Uplink sum-rate analysis of multi-cell multi-user massive MIMO system

Wang, Dongming; Chen, Ji; Gao, Xiqi; Sun, Shaojun; You, Xiaohu

doi:10.1109/icc.2013.6655448

Cited by 42 publications

(28 citation statements)

References 9 publications

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“…It is easy to verify that the expression of the sum-rate in [20] is a special case of (15). In particular, when R l,k = I M for all l and k, N = 1, and M → ∞, C inf satisfies…”

Section: A Theoretical Analysis Of Spectral Efficiencymentioning

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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“…It is easy to verify that the expression of the sum-rate in [20] is a special case of (15). In particular, when R l,k = I M for all l and k, N = 1, and M → ∞, C inf satisfies…”

Section: A Theoretical Analysis Of Spectral Efficiencymentioning

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.

Self Cite

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show abstract

“…In this paper, the circumstance of simulation is uncorrelated Rayleigh fading channels and shadow fading is ignored, so the uplink channel between the th user of cell to the BS of the target cell is denoted as , . It can be modeled by [11] …”

Section: System Model Of the Uplink Multi-cellmentioning

confidence: 99%

“…Then, we can obtain the covariance matrix of the channel estimation error vector˜, = , −ˆ, by [11] cov…”

Section: A Channel Estimation Of Massive Mimomentioning

confidence: 99%

“…Therefore, the covariance matrix of channel estimation interference and noise can be calculated as [11] Σ = cov (˜,˜) = 2 ,…”

Section: A Channel Estimation Of Massive Mimomentioning

confidence: 99%

“…The performance curves for MF-ISDIC, MMSE-ISDIC, PELD-ISDIC, and SVD-ISDIC algorithms are given and compared with theoretical result. The theoretical result for massive MI-MO systems in Fig.2 and Fig.3 is calculated according to sumrate in [11]. As channel capacity is the tightest upper bound on the rate of information that can be reliably transmitted over channel, the frame will be transmitted in error, when the sumrate is less than the transmission rate of system.…”

Section: A Simulation Set Upmentioning

confidence: 99%

See 2 more Smart Citations

On the performance of iterative receivers in massive MIMO systems with pilot contamination

Huang

Tang

Wei

et al. 2014

2014 9th IEEE Conference on Industrial Electronics and Applications

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In this paper, we study iterative detection algorithms of the multi-cell multi-user massive MIMO systems with pilot contamination. First, a iterative algorithm applying soft decision interference cancellation is introduced. It is based on minimum mean square error (MMSE) filter and derived from [1] where it was applied in block transmission. Then, by considering the algorithm complexity, two iterative soft input soft output (SISO) detection algorithms, which avoid matrix inversion, are presented: a soft interference cancellation algorithm for the polynomial expansion linear detector (PELD) and a SISO detector based on singular value decomposition (SVD). Moreover, matched filter-SISO (MF-SISO) is given as a special case of PELD-SISO. Simulation comparisons of iterative detection algorithms in massive MIMO systems are presented, which are of great significance for the future research.

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Uplink Sum-Rate Analysis of Massive MIMO System with Pilot Contamination and CSI Delay

Cao

Wang

et al. 2014

Wireless Pers Commun

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Uplink sum-rate analysis of multi-cell multi-user massive MIMO system

Cited by 42 publications

References 9 publications

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

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

On the performance of iterative receivers in massive MIMO systems with pilot contamination

Uplink Sum-Rate Analysis of Massive MIMO System with Pilot Contamination and CSI Delay

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