Christos Skeberis

Improved Antenna Array Adaptive Beamforming with Low Side Lobe Level Using a Novel Adaptive Invasive Weed Optimization Method

An improved adaptive beamforming technique of antenna arrays is introduced. The technique is implemented by using a novel Invasive Weed Optimization (IWO) variant called Adaptive Dispersion Invasive Weed Optimization (ADIWO) where the seeds produced by a weed are dispersed in the search space with standard deviation specifled by the fltness value of the weed. The adaptive seed dispersion makes the ADIWO converge faster than the conventional IWO. This behavior is verifled by applying both the ADIWO and the conventional IWO on well-known test functions. The ADIWO method is utilized here as an adaptive beamformer that makes a uniform linear antenna array steer the main lobe towards the direction of arrival (DoA) of a desired signal, form nulls towards the respective DoA of several interference signals and achieve low side lobe level (SLL). The proposed ADIWO based beamformer is compared to a Particle Swarm Optimization (PSO) based beamformer and a well known beamforming method called Minimum Variance Distortionless Response (MVDR). Several cases have been studied with difierent number of interference signals and difierent power level of additive zero-mean Gaussian noise. The results show that the ADIWO provides su-cient steering ability regarding the main lobe and the nulls, works faster than the PSO and achieves better SLL than the PSO and MVDR.

Design of a Novel Antenna Array Beamformer Using Neural Networks Trained by Modified Adaptive Dispersion Invasive Weed Optimization Based Data

A new antenna array beamformer based on neural networks (NNs) is presented. The NN training is performed by using optimized data sets extracted by a novel invasive weed optimization (IWO) variant called modified adaptive dispersion IWO (MADIWO). The trained NN is utilized as an adaptive beamformer that makes a uniform linear antenna array steer the main lobe toward a desired signal, place respective nulls toward several interference signals, and suppress the side lobe level (SLL). Initially, the NN structure is selected by training several NNs of various structures using MADIWO-based data and by making a comparison among the NNs in terms of training performance. The selected NN structure is then used to construct an adaptive beamformer, which is compared to MADIWO-based and ADIWO-based beamformers, regarding the SLL and the ability to properly steer the main lobe and the nulls. The comparison is made, considering several sets of random cases with different numbers of interference signals and different power levels of additive zero-mean Gaussian noise. The comparative results exhibit the advantages of the proposed beamformer.

Synthesis of a Near-Optimal High-Gain Antenna Array With Main Lobe Tilting and Null Filling Using Taguchi Initialized Invasive Weed Optimization

A near-optimal base-station antenna array synthesis suitable for broadcasting applications is presented. The array is required to provide a high-gain radiation pattern with a main lobe slightly tilted from the horizontal plane and null filling inside an angular sector under the main lobe. To satisfy the above requirements, a novel invasive weed optimization (IWO) variant called Taguchi initialized IWO (TI-IWO) is proposed in this paper. In TI-IWO, the Taguchis optimization method is employed to initialize effectively the positions of the weeds used by the IWO method. In this way, the fitness function starts from lower values and, thus, the TI-IWO method finds better near-optimal solutions than the conventional IWO method. The proposed method has been applied to linear arrays. Due to its easy implementation in practice, a uniform-amplitude excitation distribution is considered to be applied on the array elements. Two cases of isotropic source arrays are studied under specific requirements for maximum possible gain, main lobe tilting, and null filling. Also, the TI-IWO method is applied to optimize realistic cases of collinear wire dipole arrays in front of a mast under the same requirements and an additional one concerning the impedance matching of all the dipoles.

Optimal Wideband LPDA Design for Efficient Multimedia Content Delivery Over Emerging Mobile Computing Systems

An optimal synthesis of a wideband log-periodic dipole array (LPDA) is introduced in this paper. The LPDA optimization is performed under several requirements concerning the standing wave ratio, the forward gain, the gain flatness, the front-to-back ratio, and the sidelobe level, over a wide frequency range. The LPDA geometry that complies with the aforementioned requirements is suitable for efficient multimedia content delivery. The optimization process is accomplished by applying a recently introduced method called invasive weed optimization (IWO). The method has already been compared to other evolutionary methods and has shown superiority in solving complex nonlinear problems in telecommunications and electromagnetics. In this paper, the IWO method has been chosen to optimize an LPDA for operation in the frequency range of 800-3300 MHz. Due to its excellent performance, the LPDA can effectively be used for multimedia content reception over future mobile computing systems.

