Optimal Trajectory Learning for UAV-BS Video Provisioning System: A Deep Reinforcement Learning Approach

Kwon, Dohyun; Kim, Joongheon

doi:10.1109/icoin.2019.8718194

Cited by 11 publications

(7 citation statements)

References 6 publications

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“…III-B2. The proposed method assumes that the joint mobile charging and coverage-time extension computation conducts at a centralized controller which is connected to charging towers in order to guarantee the stabilized power supply (refer to [4], [8], [11], [12], [23], [24], [28], [30] and references therein for the detailed description).…”

Section: A Algorithm Design Rationalementioning

confidence: 99%

“…As we mentioned above, drones widely used for many applications, e.g. video provisioning [23], mobile edge computing [24], and aerial information sensing [25], can be a key solution for MBS services in cellular networks. In order to use the drones for cellular networks, theoretical performance analysis under consideration of practical antenna configurations [26] and mobility patterns [27] is essentially required.…”

Section: Introductionmentioning

confidence: 99%

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Joint Mobile Charging and Coverage-Time Extension for Unmanned Aerial Vehicles

et al. 2021

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In modern networks, the use of drones as mobile base stations (MBSs) has been discussed for coverage flexibility. However, the realization of drone-based networks raises several issues. One of critical issues is drones are extremely power-hungry. To overcome this, we need to characterize a new type of drones, so-called charging drones, which can deliver energy to MBS drones. Motivated by the fact that the charging drones also need to be charged, we deploy ground-mounted charging towers for delivering energy to the charging drones. We introduce a new energy-efficiency maximization problem, which is partitioned into two independently separable tasks. More specifically, as our first optimization task, two-stage charging matching is proposed due to the inherent nature of our network model, where the first matching aims to schedule between charging towers and charging drones while the second matching solves the scheduling between charging drones and MBS drones. We analyze how to convert the formulation containing non-convex terms to another one only with convex terms. As our second optimization task, each MBS drone conducts energy-aware time-average transmit power allocation minimization subject to stability via Lyapunov optimization. Our solutions enable the MBS drones to extend their lifetimes; in turn, network coverage-time can be extended.INDEX TERMS Cellular network, charging drone, coverage-time, mobile base station, scheduling.

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Section: A Algorithm Design Rationalementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Joint Mobile Charging and Coverage-Time Extension for Unmanned Aerial Vehicles

et al. 2021

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“…Based on the definitions of (10) and (11), the rewards R delay in (7) and R energy in (8) are defined as the difference between the value when the tasks are performed locally and the value by the decision algorithm based on DQN. As a result, the learning is carried out to minimize D dqn and E dqn while it maximizes R delay and R energy .…”

Section: Dqn-based Offloading and Compression Decisionmentioning

confidence: 99%

“…Among them, this paper considers reinforcement learning based approaches because this given problem is for stochastic sequential offloading decision-making. Among a lot of deep reinforcement learning (DRL) methodologies such as Q-learning, Markov decision process (MDP) [5], deep Q-network (DQN), and deep deterministic policy gradient (DDPG) [6,7], this paper designs a sequential offloading decision-making algorithm based on DQN. The reason why this paper considers DQN is that it is the function approximation of Q-learning using deep neural network (DNN) in order to take care of large-scale problem setting.…”

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confidence: 99%

Adaptive Real-Time Offloading Decision-Making for Mobile Edges: Deep Reinforcement Learning Framework and Simulation Results

et al. 2020

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This paper proposes a novel dynamic offloading decision method which is inspired by deep reinforcement learning (DRL). In order to realize real-time communications in mobile edge computing systems, an efficient task offloading algorithm is required. When the decision of actions (offloading enabled, i.e., computing in clouds or offloading disabled, i.e., computing in local edges) is made by the proposed DRL-based dynamic algorithm in each unit time, it is required to consider real-time/seamless data transmission and energy-efficiency in mobile edge devices. Therefore, our proposed dynamic offloading decision algorithm is designed for the joint optimization of delay and energy-efficient communications based on DRL framework. According to the performance evaluation via data-intensive simulations, this paper verifies that the proposed dynamic algorithm achieves desired performance. IntroductionAccording to the fact that the 5G era has been realized based on networking technology innovation, many new computing and communications paradigms are introduced such as millimeter-wave communications, hyper-dense networks, and device-to-device proximal networking [1-3]. Among them, mobile edge computing (MEC) is one of the major technologies for realizing data/computing distribution in order to improve cellular network performance [4]. Based on the benefits of MEC technologies such as data rate improvement and quality enhancement, mobile cellular users can enjoy high quality, seamless, and real-time communication networking services.Together with the MEC technologies, many networked components are also of interest such as cloud servers and connected devices/vehicles. As the number of connected devices/vehicles increases, the amount of data transmitted to the MEC is also rapidly increasing. This obviously introduces serious network limitations such as data processing performance limits, storage capacity limits, and the battery use of terminal devices. Under the consideration of these limitations and problems, the use of cloud computing server is more efficient to deal with big data (gathered from connected vehicles/devices via MEC edges) than the local computing on terminals. On the other hand, for any cases where the data cannot be handled in the cloud servers due to delay requirements and security reasons, MEC devices should be able to handle or process the data from the connected vehicles/devices. Therefore, we can observe the trade-off between cloud computing servers and MEC servers (local edge computing servers). In summary, cloud servers have more power and centralized computing benefits comparing to MEC servers, whereas, it may have limitations when we have delay requirements and security requirements/regulations. Figure 1 shows the combined architecture of local edge computing and cloud computing. As the transmission from connected vehicles/devices (i.e., data upload and download) delay goes down, it is much more suitable for real-time processing. It enables real-time data processing and transmission using high-quality ...

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“…Recently, many research results have been introduced to deal with the use of mobile stations for enhancing communications and networking performance, e.g., (i) mobile computing for surveillance monitoring and (ii) cellular network coverage extension using drones. Among them, the use of drone wireless communications and networking is promising and a lot of related research contributions are now available for various applications and settings [1][2][3][4][5][6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Self-Controllable Super-Resolution Deep Learning Framework for Surveillance Drones in Security Applications

Park¹,

Kang²,

Park³

et al. 2020

ICST Transactions on Security and Safety

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This paper proposes a self-controllable super-resolution adaptation algorithm in drone platforms. The drone platforms are generally used for surveillance in target network areas. Thus, super-resolution algorithms which are for enhancing surveillance video quality are essential. In surveillance drone platforms, generating video streams obtained by CCTV cameras is not static, because the cameras record the video when abnormal objects are detected. The generation of streams is not predictable, therefore, this unpredictable situation can be harmful to reliable surveillance monitoring. To handle this problem, the proposed algorithm designs superresolution adaptation. With the proposed algorithm, the shallow model which is fast and low-performance will be used if the stream queue is near overflow. On the other hand, the deep model which is highperformance and slow will be used if the queue is idle to improve the performance of super-resolution.

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Optimal Trajectory Learning for UAV-BS Video Provisioning System: A Deep Reinforcement Learning Approach

Cited by 11 publications

References 6 publications

Joint Mobile Charging and Coverage-Time Extension for Unmanned Aerial Vehicles

Joint Mobile Charging and Coverage-Time Extension for Unmanned Aerial Vehicles

Adaptive Real-Time Offloading Decision-Making for Mobile Edges: Deep Reinforcement Learning Framework and Simulation Results

Self-Controllable Super-Resolution Deep Learning Framework for Surveillance Drones in Security Applications

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