An iterative SCA-based algorithm solves the non-convex trajectory optimization problem to minimize UAV propulsion power subject to cellular connectivity constraints.
Wireless Communications with Unmanned Aerial Vehicles: Opportunities and Challenges
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
eess.SP 2years
2019 2verdicts
UNVERDICTED 2representative citing papers
A distributed Q-learning algorithm optimizes trajectories, power, and sub-channel allocation for multi-ABS networks to maximize sum-rate.
citing papers explorer
-
Power Efficient Trajectory Optimization for the Cellular-Connected Aerial Vehicles
An iterative SCA-based algorithm solves the non-convex trajectory optimization problem to minimize UAV propulsion power subject to cellular connectivity constraints.
-
Reinforcement Learning-Based Trajectory Design for the Aerial Base Stations
A distributed Q-learning algorithm optimizes trajectories, power, and sub-channel allocation for multi-ABS networks to maximize sum-rate.