Compressive Sensing Multiplicative Antenna Array

Abbasi, Muhammad Ali Babar; Fusco, Vincent; Zelenchuk, Dmitry

doi:10.1109/tap.2018.2864651

Cited by 9 publications

(14 citation statements)

References 39 publications

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“…This leads to a starting value N = 441. This fourth example is of interest both for 5G base stations (BSs) and gigabit-WiFi access points (APs) [54], which are, on one hand, characterized by a planar structure that enables the possibility to host many radiating elements [55][56][57], and, on the other hand, have to manage the problem of initial user access, which requires a wide main beam to avoid too long searching procedures for identifying the region of space where a given user lies [58]. The contour plot of the pattern derived using the proposed algorithm is shown in Figure 4, while Figure 5 and Table 5 report the initial grid (red cross) with the finally active elements (blue circles) and the real excitations, respectively.…”

Section: Fourth Examplementioning

confidence: 99%

Geometrical Synthesis of Sparse Antenna Arrays Using Compressive Sensing for 5G IoT Applications

Buttazzoni

Babich

Vatta

et al. 2020

Sensors

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One of the main targets of the forthcoming fifth-generation (5G) cellular network will be the support of the communications for billions of sensors and actuators, so as to finally realize the Internet of things (IoT) paradigm. This pervasive scenario unavoidably requires the design of cheap antenna systems with beamforming capabilities for compensating the strong attenuations that characterize the millimeter-wave (mmWave) channel. To address this issue, this paper proposes an iterative algorithm for sparse antenna arrays that enables to derive the number of elements, their amplitudes, phases, and positions in the presence of constraints on the far-field pattern. The algorithm, which relies on the compressive sensing approach, is formulated by transforming the original nonconvex optimization problem into a convex one. To prove the suitability of the conceived solution for 5G IoT mmWave applications, numerical examples and comparisons with other existing methods are provided, considering synthesis problems with different pattern and aperture specifications.

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Section: Fourth Examplementioning

confidence: 99%

Geometrical Synthesis of Sparse Antenna Arrays Using Compressive Sensing for 5G IoT Applications

Buttazzoni

Babich

Vatta

et al. 2020

Sensors

View full text Add to dashboard Cite

show abstract

“…Another important point to mention, narrower beamwidth does not always require a higher number of antenna elements in an array. Thanks to antenna array synthesis techniques link compressive sensing (Abbasi, Fusco and Zelenchuk, 2018;Abbasi and Fusco, 2019) where narrower beamwidth is achievable using sparsely distributed a lesser number of antenna elements compared to fully populated high antenna number array.…”

Section: Importance Of Beamformer's Beamwidthmentioning

confidence: 99%

Development Challenges of Millimeter‐Wave 5G Beamformers

Abbasi

2020

Wiley 5G Ref

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Beamforming is the central concept that can make millimetre-wave (mmWave) a viable solution by solving challenges of link-budget, cost, power, form factor, complexity, regulatory constraint and so on. Before deployment of mmWave beamforming solution for 5G, and possibly beyond, several fundamental questions need to be answered. Some of them include hardware constraints, performance matrices, realistic propagation channel models including impairments due to obstacles such as body, blockages due to casings, phase noise, a model for Spatial-temporal variations in channels, and advanced MIMO techniques for multi-carrier and multi-user communications. The site constraints and massive non-line-of-sight transmission within the urban environment are severely questioning the conventional terrestrial low power node deployments that used to work well at low operational frequencies (sub-6 GHz). In addition to this, in beamforming technology using massive antenna arrays, the coordination of the users and beams at the transmitter and receiver end within a large network are very challenging. In this study, we comprehensively investigate some of the most prominent and best practice solutions that attempted to answer these challenging questions.

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“…This concept is generally referred to as beam steering. By carefully managing the phases of each antenna element in an array using phase shifters, we can control the beam in multiple ways and achieve beam tilting, beam scanning, multi-beam radiation and interference rejection null formation [11]. An array that uses phase shifters to control the antenna array is called a phased array ( Figure 10.1(b)).…”

Section: Aq3mentioning

confidence: 99%

“…It is importation to note that low beamwidth in a fixed array does not necessarily imply a high number of antenna elements with associated RF electronics. Using array synthesis techniques, including compressive sensing algorithms (like the ones presented in [11,100]), a considerably fewer number of antenna elements with predefined complex weights can be used to achieve a beamwidth in a sparse antenna array format that is comparable to the beamwidth achievable by a fully populated large antenna array.…”

Section: Variable Beamwidth Fixed Beamformermentioning

confidence: 99%

Beamformer development challenges for 5G and beyond

Abbasi

Fusco

2020

Antennas and Propagation for 5G and Beyond

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Antenna's beamforming technology [1] is vital in the utilization of the microwave and millimeter-wave (mmWave) frequency bands for the fifth-generation (5G) communication technology, and beyond, applications that include the sixth generation (6G), Internet of Things and Industry 4.0 [2,3]. Antenna beamforming can be defined as the ability of an antenna array to steer the maximum radiation toward a prescribed direction, or, conversely, the ability of the antenna array to estimate the direction of arrival (DOA) of an impinging signal. Beamforming is generally achieved by using multiple antennas, spatially colocated to either function as an independent radiator or as a unit cell in larger array arrangements. Recently, the term multiple-input-multiple-output (MIMO) has been used in conjugation with the beamforming technology since it also involves multiple antenna systems, thus linking it to the beamforming technology [4]. When the number of antennas used in the MIMO operation becomes very large, it is generally termed massive MIMO technology [5]. Different beamforming concepts are used for the two frequency spectrum classifications in cellular technology, i.e., sub-6 GHz and mmWave. The majority of beamformer challenges at sub-6 GHz are already resolved, and prototypes are now available [6]; however, there are still major fundamental challenges that engineers face while developing beamformers for use in the mmWave bands, i.e., >28 GHz. Challenges include, but are not limited to, high path loss, power generation, adaption to account for realistic channel estimation, hardware impairments, energy leakage in circuits, high development cost, spatial-temporal channel variations, signal blockages due to the presence of obstacles such as beamformer casing, user body and hand. Also, multiuser MIMO techniques are not easily implementable in mmWave frequencies. In addition to this, beam coordination at transmitter and receiver antenna systems especially in mmWave spectrum is very challenging. In this chapter, we will first classify the beamformers based on the system architecture, operational frequency

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Compressive Sensing Multiplicative Antenna Array

Cited by 9 publications

References 39 publications

Geometrical Synthesis of Sparse Antenna Arrays Using Compressive Sensing for 5G IoT Applications

Geometrical Synthesis of Sparse Antenna Arrays Using Compressive Sensing for 5G IoT Applications

Development Challenges of Millimeter‐Wave 5G Beamformers

Beamformer development challenges for 5G and beyond

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