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 towards a desired signal, place respective nulls towards 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 as well as 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

Precise foreground detection algorithm using motion estimation, minima and maxima inside the foreground object

In this paper the precise foreground mask is obtained in a complex environment by applying simple and effective methods on a video sequence consisting of multi-colour and multiple foreground object environment. To detect moving objects we use a simple algorithm based on block-based motion estimation, which requires less computational time. To obtain a full and improved mask of the moving object, we use an opening-and-closing-by- reconstruction mechanism to identify the minima and maxima inside the foreground object by applying a set of morphological operations. This further enhances the outlines of foreground objects at various stages of image processing. Therefore, the algorithm does not require the knowledge of the background image. That is why it can be used in real world video sequences to detect the foreground in cases where we do not have a background model in advance. The comparative performance results demonstrate the effectiveness of the proposed algorithm.The Institute of Management Sciences Peshawar (http://imsciences.edu.pk/) through Higher Education Commission Islamabad, Pakistan (http://hec.gov.pk/)

Massive MIMO Systems for 5G Communications

Massive MIMO will improve the performance of future 5G systems in terms of data rate and spectral efficiency, while accommodating a large number of users. Furthermore, it allows for 3D beamforming in order to provide more degrees of freedom and increase the number of high-throughput users. Massive MIMO is expected to provide more advantages compared to other solutions in terms of energy and spectral efficiency. This will be achieved by focusing the radiation towards the direction of the intended users, thus implementing simultaneous transmission to many users while keeping interference low. It can boost the capacity compared to a conventional antenna solution, resulting in a spectral efficiency up to 50 times greater than that provided by actual 4G technology. However, to take full advantage of this technology and to overcome the challenges of implementation in a real environment, a complicated radio system is required. The purpose of this work is to present the MIMO technology evolution and challenges in a simple introductory way and investigate potential system enhancements

Performance comparison of LS, LMMSE and Adaptive Averaging Channel Estimation (AACE) for DVB-T2

© 2015 IEEE. In this paper the performance of the Adaptive Averaging Channel Estimator (AACE-LS) which is a modified Least Square (LS) estimator and the AACE-LMMSE which is a modified Linear Minimum Mean Error (LMMSE) estimator, are compared with respect to the conventional LS and the LMMSE estimators. The AACE is an estimator which is based on the averaging of the last N Scattered Pilots (SP) from the DVB-T2 model carried in the received OFDM symbols. The proposed method could in general be applied to any pilot based estimator. The noise introduced by the channel is considered as Additive White Gaussian Noise (AWGN) with zero mean and thus an averaging process is used to eliminate it. The estimator adaptively follows the fluctuations of the amplitude envelope in the time domain and adapts the size of the buffer N, with respect to the coherence time (Tc). Finally, based on the averaged estimated channel, the LS or the LMMSE equalizer is applied to the received signal in the frequency domain. Simulations clearly show that the performance of the AACE-LS is superior to the conventional LS estimator and is near to the performance of the LMMSE with no need of a prior knowledge of the statistics and the noise of the channel and thus if the channel is unknown to the receiver, the AACE is a good choice

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 Taguchi’s 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

Direct and external intensity modulation in OFDM RoF links

This article is available Open Access to view at: http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7158985Radio-over-fiber (RoF) systems comprise light modulation and transmission of millimeter-wave signals over fiber links. The aim of this study is to investigate the performance of external and direct intensity modulation in RoF links and to analyze the drawbacks induced by different components of the optical system. In external modulation, the Mach-Zehnder modulator (MZM) is used, whereas the vertical-cavity surface-emitting laser (VCSEL) is utilized in direct modulation. Both modulation schemes are tested for a vector modulation format, i.e., the quadrature amplitude modulation (QAM), where an orthogonal frequency-division multiplexing (OFDM) scheme is used to generate signal subcarriers. The simulations are carried out with the same values of common global parameters for both schemes of intensity modulation. Although VCSEL is a promising device for future RoF systems, the external modulation shows a more robust performance compared with that of VCSEL when implemented with the OFDM modulation technique

Comparative study of broadcasting antenna array optimization using evolutionary algorithms

Broadcasting antenna array optimized design involves gain maximization, main lobe down-tilting and null filling. In this study some of the most powerful evolutionary optimization algorithms are applied to this challenging problem: Differential Evolution, Particle Swarm, Invasive Weed, Adaptive Invasive Weed, and the Taguchi method. Evolutionary algorithms use a random search approach together with mechanisms inspired by biological evolution in order to iteratively improve the precision of randomly obtained solutions. Evolutionary algorithms are shown to require very substantial computational resources due to their random search nature. However, they are also very robust in finding a quasi-optimum solution by optimizing an appropriate fitness function. It is demonstrated that the algorithm producing the best fitness, and thus the best solution to the antenna problem, is Invasive Weed Optimization (IWO), followed by Particle Swarm Optimization (PSO) and Differential Evolution (DE), in second place and with similar results