Channel Acquisition for Massive MIMO-OFDM With Adjustable Phase Shift Pilots

You, Li; Gao, Xiqi; Swindlehurst, A. Lee; Zhong, Wen

doi:10.1109/tsp.2015.2502550

Cited by 181 publications

(152 citation statements)

References 51 publications

(100 reference statements)

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“…where U k ∈ C N k ×N k and V k ∈ C M×M are both deterministic unitary matrices, representing the eigenvectors of the receive correlation matrix and the BS correlation matrix of H k , respectively. For our considered massive MIMO channels, when M → ∞, H k can be well approximated by [21], [22]…”

Section: A Channel Modelmentioning

confidence: 99%

“…It has been widely recognized that V is independent of the locations of UTs and only depends on the BS antenna array geometry in massive MIMO [28]. For example, for the uniform linear array (ULA) case with antenna spacing of half-wavelength, the discrete Fourier transform (DFT) matrix can be used to well approximate V [21], [22]. Besides, G k ∈ C N k ×M in (1) is referred to as the beam domain channel matrix [29], whose elements are zero-mean and independently distributed.…”

Section: A Channel Modelmentioning

confidence: 99%

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Spectral Efficiency and Energy Efficiency Tradeoff in Massive MIMO Downlink Transmission With Statistical CSIT

You

Xiong

Zappone

et al. 2020

IEEE Trans. Signal Process.

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As a key technology for future wireless networks, massive multiple-input multiple-output (MIMO) can significantly improve the energy efficiency (EE) and spectral efficiency (SE), and the performance is highly dependant on the degree of the available channel state information (CSI). While most existing works on massive MIMO focused on the case where the instantaneous CSI at the transmitter (CSIT) is available, it is usually not an easy task to obtain precise instantaneous CSIT. In this paper, we investigate EE-SE tradeoff in single-cell massive MIMO downlink transmission with statistical CSIT. To this end, we aim to optimize the system resource efficiency (RE), which is capable of striking an EE-SE balance. We first figure out a closed-form solution for the eigenvectors of the optimal transmit covariance matrices of different user terminals, which indicates that beam domain is in favor of performing RE optimal transmission in massive MIMO downlink. Based on this insight, the RE optimization precoding design is reduced to a real-valued power allocation problem. Exploiting the techniques of sequential optimization and random matrix theory, we further propose a low-complexity suboptimal two-layer water-filling-structured power allocation algorithm. Numerical results illustrate the effectiveness and nearoptimal performance of the proposed statistical CSI aided RE optimization approach.

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Section: A Channel Modelmentioning

confidence: 99%

Section: A Channel Modelmentioning

confidence: 99%

Spectral Efficiency and Energy Efficiency Tradeoff in Massive MIMO Downlink Transmission With Statistical CSIT

You

Xiong

Zappone

et al. 2020

IEEE Trans. Signal Process.

Self Cite

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show abstract

“…Based on a physically motivated channel model, a critical relationship for massive MIMO orthogonal frequency division multiplexing (OFDM) has been revealed between the space-frequency domain channel covariance matrix (SFCCM) and the channel power angle-delay spectrum [45]. When the number of antennas at BS is sufficiently large, the SFCCM can be well approximated by [45] …”

Section: B Channel Estimation Methodsmentioning

confidence: 99%

An overview of transmission theory and techniques of large-scale antenna systems for 5G wireless communications

WANG¹,

YOU²,

Yu³

et al. 2015

Sci. Sin.-Inf.

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To meet the future demand for huge traffic volume of wireless data service, the research on the fifth generation (5G) mobile communication systems has been undertaken in recent years. It is expected that the spectral and energy efficiencies in 5G mobile communication systems should be ten-fold higher than the ones in the fourth generation (4G) mobile communication systems. Therefore, it is important to further exploit the potential of spatial multiplexing of multiple antennas. In the last twenty years, multiple-input multiple-output (MIMO) antenna techniques have been considered as the key techniques to increase the capacity of wireless communication systems. When a large-scale antenna array (which is also called massive MIMO) is equipped in a base-station, or a large number of distributed antennas (which is also called large-scale distributed MIMO) are deployed, the spectral and energy efficiencies can be further improved by using spatial domain multiple access. This paper provides an overview of massive MIMO and large-scale distributed MIMO systems, including spectral efficiency analysis, channel state information (CSI) acquisition, wireless transmission technology, and resource allocation. Index TermsThe fifth generation mobile communication, massive MIMO, large-scale distributed antenna systems, spectral efficiency, channel state information acquisition, multi-user MIMO, resource allocation.

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“…The system sampling rate is 1 Ts . Then, the uplink channel h k,m ∈ C M ×1 from user k to the BS during the m-th block can be expressed as [26]- [28]…”

Section: System Model and Channel Characteristicsmentioning

confidence: 99%

Time-Varying Massive MIMO Channel Estimation: Capturing, Reconstruction, and Restoration

Zhang

Zhao

et al. 2019

IEEE Trans. Commun.

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On the time-varying channel estimation, the traditional downlink (DL) channel restoration schemes usually require the reconstruction for the covariance of downlink process noise vector, which is dependent on DL channel covariance matrix (CCM). However, the acquisition of the CCM leads to unacceptable overhead in massive MIMO systems. To tackle this problem, in this paper, we propose a novel scheme for the DL channel tracking. First, with the help of virtual channel representation (VCR), we build a dynamic uplink (UL) massive MIMO channel model with the consideration of off-grid refinement.Then, a coordinate-wise maximization based expectation maximization (EM) algorithm is adopted for capturing the model parameters, including the spatial signatures, the time-correlation factors, the off-grid bias, the channel power, and the noise power. Thanks to the angle reciprocity, the spatial signatures, timecorrelation factors and off-grid bias of the DL channel model can be reconstructed with the knowledge of UL ones. However, the other two kinds of model parameters are closely related with the carrier frequency, which cannot be perfectly inferred from the UL ones. Instead of relearning the DL model parameters with dedicated training, we resort to the optimal Bayesian Kalman filter (OBKF) method to accurately track the DL channel with the partially prior knowledge. At the same time, the model parameters will be gradually restored. Specially, the factor-graph and the Metropolis Hastings MCMC are utilized within the OBKF framework. Finally, numerical results are provided to demonstrate the efficiency of our proposed scheme.

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Channel Acquisition for Massive MIMO-OFDM With Adjustable Phase Shift Pilots

Cited by 181 publications

References 51 publications

Spectral Efficiency and Energy Efficiency Tradeoff in Massive MIMO Downlink Transmission With Statistical CSIT

Spectral Efficiency and Energy Efficiency Tradeoff in Massive MIMO Downlink Transmission With Statistical CSIT

An overview of transmission theory and techniques of large-scale antenna systems for 5G wireless communications

Time-Varying Massive MIMO Channel Estimation: Capturing, Reconstruction, and Restoration

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