Browsing by Author "Gunturk, Bahadir K."

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Citation Count: 1
Analysis of deep learning based path loss prediction from satellite images
(IEEE, 2021) Baykaş, Tunçer; Ates, Hasan F.; Baykas, Tuncer; Gunturk, Bahadir K.
Determining the channel model parameters of a wireless communication system, either by measurements or by running electromagnetic propagation simulations, is a time-consuming process. Any rapid deployment of network demands faster determination of at least major channel parameters. In this paper, we investigate the idea of using deep convolutional neural networks and satellite images for channel parameters (i.e., path loss exponent n and shadowing factor sigma) prediction in a cellular network with aerial base stations. Specifically, we investigate the performance dependency of the method on three different factors: height of the transmitter antenna, quantization levels of the channel parameters and architectural design of CNN. The results presented in this paper show a high prediction accuracy of the channel parameters in real-time.
Citation Count: 0
PREDICTING PATH LOSS DISTRIBUTIONS OF A WIRELESS COMMUNICATION SYSTEM FOR MULTIPLE BASE STATION ALTITUDES FROM SATELLITE IMAGES
(Ieee, 2022) Baykaş, Tunçer; Gunturk, Bahadir K.; Ates, Hasan F.; Baykas, Tuncer
It is expected that unmanned aerial vehicles (UAVs) will play a vital role in future communication systems. Optimum positioning of UAVs, serving as base stations, can be done through extensive field measurements or ray tracing simulations when the 3D model of the region of interest is available. In this paper, we present an alternative approach to optimize UAV base station altitude for a region. The approach is based on deep learning; specifically, a 2D satellite image of the target region is input to a deep neural network to predict path loss distributions for different UAV altitudes. The neural network is designed and trained to produce multiple path loss distributions in a single inference; thus, it is not necessary to train a separate network for each altitude.
Citation Count: 7
Regression of Large-Scale Path Loss Parameters Using Deep Neural Networks
(IEEE-Inst Electrical Electronics Engineers Inc, 2022) Baykaş, Tunçer; Marey, Ahmed; Ates, Hasan F.; Baykas, Tuncer; Gunturk, Bahadir K.
Path loss exponent and shadowing factor are among important wireless channel parameters. These parameters can be estimated using field measurements or ray-tracing simulations, which are costly and time-consuming. In this letter, we take a deep neural network-based approach, which takes either satellite image or height map of a target region as input, and estimates the desired channel parameters. We use the well-known VGG-16 architecture, pretrained on the ImageNet dataset, as the backbone to extract image features, modify it as a regression network to produce channel parameters, and retrain it on our dataset, which consists of satellite image or height map as input and channel parameters as target values. We demonstrate that deep networks can be successfully utilized in estimating path loss exponent and shadowing factor of a region, simply from the region's satellite image or height map. The trained models and test codes are publicly available on a Github page.