Rank Reduction and Diagonalization of Sensing Matrix for Millimeter Wave Hybrid Precoding Using Particle Swarm Optimization

This research puts forth an optimization- based analog beamforming scheme for millimeter-wave (mmWave) massive MIMO systems. Main aim is to optimize the combination of analog precoder / combiner matrices for the purpose of getting near-optimal performance. Codebook-based analog beamforming with transmit precoding and receive combining serves the purpose of compensating the severe attenuation of mmWave signals. The existing and traditional beamforming schemes involve a complex search for the best pair of analog precoder / combiner matrices from predefined codebooks. In this research, we have solved this problem by using Particle Swarm Optimization (PSO) to find the best combination of precoder / combiner matrices among all possible pairs with the objective of achieving near-optimal performance with regard to maximum achievable rate. Experiments prove the robustness of the proposed approach in comparison to the benchmarks considered.

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3D Beamforming for 5G Millimeter Wave Systems Using Singular Value Decomposition and Particle Swarm Optimization Approaches

2018 International Conference on Information and Communication Technology Convergence (ICTC) ◽

10.1109/ictc.2018.8539578 ◽

2018 ◽

Cited By ~ 3

Author(s):

Osama Alluhaibi ◽

Manish Nair ◽

Amjed Hazzaa ◽

Aza Mihbarey ◽

Jiangzhou Wang

Keyword(s):

Particle Swarm Optimization ◽

Singular Value Decomposition ◽

Millimeter Wave ◽

Particle Swarm ◽

Singular Value ◽

Swarm Optimization ◽

3D Beamforming ◽

Value Decomposition

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MULTIBAND MILLIMETER WAVE T-SHAPED ANTENNA WITH OPTIMIZED PATCH PARAMETER USING PARTICLE SWARM OPTIMIZATION

Nigerian Journal of Technology ◽

10.4314/njt.v36i3.33 ◽

2017 ◽

Vol 36 (3) ◽

pp. 904-909

Author(s):

AH Jabire ◽

A Abdu ◽

S Salisu

Keyword(s):

Particle Swarm Optimization ◽

Millimeter Wave ◽

Patch Antenna ◽

Return Loss ◽

Particle Swarm ◽

Impedance Bandwidth ◽

Electromagnetic Spectrum ◽

Radiation Characteristics ◽

Swarm Optimization ◽

High Frequency Structure Simulator

This paper proposed a simple T-shaped patch antenna for millimeter waveband frequency operation. Millimeter wave is a frequency ranges between 30GHz to 300GHz in an electromagnetic spectrum. The proposed antenna consists of T-shape radiating patch mounted on rectangular substrate (FR4-4) and microstrip line for antenna feeding. An evolutionary algorithm called particle swarm optimization was used to optimize the length and width of the proposed antenna patch. The proposed antenna gives triple bands with central frequencies at 42GHz, 51.5GHz and 60GHz. The antenna offers minimum return loss of -19db, -24db and -19.5db at 42GHz, 51.5GHz and 60GHz respectively. The return loss impedance bandwidth of 5GHz for the first band, 8.4GHz for the second band and 5GHz for the third band was obtained. The proposed antenna was analyzed using Ansoft High Frequency Structure Simulator (HFSS) and MATLAB 2013. Radiation characteristics of this patch antenna are observed at various resonating frequencies.Â Â http://dx.doi.org/10.4314/njt.v36i3.33

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Smart teaching mode based on particle swarm image recognition and human-computer interaction deep learning

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-189048 ◽

2020 ◽

Vol 39 (4) ◽

pp. 5699-5711

Author(s):

Shirong Long ◽

Xuekong Zhao

Keyword(s):

Feature Extraction ◽

Particle Swarm Optimization ◽

Deep Learning ◽

Real Time ◽

Image Recognition ◽

Particle Swarm ◽

Learning Technology ◽

Search Performance ◽

Swarm Optimization ◽

Teaching Mode

The smart teaching mode overcomes the shortcomings of traditional teaching online and offline, but there are certain deficiencies in the real-time feature extraction of teachers and students. In view of this, this study uses the particle swarm image recognition and deep learning technology to process the intelligent classroom video teaching image and extracts the classroom task features in real time and sends them to the teacher. In order to overcome the shortcomings of the premature convergence of the standard particle swarm optimization algorithm, an improved strategy for multiple particle swarm optimization algorithms is proposed. In order to improve the premature problem in the search performance algorithm of PSO algorithm, this paper combines the algorithm with the useful attributes of other algorithms to improve the particle diversity in the algorithm, enhance the global search ability of the particle, and achieve effective feature extraction. The research indicates that the method proposed in this paper has certain practical effects and can provide theoretical reference for subsequent related research.

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Sistem Kontrol Optimal Fuzzy-Particle Swarm Optimization

Jurnal Intake : Jurnal Penelitian Ilmu Teknik dan Terapan ◽

10.32492/jintake.v9i1.720 ◽

2018 ◽

Vol 9 (1) ◽

pp. 1-5

Author(s):

Fachrudin Hunaini ◽

Imam Robandi ◽

Nyoman Sutantra

Keyword(s):

Fuzzy Logic ◽

Particle Swarm Optimization ◽

Control System ◽

Membership Function ◽

Fuzzy Logic Control ◽

Particle Swarm ◽

Optimization Method ◽

Swarm Optimization ◽

Motion Error ◽

Logic Control

Fuzzy Logic Control (FLC) is a reliable control system for controlling nonlinear systems, but to obtain optimal fuzzy logic control results, optimal Membership Function parameters are needed. Therefore in this paper Particle Swarm Optimization (PSO) is used as a fast and accurate optimization method to determine Membership Function parameters. The optimal control system simulation is carried out on the automatic steering system of the vehicle model and the results obtained are the vehicle's lateral motion error can be minimized so that the movement of the vehicle can always be maintained on the expected trajectory

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