Optimal design of UHF TV band log-periodic antenna using invasive weed optimization

A powerful evolutionary method called Invasive Weed Optimization (IWO) is applied to achieve optimal designs of log-periodic antennas. The antennas are designed for operation in the UHF-TV band, i.e. 470-860 MHz, and are optimized with respect to the standing wave ratio (SWR), the front-to-rear (F/R) ratio, and the forward gain. The parameters under optimization are the dipole lengths, the dipole diameters, the distances between adjacent dipoles and the characteristic impedance of the transmission line that feeds the dipoles. The optimized antenna geometries that resulted from the above method seem to be significantly better than the respective ones derived from the classical design method.

IWO-based synthesis of log-periodic dipole array

The Invasive Weed Optimization (IWO) is an effective evolutionary and recently developed method. Due to its better performance in comparison to other well-known optimization methods, IWO has been chosen to solve many complex non-linear problems in telecommunications and electromagnetics. In the present study, the IWO is applied to optimize the geometry of a realistic log-periodic dipole array (LPDA) that operates in the frequency range 800-3300 MHz and therefore is suitable for signal reception from several RF services. The optimization is applied under specific requirements, concerning the standing wave ratio, the forward gain, the gain flatness and the side lobe level, over a wide frequency range. The optimization variables are the lengths and the radii of the dipoles, the distances between them, and the characteristic impedance of the transmission line that connects the dipoles. The optimized LPDA seems to be superior compared to the antenna derived from the practical design procedure.

Exponential Log-Periodic Antenna Design Using Improved Particle Swarm Optimization with Velocity Mutation

An improved particle swarm optimization (PSO) method applied to the design of a new wideband log-periodic antenna (LPA) geometry is introduced. This new PSO variant, called PSO with velocity mutation (PSOvm), induces mutation on the velocities of those particles that cannot improve their position. The proposed LPA consists of wire dipoles with lengths and distances varied according to an exponential rule, which is defined by two specific parameters called length factor and spacing factor. The LPA is optimized for operation in 790–6000 MHz frequency range, in order to cover the most usual wireless services in practice, and also to provide in this range the highest possible forward gain, gain flatness below 2 dB, secondary lobe level below −20 dB with respect to the main lobe peak, and standing wave ratio below 2. To demonstrate its superiority in terms of performance, PSOvm is compared with well-known optimization methods. The comparison is performed by applying all the methods on several test functions and also on the LPA optimization problem defined by the above-mentioned requirements. Furthermore, the radiation characteristics of the PSOvm-based LPA give prominence to the effectiveness of the proposed exponential geometry compared to the traditional Carrel’s geometry.

Exposure EMF measurements with spectrum analyzers using free and open source software

This paper describes a dedicated Software that was developed using Free and Open Source tools for the automation of Electromagnetic Field (EMF) exposure measurements with Spectrum Analyzers (SAs). This software is used for controlling measurement instrumentation, receiving, storing, processing and analyzing EMF measurement date, while offering a friendly user interface with many options. For the evaluation of the measurement results, all required protocols have been taken into account in order to be consistent with national (Greek Legislation) as well as other international recommendations and standards. The software runs under Ubuntu 14.04 LTS operating system and was developed in Python 3.4 programming language. Remote control and communication with the SAs is achieved with Standard Commands for Programmable Instruments (SCPI) and with the use of National Instruments Virtual Instrument Software Architecture (NI-VISA) in combination with Python Visa (PyVISA).

Implementation of antenna array beamforming by using a novel neural network structure

The present study introduces the implementation of antenna array beamforming based on a new neural network (NN) structure. The NN comprises two hidden layers, which use different interconnectivity patterns. The first one is divided in sublayers, which are equal in number to the inputs of the NN. Each sublayer communicates only with the respective input but is fully interconnected with the second hidden layer. The NN training is performed by using data sets derived by a well-known beamforming technique called minimum variance distortionless response. The trained NN is capable of serving as adaptive beamformer that makes a linear antenna array steer the main lobe towards a desired signal and place nulls towards respective interference signals in the presence of additive zero-mean Gaussian noise. The performance of the trained NN is tested by estimating the mean absolute deviation of main lobe and null directions from their respective desired directions.

ZAM distribution analysis of radiowave ionospheric propagation interference measurements

This work investigates the occurrence of radiowave propagation disturbances across a wide range of VLF and LF frequencies received prior to a seismic event (Mw=4), that took place on May 12th 2012, the epicenter of which was very close (14Km) to the receiver. The signals analysed were from 4 VLF and 6 LF European transmitters. This seismic event produced precursory ionospheric disturbances, identified as spectral distortion, before its occurrence, providing a distinct pattern. Although the basis of the ionosphere interaction with seismic phenomena has been well documented in previous studies, the close proximity of the receiver to the seismic event provides a new perspective to this study. The received signals have undergone normalisation and then they have been processed by the application of the Zhao-Atlas-Marks Distribution (ZAMD). Diagrams of the signals relevant to the phenomena are presented as well as the the result of the application of the ZAMD.