Performance Analysis of Differential Evolution Algorithm based Beamforming for Smart Antenna Systems

Rao, Amara Prakasa; Sarma, N. V. S. N.

doi:10.5815/ijwmt.2014.01.01

Cited by 10 publications

(3 citation statements)

References 10 publications

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“…Horn antennas, log periodic dipole arrays, and spiral antennas are the most popular feeding antennas [3][4][5]. Most commonly, the horn antenna is used for long distance point-to-point transmission.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Adaptive Beamforming Approach for Antenna Array fed Parabolic Reflector for C-band Applications

Bawane,

Panda

2023

JAMRIS

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This paper presents the design of a parabolic reflector fed through a patch antenna array feed to enhance the directivity and radiation properties. Adaptive beam formers steer and alter an array's beam pattern to increase signal reception and minimize interference. Weight selection is a critical difficulty in achieving low SLL and beam width. Low SLL and narrow beam reduce antenna radiation and reception. Adjusting the weights reduces SLL and tilts the nulls. Adaptive beam formers are successful signal processors if their array output converges to the required signal. Smart antenna weights can be determined using any window function. HPBW and SLL could be used to explore different algorithms. Both must be low for excellent smart antenna performance. In noisy settings, ACLMS and CLMS create narrow beams and side lobes. AANGD offers more control than CLMS and ACLMS. The blend of CLMS and ACLMS is more effective at signal convergence than CLMS and AANGD. It presents an alternative to the conventionally used horn-based feed network for C-band applications such as satellite communication. Broadside radiation patterns and 4x4 circular patch antenna arrays are used in the proposed design. 1400 aperture illumination is provided by the array's feed parabolic reflector, whose F/D ratio is 0.36. The proposed design's efficacy is assessed using simulation analysis.

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Section: Introductionmentioning

confidence: 99%

Hybrid Adaptive Beamforming Approach for Antenna Array fed Parabolic Reflector for C-band Applications

Bawane,

Panda

2023

JAMRIS

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“…The DE is a simple and effective algorithm that has a small number of adjustable control parameters. It has been successfully used in many engineering applications of several fields such as neural networks, signal processing, pattern recognition, image processing, bioinformatics, control systems, robotics, wireless communications, and semantic web [2][3][4][5][6][7][8]. Exploration and exploitation abilities of any populationbased optimization algorithm play a vital role in enhancing its accuracy and convergence speed [9].…”

Section: Introductionmentioning

confidence: 99%

An Enhanced Differential Evolution Algorithm with Multi-mutation Strategies and Self-adapting Control Parameters

Attia¹,

Arafa²,

Sallam³

et al. 2019

IJISA

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Differential evolution (DE) is a stochastic population-based optimization algorithm first introduced in 1995. It is an efficient search method that is widely used for solving global optimization problems. It has three control parameters: the scaling factor (F), the crossover rate (CR), and the population size (NP). As any evolutionary algorithm (EA), the performance of DE depends on its exploration and exploitation abilities for the search space. Tuning the control parameters and choosing a suitable mutation strategy play an important role in balancing the rate of exploration and exploitation. Many variants of the DE algorithm have been introduced to enhance its exploration and exploitation abilities. All of these DE variants try to achieve a good balance between exploration and exploitation rates. In this paper, an enhanced DE algorithm with multi-mutation strategies and self-adapting control parameters is proposed. We use three forms of mutation strategies with their associated self-adapting control parameters. Only one mutation strategy is selected to generate the trial vector. Switching between these mutation forms during the evolution process provides dynamic rates of exploration and exploitation. Having different rates of exploration and exploitation through the optimization process enhances the performance of DE in terms of accuracy and convergence rate. The proposed algorithm is evaluated over 38 benchmark functions: 13 traditional functions, 10 special functions chosen from CEC2005, and 15 special functions chosen from CEC2013. Comparison is made in terms of the mean and standard deviation of the error with the standard "DE/rand/1/bin" and five state-of-theart DE algorithms. Furthermore, two nonparametric statistical tests are applied in the comparison: Wilcoxon signed-rank and Friedman tests. The results show that the performance of the proposed algorithm is better than other DE algorithms for the majority of the tested functions.

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“…The SINR maximization is another criterion employed in the joint transmitter and receiver beamforming algorithms [12], [13], [14]. In an analysis of [15], the mixing of differential algorithm based LAA is applied to deepen nulls and lower side lobe levels (SLLs) in the unwanted direction, and they fou-nd the max null depth of -63 dB by using 20 element. The statistic numerical algorithm was proposed to obtain the requirement for the amplitude and phase error of multibeam active phased array antenna [16].…”

Section: Introductionmentioning

confidence: 99%

The Impact of Noise Label on Beampattern and SINR of MVDR Beamformer

2016

IJCCIE

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Minimum Variance Distortionless Response (MVDR) is basically a unity gain adaptive beamformer which is suffered from performance degradation due to the presence of interference and noise. Also, MVDR is sensitive to errors such as the steering vector errors, and the nulling level. MVDR combined with a linear antenna array (LAA) is used to acquire desired signals and suppress the interference and noise. This paper examines the impact of the noise variance label (σn2) and the number of interference sources by using Signal to Interference plus Noise Ratio (SINR) and array beampattern as two different Figure-of-Merits to measure the performance of the MVDR beamformer with a fixed number of array elements (L). The findings of this study indicate that the MVDR have successfully placed nulls in the nonlook angle with average SINR of 99.6, 49.6, 24.9 dB dB for σn2.of-50, 0, 50 dB, respectively. Also, the MVDR provides accurate majorlobe to the real user direction, even the σn2 are bigger than desired signal power. The proposed method was found to perform better than some existing techniques. Based on this analysis, the beampattern is not heavily relies on the σn2. Moreover, the SINR strongly depends on the σn2 and the number of SNOIs.

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Performance Analysis of Differential Evolution Algorithm based Beamforming for Smart Antenna Systems

Cited by 10 publications

References 10 publications

Hybrid Adaptive Beamforming Approach for Antenna Array fed Parabolic Reflector for C-band Applications

Hybrid Adaptive Beamforming Approach for Antenna Array fed Parabolic Reflector for C-band Applications

An Enhanced Differential Evolution Algorithm with Multi-mutation Strategies and Self-adapting Control Parameters

The Impact of Noise Label on Beampattern and SINR of MVDR Beamformer

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