Trajectory Optimization for Cellular-Enabled UAV With Connectivity Outage Constraint

Chen, Yu-Jia; Huang, Duruo

doi:10.1109/access.2020.2971772

Cited by 26 publications

(17 citation statements)

References 44 publications

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“…If problem (32) does not yield a solution then the lower bound NWAoI is not achievable. In this case, a policy different than ū should be fed to the problem in (19) to find the update time instants and locations.…”

Section: A Nwaoi Lower Bound Analysismentioning

confidence: 99%

“…Although problem (19) can be solved, it requires the knowledge of scheduling policy, i.e., each node's number and order of updates. However, finding the scheduling policy is challenging especially when the nodes are equipped with batteries of large capacities since the nodes may send updates more frequently in such case.…”

Section: A Nwaoi Lower Bound Analysismentioning

confidence: 99%

“…different orders for updating nodes. Therefore, using a brute force method, the number of times one should solve (19) to find the optimal solution for the original problem in ( 9) is:…”

Section: A Nwaoi Lower Bound Analysismentioning

confidence: 99%

See 2 more Smart Citations

Neural Combinatorial Deep Reinforcement Learning for Age-optimal Joint Trajectory and Scheduling Design in UAV-assisted Networks

Ferdowsi¹,

Abd-Elmagid²,

Saad³

et al. 2020

Preprint

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In this paper, an unmanned aerial vehicle (UAV)-assisted wireless network is considered in which a battery-constrained UAV is assumed to move towards energy-constrained ground nodes to receive status updates about their observed processes. The UAV's flight trajectory and scheduling of status updates are jointly optimized with the objective of minimizing the normalized weighted sum of Age of Information (NWAoI) values for different physical processes at the UAV. The problem is first formulated as a mixed-integer program. Then, for a given scheduling policy, a convex optimization-based solution is proposed to derive the UAV's optimal flight trajectory and time instants on updates. However, finding the optimal scheduling policy is challenging due to the combinatorial nature of the formulated problem.Therefore, to complement the proposed convex optimization-based solution, a finite-horizon Markov decision process (MDP) is used to find the optimal scheduling policy. Since the state space of the MDP is extremely large, a novel neural combinatorial-based deep reinforcement learning (NCRL) algorithm using deep Q-network (DQN) is proposed to obtain the optimal policy. However, for large-scale scenarios with numerous nodes, the DQN architecture cannot efficiently learn the optimal scheduling policy anymore. Motivated by this, a long short-term memory (LSTM)-based autoencoder is proposed to map the state space to a fixed-size vector representation in such large-scale scenarios while capturing the spatio-temporal interdependence between the update locations and time instants. A lower bound on the minimum NWAoI is analytically derived which provides system design guidelines on the appropriate choice of importance weights for different nodes. Furthermore, an upper bound on the UAV's minimum speed is obtained to achieve this lower bound value. The numerical results also demonstrate that the proposed NCRL approach can significantly improve the achievable NWAoI per process compared to the baseline policies, such as weight-based and discretized state DQN policies.

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Section: A Nwaoi Lower Bound Analysismentioning

confidence: 99%

Section: A Nwaoi Lower Bound Analysismentioning

confidence: 99%

See 1 more Smart Citation

Neural Combinatorial Deep Reinforcement Learning for Age-optimal Joint Trajectory and Scheduling Design in UAV-assisted Networks

Ferdowsi¹,

Abd-Elmagid²,

Saad³

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…Unmanned aerial vehicles (UAVs) as aerial BSs [2] in which popular contents can be cached provide several benefits to the cellular network. Firstly, UAVs can provide wider wireless coverage due to high line-of-sight (LoS) probabilities at high altitudes [3]. Secondly, cache-enabled UAVs can be dynamically deployed and moved to deliver the requested files to the desired users, thereby improving caching efficiency [4].…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, in this paper we assume a perfect communication link between the ground BS and the UAVs. However, in realistic environments, UAVs may fly out of the wireless coverage of the ground BS [43]. In this case, more complex Q-value updating mechanisms are required for ensuring the learning stability, which will be our future work.…”

mentioning

confidence: 99%