TY - GEN
T1 - UAV Position Optimization Based on Information Freshness
T2 - 13th International Conference on Wireless Communications and Signal Processing, WCSP 2021
AU - Wang, Meng
AU - Li, Lixin
AU - Lin, Wensheng
AU - Wei, Baoguo
AU - Chen, Wei
AU - Han, Zhu
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - In order to deal with the challenge of data traffic surge, unmanned aerial vehicle (UAV) is widely investigated to provide timely internet services for ground users due to its mobility and flexibility. Meanwhile, for the purpose of measuring the timeliness, reliability and security of user's data updates, the freshness of information has also received extensive attention. In this paper, we formulate an ultra-dense network of multi-UAVs assisted base stations (BSs) serving users, and minimize the age of information (AoI) of the system by optimizing the positions of the UAVs to improve information freshness. To simplify the complicated large dense network model, a mean field game (MFG) method is proposed to jointly optimize the positions of these UAVs, taking into account the interference between UAVs and the flight energy consumption. In addition, a deep deterministic policy gradient (DDPG) algorithm is implemented to obtain the mean field equilibrium solution. The simulation results verify that the proposed algorithm can effectively reduce the AoI of the system.
AB - In order to deal with the challenge of data traffic surge, unmanned aerial vehicle (UAV) is widely investigated to provide timely internet services for ground users due to its mobility and flexibility. Meanwhile, for the purpose of measuring the timeliness, reliability and security of user's data updates, the freshness of information has also received extensive attention. In this paper, we formulate an ultra-dense network of multi-UAVs assisted base stations (BSs) serving users, and minimize the age of information (AoI) of the system by optimizing the positions of the UAVs to improve information freshness. To simplify the complicated large dense network model, a mean field game (MFG) method is proposed to jointly optimize the positions of these UAVs, taking into account the interference between UAVs and the flight energy consumption. In addition, a deep deterministic policy gradient (DDPG) algorithm is implemented to obtain the mean field equilibrium solution. The simulation results verify that the proposed algorithm can effectively reduce the AoI of the system.
KW - age of information
KW - deep deterministic policy gradient
KW - Information freshness
KW - mean field game
KW - unmanned aerial vehicle
UR - http://www.scopus.com/inward/record.url?scp=85123358414&partnerID=8YFLogxK
U2 - 10.1109/WCSP52459.2021.9613407
DO - 10.1109/WCSP52459.2021.9613407
M3 - 会议稿件
AN - SCOPUS:85123358414
T3 - 13th International Conference on Wireless Communications and Signal Processing, WCSP 2021
BT - 13th International Conference on Wireless Communications and Signal Processing, WCSP 2021
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 20 October 2021 through 22 October 2021
ER -
