Electronic Beam Steering Using Switched Parasitic Smart Antenna Arrays

Varlamos, Pantelis K.; Capsalis, Christos N.

doi:10.2528/pier01100302

Cited by 39 publications

(19 citation statements)

References 5 publications

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“…The use of genetic algorithms for antenna design purposes and electronic beam steering is a well-known method introduced in the last decade [20][21][22][23][24][25]. For the design of the antenna used in this paper, roulette wheel selection, simple crossover with p crossover = 0.8 and binary mutation with p mutation = 0.04 were employed.…”

Section: Antenna Design With Genetic Algorithmmentioning

confidence: 99%

Uplink Carrier-to-Interference Improvement in a Cellular Telecommunication System When a Six-Beam Switched Parasitic Array Is Implemented

Sotiriou¹,

Trakadas²,

Capsalis³

2008

PIER B

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Abstract-Mobile broadband communication is experiencing rapid growth in the following sections: technology, range of services and marketing target groups. This growth has driven research and development activities towards advanced high-data-rate wireless systems, with improved network performance. A typical example of technology thrust in wireless communications is the use of adaptive antennas at the transceivers, in association with advanced array signal processing. Although the mass deployment of adaptive array systems has not achieved the desired levels yet, there are many examples of improved cell coverage, link quality and system capacity at several networks. The performance of a six-beam switched parasitic array, in terms of carrier-to-interference ratio (CIR) measurement at the uplink direction, is presented in this paper. The switched parasitic array is designed with the aid of the method of genetic algorithms and the simulation results are compared with respect to those obtained when an omni directional antenna is used instead. The calculated CIR improvement reveals the superiority of the adaptive system compared to the conventional one.

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Section: Antenna Design With Genetic Algorithmmentioning

confidence: 99%

Uplink Carrier-to-Interference Improvement in a Cellular Telecommunication System When a Six-Beam Switched Parasitic Array Is Implemented

Sotiriou¹,

Trakadas²,

Capsalis³

2008

PIER B

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“…Calculating the required weights needed to form a given beam is a complicated task (see: [15,13,8]). One method to overcome this problem is to pre-calculate the coefficients that yields a pencil beam of width 2∆ in any given direction θ, and use superposition to obtain a combination of pencil beams.…”

Section: Background: Scheduling Of Smart Antennasmentioning

confidence: 99%

Scheduling of a Smart Antenna: Capacitated Coloring of Unit Circular-Arc Graphs

Even

Shahar

2006

Combinatorial and Algorithmic Aspects of Networking

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We consider scheduling problems that are motivated by an optimization of the transmission schedule of a smart antenna. In these problems we are given a set of messages and a conflict graph that specifies which messages cannot be transmitted concurrently. In our model the conflict graph is a unit circular-arc graph.Two variants of the problem are considered: c-mbl and nu-c-mbl. In c-mbl, the messages have unit demands, whereas in nu-c-mbl demands are arbitrary. We present an optimal algorithm for c-mbl and a 3-approximation algorithm for nu-c-mbl.

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“…The techniques of placing nulls in the antenna patterns to suppress interference and maximizing their gain in the direction of desired signal have received considerable attention in the past and are still of great interest using evolutionary algorithms such as genetic algorithms (GA) [7,8] or the sequential quadratic programming (SQP) algorithm [9]. It is recognized that the PSO algorithm is a practical and powerful optimization tool for a variety of electromagnetic and antenna design problems [10][11][12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

A Comparison Between Circular and Hexagonal Array Geometries for Smart Antenna Systems Using Particle Swarm Optimization Algorithm

Mahmoud¹,

Eladawy²,

Ibrahem³

et al. 2007

PIER

100

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Abstract-In this paper, circular and hexagonal array geometries for smart antenna applications are compared. Uniform circular (UCA) and hexagonal arrays (UHA) with 18 half-wave dipole elements are examined; also planar (2 concentric rings of radiators) uniform circular (PUCA) and hexagonal arrays (PUHA) are considered. The effect of rotating the outer ring of the PUCA is studied. In our analysis, the method of moments is used to compute the response of the uniform circular and hexagonal dipole arrays in a mutual coupling environment. The particle swarm optimization (PSO) algorithm is used to optimize the complex excitations, amplitudes and phases, of the adaptive arrays elements for beamforming.

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Electronic Beam Steering Using Switched Parasitic Smart Antenna Arrays

Cited by 39 publications

References 5 publications

Uplink Carrier-to-Interference Improvement in a Cellular Telecommunication System When a Six-Beam Switched Parasitic Array Is Implemented

Uplink Carrier-to-Interference Improvement in a Cellular Telecommunication System When a Six-Beam Switched Parasitic Array Is Implemented

Scheduling of a Smart Antenna: Capacitated Coloring of Unit Circular-Arc Graphs

A Comparison Between Circular and Hexagonal Array Geometries for Smart Antenna Systems Using Particle Swarm Optimization Algorithm

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