Adaptive Antenna Array Control in the Presence of Interfering Signals With Stochastic Arrivals: Assessment of a GA-Based Procedure

Sacchi, Claudio; Natale, Francesco G. B. De; Donelli, Massimo; Lommi, Andrea; Massa, Andrea

doi:10.1109/twc.2004.830845

Cited by 18 publications

(8 citation statements)

References 19 publications

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“…This problem has 9 unknowns and thus the TagO algorithm makes use of orthogonal array OA (27,9,3,2). Also, the parameter rr is set equal to 0.9 and each execution of the TagO algorithm is completed when LD pi ≤ 0.001 (p = 1, .…”

Section: Main-lobe-steering and Null-steering Beamforming Problemmentioning

confidence: 99%

“…Thus, the TagO algorithm makes use of OA (27,11,3,2) considering rr = 0.9 and terminates when LD pi ≤ 0.001 (p = 1, . .…”

Section: Main-lobe-steering and Sll-minimization Beamforming Problemmentioning

confidence: 99%

“…Adaptive beamforming (ABF) is a real time procedure capable of improving the dynamic behavior of an antenna array, which receives signals from directions of arrival (DoA) that change with time [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19]. Principally, an ABF technique estimates the excitation weights w m (m = 1, .…”

Section: Introductionmentioning

confidence: 99%

“…This requirement is satisfied either by placing radiation pattern nulls towards the respective DoA of the undesired signals or by producing a radiation pattern with low side lobe level (SLL). The null-steering can be achieved either by typical ABF methods, such as the Sample Matrix Inversion (SMI) method [20], or by ABF techniques based on evolutionary optimization methods, such as Genetic Algorithms (GAs) [1][2][3], Particle Swarm Optimization (PSO) [4-9, 11, 13] and Differential Evolution (DE) [14,15].…”

Section: Introductionmentioning

confidence: 99%

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A Modified Taguchi's Optimization Algorithm for Beamforming Applications

Zaharis

2012

PIER

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Abstract-The present paper is a study of adaptive beamforming (ABF) techniques applied to antenna arrays. The structure of these techniques is based on Taguchi's Optimization (TagO) method. The high convergence speed and the ability to reach near-optimal solutions by adjusting only one parameter make the Taguchi's method an attractive choice for real time implementations like the case of ABF. Modifications are proposed in order to enhance the applicability of the TagO algorithm and decrease the computational time needed by the algorithm to terminate. The TagO method is used here to construct an ABF technique that aims at steering the main lobe of a uniform linear array towards a signal of interest, under the constraint of low side lobe level (SLL) or the constraint of placing radiation pattern nulls towards respective interference signals. Properly defined fitness functions must be minimized by the TagO algorithm to satisfy respectively the above mentioned constraints. The TagO-based ABF technique is compared with typical beamforming methods, like the Sample Matrix Inversion (SMI) and Maximum Likelihood (ML) ones, and with two evolutionary ABF techniques based on Particle Swarm Optimization (PSO) and Differential Evolution (DE), respectively. The comparison is performed regarding the convergence speed, the ability to achieve better fitness values in less time, the ability to properly steer the main lobe and finally the null-steering ability or the SLL control depending on the constraint type. The results exhibit the superiority of the TagO-based technique.

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Section: Main-lobe-steering and Null-steering Beamforming Problemmentioning

confidence: 99%

“…Thus, the TagO algorithm makes use of OA (27,11,3,2) considering rr = 0.9 and terminates when LD pi ≤ 0.001 (p = 1, . .…”

Section: Main-lobe-steering and Sll-minimization Beamforming Problemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Modified Taguchi's Optimization Algorithm for Beamforming Applications

Zaharis

2012

PIER

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“…Usually antenna arrays separate the desired signal from interfering ones by continuously tuning the weights of the array element in order to place nulls in the beam pattern and to avoid the interference effects. The control of smart antennas is a challenging task and many solutions have been proposed in literature based on advanced optimization strategies like genetic algorithms [3] or particle swarm optimization [4].…”

Section: Introductionmentioning

confidence: 99%

Implementation of a low-cost reconfigurable antenna array for SDR-based communication systems

Donelli

Sacchi

2012

2012 IEEE Aerospace Conference

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In the last years there has been a growing interest in software-defined radio communication systems (SDR). SDR technologies are attractive for communication systems because of their reconfigurable capabilities. Due to the complexity of real scenarios, usually characterized by multipath propagation and heterogeneous interfering sources, the antenna plays a key role. It is demonstrated by literature that the use of reconfigurable antennas (known also as smart antennas) could dramatically improve system performance. In this paper, we propose a compact and low-cost antenna array, based on microchip antennas, which will be integrated with a low-cost GNU-radio SDR testbed. The proposed antenna reconfigures its radiation characteristics, turning on/off the array elements, by means of radio frequency (RF) switches. The antenna is software controlled by means of a suitable optimization technique that maximizes the signal-to-noise ratio (SNR). Test results can prove the capability of the proposed antenna to improve the performance of SDR telecommunication systems in terms of increased SNR and reduced bit error rate.

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Adaptive pattern nulling design of linear array antenna by phase‐only perturbations using memetic algorithms

Hsu

Shyr

2007

Commun. Numer. Meth. Engng.

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SUMMARYIn this paper, the pattern nulling of a linear array for interference cancellation is derived by phase-only perturbations using memetic algorithms (MAs). The MAs uses improvement procedures which is obtained by incorporating local search into the genetic algorithms. It is proposed to improve the search ability of genetic algorithms. MA is a kind of an improved type of the traditional genetic algorithms. By using local search procedure, it can avoid the shortcoming of the traditional genetic algorithms, whose termination criteria are set up by using the trial and error method. The MA is applied to find the pattern nulling of the proposed adaptive antenna. This design for radiation pattern nulling of an adaptive antenna can suppress interference by placing a null at the direction of the interfering source, i.e. to increase the signal to interference ratio. This proposed method is that an innovative adaptive antenna optimization technique is also able to solve the multipath problem which exists in practical wireless communication systems. Two examples are provided to justify the proposed phase-only perturbations approach based on MAs.Computer simulation results are given to demonstrate the effectiveness of the proposed method.

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Adaptive Antenna Array Control in the Presence of Interfering Signals With Stochastic Arrivals: Assessment of a GA-Based Procedure

Abstract: Abstract

Cited by 18 publications

References 19 publications

A Modified Taguchi's Optimization Algorithm for Beamforming Applications

A Modified Taguchi's Optimization Algorithm for Beamforming Applications

Implementation of a low-cost reconfigurable antenna array for SDR-based communication systems

Adaptive pattern nulling design of linear array antenna by phase‐only perturbations using memetic algorithms

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