Chongbin Zhou

Bio: Chongbin Zhou is an academic researcher from University of Science and Technology of China. The author has contributed to research in topics: Compressed sensing & Synthetic aperture radar. The author has an hindex of 6, co-authored 11 publications receiving 73 citations. Previous affiliations of Chongbin Zhou include Chinese Academy of Sciences.

1-bit Observation for Direct-Learning-Based Digital Predistortion of RF Power Amplifiers

Abstract: In this paper, we propose a low-cost data-acquisition approach for model extraction of digital predistortion (DPD) of RF power amplifiers. The proposed approach utilizes only 1-bit-resolution analog-to-digital converters (ADCs) in the observation path to digitize the error signal between the input and output signals. The DPD coefficients are then estimated based on the direct learning architecture using the measured signs of the error signal. The proposed solution is proved feasible in theory, and the experimental results show that the proposed algorithm achieves the performance equivalent to that using the conventional method. Replacing high-resolution ADCs with 1-bit comparators in the feedback path can dramatically reduce the power consumption and cost of the DPD system. The 1-bit solution also makes DPD become practically implementable in future broadband systems since it is relatively straightforward to achieve an ultrahigh sampling speed in data conversion using only simple comparators.

Phase Error Correction for Approximated Observation-Based Compressed Sensing Radar Imaging

Abstract: Defocus of the reconstructed image of synthetic aperture radar (SAR) occurs in the presence of the phase error. In this work, a phase error correction method is proposed for compressed sensing (CS) radar imaging based on approximated observation. The proposed method has better image focusing ability with much less memory cost, compared to the conventional approaches, due to the inherent low memory requirement of the approximated observation operator. The one-dimensional (1D) phase error correction for approximated observation-based CS-SAR imaging is first carried out and it can be conveniently applied to the cases of random-frequency waveform and linear frequency modulated (LFM) waveform without any a priori knowledge. The approximated observation operators are obtained by calculating the inverse of Omega-K and chirp scaling algorithms for random-frequency and LFM waveforms, respectively. Furthermore, the 1D phase error model is modified by incorporating a priori knowledge and then a weighted 1D phase error model is proposed, which is capable of correcting two-dimensional (2D) phase error in some cases, where the estimation can be simplified to a 1D problem. Simulation and experimental results validate the effectiveness of the proposed method in the presence of 1D phase error or weighted 1D phase error.

A random demodulation based reduced sampling rate method for wideband digital predistortion

Abstract: The linearization performance of the traditional wideband digital predistortion (DPD) is seriously limited by the analog-to-digital converter (ADC) sampling rate in the feedback path. In this paper, a random demodulation based reduced sampling rate (RDRS) method is proposed to model and linearize the broadband power amplifiers (PAs). Compared with the existing spectrum extrapolation method, the proposed method can further reduce the sampling rate, but still maintain good performances. The simulation results demonstrate that the proposed method is well suitable for the ultra wideband digital predistortion.

Gridless One-Bit Direction-of-Arrival Estimation Via Atomic Norm Denoising

Abstract: In this letter, a gridless one-bit direction-of-arrival (DOA) estimation approach with one-snapshot is proposed to be robust against the off-grid errors and sign inconsistency because of the one-bit measurements corrupted by additive noise. Different with the existing off-grid DOA estimators, an atomic norm minimization is considered to mitigate the grid mismatch with atoms instead of pre-divided discretized dictionary. Then, the sign inconsistency of one-bit measurements is solved by introducing a linear loss function. The resulting optimization problem is convex and can be equivalent to a semidefinite programming (SDP) problem, which, however, is computational demanding. Therefore, alternating direction multiplier method (ADMM) is employed to speed up the implementation. To avoid the spectrum searching, an effective dual polynomial method is developed with closed-form solution for DOA estimation. Meanwhile, the proposed method does not require $a~prior$ information of the number of targets. Simulation results demonstrate the superiority and effectiveness of the proposed method.

A Sparse SAR Imaging Method Based on Multiple Measurement Vectors Model

Abstract: In recent decades, compressive sensing (CS) is a popular theory for studying the inverse problem, and has been widely used in synthetic aperture radar (SAR) image processing. However, the computation complexity of CS-based methods limits its wide applications in SAR imaging. In this paper, we propose a novel sparse SAR imaging method using the Multiple Measurement Vectors model to reduce the computation cost and enhance the imaging result. Based on using the structure information and the matched filter processing, the new CS-SAR imaging method can be applied to high-quality and high-resolution imaging under sub-Nyquist rate sampling with the advantages of saving the computational cost substantially both in time and memory. The results of simulations and real SAR data experiments suggest that the proposed method can realize SAR imaging effectively and efficiently.

Joint Angle and Doppler Frequency Estimation for MIMO Radar with One-Bit Sampling: A Maximum Likelihood-Based Method

Abstract: We consider a multiple-input multiple-output (MIMO) radar that works through one-bit sampling of received radar echoes. The application of one-bit sampling significantly reduces the hardware cost, energy consumption, and systematic complexity, but it also poses serious challenges to extracting highly accurate target information from one-bit quantized data. In this article, we propose a maximum likelihood (ML)-based method that first iteratively maximizes the likelihood function to recover a virtual array data matrix and then jointly estimates the angle and Doppler parameters from the recovered matrix. Because the ML problem is convex, we can successfully apply a computationally efficient gradient descent algorithm to solve it. Based on our analysis of the Cram $\acute{\text{e}}$ r–Rao bound of the ML-based method, a pre-estimation-assisted threshold (PET) strategy is developed to improve the estimation performance. Numerical experiments demonstrate that the proposed ML-based method, combined with the PET strategy, can provide highly accurate parameter estimation performance, close to that of the classic MIMO radar.

1-bit Observation for Direct-Learning-Based Digital Predistortion of RF Power Amplifiers

Abstract: In this paper, we propose a low-cost data-acquisition approach for model extraction of digital predistortion (DPD) of RF power amplifiers. The proposed approach utilizes only 1-bit-resolution analog-to-digital converters (ADCs) in the observation path to digitize the error signal between the input and output signals. The DPD coefficients are then estimated based on the direct learning architecture using the measured signs of the error signal. The proposed solution is proved feasible in theory, and the experimental results show that the proposed algorithm achieves the performance equivalent to that using the conventional method. Replacing high-resolution ADCs with 1-bit comparators in the feedback path can dramatically reduce the power consumption and cost of the DPD system. The 1-bit solution also makes DPD become practically implementable in future broadband systems since it is relatively straightforward to achieve an ultrahigh sampling speed in data conversion using only simple comparators.

Adaptive Deep Learning Aided Digital Predistorter Considering Dynamic Envelope

Abstract: Memory effects of radio frequency power amplifiers (PAs) can interact with dynamic transmitting signals, dynamic operations, and dynamic environment, resulting in complicated nonlinear problems of the PAs. Recently, deep learning based schemes have been proposed to deal with the memory effects. Although these schemes are powerful in constructing complex nonlinear structures, they are still direct learning-based and are relatively static. In this paper, we propose an adaptive deep learning aided digital predistortion (DL-DPD) model by optimizing a deep regression neural network. Thanks to the sequence structure of the proposed DL-DPD, we then make the linearization architecture more adaptive by using multiple sub-DPD modules and an ensemble predicting process. The results show the effectiveness of the proposed adaptive DL-DPD, and reveals that the online system handovers the sub-DPD modules more frequently than expected.