2020 IEEE Wireless Communications and Networking Conference Workshops (WCNCW) 2020
DOI: 10.1109/wcncw48565.2020.9124759
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Energy-Efficient UAV Communications with Interference Management: Deep Learning Framework

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Cited by 8 publications
(7 citation statements)
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“…The challenge of UAV height optimization was tackled in [179] using a DQN with ER, where the UAV agent adapts its height in a way to increase throughput under interference constraints. A similar study with energy constraints was presented in [180].…”
Section: Interference Managementmentioning
confidence: 77%
See 1 more Smart Citation
“…The challenge of UAV height optimization was tackled in [179] using a DQN with ER, where the UAV agent adapts its height in a way to increase throughput under interference constraints. A similar study with energy constraints was presented in [180].…”
Section: Interference Managementmentioning
confidence: 77%
“…• The works in [82,96,123,127,128,[140][141][142][143]172] succeeded in achieving realistic aerial platform deployment scenarios where multiple platforms are expected to perform cooperative decisions instead of independent decisions. • Most of the works simulated a realistic wireless channel in their system model using the probabilistic path loss model [80,93,94,104,104,107,107,108,108,118,123,127,128,148,172,172,179,180] with only a few works achieving higher realism by considering CSI estimation [79,112,131,137,158]. • In terms of energy considerations, a fair number of works presented energy-efficient factors and constraints in their formulations such as battery capacity [103], energy harvesting [112,150,157], propulsion energy [113,139], energy quanta [136,139] and others [95,96,140].…”
Section: Qualitative Analysis: Simulation Realismmentioning
confidence: 99%
“…This design satisfied the user equipment's QoS necessities; see [106]. Other approaches include partially explainable big data [94], secure UAV Communications [95], UAV pursuer and evader problem [96], Automatic Recognition [97], Path planning [98], Trajectory Design and Generalization [99], energyefficient UAV [100][101], Multi-UAV Target-Finding [102], Real-time Data Processing [103], UAV Navigation [104], Anti-Intelligent UAV Jamming [105]. Summarized challenges of UAV, hi-tech proposed models, and loopholes are illustrated in Table 4.…”
Section: Trajectory Design and Power Allocationmentioning
confidence: 98%
“…To maximize energy efficiency in terrestrial communications, [17] proposes various key performance indicators (KPIs) sought to satisfy the DRL framework and define an optimal energy efficiency strategy from heterogeneous data. They convert the deep learning problem into deep queue learning to optimize the energy efficiency for airborne users whilst minimizing interference with terrestrial users.…”
Section: Energy-efficient Machine Learning Approaches For Uavsmentioning
confidence: 99%