Generalized spatial correlation equations for antenna arrays in wireless diversity reception exact and approximate analyses

Zhou, Zhijie; Ishizawa, Kenta; Kikuchi, Hisakazu; Sengoku, Masakazu; Onozato, Yoshikuni

doi:10.1109/icnnsp.2003.1279241

Cited by 19 publications

(20 citation statements)

References 3 publications

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“…[7] concluded that the approximate analysis exhibits good agreement with the exact analysis when ∆ < 10 degrees and σ < 10 without the limitation of d/λ and R/λ. One also can see the Gaussian distribution decreases more slowly in the main lobe, but lacks the secondary correlation peaks contract with uniform distribution.…”

Section: Im[ρ(m N)]mentioning

confidence: 78%

“…For lower or intermediate σ, the approximate sin(zσ) ≈ zσ and cos(zσ) ≈ 1 that gives the approximate equation as [7] …”

Section: Figmentioning

confidence: 99%

“…From Ref. [7], the real and imaginary parts of the correlation function ρ(m, n) of circular antenna arrays for a uniform distribution can be expressed as …”

Section: B Uniform Distributionmentioning

confidence: 99%

“…Because of the multiple transmit antennas and multiple receive antennas, the multiple dimensional statistical characteristic is of foremost significance to the system. Then it is crucial for the system designers of a MIMO communication to have a spatial correlation model that appropriately presents the essential propagation characteristics [6] [7].…”

Section: Introductionmentioning

confidence: 99%

“…To our knowledge, however these literatures are still lacking a simple but accurate correlation models from an uncorrelated to a fully correlated scenario. In this paper, the goal is to introduce a simple correlation model [6] [7] for MIMO system in which the circular antenna array is considered, that is our main contribution. By investigating the MIMO channel capacity using the correlation matrix for a circular array, we prove that, for circular array, decrease in the radius is equivalent to decrease in signal to noise ratio (SNR).…”

Section: Introductionmentioning

confidence: 99%

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Performance of MIMO with Circular Antenna Array Using Correlation Matrix

Zhou

Dong

Huang

et al. 2006

2006 Asia-Pacific Conference on Communications

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Abstract-Spatial correlation is one of the impairments practical Multiple-Input Multiple-Output (MIMO) wireless communication systems have to be coped with. Capacity increases promised by MIMO systems mostly depend on the spatial correlation properties of the radio channels. This paper investigates the connection between these properties and the channel capacity. By investigating the channel capacity using the correlation matrix for a circular array, we prove that, for circular array, decrease in the radius is equivalent to decrease in signal to noise ratio (SNR).

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Section: Im[ρ(m N)]mentioning

confidence: 78%

“…For lower or intermediate σ, the approximate sin(zσ) ≈ zσ and cos(zσ) ≈ 1 that gives the approximate equation as [7] …”

Section: Figmentioning

confidence: 99%

“…From Ref. [7], the real and imaginary parts of the correlation function ρ(m, n) of circular antenna arrays for a uniform distribution can be expressed as …”

Section: B Uniform Distributionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Performance of MIMO with Circular Antenna Array Using Correlation Matrix

Zhou

Dong

Huang

et al. 2006

2006 Asia-Pacific Conference on Communications

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MIMO channel estimation with arbitrary angle of arrival incident power spectrum for wireless communications

et al. 2020

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This study proposes an approximate algorithm for the arbitrary angle of arrival (AoA) incident power spectrum in wireless environments. The approach expands approximate algorithms for small angles to the large-angle AoA PDF. To achieve this, the fading correlation of wireless channels is calculated and derived for various theoretical fittings and measured data. Then, the multiple-input multiple-output (MIMO) channel model is reconstructed for special environments using spatial fading correlation (SFC). Following this, the approximate algorithm and its complexity are investigated in depth in the SFC of multi-antenna arrays with small AoA angles under uniform, Gaussian, and Laplacian distributions, which can function in macrocell and microcell environments. Further, the closed form formulas for SFC are derived for the small angle spread of incident signals. The formulas are extended to simulate these cases with large angle spreads, which result in an increasing calculation complexity, particularly for the actual measured results. The selection of the number of samples and the weighting coefficient in the large angle spreads and its fitting accuracy are considered in detail. Based on this relationship, the properties of the temporal correlation and channel of the MIMO uniform linear array are studied and discussed.

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Elevation Beamforming With Full Dimension MIMO Architectures in 5G Systems: A Tutorial

Nadeem

Kammoun

Alouini

2019

IEEE Commun. Surv. Tutorials

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Full dimension (FD) multiple-input multiple-output (MIMO) technology has attracted substantial research attention from both wireless industry and academia in the last few years as a promising technique for next-generation wireless communication networks. FD-MIMO scenarios utilize a planar two-dimensional (2D) active antenna system (AAS) that not only allows a large number of antenna elements to be placed within feasible base station (BS) form factors, but also provides the ability of adaptive electronic beam control over both the elevation and the traditional azimuth dimensions. This paper presents a tutorial on elevation beamforming analysis for cellular networks utilizing FD Massive MIMO antenna arrays. In contrast to existing works that focus on the standardization of FD-MIMO in the 3rd Generation Partnership Project (3GPP), this tutorial is distinguished by its depth with respect to the theoretical aspects of antenna array and 3D channel modeling. In an attempt to bridge the gap between industry and academia, this preliminary tutorial introduces the relevant array and transceiver architecture designs proposed in the 3GPP Release 13 that enable elevation beamforming. Then it presents and compares two different 3D channel modeling approaches that can be utilized for the performance analysis of elevation beamforming techniques. The spatial correlation in FD-MIMO arrays is characterized and compared based on both channel modeling approaches and some insights into the impact of different channel and array parameters on the correlation are drawn. All these aspects are put together to provide a mathematical framework for the design of elevation beamforming schemes in single-cell and multi-cell scenarios. Simulation examples associated with comparisons and discussions are also presented. To this end, this paper highlights the state-of-the-art research and points out future research directions.

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Generalized spatial correlation equations for antenna arrays in wireless diversity reception exact and approximate analyses

Cited by 19 publications

References 3 publications

Performance of MIMO with Circular Antenna Array Using Correlation Matrix

Performance of MIMO with Circular Antenna Array Using Correlation Matrix

MIMO channel estimation with arbitrary angle of arrival incident power spectrum for wireless communications

Elevation Beamforming With Full Dimension MIMO Architectures in 5G Systems: A Tutorial

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