Bradley Department of Electrical and Computer Engineering

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https://hdl.handle.net/10919/18738
From pervasive computing, to smart power systems, Virginia Tech ECE faculty and students delve into all major areas of electrical and computer engineering. The main campus is in Blacksburg, and the department has additional research and teaching facilities in Arlington, Falls Church, and Hampton, Virginia.

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Browsing Bradley Department of Electrical and Computer Engineering by Subject "09 Engineering"

Now showing 1 - 3 of 3
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    Flexible fine-grained baseband processing with network functions virtualization: Benefits and impacts
    Kist, Maicon; Wickboldt, Juliano Araujo; Granville, Lisandro Zambenedetti; Rochol, Juergen; DaSilva, Luiz A.; Both, Cristiano Bonato (Elsevier, 2019-03-14)
    The increasing demand for wireless broadband connectivity is leading mobile network operators towards new means to expand their infrastructures efficiently and without increasing the cost of operation. Network Functions Virtualization (NFV) is a step towards virtualization-based, low-cost flexible and adaptable networking services. In the context of centralized baseband architectures, virtualization is already employed to run baseband processing units as software on top of conventional data center hardware. However, current virtualization solutions consider atomic virtualization, i.e., single virtual machines implementing all baseband functionalities. In this article, we propose the fine-grained virtualization of baseband processing to achieve a more flexible distribution of the processing workload in centralized architectures. We also evaluate the benefits of our approach in terms of (i) the bandwidth requirements for each fine-grained distribution option, (ii) the latency experienced by mobile users for each fine-grained distribution option, and (iii) the total CPU usage of each fine-grained baseband processing function.
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    Gated Recurrent Unit Neural Networks for Automatic Modulation Classification With Resource-Constrained End-Devices
    Utrilla, Ramiro; Fonseca, Erika; Araujo, Alvaro; DaSilva, Luiz A. (IEEE, 2020-01-01)
    The continuous increase in the number of mobile and Internet of Things (IoT) devices, as well as in the wireless data traffic they generate, represents an essential challenge in terms of spectral coexistence. As a result, these devices are now expected to make efficient and dynamic use of the spectrum by employing Cognitive Radio (CR) techniques. In this work, we focus on the Automatic Modulation Classification (AMC). AMC is essential to carry out multiple CR techniques, such as dynamic spectrum access, link adaptation and interference detection, aimed at improving communications throughput and reliability and, in turn, spectral efficiency. In recent years, multiple Deep Learning (DL) techniques have been proposed to address the AMC problem. These DL techniques have demonstrated better generalization, scalability and robustness capabilities compared to previous solutions. However, most of these techniques require high processing and storage capabilities that limit their applicability to energy- and computation-constrained enddevices. In this work, we propose a new gated recurrent unit neural network solution for AMC that has been specifically designed for resource-constrained IoT devices. We trained and tested our solution with over-the-air measurements of real radio signals. Our results show that the proposed solution has a memory footprint of 73.5 kBytes, 51.74% less than the reference model, and achieves a classification accuracy of 92.4%
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    REQIBA: Regression and Deep Q-Learning for Intelligent UAV Cellular User to Base Station Association
    Galkin, Boris; Fonseca, Erika; Amer, Ramy; DaSilva, Luiz A.; Dusparic, Ivana (IEEE, 2021)
    Unmanned Aerial Vehicles (UAVs) are emerging as important users of next-generation cellular networks. By operating in the sky, UAV users experience very different radio conditions than terrestrial users, due to factors such as strong Line-of-Sight (LoS) channels (and interference) and Base Station (BS) antenna misalignment. As a consequence, the UAVs may experience significant degradation to their received quality of service, particularly when they are moving and are subject to frequent handovers. The solution is to allow the UAV to be aware of its surrounding environment, and intelligently connect into the cellular network taking advantage of this awareness. In this paper we present REgression and deep Q-learning for Intelligent UAV cellular user to Base station Association (REQIBA), a solution that allows a UAV flying over an urban area to intelligently connect to underlying BSs, using information about the received signal powers, the BS locations, and the surrounding building topology. We demonstrate how REQIBA can as much as double the total UAV throughput, when compared to heuristic association schemes similar to those commonly used by terrestrial users. We also evaluate how environmental factors such as UAV height, building density, and throughput loss due to handovers impact the performance of our solution.