Joint CSIT Acquisition Based on Low-Rank Matrix Completion for FDD Massive MIMO Systems

Shen, Wenqian; Dai, Linglong; Shim, Byonghyo; Mumtaz, Shahid; Hanzo, Lajos

doi:10.1109/lcomm.2015.2492960

Cited by 90 publications

(60 citation statements)

References 11 publications

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“…The transmitter and receiver try all possible wide beam pairs. With the channel feedback to indicate the largest received SNR [143], [144], the best beam pair (according to some criterion) can be selected for the next phase; ii) Beam-refinement protocol (BRP): the narrow beamwidth is considered as shown in Fig. 8 (b), and the beams are trained in a similar way within the previous selected wide beam pair; iii) Beam tracking: a periodic beamrefinement is performed for the time-varying channels.…”

Section: B Channel Estimation With Hybrid Architecturementioning

confidence: 99%

Millimeter Wave Communications for Future Mobile Networks

Xiao

Mumtaz

Huang

et al. 2017

IEEE J. Select. Areas Commun.

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968

530

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Abstract-Millimeter wave (mmWave) communications have recently attracted large research interest, since the huge available bandwidth can potentially lead to rates of multiple Gbps (gigabit per second) per user. Though mmWave can be readily used in stationary scenarios such as indoor hotspots or backhaul, it is challenging to use mmWave in mobile networks, where the transmitting/receiving nodes may be moving, channels may have a complicated structure, and the coordination among multiple nodes is difficult. To fully exploit the high potential rates of mmWave in mobile networks, lots of technical problems must be addressed. This paper presents a comprehensive survey of mmWave communications for future mobile networks (5G and beyond). We first summarize the recent channel measurement campaigns and modeling results. Then, we discuss in detail recent progresses in multiple input multiple output (MIMO) transceiver design for mmWave communications. After that, we provide an overview of the solution for multiple access and backhauling, followed by analysis of coverage and connectivity. Finally, the progresses in the standardization and deployment of mmWave for mobile networks are discussed.

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Section: B Channel Estimation With Hybrid Architecturementioning

confidence: 99%

Millimeter Wave Communications for Future Mobile Networks

Xiao

Mumtaz

Huang

et al. 2017

IEEE J. Select. Areas Commun.

Self Cite

968

530

View full text Add to dashboard Cite

show abstract

“…For multiple users, can be written in the matrix form as

H = A G,

where G denotes the P × K path gain matrix. There are P fixed scatterers around the BS and all users who are geographically separated in the cell are accessible to the P scatterers as shown in Figure . Under this condition all users will have same steering matrix (ie, AoAs), which introduce the correlation between the channel vectors and hence increase in the condition number of the channel matrix. Therefore, the channel can be approximated as a low‐rank channel.…”

Section: System and Channel Modelmentioning

confidence: 99%

“…The NN optimization problem is also solved using iterative singular value thresholding (ISVT) . In ISVT, larger singular values are penalized as heavily as lower ones to achieve the desired rank of the matrix.…”

Section: Introductionmentioning

confidence: 99%

“…The NN optimization problem is also solved using iterative singular value thresholding (ISVT). 9,10 In ISVT, larger singular values are penalized as heavily as lower ones to achieve the desired rank of the matrix. However, by shrinking the larger singular value, the most important information of the matrix is lost and hence the singular value of estimated matrix deviate from the original channel matrix.…”

Section: Introductionmentioning

confidence: 99%

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Fast iterative WSVT algorithm in WNN minimization problem for multiuser massive MIMO channel estimation

Vanidevi

Selvaganesan

2017

Int J Communication

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SummaryIn this paper, channel estimation for multiuser massive MIMO system is addressed for the scenario, where the number of scatterers is small compared to the base station antennas and single antenna users in the cell. If the number of scatterers is limited, then the corresponding angle of arrivals are finite. Moreover, if all the users share the same angle of arrivals, then the correlation among the channel vector increases.Thus, the high-dimensional channel is approximated to the low-rank matrix. This rank minimization problem is formulated as the weighted nuclear norm problem and is estimated using iterative weighted singular value thresholding (IWSVT) algorithm. To increase the convergence rate, fast IWSVT algorithm is proposed and the performance is measured based on mean square error and uplink and downlink achievable sum-rate. The simulation study shows that the proposed weighted nuclear norm minimization method with fast IWSVT algorithm performs better than the conventional least square and the nuclear norm minimization method for various finite scatterers in different signal-to-noise ratio levels.

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“…From the received samples and the sampling scheme, the fusion center applies a matrix completion technique to estimate the full matrix. In [16], the authors proposed a solution to provide the CSI to the transmitter side (Tx) for FDD massive MIMO systems. The proposal is to apply an algorithm based on matrix completion concepts.…”

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confidence: 99%

A Framework to Channel Feedback and Reconstruction using Matrix Completion in Massive MIMO Systems

Valduga

Almeida²,

Silva³

et al. 2018

JCIS

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Abstract-In this paper, we are interested in the problem of channel feedback and reconstruction in frequency division duplexing (FDD)-based massive multiple-input-multiple-output (MIMO) systems. We propose a general framework that allows reducing the overhead significantly in the uplink feedback control channel when assuming massive antenna arrays at both ends of the wireless link. The fundamental idea of the proposed method is to explore the low-rank structure of the channel for its accurate reconstruction at the transmitter side (Tx) with very few uplink feedback information, using matrix completion techniques. The proposed framework consists of two stages. First, upon reception of downlink pilots, the receiver side (Rx) undersamples either the received pilot data matrix or the estimated channel matrix and feeds back only a fraction of their entries to the Tx, throwing away the remaining ones. Then, under the assumption of reduced scattering propagation, the Tx capitalizes on matrix completion to recover either the downlink pilots or to directly reconstruct the downlink channel. We consider two application examples: i) backhauling communication and ii) a multi-user scenario with perfect and imperfect instantaneous channel knowledge. Due to data/channel undersampling, energy consumption at a receiver can be reduced during the uplink feedback transmission. Simulation results show that, compared to the conventional full-feedback approach, which requires feedback of the entire data/channel matrix, the proposed solution can decrease the feedback load in more than 90% for low-rank channels, while providing good channel estimation accuracy, bit error rate (BER) and goodput assuming maximum ratio transmission (MRT) precoding.

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Joint CSIT Acquisition Based on Low-Rank Matrix Completion for FDD Massive MIMO Systems

Cited by 90 publications

References 11 publications

Millimeter Wave Communications for Future Mobile Networks

Millimeter Wave Communications for Future Mobile Networks

Fast iterative WSVT algorithm in WNN minimization problem for multiuser massive MIMO channel estimation

A Framework to Channel Feedback and Reconstruction using Matrix Completion in Massive MIMO Systems

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