Bio-inspired optimization algorithms for smart antennas

This thesis studies the effectiveness of bio-inspired optimization algorithms in controlling adaptive antenna arrays. Smart antennas are able to automatically extract the desired signal from interferer signals and external noise. The angular pattern depends on the number of antenna elements, their geometrical arrangement, and their relative amplitude and phases. In the present work different antenna geometries are tested and compared when their array weights are optimized by different techniques. First, the Genetic Algorithm and Particle Swarm Optimization algorithms are used to find the best set of phases between antenna elements to obtain a desired antenna pattern. This pattern must meet several restraints, for example: Maximizing the power of the main lobe at a desired direction while keeping nulls towards interferers. A series of experiments show that the PSO achieves better and more consistent radiation patterns than the GA in terms of the total area of the antenna pattern. A second set of experiments use the Signal-to-Interference-plus-Noise-Ratio as the fitness function of optimization algorithms to find the array weights that configure a rectangular array. The results suggest an advantage in performance by reducing the number of iterations taken by the PSO, thus lowering the computational cost. During the development of this thesis, it was found that the initial states and particular parameters of the optimization algorithms affected their overall outcome. The third part of this work deals with the meta-optimization of these parameters to achieve the best results independently from particular initial parameters. Four algorithms were studied: Genetic Algorithm, Particle Swarm Optimization, Simulated Annealing and Hill Climb. It was found that the meta-optimization algorithms Local Unimodal Sampling and Pattern Search performed better to set the initial parameters and obtain the best performance of the bio-inspired methods studied

Pattern Synthesis of Planar Nonuniform Circular Antenna Arrays Using a Chaotic Adaptive Invasive Weed Optimization Algorithm

A novel invasive weed optimization (IWO) variant called chaotic adaptive invasive weed optimization (CAIWO) is proposed and applied for the optimization of nonuniform circular antenna arrays. A chaotic search method has been combined into the modified IWO with adaptive dispersion, where the seeds produced by a weed are dispersed in the search space with standard deviation specified by the fitness value of the weed. To evaluate the performance of CAIWO, several representative benchmark functions are minimized using various optimization algorithms. Numerical results demonstrate that the proposed approach improves the performance of the algorithm significantly, in terms of both the convergence speed and exploration ability. Moreover, the scheme of CAIWO is employed to find out an optimal set of weights and antenna element separation to obtain a radiation pattern with maximum side-lobe level (SLL) reduction with different numbers of antenna element under two cases with different purposes. The design results obtained by CAIWO have comfortably outperformed the published results obtained by other state-of-the-art metaheuristics in a statistically meaningful way

Optimisation of DTV coverage and broadcasting antennas

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel UniversityThe increased use of the available radio frequency spectrum by many existing as well as new technologies brings up the need of more advanced and specialised antennas, designed for very specific purposes. A very good example is the withdrawal of analogue TV from the radio spectrum, which has given space to be used by newer technologies such as 4G and 5G. Optimising the design of an antenna for a specific purpose is a goal that becomes more and more necessary. This suggests that an investigation of methods to optimise an electromagnetic design with the best possible results at the best possible time is also necessary. Evolutionary Algorithms (EA) are a very well know method which exhibits solid results within a smallest possible time for electromagnetic problems (e.g. antenna design optimisation). EA are nature inspired algorithms, and some very popular examples, which are widely used in antenna optimisation are the Differential Evolution (DE), Particle Swarm Optimisation (PSO) and Invasive Weed Optimisation (IWO). This thesis researches a comparison between the aforementioned methods, while also proposing a novel method, which is a modified version of IWO and has proven to be very solid. To determine the efficacy of the proposed method, all the algorithms were compared on some of the most major test functions for such purposes. Some examples are Ackley’s, De Jong’s, Holder table, Rastrigin and Rosenbrock. By employing these test functions, it was possible to determine the optimum settings of the modified IWO version. These methods are compared for different antenna design optimisation simulations using software such as MATLAB and CST Microwave Studio, to determine which method yields the best results and to output novel optimised antenna designs for different purposes. Some of the novel antenna designs that were applied to EAs for optimisation are a collinear dipole array with a specifically shaped radiation pattern and a log-periodic dipole antenna (LPDA) with flat gain response across its operating spectrum for Digital TV (DTV) broadcasting purposes. Other novel designs include a planar elliptical dipole antenna for Ultra-Wideband (UWB) Electromagnetic Compatibility (EMC) applications such as EMC measurements and a pin-fed notched circular patch antenna with circular polarisation for satellite communications, in which cases the small size of the generated geometries was also a goal so that portability is achieved. The geometrical parameters of the best possible antenna design were in some cases fabricated and compared to the simulated results so that the latter is compared to real world applications

