Automated top-down pruning optimization approach in RF power amplifier designs

Kouhalvandi, Lida; Ceylan, Osman; Özoğuz, Serdar

doi:10.1007/s10470-020-01730-w

Cited by 6 publications

(1 citation statement)

References 24 publications

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“…The top-down optimization (TDO) method is opposite to the BUO method and it starts with largest sections and decreases to the smallest sections [51]. This method is employed in [52] for designing and optimizing amplifier where the input/output MNs are divided into subsections presented with scattering parameters (S-parameter) (see Fig. This method is employed for optimizing the implanted multiple-input and multiple-output (MIMO) antennas leads to generate the configuration of the implanted antenna used for bio-medical applications [53].…”

Section: Optimization Methodsmentioning

confidence: 99%

A Prospective Look on Optimization Methods For RFID Systems: Requirements, Challenges and Implementation Aspects

Kouhalvandi

2022

Balkan Journal of Electrical and Computer Engineering

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The radio frequency identification (RFID) is a configuration of wireless communication that uses radio frequency (RF) waves for following up and recognizing data. The RFID system includes important parts as antenna and integrated circuit (IC) for radiating and storing data, respectively. Hence, high performance antenna and IC circuits must be designed for assembling the energy from the radio waves and feeding the RFID chip. One of the important circuit/block in the IC part is the amplifier where the antenna is sensing the radiated output power. For this case, it is substantially important to design high performance antenna and amplifier where the specifications of these circuits must be optimized in a professional way. In this paper, we collect the recently published optimization methods that are employed for designing antenna and RF/analog-based amplifiers. Any researcher by referring to these algorithms can access and find the solutions for their problems, straightforwardly.

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Section: Optimization Methodsmentioning

confidence: 99%

A Prospective Look on Optimization Methods For RFID Systems: Requirements, Challenges and Implementation Aspects

Kouhalvandi

2022

Balkan Journal of Electrical and Computer Engineering

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Multi-objective Optimization Methods for Passive and Active Devices in mm-Wave 5G Networks

Kouhalvandi

Matekovits

2022

PoliTO Springer Series

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Conjointly active and passive modelings with deep neural networks as fully automated optimizations for upper-mid band 6G communications

Kouhalvandi,

Matekovits

2024

Sci Rep

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complex radio frequency (RF) systems [1][2][3][4] . The 5G technology is developing on the 450 MHz-1 GHz (low band), 1 GHz-7 GHz (Mid band), 7 GHz-24 GHz (upper-mid band), and 24 GHz-52 GHz (High band/mmWave) in the recent years regarding the announcement from the international telecommunication union (ITU) [5][6][7][8] . In these telecommunication systems, antennas and amplifiers are significant components that are influencing the coverage (radiation performances) and figure of merit (FoM) of RF circuits 9,10 . Transmitting high-power signal in the communication systems (i.e., combinational of active and passive devices) is a challenging task where intelligent methods including proper optimization methods are required 11,12 . Figure 1 presents the design of wireless system that can include important RF designs as: low pass filter (LPF), amplifier (AMP), monolithic microwave integrated circuit (MMIC), and antenna, where various high performance circuits are required to have successful complex system.In the above mentioned wireless communication systems, amplifiers as low-noise amplifiers (LNAs) with high power amplifiers (HPAs) and antennas play an important role in receiving and transferring large signals.For this case, active device (i.e., amplifiers) and passive component (i.e., antenna) must have very satisfied output performance in the determined band frequency. The design and optimization of these components can face with the problems due to the nonlinear behavior of used transistor models in the amplifiers, quality factor of passive components, environment effects, etc. Hence, strong and multi-objective optimization algorithms are required to help engineers in designing these circuits. In a recently published papers, applying nonlinear optimizations gets the attention of engineers in optimizing RF circuits 13 . In designing these circuits, the more accurate platform for applying the optimization algorithms must be selected considerably. Optimizations as support vector machine (SVM) 14 , Kriging 15 , polynomial-based surrogate modeling 16 , particle swarm optimization [17][18][19] , and genetic algorithm [20][21][22] are suitable optimization methods for designing RF designs; however, when the design parameters with circuit designs are lot and complex these methods can not be powerful enough. To tackle this problem, intelligent-based optimization approach can be a powerful one [23][24][25][26] .Artificial neural networks (ANNs) are presented as an accurate modeling network that can model the nonlinear circuits in a remarkably successful way 27 . In 28 , the optimization process based on the ANN is applied for designing the active antenna that can be suitable for 5G networks. These methods can help electronic design automation (EDA) tools in improving the modeling problems. The DNN (network includes multi hidden layers) is used in 29 for designing and optimizing a receiver that is suitable for low earth orbit (LEO) satellite communications. The works in 28,29 present optimization methods for designing either...

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Automated top-down pruning optimization approach in RF power amplifier designs

Cited by 6 publications

References 24 publications

A Prospective Look on Optimization Methods For RFID Systems: Requirements, Challenges and Implementation Aspects

A Prospective Look on Optimization Methods For RFID Systems: Requirements, Challenges and Implementation Aspects

Multi-objective Optimization Methods for Passive and Active Devices in mm-Wave 5G Networks

Conjointly active and passive modelings with deep neural networks as fully automated optimizations for upper-mid band 6G communications

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