Browsing by Author "Fonseca, Erika"

Now showing 1 - 6 of 6
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    Adaptive Height Optimization for Cellular-Connected UAVs: A Deep Reinforcement Learning Approach
    Fonseca, Erika; Galkin, Boris; Amer, Ramy; DaSilva, Luiz A.; Dusparic, Ivana (IEEE, 2023-01-19)
    Providing reliable connectivity to cellular-connected Unmanned Aerial Vehicles (UAVs) can be very challenging; their performance highly depends on the nature of the surrounding environment, such as density and heights of the ground Base Stations (BSs). On the other hand, tall buildings might block undesired interference signals from ground BSs, thereby improving the connectivity between the UAVs and their serving BSs. To address the connectivity of UAVs in such environments, this paper proposes a Reinforcement Learning (RL) algorithm to dynamically optimise the height of a UAV as it moves through the environment, with the goal of increasing the throughput or spectrum ef ciency that it experiences. The proposed solution is evaluated in two settings: using a series of generated environments where we vary the number of BS and building densities, and in a scenario using real-world data obtained from an experiment in Dublin, Ireland. Results show that our proposed RL-based solution improves UAV Quality of Service (QoS) by 6% to 41%, depending on the scenario. We also conclude that, when ying at heights higher than the buildings, building density variation has no impact on UAV QoS. On the other hand, BS density can negatively impact UAV QoS, with higher numbers of BSs generating more interference and deteriorating UAV performance.
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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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    Intelligent Base Station Association for UAV Cellular Users: A Supervised Learning Approach
    Galkin, Boris; Amer, Ramy; Fonseca, Erika; DaSilva, Luiz A. (IEEE, 2020-01-01)
    Fifth Generation (5G) cellular networks are expected to provide cellular connectivity for vehicular users, including Unmanned Aerial Vehicles (UAVs). When flying in the air, these users experience strong, unobstructed channel conditions to a large number of Base Stations (BSs) on the ground. This creates very strong interference conditions for the UAV users, while at the same time offering them a large number of BSs to potentially associate with for cellular service. Therefore, to maximise the performance of the UAV-BS wireless link, the UAV user needs to be able to choose which BSs to connect to, based on the observed environmental conditions. This paper proposes a supervised learning-based association scheme, using which a UAV can intelligently associate with the most appropriate BS. We train a Neural Network (NN) to identify the most suitable BS from several candidate BSs, based on the received signal powers from the BSs, known distances to the BSs, as well as the known locations of potential interferers. We then compare the performance of the NN-based association scheme against strongest-signal and closest-neighbour association schemes, and demonstrate that the NN scheme significantly outperforms the simple heuristic schemes.
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    Mobility for Cellular-Connected UAVs: Challenges for the Network Provider
    Fonseca, Erika; Galkin, Boris; Kelly, Marvin; DaSilva, Luiz A.; Dusparic, Ivana (IEEE, 2021-01-01)
    Unmanned Aerial Vehicle (UAV) technology is becoming more prevalent and more diverse in its application. 5G and beyond networks must enable UAV connectivity. This will require the network operator to consider this new type of user in the planning and operation of the network. This work presents the challenges an operator will encounter and should consider in the future as UAVs become users of the network. We analyse the 3GPP specifications, the existing research literature, and a publicly available UAV connectivity dataset, to describe the challenges. We classify these challenges into network planning and network optimisation categories. We discuss the challenge of planning network coverage when considering coverage for flying users and the PCI collision and confusion issues that can be aggravated by these users. In discussing network optimisation challenges, we introduce Automatic Neighbouring Relation (ANR) and handover challenges, specifically the number of neighbours in the Neighbour Relation Table (NRT), and their potential deletion and block-listing, the frequent number of handovers and the possibility that the UAV disconnects because of handover issues. We discuss possible approaches to address the presented challenges and use a real-world dataset to support our findings about these challenges and their importance.
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    Radio Access Technology characterisation through object detection
    Fonseca, Erika; Santos, Joao F.; Paisana, Francisco; DaSilva, Luiz A. (Elsevier, 2021-02-15)
    Radio Access Technology (RAT) classification and monitoring are essential for efficient coexistence of different communication systems in shared spectrum. Shared spectrum, including operation in license-exempt bands, is envisioned in the fifth generation of wireless technology (5G) standards (e.g., 3GPP Rel. 16). In this paper, we propose a Machine Learning (ML) approach to characterise the spectrum utilisation and facilitate the dynamic access to it. Recent advances in Convolutional Neural Networks (CNNs) enable us to perform waveform classification by processing spectrograms as images. In contrast to other ML methods that can only provide the class of the monitored RATs, the solution we propose can recognise not only different RATs in shared spectrum, but also identify critical parameters such as inter-frame duration, frame duration, centre frequency, and signal bandwidth by using object detection and a feature extraction module to extract features from spectrograms. We have implemented and evaluated our solution using a dataset of commercial transmissions, as well as in a Software-Defined Radio (SDR) testbed environment. The scenario evaluated was the coexistence of WiFi and LTE transmissions in shared spectrum. Our results show that our approach has an accuracy of 96% in the classification of RATs from a dataset that captures transmissions of regular user communications. It also shows that the extracted features can be precise within a margin of 2%, and can detect above 94% of objects under a broad range of transmission power levels and interference conditions.
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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.