Design and Optimization Method of Broadband High‐Efficiency Power Amplifier Using Irregular Matching Network

A novel hybrid gravitational search and pattern search algorithm for load frequency control of nonlinear power system

In advanced wireless communication systems, a rapid increase in the mobile data traffic and broad information bandwidth requirement can lead to the use of complex spectrally efficient modulation schemes such as orthogonal frequency-division multiplexing (OFDM). Generally, complex non-constant envelope modulated signals have very high peak-to-average ratios (PAPR). Doherty Power Amplifier (DPA) is the most commonly used power amplifier (PA) architecture for meeting high efficiency requirement in advanced communication systems, in the presence of high PAPR signals. However, limited bandwidth of the conventional DPA is often identified as a bottleneck for widespread deployment in base-station application for multi-standard communication signals. The research in this thesis focuses on the development of new designs to overcome the bandwidth limitations of a conventional PA. In particular, the bandwidth limitation factors of a conventional DPA architecture are studied. Moreover, a novel design technique is proposed for DPA's bandwidth extension. In the first PA design, limited bandwidth and linearity problems are addressed simultaneously. For this purpose, a new Class-AB PA with extended bandwidth and improved linearity is presented for LTE 5 W pico-cell base-station over a frequency range of 1.9-2.5 GHz. A two-tone load/source-pull and bias point optimization techniques are used to extract the sweet spots for optimum efficiency and linearity from the 6 W Cree GaN HEMT device for the whole frequency band. The realized prototype presented saturated PAE higher than 60%, a power gain of 13 dB and an average output power of 36.5 dBm over the desired bandwidth. The proposed PA is also characterized by QAM-256 and LTE input communication signals for linearity characterization. Measured ACPRs are lower than -40 dBc for an input power of 17 dBm. The documented results indicate that the proposed Class-AB architecture is suitable for pico-cell base-station application. In the second PA design, an inherent bandwidth limitation of Class-F power amplifier forced by the improper load harmonics terminations at multiple harmonics is investigated and analyzed. It is demonstrated that the impedance tuning of the second and third harmonics at the drain terminal of a transistor is crucial to achieve a broadband performance. The effect of harmonics terminations on power amplifier's bandwidth up to fourth harmonics is investigated. The implemented broadband Class-F PA achieved maximum saturated drain efficiency 60-77%, and 10 W output power throughout (1.1-2.1 GHz) band. The simulated and measured results verify that the presented Class-F PA is suitable for a high-efficiency system application in wireless communications over a wide range of frequencies. In the third PA design, a single- and dual-input DPA for LTE application in the 3.5 GHz frequency band are presented and compared. The main goal of this study is to improve the performance of gallium-nitride (GaN) Doherty transmitters over a wide bandwidth in the 3.5 GHz frequency band. For this purpose, the linearity-efficiency trade-off for the two proposed architectures is discussed in detail. Simulated results demonstrate that the single- and dual-input DPA exhibited a peak drain efficiency (DE) of 72.4% and 77%, respectively. Both the circuits showed saturated output power more than 42.9 dBm throughout the designed band. Saturated efficiency, gain and bandwidth of dual-input DPA are higher than that of the single-input DPA. On the other side, dual-input DPA linearity is worse as compared to the single-input DPA. In the last PA design, a novel design methodology for ultra-wide band DPA is presented. The bandwidth limitation factors of the conventional Doherty amplifier are discussed on the ground of broadband matching with impedance variation. To extend the DPA bandwidth, three different methods are used such as post-matching, low impedance transformation ratio and the optimization of offset line for wide bandwidth in the proposed design. The proposed Doherty power amplifier was designed and realized based on two 10 W GaN HEMT devices from Cree Inc. The measured results exhibited 42-57% of efficiency at the 6-dB back-off and saturated output power ranges from 41.5 to 43.1 dBm in the frequency range of 1.15 to 2.35 GHz (68.5% fractional bandwidth). Moreover, less than -25 dBc ACPRs are measured at 42 dBm peak output power throughout the designed band. In a nutshell, all power amplifiers presented in this thesis are suitable for wideband operation and their performances are satisfying the required operational standard. Therefore, this thesis has a significant contribution in the domain of high efficiency and broadband power amplifiers.

