A Unified Transmission Strategy for TDD/FDD Massive MIMO Systems With Spatial Basis Expansion Model

Xie, Huimin; Gao, Feifei; Zhang, Shun; Jin, Shi

doi:10.1109/tvt.2016.2594706

Cited by 460 publications

(325 citation statements)

References 33 publications

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“…Angle estimation has been studied in other wireless networks without considering cell-free massive MIMO networks (see e.g. [16], [22]- [30]). For instance, subspace-based angle estimation algorithms, such as multiple signal classification (MUSIC), estimation of signal parameters via rotational invariance technique (ESPRIT) and their extensions have gained interest in the array processing community due to their high resolution angle estimation capability [22]- [24].…”

Section: A Related Workmentioning

confidence: 99%

“…In [16], [29], [30], an AoA estimation scheme for a conventional mmWave massive MIMO system with a uniform planar array at the base station is presented. The initial AoAs of each uplink path are estimated through the two-dimensional discrete Fourier transform (2D-DFT), and then the estimation accuracy is further enhanced via an angle rotation technique.…”

Section: A Related Workmentioning

confidence: 99%

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Efficient Angle-Domain Processing for FDD-Based Cell-Free Massive MIMO Systems

Abdallah

Mansour

2020

IEEE Trans. Commun.

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Cell-free massive MIMO communications is an emerging network technology for 5G wireless communications wherein distributed multi-antenna access points (APs) serve many users simultaneously. Most prior work on cell-free massive MIMO systems assume time-division duplexing mode, although frequency-division duplexing (FDD) systems dominate current wireless standards. The key challenges in FDD massive MIMO systems are channel-state information (CSI) acquisition and feedback overhead. To address these challenges, we exploit the socalled angle reciprocity of multipath components in the uplink and downlink, so that the required CSI acquisition overhead scales only with the number of served users, and not the number of AP antennas nor APs. We propose a low complexity multipath component estimation technique and present linear angle-ofarrival (AoA)-based beamforming/combining schemes for FDDbased cell-free massive MIMO systems. We analyze the performance of these schemes by deriving closed-form expressions for the mean-square-error of the estimated multipath components, as well as expressions for the uplink and downlink spectral efficiency. Using semi-definite programming, we solve a maxmin power allocation problem that maximizes the minimum user rate under per-user power constraints. Furthermore, we present a user-centric (UC) AP selection scheme in which each user chooses a subset of APs to improve the overall energy efficiency of the system. Simulation results demonstrate that the proposed multipath component estimation technique outperforms conventional subspace-based and gradient-descent based techniques. We also show that the proposed beamforming and combining techniques along with the proposed power control scheme substantially enhance the spectral and energy efficiencies with an adequate number of antennas at the APs.Index Terms-FDD mode, cell-free massive MIMO, multipath component estimation, array signal processing, angle-based beamforming/combining, power control.

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Section: A Related Workmentioning

confidence: 99%

Section: A Related Workmentioning

confidence: 99%

Efficient Angle-Domain Processing for FDD-Based Cell-Free Massive MIMO Systems

Abdallah

Mansour

2020

IEEE Trans. Commun.

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“…2 A related low-rank basis expansion model has recently also been investigated in [78]; however, in [78] the authors consider an orthogonal basis expansion model, whereas we consider an over-complete basis set containing far more entries |D| than the dimension N t of the vector space. 3 We investigate a related max-min problem that accounts for uncertainty in the signal direction in [39]; it can only be approximately solved via SDR.…”

Section: Endnotesmentioning

confidence: 99%

Robust full-dimension MIMO transmission based on limited feedback angular-domain CSIT

Schwarz

2018

J Wireless Com Network

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In this paper, we propose robust multi-user beamforming and precoding techniques for full-dimension MIMO transmission based on limited feedback. We propose to employ an over-complete basis decomposition in the angular domain to approximate the channel matrices as sums of few dominant specular components, facilitating efficient channel state information (CSI) quantization at the users. The selected expansion vectors of such a sparse approximation, parametrized by azimuth and elevation angles, are relatively robust with respect to channel estimation errors as well as channel variations over time. Based on this CSI feedback, we propose incoherent beamforming/precoding methods that make use only of the azimuth and elevation angles as well as the norm of the expansion coefficients and do not rely on coherent multipath interference to eliminate inter-user interference. Our optimization aims at maximizing the signal power or the achievable rate of a user, while limiting the amount of interference leakage caused to other users. To further improve robustness, we account for uncertainty in the angular channel decomposition in the proposed precoder optimization.

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“…Hence, the angular spread seen by the BS is small, and the number of incoming signal paths is limited. Also, several related works assume similar insights [7], [35], [41]- [47]. Thereby, for the considered scenarios in this work, the channel matrix is assumed to have low-rank.…”

Section: Matrix Completion Problemmentioning

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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A Unified Transmission Strategy for TDD/FDD Massive MIMO Systems With Spatial Basis Expansion Model

Cited by 460 publications

References 33 publications

Efficient Angle-Domain Processing for FDD-Based Cell-Free Massive MIMO Systems

Efficient Angle-Domain Processing for FDD-Based Cell-Free Massive MIMO Systems

Robust full-dimension MIMO transmission based on limited feedback angular-domain CSIT

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

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