Performance optimisation of small antenna arrays

This thesis addresses radiation pattern synthesis problems for small linear periodic phased arrays (with array elements less then 10). Due to the small array size conventional pattern synthesis techniques fail to produce the required results. In the case of practical small arrays, mutual coupling and element pattern asymmetric effect degrade the array radiation performance. The main performance metrics considered in this thesis include side lobe level (SLL), gain, halfpower beamwidth (HPBW) and mainbeam scan direction. The conventional pattern synthesis approaches result in sub optimal gain, SLL and HPBW due to the limited number of elements and the mutual coupling involved. In case of difference pattern synthesis these factors resulted in lower difference pattern slope, degraded SLL and difference peak asymmetry. The sum and difference patterns are used in monopulse arrays and a simplified feed that could produce both patterns with acceptable radiation properties is of interest and has been examined (chapter 5). A conventional technique is applied to small arrays to synthesise a sector beam and there is limited control over the radiation pattern. It is shown that the mutual coupling has significant effect on the array radiation pattern and mitigation is necessary for optimum performance (chapter 6). Furthermore, wideband phased arrays may have a natural limitation of the HPBW in low gain applications and minimisation of the variation becomes important. Also the SLL variations for wideband antenna arrays in the presence of mutual coupling considerably degrade the radiation pattern. The mutual coupling degrades significantly the radiation pattern performance in case of small scanning wideband arrays (chapter 7). It is the primary goal of this thesis to develop an optimisation scheme thatis applied in the above scenarios (chapters 3 & 4). The only degree of freedom assumed is the array excitation. Optimised amplitude and phase for each element in the array are determined by the proposed scheme, concurrently. The deterministic optimisation techniques reported in the literature for the pattern synthesis may involve complicated problem modelling. The heuristic opti-misation techniques generally are computationally expensive. The proposedIntelligent z-space Boundary Condition-Particle Swarm Optimiser (IzBC-PSO)is based on a heuristic algorithm. This scheme can be applied to a wider rangeof problems without significant modifications and requires fewer computationscompared to the competing techniques.In order to verify the performance of IzBC-PSO antenna array measure-ments were performed in the receiving mode only using the online and offlinedigital beamforming setups described in chapter 8. The measurement resultsshow that the proposed scheme may be successfully applied with both onlineand offline digital beamformers for a practical small array (chapter 8).EThOS - Electronic Theses Online ServiceCOMSATS Institute of Information Technology (CIIT), Islamabad, PakistanGBUnited Kingdo

Pattern Nulling of Linear Antenna Arrays Using Backtracking Search Optimization Algorithm

An evolutionary method based on backtracking search optimization algorithm (BSA) is proposed for linear antenna array pattern synthesis with prescribed nulls at interference directions. Pattern nulling is obtained by controlling only the amplitude, position, and phase of the antenna array elements. BSA is an innovative metaheuristic technique based on an iterative process. Various numerical examples of linear array patterns with the prescribed single, multiple, and wide nulls are given to illustrate the performance and flexibility of BSA. The results obtained by BSA are compared with the results of the following seventeen algorithms: particle swarm optimization (PSO), genetic algorithm (GA), modified touring ant colony algorithm (MTACO), quadratic programming method (QPM), bacterial foraging algorithm (BFA), bees algorithm (BA), clonal selection algorithm (CLONALG), plant growth simulation algorithm (PGSA), tabu search algorithm (TSA), memetic algorithm (MA), nondominated sorting GA-2 (NSGA-2), multiobjective differential evolution (MODE), decomposition with differential evolution (MOEA/D-DE), comprehensive learning PSO (CLPSO), harmony search algorithm (HSA), seeker optimization algorithm (SOA), and mean variance mapping optimization (MVMO). The simulation results show that the linear antenna array synthesis using BSA provides low side-lobe levels and deep null levels