New generations of wireless communication systems require linear efficient RF power amplifiers (PAs) for higher transmission data rates and longer battery life. On the contrary, conventional PAs are normally designed for peak efficiency under maximum output power (Pout). Thus, in power back-off, the overall efficiency degrades significantly and the average efficiency is much lower than the efficiency at maximum Pout. Chireix outphasing PA, also called LINC (Linear amplification using Non-linear Components), is one of the most promising techniques to improve the efficiency at power back-off. In this method, a variable envelope input signal is first decomposed into two constant-envelope phase-modulated signals and then amplified using two highly efficient non-linear PAs. The output signals are combined preferably in a loss-less power combiner to build the desired output signal. In this way, the PA exhibits high efficiency with good linearity. In this thesis, first we analyze a complex model of outphasing combiner considering its nonidealities such as reflection and loss in transmission lines (TL). Then we propose a compact model with analytical formula that is validated through several comparative tests using ADS and Spectre RF. Furthermore, we analyze the effect of reactive load in Chireix combiner with stubs (a parallel inductor and capacitor), while distinguishing between its capacitive and inductive parts. It is demonstrated that only the capacitive part of the reactive load degrades the performances. Based on this, a new architecture (Z LINC) is proposed where the power combiner is designed to provide a zero capacitive load to the PAs whatever the outphasing angle. The theory describing the operations of the system is developed and a 900 MHz classical LINC and Z-LINC PAs are designed and measured. In addition, a miniaturization technique is proposed which employs I»/8 or smaller TLs instead of conventional I»/4 TLs in outphasing power combiner. This technique is applied to implement a 900 MHz PA using LDMOS power transistors. Besides single-band PAs, dual-band PAs are more and more needed because of an increasing demand for wireless communication terminals to handle multi-band operation. In chapter 5, a new compact design approach for dual-band transmitters based on a reconfigurable outphasing combiner is proposed. The objective is to avoid the cumbersome implementations where several PAs and matching network are used in parallel. The technique is applied to design a dual band PA with a fully integrated power combiner in 90 nm CMOS technology. An inverter-based class D PA topology, particularly suitable for outphasing and multimode operations is presented. The TLs in the combiner, realized using a network of on-chip series inductors and parallel capacitors, are reconfigurable from I»/4 in 1800 MHz to I»/8 in 900 MHz. In order to maximize the efficiency, the on-chip inductors are implemented using high quality factor on chip slab inductors. The measured maximum Pout at 900/1800 MHz are 24.3 and 22.7 dBm with maximum efficiencies of 51% and 34% respectively.

Gravitational Search Algorithm (GSA) is one of the recent nature inspired algorithms which is capable to solve optimization problems. GSA is inspired by the Newtonian's law of gravity and the law of motion. The aim of the paper is to investigate GSA utilization in various optimization problems. The paper provides a brief explanation on GSA and also presents the previous optimization works based on GSA. Based on the literature, GSA is capable of providing more accurate, effective and robust high-quality solution for most of the optimization problems. The algorithm has been applied in various applications and has solved various optimization problems such as in power system, controller design, network routing, sensor networks, software design and many more. GSA has been adapted in the optimization of parameters, settings, strategies, cost, voltage control and also power dispatch. The algorithm also has been adapted to optimize the design of controllers, software, antenna and microgrids. Based on previous works, GSA has showed better performance in solving the optimization problems compared to other previous algorithms such as PSO, ACO and ABC. It is expected that more studies are to be done based on GSA in future as the algorithm has a high potential to solve various optimization problems in different areas.

The fuel and cost efficiency in automotive industry is related to the designing of light-weight vehicle components. Main aim of this paper is to show effectiveness of two new optimization algorithms in solving structural design optimization problems. The gravitational search algorithm (GSA) and charged system search algorithm (CSS) are applied to the optimum design of a vehicle component. The results show that both GSA and CSS have a better search capability than the genetic algorithms to find optimum solutions.

A novel scheme for a multi-band power amplifier (PA) that employs a low-loss reconfigurable matching network is presented and discussed. The matching network basically consists of a cascade of single-stub tuning circuits, in which each stub is connected to a transmission line via a Single-Pole-Single-Throw (SPST) switch. By controlling the on/off status of each switch, the matching network works as a band-switchable matching network. Based on a detailed analysis of the influence of non-ideal switches in the matching network, we conceived a new design perspective for the reconfigurable matching network that achieves low loss. A 900/1900-MHz dual-band, 1 W class PA is newly designed following the new design perspective, and fabricated with microelectro mechanical system (MEMS) SPST switches. Owing to the new design and sufficient characteristics of the MEMS switches, the dual-band PA achieves over 60% of the maximum power-added efficiency with an output power for each band exceeding 30dBm. These results are comparable to the estimated results for a single-band PA. This shows that the proposed scheme provides a band-switchable highly efficient PA that has superior performance compared to the conventional multi-band PA that has a complex structure.

