3D vs. 2D channel models: Spatial correlation and channel capacity comparison and analysis

Yu, Yawei; Smith, Peter J.; Dmochowski, Pawel A.; Zhang, Jianhua; Shafi, Mansoor

doi:10.1109/icc.2017.7996405

Cited by 10 publications

(4 citation statements)

References 8 publications

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“…Firstly, E AOA (w * m p ) in ( 9) and ( 10) can be expressed as (11), where ∆φ = φ−φ 0 and ∆θ = θ−θ 0 in Table 1. Then we utilize the product-to-sum transform, Bessel expansion in (12), and Euler transform cos(n∆φ) = e jn∆φ +e −jn∆φ 2 and compute the integral according to the integral representation of Bessel function in (13).…”

Section: Von Mises and Truncated Laplacian Distributionsmentioning

confidence: 99%

“…, b m = 2π λ sin θ sec Ω m x m and substitute e κ cos(∆φ) as (12), we obtain Equation (A3). Furtherly, Equation (A4) is derived based on the integral representation of Bessel function in (13).…”

Section: Data Availability Statementmentioning

confidence: 99%

“…Massive MIMO brings more resources to the spatial domain and improves the communication system capacity [5] by using the channel degrees of freedom (DoFs). Full or three dimensional (3D) MIMO [10][11][12][13] is a typical massive antenna realization, which deploys antenna elements in both horizontal and elevation domains. Holographic MIMO is another novel spatially-constrained massive antenna realization with densely deployed antennas in a limited array aperture [14,15].…”

Section: Introductionmentioning

confidence: 99%

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A Theoretical Analysis of Favorable Propagation on Massive MIMO Channels with Generalized Angle Distributions

Zhang

et al. 2022

Electronics

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Massive MIMO obtains the multiuser performance gain based on the favorable propagation (FP) assumption, defined as the mutual orthogonality of different users’ channel vectors. Until now, most of the theoretical analyses of FP are based on uniform angular distributions and only consider the horizontal dimension. However, the real propagation channel contains full dimensions, and the spatial angle varies with the environment. Thus, it remains unknown whether the FP condition holds in real deployment scenarios and how it impacts the massive MIMO system performance. In this paper, we analyze the FP condition theoretically based on a cluster-based three-dimensional (3D) MIMO channel with generalized angle distributions. Firstly, the FP condition’s unified mathematical expectation and variance expressions with full-dimensional angular integral are given. Since the closed-form expressions are hard to derive, we decompose generalized angle distributions, i.e., wrapped Gaussian (WG), Von Mises (VM), and truncated Laplacian (TL) into the functions of Bessel and Cosine basis by introducing Jacobi-Anger expansions and Fourier series. Thus the closed-form expressions of the FP condition are derived. Based on the above, we theoretically analyze the asymptotically FP condition under generalized angle distributions and then compare the impact of angular spreads on the FP performance. Furtherly, the FP condition is also investigated by numerical simulations and practical measurements. It is observed that environments with larger angle spreads and larger antenna spacing are more likely to realize FP. This paper provides valuable insights for the theoretical analysis of the practical application of massive MIMO systems.

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Section: Von Mises and Truncated Laplacian Distributionsmentioning

confidence: 99%

Section: Data Availability Statementmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Theoretical Analysis of Favorable Propagation on Massive MIMO Channels with Generalized Angle Distributions

Zhang

et al. 2022

Electronics

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“…However, in the actual propagation process, the assumption of 2D is extremely insufficient. Research results, as well as field measurements, have shown that there exists a large capacity gap as predicted by three-dimensional (3D) and 2D models, and the gap of correlation grows quadratically due to a slight elevation angle spread [ 28 , 29 , 30 ]. This highlights the importance of an accurate 3D channel model when evaluating multiple-antennas systems.…”

Section: Introductionmentioning

confidence: 99%

3D Multiple-Antenna Channel Modeling and Propagation Characteristics Analysis for Mobile Internet of Things

Zeng

et al. 2021

Sensors

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The demand for optimization design and performance evaluation of wireless communication links in a mobile Internet of Things (IoT) motivates the exploitation of realistic and tractable channel models. In this paper, we develop a novel three-dimensional (3D) multiple-antenna channel model to adequately characterize the scattering environment for mobile IoT scenarios. Specifically, taking into consideration both accuracy and mathematical tractability, a 3D double-spheres model and ellipsoid model are introduced to describe the distribution region of the local scatterers and remote scatterers, respectively. Based on the explicit geometry relationships between transmitter, receiver, and scatterers, we derive the complex channel gains by adopting the radio-wave propagation model. Subsequently, the correlation-based approach for theoretical analysis is performed, and the detailed impacts with respect to the antenna deployment, scatterer distribution, and scatterer density on the vital statistical properties are investigated. Numerical simulation results have shown that the statistical channel characteristics in the developed simulation model nicely match those of the corresponding theoretical results, which demonstrates the utility of our model.

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3D MIMOAntenna Designs

Sharawi

2020

Wiley 5G Ref

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Multiple‐input multiple‐output (MIMO) technology has been a key driver in the development of fourth‐generation wireless systems and will continue to be utilized in the upcoming fifth‐generation one. MIMO was an excellent technology to make use of multipath effects and enhance the system capacity dramatically. A new technology that is based on MIMO that utilizes large amounts of antennas at the base‐station side to simultaneously send dedicated beams and create dedicated channels for various users has been identified and will be deployed with fifth‐generation systems to enhance the channel capacity even more. This technology is called massive‐MIMO (MaMI). The majority of works that addressed MaMI focused on one dimensional (azimuthal) beam steering. Adding the second dimension (elevation) to the original MaMI systems has been denoted as three‐dimensional MIMO (3D‐MIMO) since the beams will cover the 3D space in front of the MaMI array, thus enhancing coverage, reducing interference and enhancing capacity. This article focuses on 3D‐MIMO antenna array systems, their performance metrics and some examples of actual implementations at microwave and mm‐wave bands.

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3D vs. 2D channel models: Spatial correlation and channel capacity comparison and analysis

Cited by 10 publications

References 8 publications

A Theoretical Analysis of Favorable Propagation on Massive MIMO Channels with Generalized Angle Distributions

A Theoretical Analysis of Favorable Propagation on Massive MIMO Channels with Generalized Angle Distributions

3D Multiple-Antenna Channel Modeling and Propagation Characteristics Analysis for Mobile Internet of Things

3D MIMOAntenna Designs

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