Adaptive Grouping Sparse Bayesian Learning for Channel Estimation in Non-Stationary Uplink Massive MIMO Systems

Cheng, Xiantao; Xu, Ke; Sun, Jingjing; Li, Shaoqian

doi:10.1109/twc.2019.2922913

Cited by 30 publications

(14 citation statements)

References 35 publications

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“…Most of the existing DL channel estimation works i.e., [3]- [5] fail to capture the cluster-specific sparsity, which occurs due to co-located users. Cheng et al [17] proposed a Dirichlet-Gaussian hybrid prior which exploits user-specific, cluster-specific and common sparsities by adaptively grouping the users. However, it is difficult to extend this algorithm to a decentralized architecture, due to its adaptive user grouping.…”

Section: Introductionmentioning

confidence: 99%

“…3) We analyze the convergence of our D-VBL algorithm and show that the upper bound on the absolute error between the C-and D-VBL updates tends to zero, when the SNR-based criterion detects the non-zero support accurately. 4) We numerically show that the proposed D-VBL and C-VBL algorithms have much lower normalized mean squared error (NMSE) and bit error rate (BER) than the centralized algorithms e.g., J-OMP [4], variational expectation maximization (VEM) [3], adaptive grouping sparse Bayesian learning (AG-SBL) [17], and the decentralized ones in [13], [24]. We also show that proposed algorithms outperform the existing ones in terms of spectral efficiency (SE) and energy efficiency (EE), and that the D-VBL has better EE than the C-VBL.…”

Section: Introductionmentioning

confidence: 99%

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Centralized and Decentralized Channel Estimation in FDD Multi-User Massive MIMO Systems

Rajoriya

Budhiraja

Hanzo

2022

IEEE Trans. Veh. Technol.

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We design a centralized and a decentralized variational Bayesian learning (C-and D-VBL) algorithms for the base station (BS) of a frequency division duplex massive multiple input multiple output (mMIMO) cellular system, wherein users send compressed information for it to estimate their downlink channels. The BS in the decentralized algorithm consists of multiple processing units (PUs), and each PU separately estimates the channels of a group of users, by employing the proposed D-VBL algorithm. To reduce channel estimation error, the PUs exploit the structured sparsity inherent in multi-user mMIMO channels by exchanging information among themselves. We investigate the proposed C-VBL and low-complexity D-VBL algorithms and show that i) they substantially outperform the state-of-the-art centralized and decentralized algorithms in terms of the normalized mean squared error and the bit error rate. This is because they beneficially exploit the inherent channel sparsity, while the existing state-of-the-art solutions fail to do so. The proposed D-VBL is also robust to PU failures, and provides a similar performance as its centralized counterpart (C-VBL), but with a much reduced complexity.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Centralized and Decentralized Channel Estimation in FDD Multi-User Massive MIMO Systems

Rajoriya

Budhiraja

Hanzo

2022

IEEE Trans. Veh. Technol.

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“…If users and scatterers are located inside the Rayleigh distance of the array, then the array will experience spherical wavefronts instead of planar wavefronts [2]- [4]. Another difference is spatial non-stationarity [5]- [8]. The channel measurement results of [5] show that different regions of a large aperture array receive varying levels of powers due to the different propagation paths (scatterers) that they can see, whereas some of them cannot see a path.…”

Section: Introductionmentioning

confidence: 99%

“…Traditional channel estimation methods, such as least squares (LS) and linear minimum mean square error [9], are unable to satisfy these requirements. Although numerous studies have been conducted on positioning the scatterers in near-field stationary channels [3], [4], and more research has begun to focus on the estimation of visible regions of scatterers [8], only a few studies are available on positioning the scatterers and identifying the visible regions simultaneously. This letter proposes a subarray-wise and a scatterer-wise channel estimation methods to draw up the near-field non-stationary massive MIMO channel.…”

Section: Introductionmentioning

confidence: 99%

Channel Estimation for Extremely Large-Scale Massive MIMO Systems

Han

Jin

Wen

et al. 2020

IEEE Wireless Commun. Lett.

146

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Extremely large-scale massive multiple-input multiple-output (MIMO) has shown considerable potential in future mobile communications. However, the use of extremely large aperture arrays has led to near-field and spatial non-stationary channel conditions, which result in changes to transceiver design and channel state information that should be acquired. This letter focuses on the channel estimation problem and describes the non-stationary channel through mapping between subarrays and scatterers.We propose subarray-wise and scatterer-wise channel estimation methods to estimate the near-field nonstationary channel from the view of subarray and scatterer, respectively. Numerical results demonstrate that subarray-wise method can derive accurate channel estimation results with low complexity, whereas the scatterer-wise method can accurately position the scatterers and identify almost all the mappings between subarrays and scatterers. Index TermsExtremely large-scale massive MIMO, spherical wave, non-stationary, channel estimation.

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“…Another crucial difference is that users may not be far apart from the BS, such that the exchanged signals experience near-field propagation with spherical wavefronts. To account for these new propagation challenges in XL-MIMO, adapted channel estimation methods [4], [5] and novel low-complexity detection schemes [6]- [8] have been proposed. However, little attention…”

Section: Introductionmentioning

confidence: 99%

Low-Complexity Zero-Forcing Precoding for XL-MIMO Transmissions

Ribeiro

Schwarz

Haardt

2021

2021 29th European Signal Processing Conference (EUSIPCO)

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Deploying antenna arrays with an asymptotically large aperture will be central to achieving the theoretical gains of massive MIMO in beyond-5G systems. Such extra-large MIMO (XL-MIMO) systems experience propagation conditions which are not typically observed in conventional massive MIMO systems, such as spatial non-stationarities and near-field propagation. Moreover, standard precoding schemes, such as zeroforcing (ZF), may not apply to XL-MIMO transmissions due to the prohibitive complexity associated with such a large-scale scenario. We propose two novel precoding schemes that aim at reducing the complexity without losing much performance. The proposed schemes leverage a plane-wave approximation and user grouping to obtain a low-complexity approximation of the ZF precoder. Our simulation results show that the proposed schemes offer a possibility for a performance and complexity trade-off compared to the benchmark schemes.

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Adaptive Grouping Sparse Bayesian Learning for Channel Estimation in Non-Stationary Uplink Massive MIMO Systems

Cited by 30 publications

References 35 publications

Centralized and Decentralized Channel Estimation in FDD Multi-User Massive MIMO Systems

Centralized and Decentralized Channel Estimation in FDD Multi-User Massive MIMO Systems

Channel Estimation for Extremely Large-Scale Massive MIMO Systems

Low-Complexity Zero-Forcing Precoding for XL-MIMO Transmissions

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