Poster Abstract: A QoS-aware, Energy-efficient Trajectory Optimization for UAV Base Stations using Q-Learning

Salehi, Shavbo; Hassan, Jahan; Bokani, Ayub; Hoseini, Sayed Amir; Kanhere, Salil S.

doi:10.1109/ipsn48710.2020.00-22

Cited by 13 publications

(8 citation statements)

References 3 publications

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“…Furthermore, designing UAVs communication trajectories improve energy efficiency [56,108]. The trajectory optimization uses for recharging UAVs using Q-learning [54] and balances energy consumption and QoS [53]. In [109], the maximization of UAVs' energy efficiency is discussed, in which the UAVs are deployed as a relay station to amplify the signal strength between the IoT devices and end node (center).…”

Section: Energy Efficiencymentioning

confidence: 99%

“…The authors in [41] studied a UAVs-assisted wireless system and focused on maximizing the vehicle's rate on the ground to join the UAV's trajectory while maintaining UAV's energy constraints. Furthermore, the optimal trajectory is introduced for balancing QoS and energy efficiency intelligently using Q-learning [53][54][55][56], RF band allocation [57], and wireless power transfer to recharge the UAVs [58].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Green internet of things using UAVs in B5G networks: A review of applications and strategies

et al. 2021

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Section: Energy Efficiencymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Green internet of things using UAVs in B5G networks: A review of applications and strategies

et al. 2021

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“…In [2], a maximum-minimum data collection rate problem is solved for UAV wireless sensor networks in urban areas, where the three-dimensional UAV trajectory and transmission scheduling of sensors are jointly optimized by convex approximation. In [16], the UAV trajectory is optimized via Q-learning to improve energy efficiency while ensuring QoS of ground users. For multiple UAVs, the interference problem becomes a key issue dominating wireless communication performance and requires new solutions to exploit its inherent spatial degree of freedom.…”

Section: A Literature Surveymentioning

confidence: 99%

“…represents the accumulated sum data rate for the kth WN up to the (n−1)th time, and R k,n is calculated by (16) with respect to the action a n and the state s n at the time instant n. The idea of the WASR method is to simply use the worst accumulated sum rate of the WNs as the reward according to the objective function of the optimization problem (P1). The DWASR method is conceptualized in accordance with [29], for which the difference of the worst accumulated sum rate at two adjacent time slots n and n − 1 is computed as the reward.…”

Section: Reward Designmentioning

confidence: 99%

UAV Trajectory, User Association and Power Control for Multi-UAV Enabled Energy Harvesting Communications: Offline Design and Online Reinforcement Learning

Fu¹,

Ku²,

Chen³

et al. 2022

Preprint

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In this paper, we consider multiple solar-powered wireless nodes which utilize the harvested solar energy to transmit collected data to multiple unmanned aerial vehicles (UAVs) in the uplink. In this context, we jointly design UAV flight trajectories, UAV-node communication associations, and uplink power control to effectively utilize the harvested energy and manage co-channel interference within a finite time horizon. To ensure the fairness of wireless nodes, the design goal is to maximize the worst user rate. The joint design problem is highly non-convex and requires causal (future) knowledge of the instantaneous energy state information (ESI) and channel state information (CSI), which are difficult to predict in reality. To overcome these challenges, we propose an offline method based on convex optimization that only utilizes the average ESI and CSI. The problem is solved by three convex subproblems with successive convex approximation (SCA) and alternative optimization. We further design an online convex-assisted reinforcement learning (CARL) method to improve the system performance based on real-time environmental information. An idea of multi-UAV regulated flight corridors, based on the optimal offline UAV trajectories, is proposed to avoid unnecessary flight exploration by UAVs and enables us to improve the learning efficiency and system performance, as compared with the conventional reinforcement learning (RL) method. Computer simulations are used to verify the effectiveness of the proposed methods. The proposed CARL method provides 25% and 12% improvement on the worst user rate over the offline and conventional RL methods.

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“…However, this work in [20] and other relevant studies [21] provided a one-way trajectory along only three GNs. To explore a more general and complex scenario, Shavbo Salehi et al considered more GNs but still a one-way trajectory in the case that the UAV flew and hovered above each node [22], [23]. Tingting Lan et al [24] obtained the one-way energyefficiency trajectory among a large number of GNs under certain communication demands but they have not analyzed the effect of the changing communication throughput requirements on the trajectory design.…”

Section: Introductionmentioning

confidence: 99%

Closed Trajectory Optimization for Aerial Base Station: An Energy Efficiency Design

Tian¹

2023

FCIS

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By optimizing the energy-efficient flight trajectory of unmanned aerial vehicles (UAVs), UAV-based aerial base stations (BSs) can provide longer communication services to more ground nodes (GNs) under the UAV battery limitation. Path discretization method and successive convex approximation (SCA) algorithm have been shown to be effective techniques in the design of the energy-aware UAV one-way trajectory along a few GNs. Compared with current studies, this paper designed and compared different optimal closed UAV trajectories among multiple GNs under a wide range of changing communication requirements. Meanwhile, a more complex and realistic scenario is considered where the UAV visits the ground nodes sequentially without repetition from a centrally located terrestrial base station and returns to the starting point in the end. To thoroughly analyze this problem, two UAV trajectory designs are proposed in this paper. One is to use the fly-hover-communicate protocol as a benchmark for comparison, and the other is to require that the UAV should fly and communicate with ground nodes simultaneously, named the fly-over scheme. The simulation results show that, in general, the proposed fly-over scheme outperforms the benchmark results, while in the case of high and low communication demands, the energy-saving performance of both is very similar. Therefore, advice can be given on the design of the UAV trajectory depending on different situations.

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Poster Abstract: A QoS-aware, Energy-efficient Trajectory Optimization for UAV Base Stations using Q-Learning

Cited by 13 publications

References 3 publications

Green internet of things using UAVs in B5G networks: A review of applications and strategies

Green internet of things using UAVs in B5G networks: A review of applications and strategies

UAV Trajectory, User Association and Power Control for Multi-UAV Enabled Energy Harvesting Communications: Offline Design and Online Reinforcement Learning

Closed Trajectory Optimization for Aerial Base Station: An Energy Efficiency Design

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