Particle Swarm Optimization for Antenna Selection in MIMO System

Hei, Yongqiang; Li, Wentao; Li, Xiaohui

doi:10.1007/s11277-011-0496-z

Cited by 14 publications

(11 citation statements)

References 17 publications

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“…Sun et al [251] applied PSO algorithm to design ultrawideband (UWB) pulse waveform, which was converted into a constraint problem with multiband chirp signals. Yongqiang et al [252] investigated the receive antenna selection problem to maximize capacity in wireless MIMO communication system, which could be formulated as an integer programming optimization problem and could not be directly solved because of its nonconvex characteristics caused by the discrete binary antenna selection factor. Hence, they introduced PSO, in which the particle was defined as the discrete binary antenna selection factor and the objective function was associated with the capacity corresponding to the specified antenna subsection represented by the particle.…”

Section: Communicationmentioning

confidence: 99%

“…Ganguly et al [221], Komsiyah [222], Feng et al [223], Pekşen et al [224], Yang et al [225], de Mendonça et al [226], Liu et al [227], Aich and Banerjee [228], Chou et al [229], Lee et al [230], Thakral and Bakhshi [231], Fister et al [232], Aghaei et al [233], Selakov et al [234], Shirvany et al [235], and Tungadio et al [236] Automatic control Cai and Yang [237], Kolomvatsos and Hadjieftymiades [238], Pandey et al [239],Štimac et al [240], Nedic et al [241], Chang and Chen [242], Xiang et al [243], Danapalasingam [244], Mahmoodabadi et al [245], Zhong et al [246], Perng et al [247], Huang and Li [248], and Nisha and Pillai [249] Communication Yousefi et al [250], Sun et al [251], Yongqiang et al [252], Chiu et al [253], Zubair and Moinuddin [255], Kim and Lee [256], Yazgan and Hakki Cavdar [257], Rabady and Ababneh [258], Das et al [259], Scott-Hayward and Garcia-Palacios [260], Omidvar and Mohammadi [261], and Kuila and Jana [262] Operations Liu and Wang…”

Section: Area Publication Electrical and Electronic Engineeringmentioning

confidence: 99%

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A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

Zhang

Wang

2015

Mathematical Problems in Engineering

697

380

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Particle swarm optimization (PSO) is a heuristic global optimization method, proposed originally by Kennedy and Eberhart in 1995. It is now one of the most commonly used optimization techniques. This survey presented a comprehensive investigation of PSO. On one hand, we provided advances with PSO, including its modifications (including quantum-behaved PSO, bare-bones PSO, chaotic PSO, and fuzzy PSO), population topology (as fully connected, von Neumann, ring, star, random, etc.), hybridization (with genetic algorithm, simulated annealing, Tabu search, artificial immune system, ant colony algorithm, artificial bee colony, differential evolution, harmonic search, and biogeography-based optimization), extensions (to multiobjective, constrained, discrete, and binary optimization), theoretical analysis (parameter selection and tuning, and convergence analysis), and parallel implementation (in multicore, multiprocessor, GPU, and cloud computing forms). On the other hand, we offered a survey on applications of PSO to the following eight fields: electrical and electronic engineering, automation control systems, communication theory, operations research, mechanical engineering, fuel and energy, medicine, chemistry, and biology. It is hoped that this survey would be beneficial for the researchers studying PSO algorithms.

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Section: Communicationmentioning

confidence: 99%

Section: Area Publication Electrical and Electronic Engineeringmentioning

confidence: 99%

A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

Zhang

Wang

2015

Mathematical Problems in Engineering

697

380

View full text Add to dashboard Cite

show abstract

“…(28) and Eq. (29). If the criteria are not met then again repeat the procedure as a satisfied best solution being developed.…”

Section: Fitness Function Generationmentioning

confidence: 99%

An Energy Efficient Resource Allocation and Transmit Antenna Selection Scheme in mm-Wave Using Massive MIMO Technology

Singh

Kishoreraja

2021

Preprint

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The massive Multiple-Input Multiple-Output (MIMO) improves the reliability of transmission and capacity of the channel. Resource allocation (RA) and Transmit Antenna Selection (TAS) can minimize the complexity in implementation and hardware costs. In this research, both the RA as well as the TAS of wireless communication in millimetre- wave (mm-wave) with massive MIMO technology is considered. Two different solutions are developed for this research such as the Deep Learning method for efficient resource allocation process and optimization algorithm for Transmit Antenna Selection (TAS) process. Here, the RA process is done with the help of Attention Based Capsule Auto-Encoder (ACAE) architecture which allocates the radio resources like power, space, time and frequency to all the available users in the system. Further, Battle Royale Optimization (BRO) algorithm is utilized to select an efficient antenna from multiple antennas at BS. This optimization algorithm optimally selects an efficient antenna so that, user equipments (UEs) can create high quality links and achieves a reduced power consumption rate of the whole architecture. The overall system performance depends on the selection of optimal antenna which in terms enhances Spectral Efficiency (SE), Energy Efficiency (EE), reliability, and diversity gain of MIMO technology. In this way, both RA and optimal antenna selection schemes are performed to maximize the overall performance of wireless communication with massive MIMO technology for 5G wireless communication applications. The implementation of the proposed methodology is evaluated on MATLAB. Finally, the efficiency of the developed method is improved with respect to the capacity, EE and SE.

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“…The average capacity obtained from the simulation experiment shows that PSO algorithm is a sub-optimal selection. Based on the maximum capacity selection criteria, several AS algorithms using binary PSO (BPSO) are presented in literature [25][26][27][28]. The simulating experiments show that these AS algorithms can get high capacity.…”

Section: Introductionmentioning

confidence: 99%

A Novel Joint Transmitting and Receiving Antenna Selection for Spatial Multiplexing Systems

Zhuang

Lin

2020

Applied Mathematics and Nonlinear Sciences

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How to reduce the hardware cost and high power consumption of RF link of communication device is the key problem to be solved for multi-transmitting antenna and multi-receiving antenna system (MIMO). Always choose the best antennas connection a limited number of RF circuits, which is called antenna selection technology (AS), are a perfect solution to the problem, Assuming that the spatial range of the antenna meets the requirements of signal multiplexing and based on the maximum capacity criterion of the selected MIMO system, the manuscript proposes a low computational complexity (CC) and high performance joint transmitting and receiving antenna selection technique (JTRAS). Starting from the traditional capacity formula and the full matrix of MIMO channel, we utilize a simplified channel capacity expression through repeatedly iterating to delete a row and a column of the equivalent decrement channel matrix, which is to remove a pair of transmitting and receiving antennas. Based on the decreasing JTRAS (DJTRAS) algorithm, the capacity results of simulating calculation indicate that its median capacity overtakes other ones, such as optimum selection (OS), AS based on Frobenius 2 norm (NBS), and concise joint AS criterion (CJAS) etc., and the novel DJTRAS scheme can significantly reduce computational complexity (CC) compared to the exhaustive search method with maximum capacity, which defined as optimal algorithm in the curve graphs. This new technology of the AS is particularly suited to large number of selected antennas, such as Lt ≥ NT/2,Lr ≥ NR/2.

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Particle Swarm Optimization for Antenna Selection in MIMO System

Cited by 14 publications

References 17 publications

A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

An Energy Efficient Resource Allocation and Transmit Antenna Selection Scheme in mm-Wave Using Massive MIMO Technology

A Novel Joint Transmitting and Receiving Antenna Selection for Spatial Multiplexing Systems

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