Marcelo M.M. de Oliveira

Bio: Marcelo M.M. de Oliveira is an academic researcher from Federal University of Rio Grande do Norte. The author has contributed to research in topics: LTE Advanced & Path loss. The author has an hindex of 1, co-authored 1 publications receiving 21 citations.

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.

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.

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.

Path Loss Exponent and Shadowing Factor Prediction From Satellite Images Using Deep Learning

Abstract: Optimal network planning for wireless communication systems requires the detailed knowledge of the channel parameters of the target coverage area. Channel parameters can be estimated through extensive measurements in the environment. Alternatively, ray tracing simulations can be done if the 3D model of the environment is available. One drawback of ray tracing simulations is the high computational complexity; therefore, ray tracing is not suitable for real-time coverage optimization. In this paper, we present a deep convolutional neural network-based approach to estimate channel parameters (specifically, path loss exponent and standard deviation of shadowing) directly from 2D satellite images. While deep learning methods require high computational resources for training and large amount of training data, once trained, the network can make predictions fast. Also, unlike the ray tracing simulations, there is no need for 3D model generation, and therefore, it can be applied easily using the images obtained from satellites or aerial vehicles. These make the proposed method a computationally efficient and reliable alternative to ray tracing simulations. The experimental results show that path loss exponent and large-scale shadowing factor at 900 MHz can be correctly classified by 88% and 76% accuracy, respectively.

Path Loss Exponent Prediction for Outdoor Millimeter Wave Channels through Deep Learning

Abstract: In this paper, we propose a new algorithm for predicting the path loss exponent of outdoor millimeter-wave band channels through deep learning method. The proposed algorithm has the advantage of requiring less inference time compared to existing deterministic channel models while concretely considering the topographical characteristics. We used three-dimensional ray tracing to generate the outdoor millimeterwave band channel and path loss exponent. We trained a neural network with generated path loss exponent. To evaluate the performance of the proposed method, we analyzed the influence of the hyperparameters and environmental features, for example, building density and average distance from the transmitter.