Three-Dimensional Beamforming for Large-Scale FD-MIMO Systems Exploiting Statistical Channel State Information

Li, Xiao; Jin, Shi; Gao, Xiqi; Heath, Robert W.

doi:10.1109/tvt.2016.2519141

Cited by 63 publications

(48 citation statements)

References 29 publications

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“…Specifically, for the arbitrarily distributed phases of {s k,l }, the sum in (18) can be zero in the worst case when the phases of {s k,l } are destructive. To tackle this issue, we propose to combine BS-SBF with an angular-domain version of the space-time block coding (STBC), referred to as the beam-time block coding (BTBC).…”

Section: A Beamformingmentioning

confidence: 99%

Statistical Beamforming for FDD Downlink Massive MIMO via Spatial Information Extraction and Beam Selection

Liu

Yuan

Zhang

2020

IEEE Trans. Wireless Commun.

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In this paper, we study the beamforming design problem in frequency-division duplexing (FDD) downlink massive MIMO systems, where instantaneous channel state information (CSI) is assumed to be unavailable at the base station (BS). We propose to extract the information of the angle-of-departures (AoDs) and the corresponding large-scale fading coefficients (a.k.a. spatial information) of the downlink channel from the uplink channel estimation procedure, based on which a novel downlink beamforming design is presented. By separating the subpaths for different users based on the spatial information and the hidden sparsity of the physical channel, we construct near-orthogonal virtual channels in the beamforming design. Furthermore, we derive a sum-rate expression and its approximations for the proposed system. Based on these closed-form rate expressions, we develop two low-complexity beam selection schemes and carry out asymptotic analysis to provide valuable insights on the system design.Numerical results demonstrate a significant performance improvement of our proposed algorithm over the state-of-the-art beamforming approach.

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Section: A Beamformingmentioning

confidence: 99%

Statistical Beamforming for FDD Downlink Massive MIMO via Spatial Information Extraction and Beam Selection

Liu

Yuan

Zhang

2020

IEEE Trans. Wireless Commun.

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“…Section IV-C]. If the transmitter has knowledge of statistical CSI, the dominant eigen-directions of the channel covariance matrix (Σ k , ∀k) can be used as metrics to identify compatible users, e.g., [58], [129], [131], [134], [135], [138], [270]. Spatial clustering based on Σ k , ∀k, has been recently proposed to identify users with similar channel statistics in massive MIMO settings, see [13], [129], [138].…”

Section: Discussionmentioning

confidence: 99%

An Overview on Resource Allocation Techniques for Multi-User MIMO Systems

Castañeda

Silva

Gameiro

et al. 2017

IEEE Commun. Surv. Tutorials

179

123

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Abstract-Remarkable research activities and major advances have been occurred over the past decade in multiuser multipleinput multiple-output (MU-MIMO) systems. Several transmission technologies and precoding techniques have been developed in order to exploit the spatial dimension so that simultaneous transmission of independent data streams reuse the same radio resources. The achievable performance of such techniques heavily depends on the channel characteristics of the selected users, the amount of channel knowledge, and how efficiently interference is mitigated. In systems where the total number of receivers is larger than the number of total transmit antennas, user selection becomes a key approach to benefit from multiuser diversity and achieve full multiplexing gain. The overall performance of MU-MIMO systems is a complex joint multi-objective optimization problem since many variables and parameters have to be optimized, including the number of users, the number of antennas, spatial signaling, rate and power allocation, and transmission technique. The objective of this literature survey is to provide a comprehensive overview of the various methodologies used to approach the aforementioned joint optimization task in the downlink of MU-MIMO communication systems.

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“…Therefore, we make H = H sl in the rest of the paper. We could also write (13) in a more compact expression as…”

Section: B Sld Operation and The Equivalent Spatial Lobes Channelmentioning

confidence: 99%

Spatial Lobes Division-Based Low Complexity Hybrid Precoding and Diversity Combining for mmWave IoT Systems

Cui

Chen

Tian

et al. 2019

IEEE Internet Things J.

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This paper focuses on the design of low complexity hybrid analog/digital precoding and diversity combining in millimeter wave (mmWave) Internet of things (IoT) systems. Firstly, by exploiting the sparseness property of the mmWave in the angular domain, we propose a spatial lobes division (SLD) to group the total paths of the mmWave channel into several spatial lobes, where the paths in each spatial lobe form a low-rank sub-channel. Secondly, based on the SLD operation, we propose a low complexity hybrid precoding scheme, named HYP-SLD. Specifically, for each low-rank sub-channel, we formulate the hybrid precoding design as a sparse reconstruction problem and separately maximizes the spectral efficiency. Finally, we further propose a maximum ratio combining based diversity combining scheme, named HYP-SLD-MRC, to improve the bit error rate (BER) performance of mmWave IoT systems. Simulation results demonstrate that, the proposed HYP-SLD scheme significantly reduces the complexity of the classic orthogonal matching pursuit (OMP) scheme. Moreover, the proposed HYP-SLD-MRC scheme achieves great improvement in BER performance compared with the fully digital precoding scheme.

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Three-Dimensional Beamforming for Large-Scale FD-MIMO Systems Exploiting Statistical Channel State Information

Cited by 63 publications

References 29 publications

Statistical Beamforming for FDD Downlink Massive MIMO via Spatial Information Extraction and Beam Selection

Statistical Beamforming for FDD Downlink Massive MIMO via Spatial Information Extraction and Beam Selection

An Overview on Resource Allocation Techniques for Multi-User MIMO Systems

Spatial Lobes Division-Based Low Complexity Hybrid Precoding and Diversity Combining for mmWave IoT Systems

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