Comparisons between 2D and 3D Uniform Array Antennas

Vesa, Andy; Alexa, Florin; Balta, Horia

doi:10.15439/2015f266

Cited by 12 publications

(8 citation statements)

References 3 publications

(1 reference statement)

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“…We use the term planar to indicate that the array can scan the beam along the elevation plane θ and the azimuth plane ϕ as opposed to the linear arrays that scan the main beam only along θ or ϕ [21]. Planar arrays offer more gain and lower sidelobes than linear arrays at the expense of using more elements [22].…”

Section: The Massive Mimo Systemsmentioning

confidence: 99%

“…Basically, a URPA is a two-dimensional matrix filled with a certain number of antenna elements (the circles in the figure) both along the x-and y-axis; these antenna elements are equally spaced between any successive pair of elements and this spacing is usually expressed in wavelengths. If we denote the number of elements placed on the x-axis with M (the rows of the matrix) and with N the number of antennas lying in the y-axis (the columns of the matrix), the total number of elements of the URPA is given by [22]:…”

Section: Massive Mimo Urpamentioning

confidence: 99%

“…In the case of URPA, the array factor equation is very similar to the ULA with the only difference that is designed by considering two dimensions [15,22]: With:…”

Section: Massive Mimo Urpamentioning

confidence: 99%

See 2 more Smart Citations

Smart Antenna Systems Model Simulation Design for 5G Wireless Network Systems

Inzillo¹,

Rango²,

Zampogna³

et al. 2019

Array Pattern Optimization

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The most recent antenna array technologies such as smart antenna systems (SAS) and massive multiple input multiple output (MIMO) systems are giving a strong increasing impact relative to 5G wireless communication systems due to benefits that they could introduce in terms of performance improvements with respect to omnidirectional antennas. Although a considerable number of theoretical proposals already exist in this field, the most common used network simulators do not implement the latest wireless network standards and, consequently, they do not offer the possibility to emulate scenarios in which SAS or massive MIMO systems are employed. This aspect heavily affects the quality of the network performance analysis with regard to the next generation wireless communication systems. To overcome this issue, it is possible, for example, to extend the default features offered by one of the most used network simulators such as Omnet++ which provides a very complete suite of network protocols and patterns that can be adapted in order to support the latest antenna array systems. The main goal of the present chapter is to illustrate the improvements accomplished in this field allowing to enhance the basic functionalities of the Omnet++ simulator by implementing the most modern antenna array technologies.

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Section: The Massive Mimo Systemsmentioning

confidence: 99%

Section: Massive Mimo Urpamentioning

confidence: 99%

See 1 more Smart Citation

Smart Antenna Systems Model Simulation Design for 5G Wireless Network Systems

Inzillo¹,

Rango²,

Zampogna³

et al. 2019

Array Pattern Optimization

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show abstract

“…This paper analyzed various array geometries such as uniform linear (LA), uniform rectangular (PA), and circular arrays (CA). The LA has excellent direct and narrowest main beam lobe in a wanted direction, but in all azimuthal directions it does not work equally well, a major drawback of the PA is the question of presence on the opposite side of an additional large lobe of the same strength [6] the symmetry of the CA structure provides an obvious benefit since, it has no edge components, without a major change in the beamform, directional patterns synthesized with a CA can be rotated electronically in the array plane [7]. Many algorithms have been introduced to applicants on SA [8,9], one of the most public algorithms is least mean square (LMS) A commonly employed algorithm due to its low computational complexity and ease of implementation [10].…”

Section: Introductionmentioning

confidence: 99%

Rabid Euclidean direction search algorithm for various adaptive array geometries

2021

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One of the exciting technologies used to meet the increasing demand for wireless communication services is a smart antenna. A smart antenna is basically confirmed by an array of antennas and a digital beamformer unit through which cellular base station can direct the beam toward the desired user and set nulls toward interfering users. In this paper, different array configurations (linear, circular, and planer) with the REDS algorithm are implemented in the digital beam-forming unit. The wireless system performance is investigated to check the smart antenna potentials assuming Rayleigh fading channel environment beside the AWGN channel. Results show how the REDS algorithm offers a significant improvement through antenna radiation pattern optimization, sidelobe level, and interference reduction, and also the RDES algorithm proves fast convergence with minimum MSE and better sidelobe level reduction comparing with other algorithms.

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“…The favorable propagation offers a mutual orthogonality between channel vectors from different mobile terminals (MTs); hence, the application of linear signal processing techniques at the receiver side can result in optimal performance 4,7 . The antenna elements may be arranged in different structures and generate a radiation pattern according to its structure 8 . The majority of the works consider a uniform linear array (ULA) structure; however, only 2D or 3D structures such as uniform planar array (UPA) and uniform cylindrical array (UCA) offer control of angle of elevation, resulting in an increase of spatial resolution, that is, an increasing on the desired signal strength while simultaneously provide reduction in users' interference 9 …”

Section: Introductionmentioning

confidence: 99%

Stochastic channel models for massive and extreme large multiple‐input multiple‐output systems

Taniguchi

Abrão

2020

Trans Emerging Tel Tech

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In this article, stochastic channel models for massive multiple‐input multiple‐output (M‐MIMO) and extreme large MIMO (XL‐MIMO) system applications are described, evaluated, and systematically compared. This work aims to cover new aspects of M‐MIMO stochastic channel models in a comprehensive and systematic way. For that, we compare different models, presenting graphically and intuitively the behavior of each model. Each M‐MIMO channel model emulates the environment using different methodologies and properties. Using metrics such as capacity, SINR, singular values decomposition, and condition number, one can understand the influence of each characteristic on the modeling and how it differentiates from other models. Moreover, in new XL‐MIMO scenarios, where the near‐field and visibility region effects arise, our finding demonstrate that for the two assumed schemes of clusters distribution, the clusters location influences the performance of the conjugate beamforming and zero‐forcing precoding due to the correlation effect, which have been analyzed from the geometric M‐MIMO channel models.

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Comparisons between 2D and 3D Uniform Array Antennas

Cited by 12 publications

References 3 publications

Smart Antenna Systems Model Simulation Design for 5G Wireless Network Systems

Smart Antenna Systems Model Simulation Design for 5G Wireless Network Systems

Rabid Euclidean direction search algorithm for various adaptive array geometries

Stochastic channel models for massive and extreme large multiple‐input multiple‐output systems

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