoor localization system that employs modern mathematical concepts based on the Sparse Representations and Matrix Completion theories. The contribution of our work is threefold. First, we investigate the impact of channel changes on the fingerprint characteristics and the effects of channel mismatch on state-of-the-art localization schemes. Second, we propose a novel fingerprint collection technique that significantly reduces the calibration time, by formulating the map construction as an instance of the Matrix Completion problem. Third, we propose the use of sparse Bayesian learning to achieve accurate location estimation. Experimental evaluation on real data highlights the superior performance of the proposed framework in terms of reconstruction error and localization accuracy.

Hybrid Minimum Pearson and Euclidean Distance Detection

The reliability of mass storage systems, such as optical data recording and non-volatile memory (Flash), is seriously hampered by uncertainty of the actual value of the offset (drift) or gain (amplitude) of the retrieved signal. The recently introduced minimum Pearson distance detection is immune to unknown offset or gain, but this virtue comes at the cost of a lessened noise margin at nominal channel conditions. We will present a novel hybrid detection method, where we combine the outputs of the minimum Euclidean distance and Pearson distance detectors so that we may trade detection robustness versus noise margin. We will compute the error performance of hybrid detection in the presence of unknown channel mismatch and additive noise.

Modeling of channel mismatch in time-interleaved SAR ADC**Project supported by the National Natural Science Foundation of China (Nos. 61234002, 61322405, 61306044, 61376033) and the National High-Tech Program of China (No. 2013AA014103).

In a time-interleaved analog-to-digital converter (TI ADC), several individual ADCs operate in parallel to achieve a higher sampling rate. Low power consumption as well as good linearity can be obtained by applying successive approximation register (SAR) converters as sub-channel ADCs. In spite of the advantages, this structure suffers from three mismatches, which are offset mismatch, gain mismatch, and time skew. This paper focuses on a TI SAR ADC with a number of channels. The mismatch effects in the frequency domain are analyzed and the derived close form formulas are verified based on Matlab. In addition, we clarify that the standard deviation of DNL and INL of an M-channel TI ADC is reduced by a factor of compared to a single channel ADC. The formulas can be used to derive the corresponding requirements when designing a TI ADC. Our analysis process is able to inform the study of calibration algorithms.

Speaker height estimation from speech: Fusing spectral regression and statistical acoustic models.

Estimating speaker height can assist in voice forensic analysis and provide additional side knowledge to benefit automatic speaker identification or acoustic model selection for automatic speech recognition. In this study, a statistical approach to height estimation that incorporates acoustic models within a non-uniform height bin width Gaussian mixture model structure as well as a formant analysis approach that employs linear regression on selected phones are presented. The accuracy and trade-offs of these systems are explored by examining the consistency of the results, location, and causes of error as well a combined fusion of the two systems using data from the TIMIT corpus. Open set testing is also presented using the Multi-session Audio Research Project corpus and publicly available YouTube audio to examine the effect of channel mismatch between training and testing data and provide a realistic open domain testing scenario. The proposed algorithms achieve a highly competitive performance to previously published literature. Although the different data partitioning in the literature and this study may prevent performance comparisons in absolute terms, the mean average error of 4.89 cm for males and 4.55 cm for females provided by the proposed algorithm on TIMIT utterances containing selected phones suggest a considerable estimation error decrease compared to past efforts.

Digital estimation and compensation method for nonlinearity mismatches in time-interleaved analog-to-digital converters

Channel mismatches in time-interleaved analog-to-digital converters (TIADCs) typically result in significant degradation of the ADC's dynamic performance. Offset, gain, and timing mismatches have been widely investigated whereas nonlinearity mismatches have not. In this work, we analyze the influence of nonlinearity mismatches by using a polynomial model. As a cost measure we use the signal-to-noise and distortion ratio (SNDR) and then derive a compact formula describing the dependency on nonlinearity mismatches. Based on the spectral characteristics of the TIADCs, we propose a foreground estimation method and a compensation method using a cascaded structure of adders and multipliers. Through behavioral-level simulations, we prove the validity of the derivations and demonstrate that the proposed estimation and compensation method can bring a considerable amount of improvement in the combined TIADCs dynamic performance. The proposed method is efficient assuming that a smooth approximation of the nonlinearity mismatches is sufficient.

Estimation of channel mismatches in time-interleaved analog-to-digital converters based on fractional delay and sine curve fitting

This paper proposes an algorithm to estimate the channel mismatches in time-interleaved analog-to-digital converter (TIADC) based on fractional delay (FD) and sine curve fitting. Choose one channel as the reference channel and apply FD to the output samples of reference channel to obtain the ideal samples of non-reference channels with no mismatches. Based on least square method, the sine curves are adopted to fit the ideal and the actual samples of non-reference channels, and then the mismatch parameters can be estimated by comparing the ideal sine curves and the actual ones. The principle of this algorithm is simple and easily understood. Moreover, its implementation needs no extra circuits, lowering the hardware cost. Simulation results show that the estimation accuracy of this algorithm can be controlled within 2%. Finally, the practicability of this algorithm is verified by the measurement results of channel mismatch errors of a two-channel TIADC prototype.

Theoretical and experimental analysis of channel mismatch in time-wavelength interleaved optical clock based on mode-locked laser.

We demonstrate a theoretical model of channel mismatch effect in a mode-locked laser based time-wavelength interleaved optical clock generation system. The channel mismatch effect includes clock timing mismatch, amplitude mismatch, and pulse shape mismatch. An explicit expression of this model is derived for 2-channel simple system and a numerical simulation of multiple-channel complicated system is carried out. In comparison with the experimental measurement, the feasibility of the theoretical model is verified for calibration and compensation of the channel mismatches.

