Gridless Channel Estimation for Mixed One-Bit Antenna Array Systems

Wang, Chang-Jen; Wen, Chao-Kai; Jin, Shi; Tsai, Shi-Chun

doi:10.1109/twc.2018.2877757

Cited by 27 publications

(21 citation statements)

References 35 publications

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“…To exploit low-resolution information, L r must be utilized in channel estimation. However, it is also essential to include H r in channel estimation since H r mainly determines the accuracy of the estimation [21]. Therefore, denote g the output vector of our utilization scheme, we devise three schemes to utilize the pilot signals at low resolution antennas in both approaches as below.…”

Section: Let Hmentioning

confidence: 99%

“…This indicates that the argument of received pilot signals at low-resolution antennas is more useful than their modulus at improving estimation performance. Since the observations in [21] show that the addition of low-bit antenna information, though not helpful for estimating the amplitude, can provide useful information for the estimation of AoA, this indicates that argument can provide more AoA related information than modulus and thus can help improve the estimation performance. In addition, by observing Fig.…”

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confidence: 99%

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Deep Learning-Based Channel Estimation for Massive-MIMO With Mixed-Resolution ADCs and Low-Resolution Information Utilization

et al. 2021

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In this paper, we propose two deep-learning based uplink channel estimation approaches that can utilize not only high-resolution-ADC-quantized but also low-resolution-ADC-quantized received pilot signals to improve estimation performance for mixed analog-to-digital converters (ADCs) massive multipleinput multiple-output (MIMO) systems. In each approach, low-resolution-ADC-quantized received pilot signals are utilized with one of three different schemes, i.e., High-resolution quantized pilot + All lowresolution quantized pilot(High+All), High-resolution quantized pilot + Argument of low-resolution quantized pilot (High+Arg) or High-resolution quantized pilot + Modulus of low-resolution quantized pilot (High+Mod). All three schemes include the intact quantized pilot signals at high-resolution antennas, but the quantized pilot signals at low-resolution ADCs are exploited differently in each scheme. Modified selectiveinput prediction deep neural network (Modified SIP-DNN) is developed to predict more realistic channels and test the effectiveness of the utilization scheme. To achieve further performance improvement, a deep neural network (DNN) based two-stage network is proposed where the recovering DNN (RC-DNN) in the first stage forms a coarse estimation for channels at antennas with low-resolution ADCs and the refining DNN (Ref-DNN) in the second stage outputs a refined estimation for channels at all antennas. Simulation results show that our proposed approaches outperform state-of-the-art channel estimation method especially when most antennas are equipped with low-resolution ADCs.

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Section: Let Hmentioning

confidence: 99%

mentioning

confidence: 99%

Deep Learning-Based Channel Estimation for Massive-MIMO With Mixed-Resolution ADCs and Low-Resolution Information Utilization

et al. 2021

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“…In the following simulation, we would compare our EM-GAMP-based method 4 with the BIHT algorithm proposed in [5], the LASSO problem and the BPDN problem, where the LASSO and BPDN problems are solved by using the software in [14]. Fig.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…Compressed sensing (CS) based methods for DoA estimation with 1-bit quantization have been investigated in [2]- [4]. In [2], the binary iterative hard thresholding (BIHT) algorithm from [5] was extended and applied to 1-bit DoA estimation.…”

Section: Introductionmentioning

confidence: 99%

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Gridless Angular Domain Channel Estimation for mmWave Massive MIMO System with One-Bit Quantization via Approximate Message Passing

Gao

Cheng

2019

2019 IEEE Global Communications Conference (GLOBECOM)

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We develop a direction of arrival (DoA) and channel estimation algorithm for the one-bit quantized millimeterwave (mmWave) massive multiple-input multiple-output (MIMO) system. By formulating the estimation problem as a noisy one-bit compressed sensing problem, we propose a computationally efficient gridless solution based on the expectationmaximization generalized approximate message passing (EM-GAMP) approach. The proposed algorithm does not need the prior knowledge about the number of DoAs and outperforms the existing methods in distinguishing extremely close DoAs for the case of one-bit quantization. Both the DoAs and the channel coefficients are estimated for the case of one-bit quantization. The simulation results show that the proposed algorithm has effective estimation performances when the DoAs are very close to each other.

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A two-stage approach to estimate CFO and channel with one-bit ADCs

Zhu

Cao

2020

Signal Processing

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In this letter, we propose a two-stage approach to estimate the carrier frequency offset (CFO) and channel with one-bit analog-to-digital converters (ADCs). Firstly, a simple metric which is only a function of the CFO is proposed, and the CFO is estimated via solving the one-dimensional optimization problem.Secondly, the generalized approximate message passing (GAMP) algorithm combined with expectation maximization (EM) method is utilized to estimate the channel. In order to provide a benchmark of our proposed algorithm in terms of the CFO estimation, the corresponding Cramér-Rao bound (CRB) is derived. Furthermore, numerical results demonstrate the effectiveness of the proposed approach when applied to the general Gaussian channel and mmWave channel. keywords: CFO, channel estimation, millimeter wave system, one-bit quantization I. INTRODUCTIONTo provide a high-speed data rate in celluar systems, the mmWave multiple input multiple output (MIMO) system has been proposed as the key technology of the fifth generation (5G) cellular system [1,2]. Because of the larger bandwidths that accompany mmWave, the cost and power consumption are huge due to high precision (e.g., 10-12 bits) analog-to-digital converters (ADCs) [3]. As a result, a low precision (e.g., 1-4 bits) ADC is employed to relieve this ADC bottleneck [4,5]. However, as low precision quantization is severely nonlinear, traditional algorithms designed for high precision systemscan not be applied directly because of significant performance degradation. As a consequence, new signal processing algorithms dealing with channel estimation and transmit precoding have been proposed, which work well in systems with low precision ADCs [6][7][8][9]. For the channel estimation in mmWave systems, it can be regarded as one-bit compressed sensing (CS) problems [10][11][12][13][14], as the mmWave MIMO channel is approximately sparse in angle domain [15]. Therefore, many CS-based algorithms have been proposed to estimate the mmWave MIMO channel. In [16,17], a modified expectation maximization (EM) algorithm

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Gridless Channel Estimation for Mixed One-Bit Antenna Array Systems

Cited by 27 publications

References 35 publications

Deep Learning-Based Channel Estimation for Massive-MIMO With Mixed-Resolution ADCs and Low-Resolution Information Utilization

Deep Learning-Based Channel Estimation for Massive-MIMO With Mixed-Resolution ADCs and Low-Resolution Information Utilization

Gridless Angular Domain Channel Estimation for mmWave Massive MIMO System with One-Bit Quantization via Approximate Message Passing

A two-stage approach to estimate CFO and channel with one-bit ADCs

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