Browsing by Author "Dusparic, Ivana"

Now showing 1 - 4 of 4
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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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    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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    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.
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    Timely and sustainable: Utilising correlation in status updates of battery-powered and energy-harvesting sensors using Deep Reinforcement Learning
    Hribar, Jernej; DaSilva, Luiz A.; Zhou, Sheng; Jiang, Zhiyuan; Dusparic, Ivana (Elsevier, 2022-08-01)
    In a system with energy-constrained sensors, each transmitted observation comes at a price. The price is the energy the sensor expends to obtain and send a new measurement. The system has to ensure that sensors' updates are timely, i.e., their updates represent the observed phenomenon accurately, enabling services to make informed decisions based on the information provided. If there are multiple sensors observing the same physical phenomenon, it is likely that their measurements are correlated in time and space. To take advantage of this correlation to reduce the energy use of sensors, in this paper we consider a system in which a gateway sets the intervals at which each sensor broadcasts its readings. We consider the presence of battery-powered sensors as well as sensors that rely on Energy Harvesting (EH) to replenish their energy. We propose a Deep Reinforcement Learning (DRL)-based scheduling mechanism that learns the appropriate update interval for each sensor, by considering the timeliness of the information collected measured through the Age of Information (AoI) metric, the spatial and temporal correlation between readings, and the energy capabilities of each sensor. We show that our proposed scheduler can achieve near-optimal performance in terms of the expected network lifetime.