Laercio M. Mendonca

Bio: Laercio M. Mendonca is an academic researcher from Federal University of Rio Grande do Norte. The author has contributed to research in topics: Microstrip antenna & Antenna (radio). The author has an hindex of 6, co-authored 17 publications receiving 109 citations.

Microwave Metamaterial-Based Sensor for Dielectric Characterization of Liquids

Abstract: This article proposed to build a system founded on metamaterial sensor antennas, which can be used to evaluate impurities in aqueous substances according to the quality of transmission between the sensor antennas. In order to do this, a dedicated setup with tests in several frequencies was deployed so as to monitor the behavior of transmission variation between sensors. These sensors are microstrip antennas with a ground plane of resonant cleaved metallic rings; the substrate functions as a metamaterial for the irradiating element. In this study, an analysis was made of transmission between the sensors, looking for variation in angles of incidence of signal and of distance between the antennas. The sensor was tested at various operating frequencies, as such 1.8 GHz, 2.4 GHz, 3.4 GHz and 4.1 GHz, resulting in different values of sensitivity. The prototypes were constructed and tested so as to analyze the dielectric effects of the impurities on NaCl and C2H4O2 substances. The research aims to use these control systems of impurities in industrial premises.

A Hybrid Path Loss Prediction Model based on Artificial Neural Networks using Empirical Models for LTE And LTE-A at 800 MHz and 2600 MHz

Abstract: This article presents the development and analysis of a hybrid, error correction-based, neural network to predict the path loss for suburban areas at 800 MHz and 2600 MHz, obtained by combining empirical propagation models, ECC-33, Ericsson 9999, Okumura Hata, and 3GPP's TR 36.942, with a backpropagation Artificial Neural Network (ANN). The network performance was tested along with two optimization techniques - Genetic Algorithm (GA) and Least Mean Square (LMS). Results were compared with data obtained by measurements performed in the vicinity of the Federal University of Rio Grande do Norte (UFRN), in the city of Natal, Brazil. In the end, the hybrid neural network presented the best results, indicating greater similarity with experimental data. The results developed in this research will help to achieve better signal estimation, reducing errors in planning and implementation of LTE and LTE-A systems.

Miniaturization of a Microstrip Patch Antenna with a Koch Fractal Contour Using a Social Spider Algorithm to Optimize Shorting Post Position and Inset Feeding

Abstract: This paper presents a social spider optimization (SSO) design of a small-size microstrip antenna. Two antenna miniaturization techniques, based on the use of a Koch fractal contour and a shorting post (connecting the patch to the ground plane), are combined to enable a major size reduction. The antenna is inset fed by a microstrip line. The developed SSO algorithm is used to find out the best radius and position of the shorting post and the length of the inset feed, to achieve the desired resonant frequency with good impedance matching. Antenna prototypes have been fabricated and measured. The good agreement obtained between numerical simulation and experimental results has validated the design procedure. Compared with a conventional rectangular patch, the antenna resonance frequency is reduced from 2.45 GHz to 730 MHz, which corresponds to a remarkable miniaturization of about 70%. The proposed antenna is suitable for applications in the 700-800 MHz frequency range, such as 4G mobile communication systems.

Simulation and measurement of inset-fed microstrip patch antennas on BiNbO4 substrates

Abstract: This work presents an experimental investigation of the properties of microstrip patch antennas on bismuth niobate ceramic substrates. The substrate fabrication process and characterization are described. Several prototypes were built and measured for wireless communication systems. A good agreement was observed between measured and simulated results. The very high electrical permittivity of the ceramic substrate provided a reduction of the antenna dimensions

A new triple band microstrip fractal antenna for c-band and s-band applications

Abstract: Recently research show that some parameters such as the shapes of antenna patch and the ground plane when geometrically altered produces changes in the current density distribution of the planar structure and consequently in the resonant modes. This paper presents a new microstrip fractal antenna using the technique of inserting slots of shape fractal in ground plane in order to increase the bandwidth and insertion discontinuities in the feed line to reach specific behaviors in three resonant modes. The FR-4 substrate with dimensions 85.0 x 85.0 x 1.57 mm3 is used. Also, it used different techniques of impedance matching in feed line of antenna with changes of the width of the transmission line in order to obtain a variation in the current distribution and consequently of the impedance bandwidth for S11 ≤ -10dB for C-band (3.625 GHz – 4.2 GHz) and S-band (2.0 GHz – 4.0 GHz). Good agreement between measured and simulated results is achieved. Proposed fractal microstrip antenna can be easily designed, built and applied in wireless communication.

