DoA estimation in EM lens assisted massive antenna system using subsets based antenna selection and high resolution algorithms

In recent times, massive antenna array technology has captured significant attention among wireless communication researchers. This is a field with strong potential to increase rates of data transfer; mitigate interference and serve a large number of users simultaneously. To contribute further to this emerging technology, this paper presents an approach for the line-of-sight (LoS) based direction of arrival (DoA) estimation using the electromagnetic (EM) lens-focusing antenna concept. The EM lens focuses the received signal energy as a function of the angle of arrival (AoA) to a small subset/area of the antenna array. This is advantageous, as it helps to reduce both hardware implementation (RF chains) and the complexity of signal processing in the large number of antennas system. Furthermore, this focusing capability of the EM lens provides additional interference rejection gain which leads to estimate the DoA of user terminals precisely. Hence, in this work, subsets based antenna selection approach and subspace-based high resolution DoA estimation algorithms have been considered in combination with the EM lens assisted massive antenna system. In simulations where the DoA is estimated with the EM lens, the results are comparable with conventional methods of DoA estimation without an EM lens, despite the significantly reduced overall system complexity

Stochastic trajectory generation using particle swarm optimization for quadrotor unmanned aerial vehicles (UAVs)

The aim of this paper is to provide a realistic stochastic trajectory generation method for unmanned aerial vehicles that offers a tool for the emulation of trajectories in typical flight scenarios. Three scenarios are defined in this paper. The trajectories for these scenarios are implemented with quintic B-splines that grant smoothness in the second-order derivatives of Euler angles and accelerations. In order to tune the parameters of the quintic B-spline in the search space, a multi-objective optimization method called particle swarm optimization (PSO) is used. The proposed technique satisfies the constraints imposed by the configuration of the unmanned aerial vehicle (UAV). Further particular constraints can be introduced such as: obstacle avoidance, speed limitation, and actuator torque limitations due to the practical feasibility of the trajectories. Finally, the standard rapidly-exploring random tree (RRT*) algorithm, the standard (A*) algorithm and the genetic algorithm (GA) are simulated to make a comparison with the proposed algorithm in terms of execution time and effectiveness in finding the minimum length trajectory

Performance analysis of 180\ub0 HRR coupler used for direction finding with an antenna array

This paper presents the performance analysis of  hybrid rat race coupler, widely used in radio frequency (RF)/wireless communication systems to couple the power in the desired way. The  hybrid ring coupler consists of 4 ports, two for the input signals and two for the output signals, where sum and difference patterns of the applied two signals can be obtained at two output ports and usually called sum and difference ports. In this work, the couplers have been designed and simulated at central frequencies (fo) of 2.4 and 10 GHz using different types of substrates such as RT Duroid 5880 and FR4.  Furthermore, the coupler has been used for direction finding (angle-of-arrival estimation) application, where we combine the designed  hybrid rat race coupler with a simple two antenna elements array  (at fo=10GHz and RT Duroid =2.2) and fabricate the circuit in order to validate the performance of the coupler by measuring the direction of arrival (DoA) from  and  ports. The obtained results show that good performance can be achieved with the designs considered in this paper.

Copula Density Neural Estimation

Probability density estimation from observed data constitutes a central task in statistics. Recent advancements in machine learning offer new tools but also pose new challenges. The big data era demands analysis of long-range spatial and long-term temporal dependencies in large collections of raw data, rendering neural networks an attractive solution for density estimation. In this paper, we exploit the concept of copula to explicitly build an estimate of the probability density function associated to any observed data. In particular, we separate univariate marginal distributions from the joint dependence structure in the data, the copula itself, and we model the latter with a neural network-based method referred to as copula density neural estimation (CODINE). Results show that the novel learning approach is capable of modeling complex distributions and it can be applied for mutual information estimation and data generation.Comment: 5 pages, 4 figures. This work has been submitted to the IEEE for possible publicatio

Interplay of Kerr and Raman beam cleaning with a multimode microstructure fiber

We experimentally study the competition between Kerr beam self-cleaning and Raman beam cleanup in a multimode air-silica microstructure optical fiber. Kerr beam self-cleaning of the pump is observed for a certain range of input powers only. Raman Stokes beam generation and cleanup lead to both depletion and degradation of beam quality for the pump. The interplay of modal four-wave mixing and Raman scattering in the infrared domain lead to the generation of a multimode supercontinuum ranging from 500 nm up to 1800 nm

Self-cleaning on a higher order mode in ytterbium-doped multimode fiber with parabolic profile

We experimentally demonstrate polarization-dependent Kerr spatial beam self-cleaning into the LP11 mode of an Ytterbium-doped multimode optical fiber with parabolic gain and refractive index profiles

Ge-Doped microstructured multicorefiber for customizable supercontinuum generation

Supercontinuum generation in a multicore fiber in which several uncoupled cores were doped with dissimilar concentrations of germanium was studied experimentally. Germanium doping provided control over the separation between the zero-dispersion wavelength and the 1064-nm wavelength of a Q-switched Nd:YAG pump laser. Supercontinua generated independently in each core of the same piece of fiber displayed clear and repeatable differences due to the influence of germanium doping on refractive index and four-wave mixing. The spectral evolution of the subnanosecond pump pulses injected into the different cores was accurately reproduced by numerical simulations