An Overview of Enhanced Massive MIMO With Array Signal Processing Techniques

Wang, Mingjin; Gao, Feifei; Jin, Shi; Lin, Hai

doi:10.1109/jstsp.2019.2934931

Cited by 140 publications

(72 citation statements)

References 157 publications

(264 reference statements)

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“…, √ P K is a diagonal power allocation matrix that satisfies the power constraint, i.e., the total transmit power K j=1 P j = P . For ease of exposition, the power is equally allocated amongst users, 5 i.e., P = [14], [24], [27]. Furthermore, it is observed that some of the r k nonzero eigenvalues of Σ k are extremely small, and as such could be ignored, resulting in an effective rank of r eff, k r k N BS .…”

Section: B Notationsmentioning

confidence: 99%

“…Traditionally, in FDD systems, the acquisition of channel statistics information at the transmitter is acquired through long-term DL training sequences and low-rate UL feedback. However, channel statistics reciprocity is being leveraged for FDD systems more recently [24], [32]- [34]. This key observation eliminates the need for channel statistics estimation and feedback in FDD systems, and results in significant training and feedback overhead reduction.…”

Section: Statistical Csitmentioning

confidence: 99%

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Hybrid Precoding for Massive MIMO With Low Rank Channels: A Two-Stage User Scheduling Approach

Almradi

Matthaiou

Xiao

et al. 2020

IEEE Trans. Commun.

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To fully reap the benefits of massive multiple-input multiple-output hybrid analog and digital precoding in frequency division duplexing single-cell systems, a two-stage precoder is developed utilizing the signal-to-leakage-plus-noise ratio metric. The main idea of this technique is to jointly design the analog precoder based only on the long-term channel statistics information at the transmitter, i.e., the channel mean and reconstructed reduced rank covariance statistics, while the digital precoder is designed based on the instantaneous channel state information of the reduced dimensionality effective reconstructed channel. Consequently, we can significantly reduce the downlink training and uplink feedback overhead analogously to the rank of the resultant effective channel. The two extremes of full channel state information at the transmitter (CSIT) and statistical CSIT are also investigated. The performance gap between the full and statistical CSIT corroborates the importance of the proposed two-stage CSIT approach. These precoders are then extended to multi-cell systems. It is shown that the digital baseband precoder design problem reduces to the generalized Rayleigh quotient problem, while the analog precoder design problem reduces to the quotient trace problem, also known as the ratio trace problem. These dimensionality reduction problems are solved via the generalized eigenvalue decomposition method. Finally, in the presence of multiuser diversity where only a subset of the users are scheduled, to considerably alleviate the channel estimation and feedback overhead burden, a low-complexity one-stage and two-stage CSIT joint user scheduler and precoder algorithms are developed. Index Terms-Hybrid analog and digital precoding, multiuser MIMO systems, sum spectral efficiency, statistical CSIT, signalto-leakage-plus-noise ratio (SLNR), two-stage precoding, zeroforcing (ZF). I. INTRODUCTION M ULTIUSER massive multiple-input multiple-output (MIMO) systems have attracted a lot of research interest due to their ability to significantly improve the achievable

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Section: B Notationsmentioning

confidence: 99%

Section: Statistical Csitmentioning

confidence: 99%

Hybrid Precoding for Massive MIMO With Low Rank Channels: A Two-Stage User Scheduling Approach

Almradi

Matthaiou

Xiao

et al. 2020

IEEE Trans. Commun.

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“…where α k , d, and λ denote the propagation gain of the k th downlink path and the antenna spacing at the BS and carrier wavelength, respectively. d = 1 2 λ is widely adopted here [32]. δ T,k ∈ [0, 2π] is the AoD of the k th path.…”

Section: System Modelmentioning

confidence: 99%

Low-Complexity Joint Channel Estimation for Multi-User mmWave Massive MIMO Systems

et al. 2020

Electronics

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For multi-user millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems, the precise acquisition of channel state information (CSI) is a huge challenge. With the increase of the number of antennas at the base station (BS), the traditional channel estimation techniques encounter the problems of pilot training overhead and computational complexity increasing dramatically. In this paper, we develop a step-length optimization-based joint iterative scheme for multi-user mmWave massive MIMO systems to improve channel estimation performance. The proposed estimation algorithm provides the BS with full knowledge of all channel parameters involved in up-and down-links. Compared with existing algorithms, the proposed algorithm has higher channel estimation accuracy with low complexity. Moreover, the proposed scheme performs well even with a small number of training sequences and a large number of users. Simulation results are shown to demonstrate the performance of the proposed channel estimation algorithm.

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“…In [ 27 ], a virtual channel with common sparsity because users are sharing the same local scatters, is explained in which the information of unitary dictionary matrix is available at a base station (BS). A comprehensive study on signal processing techniques used for mmWave MIMO communications is briefly explained in [ 28 ].…”

Section: Introductionmentioning

confidence: 99%

On the Performance of Efficient Channel Estimation Strategies for Hybrid Millimeter Wave MIMO System

Srivastav

Chen

Wahla

2020

Entropy

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Millimeter wave (mmWave) relying upon the multiple output multiple input (MIMO) is a new potential candidate for fulfilling the huge emerging bandwidth requirements. Due to the short wavelength and the complicated hardware architecture of mmWave MIMO systems, the conventional estimation strategies based on the individual exploitation of sparsity or low rank properties are no longer efficient and hence more modern and advance estimation strategies are required to recapture the targeted channel matrix. Therefore, in this paper, we proposed a novel channel estimation strategy based on the symmetrical version of alternating direction methods of multipliers (S-ADMM), which exploits the sparsity and low rank property of channel altogether in a symmetrical manner. In S-ADMM, at each iteration, the Lagrange multipliers are updated twice which results symmetrical handling of all of the available variables in optimization problem. To validate the proposed algorithm, numerous computer simulations have been carried out which straightforwardly depicts that the S-ADMM performed well in terms of convergence as compared to other benchmark algorithms and also able to provide global optimal solutions for the strictly convex mmWave joint channel estimation optimization problem.

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An Overview of Enhanced Massive MIMO With Array Signal Processing Techniques

Cited by 140 publications

References 157 publications

Hybrid Precoding for Massive MIMO With Low Rank Channels: A Two-Stage User Scheduling Approach

Hybrid Precoding for Massive MIMO With Low Rank Channels: A Two-Stage User Scheduling Approach

Low-Complexity Joint Channel Estimation for Multi-User mmWave Massive MIMO Systems

On the Performance of Efficient Channel Estimation Strategies for Hybrid Millimeter Wave MIMO System

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