Model-Aided Deep Learning Method for Path Loss Prediction in Mobile Communication Systems at 2.6 GHz

Abstract: Accurate channel models are essential to evaluate mobile communication system performance and optimize coverage for existing deployments. The introduction of various transmission frequencies for 5G imposes new challenges for accurate radio performance prediction. This paper compares traditional channel models to a channel model obtained using Deep Learning (DL)-techniques utilizing satellite images aided by a simple path loss model. Experimental measurements are gathered and compose the training and test set. This paper considers path loss modelling techniques offered by state-of-the-art stochastic models and a ray-tracing model for comparison and evaluation. The results show that 1) the satellite images offer an increase in predictive performance by ≈ 0.8 dB, 2) The model-aided technique offers an improvement of ≈ 1 dB, and 3) that the proposed DL model is capable of improving path loss prediction at unseen locations for 811 MHz with ≈ 1 dB and ≈ 4.7 dB for 2630 MHz.

Machine Learning for Wireless Communication Channel Modeling: An Overview

Abstract: Channel modeling is fundamental to design wireless communication systems. A common practice is to conduct tremendous amount of channel measurement data and then to derive appropriate channel models using statistical methods. For highly mobile communications, channel estimation on top of the channel modeling enables high bandwidth physical layer transmission in state-of-the-art mobile communications. For the coming 5G and diverse Internet of Things, many challenging application scenarios emerge and more efficient methodology for channel modeling and channel estimation is very much needed. In the mean time, machine learning has been successfully demonstrated efficient handling big data. In this paper, applying machine learning to assist channel modeling and channel estimation has been introduced with evidence of literature survey.

A Low-Cost Multiple Complementary Split-Ring Resonator-Based Microwave Sensor for Contactless Dielectric Characterization of Liquids

Abstract: We propose a low-cost, easy-to-fabricate, contactless microwave sensor for dielectric characterization of liquids. The design of the proposed sensor is based on a multiple complementary split-ring resonator (MCSRR) fabricated on a low-cost FR-4 substrate. A glass capillary tube having an inner diameter of $0.008\lambda _{{0}}$ is inserted in the high electric field region of the MCSRR to carry the liquid under test. The sensor is designed to operate at a resonant frequency of 2.45GHz for an empty tube and shifted resonant peaks are utilized for the dielectric characterization of different liquids. The maximum observed shifts in resonant frequency and Q factor are up to 400MHz and 31, respectively. The numerically established relations are experimentally verified through fabricated sensor for various binary mixtures of water and ethanol. The percentage errors between the calculated and reference permittivity of different samples are noticed to be less than 5%. The proposed device promises to be a cost-effective and convenient solution for accurate dielectric characterization of liquids and their binary aqueous solutions.

Predicting Path Loss Distribution of an Area From Satellite Images Using Deep Learning

Abstract: Path loss prediction is essential for network planning in any wireless communication system. For cellular networks, it is usually achieved through extensive received signal power measurements in the target area. When the 3D model of an area is available, ray tracing simulations can be utilized; however, an important drawback of such an approach is the high computational complexity of the simulations. In this paper, we present a fundamentally different approach for path loss distribution prediction directly from 2D satellite images based on deep convolutional neural networks. While training process is time consuming and completed offline, inference can be done in real time. Another advantage of the proposed approach is that 3D model of the area is not needed during inference since the network simply uses an image captured by an aerial vehicle or satellite as its input. Simulation results show that the path loss distribution can be accurately predicted for different communication frequencies and transmitter heights.

Differential Microfluidic Sensors Based on Dumbbell-Shaped Defect Ground Structures in Microstrip Technology: Analysis, Optimization, and Applications.

Abstract: A microstrip defect ground structure (DGS) based on a pair of dumbbell-shaped slots is used for sensing. The device is a differential sensor consisting of a pair of mirrored lines loaded with a dumbbell-shaped DGS, and the output variable is the cross-mode transmission coefficient. Such a variable is very sensitive to asymmetries in the line pair, e.g., caused by an asymmetric dielectric load in the dumbbell-shaped DGSs. Therefore, the sensor is of special interest for the dielectric characterization of solids and liquids, or for the measurement of variables related to complex permittivity changes. It is shown in this work that by adding fluidic channels on top of the dumbbell-shaped DGSs, the device is useful for liquid characterization, particularly for the measurement of solute concentration in very diluted solutions. A sensitivity analysis useful for sensor design is carried out in this paper.