Power-Constrained Trajectory optimization for Wireless UAV Relays with Random Requests

Bliss, Matthew; Michelusi, Nicolò

doi:10.1109/icc40277.2020.9149029

Cited by 10 publications

(4 citation statements)

References 12 publications

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“…Authors of [7] addressed the total energy minimization of wireless communication with rotary-wing UAV by optimizing the propulsion energy as well as the communication energy in the trajectory design. Work by authors in [13] focused on the trajectory optimization of a rotary-wing UAV, acting as a relay node in a cellular network setting, whereby they aim to achieve a trade-off between long term communication delay and power consumption for UAV's mobility.…”

Section: Related Workmentioning

confidence: 99%

Energy and Service-priority aware Trajectory Design for UAV-BSs using Double Q-Learning

Hoseini¹,

Bokani²,

Hassan³

et al. 2020

Preprint

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Next generation mobile networks have proposed the integration of Unmanned Aerial Vehicles (UAVs) as aerial base stations (UAV-BS) to serve ground nodes. Despite having advantages of using UAV-BSs, their dependence on the on-board, limited-capacity battery hinders their service continuity. Shorter trajectories can save flying energy, however UAV-BSs must also serve nodes based on their service priority since nodes' service requirements are not always the same. In this paper, we present an energy-efficient trajectory optimization for a UAV assisted IoT system in which the UAV-BS considers the IoT nodes' service priorities in making its movement decisions. We solve the trajectory optimization problem using Double Q-Learning algorithm. Simulation results reveal that the Q-Learning based optimized trajectory outperforms a benchmark algorithm, namely Greedilyserved algorithm, in terms of reducing the average energy consumption of the UAV-BS as well as the service delay for high priority nodes.

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Section: Related Workmentioning

confidence: 99%

Energy and Service-priority aware Trajectory Design for UAV-BSs using Double Q-Learning

Hoseini¹,

Bokani²,

Hassan³

et al. 2020

Preprint

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“…To address the aforementioned challenges, our previous work [33] investigated the UAV trajectory and communication scheduling design subject to random data traffic from two ground nodes, but did not consider the UAV mobility power, and assumed the UAV to be the ultimate destination of the GN data traffic. The model was extended in our work [1] to account for the UAV power constraint and the scenario in which the UAV relays random data traffic generated by densely deployed GNs and a BS. However, UAV relaying periods utilized a fly-hovercommunicate strategy, which is inefficient for network performance and UAV endurance, but easier to analyze than protocols in which the UAV communicates while moving [8], as done in this paper.…”

Section: A Related Workmentioning

confidence: 99%

“…where R GB (r) is the communication rate for the direct transmission defined in (1), and the other two terms equal 0 since the system moves immediately to the waiting state (r U , θ U , w 0 ) ∈ S wait with probability 1, and the corresponding action duration and energy consumption are 0.…”

Section: A Smdp Formulationmentioning

confidence: 99%

MAESTRO-X: Distributed Orchestration of Rotary-Wing UAV-Relay Swarms

Keshavamurthy¹,

Bliss²,

Michelusi³

2020

Preprint

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This paper investigates the adaptive trajectory and communication scheduling design for an unmanned aerial vehicle (UAV) relaying random data traffic generated by ground nodes to a base station.The goal is to minimize the expected average communication delay to serve requests, subject to an average UAV mobility power constraint. It is shown that the problem can be cast as a semi-Markov decision process with a two-scale structure, which is optimized efficiently: in the outer decision, the UAV radial velocity for waiting phases and end radius for communication phases optimize the average longterm delay-power trade-off; given outer decisions, inner decisions greedily minimize the instantaneous delay-power cost, yielding the optimal angular velocity in waiting states, and the optimal relay strategy and UAV trajectory in communication states. A constrained particle swarm optimization algorithm is designed to optimize these trajectory problems, demonstrating 100× faster computational speeds than successive convex approximation methods. Simulations demonstrate that an intelligent adaptive design exploiting realistic UAV mobility features, such as helicopter translational lift, reduces the average communication delay and UAV mobility power consumption by 44% and 7%, respectively, with respect to an optimal hovering strategy and by 2% and 13%, respectively, with respect to a greedy delay minimization scheme.

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“…A previous version of this research [16] focused only on single UAV-relay deployments, assumed an FSPL channel model, and employed PSO for trajectory optimization: the challenges associated with these have been discussed above. Novelties: In this paper, with rate adaptation to exploit A2G channel stochastics, we first constrain our study to single relay settings, wherein the problem of minimizing the time-average service delay subject to an average UAV power constraint, is formulated as a Semi-Markov Decision Process (SMDP).…”

Section: Introductionmentioning

confidence: 99%

Multiscale Adaptive Scheduling and Path-Planning for Power-Constrained UAV-Relays via SMDPs

Keshavamurthy¹,

Michelusi²

2022

Preprint

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We describe the orchestration of a decentralized swarm of rotary-wing UAV-relays, augmenting the coverage and service capabilities of a terrestrial base station. Our goal is to minimize the time-average service latencies involved in handling transmission requests from ground users under Poisson arrivals, subject to an average UAV power constraint. Equipped with rate adaptation to efficiently leverage air-to-ground stochastics, we first derive the optimal control policy for a single relay via a semi-Markov decision process formulation, with competitive swarm optimization for UAV trajectory design. Accordingly, we detail a multiscale decomposition of this construction: outer decisions on radial wait velocities and end positions optimize the expected long-term delay-power trade-off; consequently, inner decisions on angular wait velocities, service schedules, and UAV trajectories greedily minimize the instantaneous delay-power costs. Next, generalizing to UAV swarms via replication and consensus-driven command-and-control, this policy is embedded with spread maximization and conflict resolution heuristics. We demonstrate that our framework offers superior performance vis-à-vis average service latencies and average per-UAV power consumption: 11× faster data payload delivery relative to static UAV-relay deployments and 2× faster than a deep-Q network solution; remarkably, 1 relay with our scheme outclasses 3 relays under a joint successive convex approximation policy by 62%.

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Power-Constrained Trajectory optimization for Wireless UAV Relays with Random Requests

Cited by 10 publications

References 12 publications

Energy and Service-priority aware Trajectory Design for UAV-BSs using Double Q-Learning

Energy and Service-priority aware Trajectory Design for UAV-BSs using Double Q-Learning

MAESTRO-X: Distributed Orchestration of Rotary-Wing UAV-Relay Swarms

Multiscale Adaptive Scheduling and Path-Planning for Power-Constrained UAV-Relays via SMDPs

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