Optimization of linear wire antenna arrays to increase MIMO capacity using swarm intelligence

Olgun, Uğur; Tunc, Celal Alp; Aktas, D.; Ertürk, Vakur B.; Altintas, A.

doi:10.1049/ic.2007.1463

Cited by 7 publications

(12 citation statements)

References 12 publications

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“…They do not take into account the fact that mobile channels may vary considerably over time or over changing transmitter or receiver positions whereas our approach aims at optimizing radiation patterns for an average propagation behaviour of variant mobile channels. There are publications that deal with capacity maximizing geometrical array configurations using channel models that incorporate spatial correlation and mutual coupling, e.g., [4,5]. However our work gives an approach of a radiation pattern synthesis for realistic variant mobile channels.…”

Section: Introductionmentioning

confidence: 99%

Channel Based Design of Systems With Multiple Antennas

Mahler¹,

Reichardt²,

Heine³

et al. 2015

PIER B

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In this article a method of optimizing wireless communication systems using multiple antennas is presented. This method focuses on the synthesis of antenna radiation patterns that are optimized in terms of mutual information, taking into account the specific limitations of the antenna design, such as the available space (for the antenna structure), polarization, number and arrangement of the antennas.

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Section: Introductionmentioning

confidence: 99%

Channel Based Design of Systems With Multiple Antennas

Mahler¹,

Reichardt²,

Heine³

et al. 2015

PIER B

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“…On the other hand, ULA has less spatial correlation than UCA for near broadside angle-of-arrivals with moderate AS. Regarding nonuniform geometries designs, the free standing linear arrays (FSLA) optimized using particle swarm optimization (PSO) was introduced in [19]. Throughout this work, a MIMO channel model based on electric fields including mutual coupling effects is presented.…”

Section: Introductionmentioning

confidence: 99%

Optimization of Channel Capacity for Indoor Mimo Systems Using Genetic Algorithm

Mangoud¹

2009

PIER C

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Abstract-The geometrical parameters of antenna arrays in a multiple-input multiple-output (MIMO) link that will maximize the ergodic channel capacity of the system are investigated. The problem of selecting the optimum number of the elements at the base station antenna array is also included. The genetic algorithm technique is applied for the optimisation process, using the ergodic capacity as a fitness function. A discrete model based on statistical distribution of the Angle of Arrival (AoA) of the incoming rays is considered. Searching for more compactness in the antenna size and higher system capacity, different array configurations with non-uniform inter-element spacing are considered, such as linear array, circular array and multicircular array (Star) geometries. Numerical examples that illustrate new designs of non-uniform spaced arrays are presented to show the capability of the developed procedures to optimize the capacity of indoor MIMO systems.

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“…For convenience, a widely used "rule" is that half a wavelength 0.5 separation is required to obtain decorrelation between antenna elements. However, it has been observed that different array configurations obtained by PSO outperform the conventional uniform linear arrays with 0.5 length spacing in terms of MIMO capacity [1]. Various evolutionary metaheuristic algorithms have been used to solve antenna topology problems by using different objective functions, for example minimizing sidelobe level (SLL) for high directionality and interference suppression [2], minimizing lower bound of the mean square estimation error for enhancement of direction of arrival (DOA) estimation accuracy [3] and so on.…”

Section: Introductionmentioning

confidence: 99%

Multi-Antenna Array Topologies Optimization for Future Wireless Networks by Employing Particle Swarm Optimization

Huang

Karadimas

Sohrab

2019

2019 PhotonIcs &Amp; Electromagnetics Research Symposium - Spring (PIERS-Spring)

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The purpose of this work is to design novel antenna array topologies that maximize channel capacity. Accordingly, we employ the particle swarm optimization (PSO) algorithm as an effective technique in similar electromagnetic problems. We use a novel modelling approach to derive the spatial fading correlation function by employing a generic three-dimensional (3-D) angle of arrival (AOA) model. We then employ the PSO algorithm to find the antenna array topology that maximizes the channel capacity. The use of the PSO algorithm together with the new modelling approach for the spatial correlation is sufficiently tested and verified in terms of efficiency and correctness of addressing similar type of electromagnetic problems. Moreover, the general AOA model of this work is adaptable to different propagation environments, thus our approach is very promising in this regard.

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Optimization of linear wire antenna arrays to increase MIMO capacity using swarm intelligence

Cited by 7 publications

References 12 publications

Channel Based Design of Systems With Multiple Antennas

Channel Based Design of Systems With Multiple Antennas

Optimization of Channel Capacity for Indoor Mimo Systems Using Genetic Algorithm

Multi-Antenna Array Topologies Optimization for Future Wireless Networks by Employing Particle Swarm Optimization

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