The classical optimization algorithms are not efficient in solving complex search and optimization problems. Thus, some heuristic optimization algorithms have been proposed. In this paper, exploration of association rules within numerical databases with Gravitational Search Algorithm (GSA) has been firstly performed. GSA has been designed as search method for quantitative association rules from the databases which can be regarded as search space. Furthermore, determining the minimum values of confidence and support for every database which is a hard job has been eliminated by GSA. Apart from this, the fitness function used for GSA is very flexible. According to the interested problem, some parameters can be removed from or added to the fitness function. The range values of the attributes have been automatically adjusted during the time of mining of the rules. That is why there is not any requirements for the pre-processing of the data. Attributes interaction problem has also been eliminated with the designed GSA. GSA has been tested with four real databases and promising results have been obtained. GSA seems an effective search method for complex numerical sequential patterns mining, numerical classification rules mining, and clustering rules mining tasks of data mining.

— In this paper, two optimization algorithms i.e., Particle Swarm Optimization (PSO) and Gravitational Search Algorithm (GSA) are combined to produce a hybrid population-based algorithm PSOGSA. The approach of HPSOGSA is to use the assessment of PSO using the social thinking capability and the manipulation of GSA using the local search proficiency. To increase the performance of HPSOGSA, some benchmark functions are used. Two standard database ORL and YALE is used to assess the classification performance of the proposed method. Classification accuracy is compared for diverse number of training samples per class. Further lead of proposed method is confirmed by analyzing percentage improvement in classification accuracy. With limited accessibility of training sample, percentage enhancement is very successful for face recognition applications. Keywords: Particle Swarm Optimization (PSO), Gravitational Search Algorithm (GSA), Particle Swarm Optimization and Gravitational Search Algorithm (PSOGSA), Hybrid Particle Swarm Optimization and Gravitational Search Algorithm (HPSOGSA). Cite this Article Purvaa Saxena, Gargi Mishra, Apoorva Aggarwal, Ayush Priyadarshi, Nitin Kumar, Rahul. Pattern Recognition Using Hybrid Meta-Heuristic. Journal of Artificial Intelligence Research & Advances. 2019; 6(2): 100–108p.

An effective method for optimal design of water distribution network (WDN) can significantly benefit to develop commercial software for component sizing. This research investigates Gravitational Search Algorithm (GSA) for pipe cost optimization model problems. GSA is a meta-heuristic (MH) algorithm which makes ease of its applicability to the design of WDNs due to its minimum number of algorithm parameters and requiring least effort in fine-tuning the parameters. Three well-known benchmark networks (Hanoi network, Two-Reservoir network and New York tunnels network) and a real-world WDN located Khorramshahr city in Iran were used. The GSA results were compared with the solutions obtained through various Evolutionary Algorithms. Experimental results show success of GSA in arriving minimum cost solution. GSA achieved to the best so far solution reported for one case (Hanoi network), and it could find the least cost for three other networks compared to the best results of other optimization algorithms. In addition, for two case studies (Hanoi network and Khorramshahr city network) number of function evaluations were less than other algorithms. Further, the study reveals that GSA achieved the maximum number of times the best so far solution and confirming rapid convergence without struck up at local optimum.

A new optimization algorithm is proposed, since a huge problem that many algorithms faced was not being able to effectively balance the global and local search ability. Matter exists in three states: solid, liquid, and gas, which presents different motion characteristics. Inspired by multi- states of matter, individuals of optimization algorithm have different motion characteristics of matter, which could present different search ability. The Finite Element Analysis (FEA) approach can simulate multi- states of matter, which can be adopted to effectively balance the global search ability and local search ability in new optimization algorithm. The new algorithm is creative application of Finite Element Analysis at optimization algorithm field. Artificial Physics Optimization (APO) and Gravitational Search Algorithm (GSA) belongs to the algorithm types defined by force and mass. According to FEA approach, node displacement caused by force and stiffness could be equivalent to motion caused by force and mass of APO and GSA. In the new algorithm framework, stiffness replaces mass of APO and GSA algorithm. This paper performs research on two different algorithms based on APO and GSA respectively. The individuals of new optimization algorithm are divided into solid state, liquid state, and gas state. The effects of main parameters on the performance were studied through experiments of 6 static test functions. The performance is compared with PSO, basic APO, or GSA for four complex models which made up of solid individual, liquid individual, and gas individual in iterative process. The reasonable complex model can be confirmed experimentally. Based on the reasonable complex model, the article conducted complete experiments against Enhancing artificial bee colony algorithm with multi-elite guidance (MGABC), Artificial bee colony algorithm with an adaptive greedy position update strategy (AABC), Multi-strategy ensemble artificial bee colony (MEABC), Self-adaptive heterogeneous PSO (fk-PSO), and APO with 28 CEC2013 test problem. Experimental results show that the proposed method achieves a good performance in comparison to its counterparts as a consequence of its better exploration– exploitation balance. The algorithm supplies a new method to improve physics optimization algorithm.