Adaptive weighted constrained least squares algorithm based microphone array robustness beamforming algorithm

In order to solve the problem of robustness of beamforming algorithm with microphone array channel mismatch, an adaptive dynamic-weighted constrained least square algorithm-based microphone array robustness frequency invariant beamforming algorithm is proposed. In the proposed algorithm, by analyzing the microphone array model, with or without channel mismatch, the disadvantages of the constrained least square frequency invariant beamforming algorithm with channel mismatch are studied. After the probability density functions of the microphones are defined as the robustness factors and added to the constraint least square frequency invariant beamforming algorithm, the robustness is improved to a certain extent, but it is still poor. In order to further improve the robustness of the algorithm, dynamic-weighted coefficients for controlling frequency invariance in the cost function are used to regulate the sidelobe spectrum energy. The fluctuation error is defined as the ratio of the maximum to minimum value of array response formed by the same angle of arrival at different frequencies, within the frequency range of frequency invariant, to compare the proposed algorithm with the constrained least square robustness frequency invariant and minmax robustness broadband beamforming algorithm. Experimental results of the algorithms show that the fluctuation errors of the proposed algorithm are the smallest and its robustness is the best; it can effectively overcome the poor robustness of the beamforming algorithm caused by microphone array channel mismatch, and can be applied to any arbitrary array structure.

Robust Feedback Design for Time Reversal UWB Communication Systems Under CSIT Imperfections

We propose a robust design for digital feedback of the channel impulse response in a time-reversal ultrawideband communication system. The proposed scheme employs channel optimized vector quantization to reduce distortion that occurs due to channel noise on the feedback link. Utilizing experimental channel measurements in a typical indoor environment, we indicate the robustness of the proposed strategy by showing that the scheme's performance remains resilient under channel mismatch conditions that arise due to the combined effect of quantization, feedback delay, and noise on the feedback channel.

Adaptive Background Estimation for Static Nonlinearity Mismatches in Two-Channel TIADCs

Due to channel mismatches in time-interleaved analog-to-digital converters (TIADCs), estimation and compensation methods are required to restore the resolution of the individual converters. Whereas several methods exist for linear mismatches, nonlinearity mismatches have not been widely investigated. This brief presents an adaptive background estimation method for nonlinearity mismatches in two-channel TIADCs. It utilizes a normalized least-mean-square algorithm and assumes slight oversampling as well as a polynomial nonlinearity model that is appropriate when smooth errors dominate. Furthermore, two implementation strategies are proposed to enhance its ability for different applications. The estimation performance of the proposed method is evaluated through behavioral-level simulations.

Prefilter-Based Reconfigurable Reconstructor for Time-Interleaved ADCs With Missing Samples

This brief proposes a reconstruction scheme for the compensation of frequency-response mismatch errors at the output of a time-interleaved analog-to-digital converter (TI-ADC) with missing samples. The missing samples are due to sampling instants reserved for estimating the channel mismatch errors in the TI-ADC. Compared with previous solutions, the proposed scheme offers substantially lower computational complexity.

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.

Optimization of Learning Time for Learning-Assisted Rendezvous Channel in Cognitive Radio System

SUMMARY This paper derives the optimal learning time for the learning-assisted rendezvous channel. One problem with the dynamic spectrum access system of cognitive radio is access channel mismatch between two wireless terminals. In the learning-assisted rendezvous channel, before exchanging packets for link connection, the rate of channel occupancy by the other system is estimated within the learning time; it is referred to as the channel occupancy rate (COR). High speed packet exchange is made possible by selecting a low COR channel. However, the optimal learning time and the impact of COR estimation errors have not been clarified yet. This paper analyzes the time to rendezvous channel (TTR), where TTR is the time needed to complete the rendezvous with a certain probability. The results indicate that the learning time and TTR have a concave relationship which means that the optimal learning time can be determined.

Robust Spectral Features for Automatic Speaker Recognition in Mismatch Condition

Abstract The widespread use of automatic speaker recognition technology in real world applications demands for robustness against various realistic conditions. In this paper, a robust spectral feature set, called NDSF (Normalized Dynamic Spectral Features) is proposed for automatic speaker recognition in mismatch condition. Magnitude spectral subtraction is performed on spectral features for compensation against additive noise. A spectral domain modification is further performed using time-difference approach followed by Gaussianization Non-linearity. Histogram normalization is applied to these dynamic spectral features, to compensate the effect of channel mismatch and some non-linear effects introduced due to handset transducers. Feature extraction using proposed features is carried out for a text independent automatic speaker recognition (identification) system. The performance of proposed feature set is compared with conventional cepstral features like (mel-frequency cepstral coefficients and linear prediction cepstral coefficients), for acoustic mismatch condition caused by use of different sensors. Studies are performed on two databases: A multi-variability speaker recognition (MVSR) developed by IIT-Guwahati and Multi-speaker continuous (Hindi) speech database (By Department of Information Technology, Government of India). From experimental analysis, it is observed that, spectral domain dynamic features enhance the robustness by reducing additive noise and channel effects caused by sensor mismatch. The proposed NDSF features are found to be more robust than cepstral features for both datasets.

On the Baseband Doppler Centroid Estimation for Multichannel HRWS SAR Imaging

In multichannel high-resolution and wide-swath (HRWS) synthetic aperture radar (SAR) systems, Doppler centroid (DC) is essential for SAR focusing. However, conventional phase-dependent DC estimators for single-channel SAR systems face many problems in multichannel HRWS SAR systems because of the azimuth undersampling of each channel and channel mismatches in phase. To estimate the baseband DC, the spatial cross-correlation coefficients (SCCCs) of multichannel HRWS SAR signals are exploited, and a novel baseband DC estimator named SCCC method is proposed in this letter. Validation of the proposed method is demonstrated with the real airborne multichannel SAR data.