Browsing by Author "Galkin, Boris"

Now showing 1 - 5 of 5
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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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    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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    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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    UAVs as Mobile Infrastructure: Addressing Battery Lifetime
    Galkin, Boris; Kibilda, Jacek; DaSilva, Luiz A. (IEEE, 2019-06-01)
    Unmanned Aerial Vehicles (UAVs) can play an important role in next generation cellular networks, acting as flying infrastructure which can serve ground users when regular infrastructure is overloaded or unavailable. As these devices operate wirelessly they rely on an internal battery for their power supply, which limits the amount of time they can operate over an area of interest before having to recharge. To accommodate this limitation UAV networks will have to rely on dedicated infrastructure to recharge the UAVs in-between deployments. In this article, we outline three battery charging options that may be considered by a network operator and use simulations to demonstrate the performance impact of incorporating those options into a cellular network where UAV infrastructure provides wireless service.