In this paper, a compact Ka-band power amplifier (PA) with good bandstop responses from the matching network is designed for satellite communications. Compared with the conventional PA, it can suppress the undesired signal and meanwhile ensure the low loss and good performance. For demonstration, a 0.15 μm GaAs pseudomorphic high electron mobility (pHEMT) two-stage PA based on the matching filtering technique is implemented, which exhibits the small-signal gain of 15.5 dB in the Ka-band and the suppression greater than 55 dB in the K-band, the output saturation power (P <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">sat</sub> ) of 21 dBm and the power-added efficiency of 35% at P <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">sat</sub> .

Gravitational Search Algorithm (GSA) is a memory-less, nature-inspired algorithm for nonlinear continuous optimization problems. In Singh et al. (a new Improved Gravitational Search Algorithm for function optimization using a novel “best-so-far” update mechanism. IEEE, pp. 35–39 (2015) [21]), Singh and Deep proposed an Improved GSA using best-so-far mechanism. In this paper, the problem of 3D reconstruction is modelled as a nonlinear optimization problem. GSA and Improved GSA are used to solve three reconstruction problems. Based on the several computational experiments and analysis, it is concluded that the performance of improved GSA is better than original GSA in terms of convergence and solution quality.

Particle swarm optimization (PSO) is a relatively new optimization algorithm that has been applied to a variety of problems. However, it may easily get trapped into local optima when solving complex multimodal problems. To address this concerning issue, a novel approach, namely hybrid particle swarm optimization and gravitational search algorithm (GSA) method by introducing GSA into PSO (HPSO-GSA), is proposed in this paper for global numerical optimization. The proposed algorithm incorporates both the different concepts from PSO and GSA, updating particle positions offered by both PSO algorithm and GSA tool. The hybrid approach makes full use of the fast convergence capability of PSO and the exploitation ability of GSA. To efficiently decrease the computational cost in the hybrid algorithm, GSA is introduced when adaptive evolution stagnation cycle is met. HPSO-GSA is tested on a commonly used set of benchmark functions and is compared to other algorithms presented in the literature. Experimental results show that HPSO-GSA obtains better performance on the tested functions.

Optimization problems are interest and common problems that are often encountered in life. Optimization can be applied to solve various problems, for example development, government, business, social, economic and something related to the limitation of resource capacity. The most frequently encountered, optimization is often used to find the best solution, that is maximizing profits or minimizing production costs. One of the optimization problems that often occurs is the knapsack problem. There are several types of knapsack problems, one of which is Modified Bounded Knapsack with Multiple Constraints (MBKMC) problem. In popular mathematical studies, metaheuristic algorithms are very often used to solve optimization problems. In this paper the authors did not only use one algorithm, but implemented two metaheuristic algorithms which were combined into one, namely the Gravitational Search Algorithm (GSA) and the Cat Swarm Optimization (CSO) algorithm. The combined algorithm uses the entire GSA algorithm mechanism which is added with the CSO algorithm seeking mode to become the GSA&sCSO algorithm. The author uses the GSA&sCSO algorithm to solve the MBKMC problem of uncertain coefficient. Based on the results of this research, the GSA&sCSO algorithm produces a better solution (higher profit) than the GSA algorithm and the CSO algorithm and earn a better advantage in accordance with the knapsack capacity. In addition, the uncertain coefficient greatly affects the solution obtained, i.e if there is a change of the coefficient, then the solution also changes.

Today, many metaheuristics algorithms have been developed are inspired by the physical phenomena or behaviors of natural creatures that are very effective in solving complex engineering optimization problems. These algorithms seek to find optimal solutions in various engineering problems such as machine learning, image processing, pattern matching, decision making, and so on. These algorithms can be implemented in both single-objective and multi-objective optimization problems. Among the most famous techniques in this category include the ant colony optimization (ACO) algorithm, which is designed based on the navigation of ants; particle swarm optimization (PSO), which is based on the movement of organisms in a bird flock; and gravitational search algorithm (GSA). GSA is one of the most popular metaheuristics methods. It is inspired by the law of gravity and motion and creating the appropriate balance between exploration and exploitation capabilities. In this chapter, we are going to introduce the basic GSA and review the newer versions. First, we will introduce the original GSA that is proposed for continuous problems. Then we will discuss other versions of GSA for various optimization problems and analyze the convergence properties of this algorithm. We will also review the GSA in various engineering applications.