A greedy-model-based reinforcement learning algorithm for Beyond-5G cooperative data collection

Liu, Xinyu; Zhou, Qingfeng; Cheng, Chi‐Tsun; Liu, Chanzi

doi:10.1016/j.phycom.2021.101496

Cited by 3 publications

(1 citation statement)

References 17 publications

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“…19 Furthermore, Khan and Yau 20 proposed the application of deep RL in determining the optimal UAV trajectory while addressing the limited energy resources of UAVs, and thus, optimization criteria were proposed for when the target was to increase the network lifetime of the UAV fleet. Finally, the work presented in Liu et al 21 proposes a multiagent greedy-model-based RL approach that accounts for multiple UAVs with different parameters to explore environments in parallel to accelerate training.…”

Section: Related Workmentioning

confidence: 99%

Fast and computationally efficient generative adversarial network algorithm for unmanned aerial vehicle–based network coverage optimization

Růžička

Vološin

Gazda

et al. 2022

International Journal of Distributed Sensor Networks

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The challenge of dynamic traffic demand in mobile networks is tackled by moving cells based on unmanned aerial vehicles. Considering the tremendous potential of unmanned aerial vehicles in the future, we propose a new heuristic algorithm for coverage optimization. The proposed algorithm is implemented based on a conditional generative adversarial neural network, with a unique multilayer sum-pooling loss function. To assess the performance of the proposed approach, we compare it with the optimal core-set algorithm and quasi-optimal spiral algorithm. Simulation results show that the proposed approach converges to the quasi-optimal solution with a negligible difference from the global optimum while maintaining a quadratic complexity regardless of the number of users.

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Section: Related Workmentioning

confidence: 99%