Comparative Analysis of Linear and Nonlinear Pattern Synthesis of Hemispherical Antenna Array Using Adaptive Evolutionary Techniques

Hemispherical antenna arrays are subjected to linear and nonlinear synthesis and are optimized using adaptive based differential evolution (ADE) and fire fly (AFA) algorithm. The hemispherical shaped array with isotropic elements is considered. Antenna element parameters that are used for synthesis are excitation amplitude and angular position. Linear synthesis is termed as the variation in the element excitation amplitude and nonlinear synthesis is process of variation in element angular position. Both ADE and AFA are a high-performance stochastic evolutionary algorithm used to solve N-dimensional problems. These methods are used to determine a set of parameters of antenna elements that provide the desired radiation pattern. The effectiveness of the algorithms for the design of conformal antenna array is shown by means of numerical results. Comparison with other methods is made whenever possible. The results reveal that nonlinear synthesis, aided by the discussed techniques, provides considerable enhancements compared to linear synthesis

Particle Swarm Optimization

Particle swarm optimization (PSO) is a population based stochastic optimization technique influenced by the social behavior of bird flocking or fish schooling.PSO shares many similarities with evolutionary computation techniques such as Genetic Algorithms (GA). The system is initialized with a population of random solutions and searches for optima by updating generations. However, unlike GA, PSO has no evolution operators such as crossover and mutation. In PSO, the potential solutions, called particles, fly through the problem space by following the current optimum particles. This book represents the contributions of the top researchers in this field and will serve as a valuable tool for professionals in this interdisciplinary field

Characteristics of different focusing antennas in the near field region

Focusing antennas are of interest in many application including microwave wireless power transmission, remote (non-contact) sensing, and medical applications. Different kinds of antennas such as array antennas, reflector antennas and Fresnel zone plate (FZP) antennas have been used for these applications. Here, first, a new scheme in designing focused array antennas with desired sidelobe levels (SLLs) in the near field region is presented. The performance of the large focused array antennas is predicted based on the knowledge of the mutual admittances of a smaller array. The effects of various focal distances on the near field pattern of these antennas are investigated. Then, electric field pattern characteristics of the focused Fresnel zone plate lens antennas in the near-field region are presented. The FZP antenna fed by a circular horn is implemented and the effects of various focal lengths on the near field pattern of this antenna are examined. It is shown that the maximum field intensity occurs closer to the antenna aperture than to the focal point and this displacement increases as the focal point moves away from the antenna aperture. The focusing properties of ultra-wideband (UWB) array antennas are also presented. Large current radiator (LCR) antennas are modeled by replacing the antenna with a set of infinitesimal dipoles producing the same near field of the antenna. LCR antenna arrays are used to provide high concentration of microwave power into a small region. It is shown that the defocusing effect occurs in pulse radiating antennas as well. Invasive weed optimization (IWO), a new optimization algorithm, is also employed to optimize the pulsed array antenna. In the attempt of optimizing the focused arrays, a new scenario for designing thinned array antennas using this optimization method is introduced. It is shown that by using this method, the number of elements in the array can be optimized, which yields a more efficient pattern with less number of elements. By applying this new optimization method to UWB arrays, the peak power delivered to a localized region can be increased

Optimization methods in problems of the lineаr antenna array synthesis

Предмет истраживања ове докторске дисертације је анализа линеарног антенског низа, анализа методе комбинације две претраге за синтезу линеарног антенског низа и примена представљене методе на проблеме синтезе линеарног антенског низа...The primary goals of this dissertation are: analysis of the linear antenna array; analysis of a method combining two searches for the linear antenna array synthesis; and the application of the method presented to the problems of the linear antenna array synthesis..