Trajectory Design and Power Control for Multi-UAV Assisted Wireless Networks: A Machine Learning Approach
By Xiao Liu, Yuanwei Liu, Yue Chen, Lajos Hanzo. IEEE Transactions on Vehicular Technology, 2018
306 citations recorded by the source corpus.
A novel framework is proposed for the trajectory design of multiple unmanned aerial vehicles (UAVs) based on the prediction of users’ mobility information. The problem of joint trajectory design and power control is formulated for maximizing the instantaneous sum transmit rate while satisfying the rate requirement of users. In an effort to solve this pertinent problem, a three-step approach is proposed, which is based on machine learning techniques to obtain both the position information of users and the trajectory design of UAVs. First, a multi-agent Q-learning-based placement algorithm is proposed for determining the optimal positions of the UAVs based on the initial location of the users. Second, in an effort to determine the mobility information of users based on a real dataset, their position data is collected from Twitter to describe the anonymous user-trajectories in the physical world. In the meantime, an echo state network (ESN) based prediction algorithm is proposed for predicting the future positions of users based on the real dataset. Third, a multi-agent Q-learning-based algorithm is conceived for predicting the position of UAVs in each time slot based on the movement of users. In this algorithm, multiple UAVs act as agents to find optimal actions by interacting with their environment and learn from their mistakes. Additionally, we also prove that the proposed multi-agent Q-learning-based trajectory design and power control algorithm can converge under mild conditions. Numerical results are provided to demonstrate that as the size of the reservoir increases, the proposed ESN approach improves the prediction accuracy. Finally, we demonstrate that the throughput gains of about $17\%$ are achieved.
Subfields: Aerospace Engineering, Computer Networks and Communications
View Paper: https://www.semanticscholar.org/paper/12d8f42595f78f9b50bef817b77bee7f3947d1f1
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Aerospace Engineering
- Yuanwei Liu
- Robert Schober
- Xidong Mu
- Naofal Al-Dhahir
- Cunhua Pan
- M. Di Renzo
- Shuowen Zhang
- Lajos Hanzo
In Computer Networks and Communications
- M. Di Renzo
- Lajos Hanzo
- Robert Schober
- Yuanwei Liu
- Naofal Al-Dhahir
- Xuemin Shen
- Zhiguo Ding
- Xidong Mu
Citations in the corpus
- Evolution of NOMA Toward Next Generation Multiple Access (NGMA) for 6G (2021, background): “On the one hand, a notable feature of DRL is its benefits in long-term optimization [205], [216], [222], [234].”
- Multi-Agent Deep Reinforcement Learning-Based Trajectory Planning for Multi-UAV Assisted Mobile Edge Computing (2020, methodology): “We set the initial coordinates of UAVs to [10, 10], [90, 90], [10, 90] and [90, 10] m.”
- Reconfigurable Intelligent Surfaces: Principles and Opportunities (2020, methodology): “Additionally, by mounting a compact distributed laser charging (DLC) receiver or wireless power transmission (WPT) receiver antenna inside the UAVs, while a DLC/WPT transmitter is deployed on the ground or the building roof, the UAVs can be charged as long as they are flying within the coverage rang”
- Voronoi-Based Multi-Robot Autonomous Exploration in Unknown Environments via Deep Reinforcement Learning (2020, background): “[4], [5], vehicle-assisted wireless communication networks [6], autonomous shepherding [7], connected vehicle platoons [8], [9], etc.”