Browsing by Author "Wang, Chang-Jen"

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Bayes-Optimal Joint Channel-and-Data Estimation for Massive MIMO With Low-Precision ADCs
(2016-05-15)
Wen, Chao-Kai
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Wang, Chang-Jen
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Jin, Shi
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Wong, Kai-Kit
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Ting, Pangan
This paper considers a multiple-input multipleoutput (MIMO) receiver with very low-precision analog-to-digital convertors (ADCs) with the goal of developing massive MIMO antenna systems that require minimal cost and power. Previous studies demonstrated that the training duration should be relatively long to obtain acceptable channel state information. To address this requirement, we adopt a joint channel-and-data (JCD) estimation method based on Bayes-optimal inference. This method yields minimal mean square errors with respect to the channels and payload data. We develop a Bayes-optimal JCD estimator using a recent technique based on approximate message passing. We then present an analytical framework to study the theoretical performance of the estimator in the large-system limit. Simulation results confirm our analytical results, which allow the efficient evaluation of the performance of quantized massive MIMO systems and provide insights into effective system design.
Decentralized Expectation Consistent Signal Recovery for Phase Retrieval
(2020-01-01)
Wang, Chang-Jen
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Wen, Chao-Kai
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Tsai, Shang-Ho
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Jin, Shi
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電子工程學系及電子研究所
In this study, we present a phase retrieval solution that aims to recover signals from noisy phaseless measurements. A recently proposed scheme known as generalized expectation consistent signal recovery (GEC-SR), has shown better accuracy, speed, and robustness than many existing methods. However, sensing high-resolution images with large transform matrices presents a computational burden for GEC-SR, thereby limiting its applications to areas, such as real-time implementation. Moreover, GEC-SR does not support distributed computing, which is an important requirement to modern computing. To address these issues, we propose a novel decentralized algorithm called & x201C;deGEC-SR & x201D; by leveraging the core framework of GEC-SR. deGEC-SR exhibits excellent performance similar to GEC-SR but runs tens to hundreds of times faster than GEC-SR. We derive the theoretical state evolution for deGEC-SR and demonstrate its accuracy using numerical results. Analysis allows quick generation of performance predictions and enriches our understanding on the proposed algorithm.
Finite-Alphabet Precoding for Massive MU-MIMO With Low-Resolution DACs
(2018-07-01)
Wang, Chang-Jen
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Wen, Chao-Kai
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Jin, Shi
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Tsai, Shang-Ho
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電子工程學系及電子研究所
Massive multiuser multiple-input multiple-output (MU-MIMO) systems are expected to he the core technology in fifth-generation wireless systems because they significantly improve spectral efficiency. However, the requirement for a large number of radio frequency (RF) chains results in high hardware costs and power consumption, which obstruct the commercial deployment of massive MIMO systems. A potential solution is to use low-resolution digital-to-analog converters (DAC)/analogto-digital converters for each antenna and RF chain. However, using low-resolution DACs at the transmit side directly limits the degree of freedom of output signals and thus poses a challenge to the precoding design. In this paper, we develop efficient and universal algorithms for a downlink massive MU-MIMO system with finite-alphabet precodings. Our algorithms are developed based on the alternating direction method of multipliers (ADMM) framework. The original ADMM does not converge in a nonlinear discrete optimization problem. The primary cause of this problem is that the alternating (update) directions in ADMM on one side are biased, and those on the other side are unbiased. By making the two updates consistent in an unbiased manner, we develop two algorithms called iterative discrete estimation (IDE) and IDE2. IDE demonstrates excellent performance and IDE2 possesses a significantly low computational complexity. Compared with state-of-the-art techniques, the proposed preceding algorithms present significant advantages in performance and computational complexity.
Gridless Channel Estimation for Mixed One-Bit Antenna Array Systems
(2018-12-01)
Wang, Chang-Jen
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Wen, Chao-Kai
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Jin, Shi
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Tsai, Shang-Ho
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電子工程學系及電子研究所
A receiver architecture with low-resolution analogto-digital converters (ADCs) coupled with large antenna arrays has drawn considerable interest in the millimeter wave (mm-wave) system. Although architecture with pure one-bit ADCs has low power cost, such a system presents many challenges for synchronization, channel estimation, and power level estimation. Research has been conducted recently on the so-called mixed one-bit system, in which most antennas are equipped with one-bit ADCs, and a few have high-resolution ADCs. Despite the advantages of this system, studies on how to efficiently use high-resolution outputs to aid the one-bit system are lacking. This research considers the channel estimation problem to fill this gap and proposes a two-step channel estimator by utilizing the different features of mixed outputs. The channel gain can he extracted from high-resolution ADCs, and the channel angle can be extracted by the combination of additional one-bit ADCs. The proposed estimator leverages the sparsity feature of the channel in mm-wave. In contrast to previous works that used compressive sensing techniques, which confine the estimate to the set of grid angle points and induce estimation bias, the proposed estimator is gridless and treats the angle as a continuous parameter. The simulation results demonstrate that the proposed method yields significantly lower mean square errors than the conventional maximum likelihood estimator. In addition, this paper investigates ways to further improve the channel estimate by arranging the locations of high-resolution ADCs. Several useful observations on system design are obtained through our analysis.
Phase Retrieval With Learning Unfolded Expectation Consistent Signal Recovery Algorithm
(2020-01-01)
Wang, Chang-Jen
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Wen, Chao-Kai
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Tsai, Shang-Ho
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Jin, Shi
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電子工程學系及電子研究所
Phase retrieval algorithms are now an important component of many modern computational imaging systems. A recently proposed scheme called generalized expectation consistent signal recovery (GEC-SR) shows better accuracy, speed, and robustness than numerous existing methods. Decentralized GEC-SR (deGEC-SR) addresses the scalability issue in high-resolution images. However, the convergence speed and stability of these algorithms heavily rely on the settings of several handcrafted tuning factors with inefficient turning process. In this work, we propose deGEC-SR-Net by unfolding the iterative deGEC-SR algorithm into a learning network architecture with trainable parameters. The parameters of deGEC-SR-Net are determined by data-driven training. Numerical results show that deGEC-SR-Net provides substantially faster convergence than deGEC-SR and exhibits superior robustness to noise and prior mis-specifications.