A memory-based gravitational search algorithm for enhancing minimum variance distortionless response beamforming

Darzi, Soodabeh; Kiong, Tiong Sieh; Islam, Mohammad Tariqul; Soleymanpour, Hassan Rezai; Kibria, Salehin

doi:10.1016/j.asoc.2016.05.045

Cited by 24 publications

(9 citation statements)

References 24 publications

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“…There are several advantages of digital beamforming in large antenna array systems. These include high-resolution DOA estimation, high spectral efficiency, high system security and enhanced energy efficiency [16].…”

Section: Digital Beamformermentioning

confidence: 99%

Beamformer development challenges for 5G and beyond

Abbasi

Fusco

2020

Antennas and Propagation for 5G and Beyond

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Antenna's beamforming technology [1] is vital in the utilization of the microwave and millimeter-wave (mmWave) frequency bands for the fifth-generation (5G) communication technology, and beyond, applications that include the sixth generation (6G), Internet of Things and Industry 4.0 [2,3]. Antenna beamforming can be defined as the ability of an antenna array to steer the maximum radiation toward a prescribed direction, or, conversely, the ability of the antenna array to estimate the direction of arrival (DOA) of an impinging signal. Beamforming is generally achieved by using multiple antennas, spatially colocated to either function as an independent radiator or as a unit cell in larger array arrangements. Recently, the term multiple-input-multiple-output (MIMO) has been used in conjugation with the beamforming technology since it also involves multiple antenna systems, thus linking it to the beamforming technology [4]. When the number of antennas used in the MIMO operation becomes very large, it is generally termed massive MIMO technology [5]. Different beamforming concepts are used for the two frequency spectrum classifications in cellular technology, i.e., sub-6 GHz and mmWave. The majority of beamformer challenges at sub-6 GHz are already resolved, and prototypes are now available [6]; however, there are still major fundamental challenges that engineers face while developing beamformers for use in the mmWave bands, i.e., >28 GHz. Challenges include, but are not limited to, high path loss, power generation, adaption to account for realistic channel estimation, hardware impairments, energy leakage in circuits, high development cost, spatial-temporal channel variations, signal blockages due to the presence of obstacles such as beamformer casing, user body and hand. Also, multiuser MIMO techniques are not easily implementable in mmWave frequencies. In addition to this, beam coordination at transmitter and receiver antenna systems especially in mmWave spectrum is very challenging. In this chapter, we will first classify the beamformers based on the system architecture, operational frequency

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Section: Digital Beamformermentioning

confidence: 99%

Beamformer development challenges for 5G and beyond

Abbasi

Fusco

2020

Antennas and Propagation for 5G and Beyond

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“…Since the rotors always rotate about their Z-axes at the rate of Ω with the moment of inertia about Z-axis of and have very low masses, and can then be omitted and the dynamics of each rotor reduces to: (15) Note that Eq. (15) is a function of rotor speed Ω.…”

Section: Rotor Dynamicsmentioning

confidence: 99%

“…Yet, the MBGSA uses the overall best solution of the agents from previous iterations in the calculation of agents' positions. Consequently, the agents try to improve their positions by always searching around overall best solutions [15]. The following equation will be modified:…”

Section: Mbgsa Algorithmmentioning

confidence: 99%

“…Since rotor 1 and 3 rotate in the opposite direction of rotor 2 and 4, one can define the total rotor speed as: (16) From Eq. (14) and (15), the total moment due to gyro effect from all rotors can be expressed as:…”

Section: Rotor Dynamicsmentioning

confidence: 99%

“…Thus in order to get the best (optimal) setting to achieve the objective an optimization algorithm must be used. In this work the Gravitational Search Algorithm was applied which is inspired by [1], then it was improved to generate new Hybrid algorithm called Memory Based Gravitational Search Algorithm -Particle Swarm Optimization (MBGSA) which achieved better results it combine the work in [14,15]. As much of research done in this field still not all tuning algorism was tested, Gravitational Search Algorithm is one of them.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Tuning of PID Controllers for Quadcopter System using Hybrid Memory based Gravitational Search Algorithm – Particle Swarm Optimization

Abbas¹,

Sami²

2017

IJCA

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Quadrotors are coming up as an attractive platform for unmanned aerial vehicle (UAV) research, due to the simplicity of their structure and maintenance, their ability to hover, and their vertical take-off and landing (VTOL) capability. With the vast advancements in small-size sensors, actuators and processors, researches are now focusing on developing mini UAV's to be used in both research and commercial applications. This work presents a detailed mathematical nonlinear dynamic model of the quadrotor which is formulated using the Newton-Euler method. Although the quadrotor is a 6 DOF under-actuated system, the derived rotational subsystem is fully actuated, while the translational subsystem is under-actuated. The derivation of the mathematical model was followed by the development of the controller to control the altitude, attitude, heading and position of the quadrotor in space, which is, based on the linear Proportional-Derivative-Integral (PID) controller; thus, a simplified version of the model is obtained. The gains of the controllers will be tuned using optimization techniques to improve the system's dynamic response. The standard Gravitational Search Algorithm (GSA) was applied to tune the PID parameters and then it was compared to Hybrid Memory Based Gravitational Search Algorithm -Particle Swarm Optimization tuning, and the results shows improvement in the new algorithm, which produced enhancements by ( %) compared to the standard algorithm.

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Multi-beam and Beamforming Terahertz Array Antenna for 6G Communication

Nissanov

Singh

2023

Antenna Technology for Terahertz Wireless Communication

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A memory-based gravitational search algorithm for enhancing minimum variance distortionless response beamforming

Cited by 24 publications

References 24 publications

Beamformer development challenges for 5G and beyond

Beamformer development challenges for 5G and beyond

Tuning of PID Controllers for Quadcopter System using Hybrid Memory based Gravitational Search Algorithm – Particle Swarm Optimization

Multi-beam and Beamforming Terahertz Array Antenna for 6G Communication

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