TY - JOUR
T1 - aI-aided Downlink Interference Control in Dense Interference-aware Drone Small Cells Networks
AU - Li, Lixin
AU - Zhang, Zihe
AU - Xue, Kaiyuan
AU - Wang, Meng
AU - Pan, Miao
AU - Han, Zhu
N1 - Publisher Copyright:
© 2013 IEEE.
PY - 2020
Y1 - 2020
N2 - Recently, drone small cells (DSCs) has been brought into significant focus, which is the one key enabler for potentially facilitating terrestrial wireless communication systems. Meanwhile, in ultra-dense unmanned networks, artificial intelligence (aI) has been a useful and efficient tool for control and management of the multi-agents. This paper investigates a downlink interference control problem in ultra-dense unmanned networks with aI-aided approach, that each DSC can adjust its altitude to increase the data-rate. This problem is formulated as a mean field game (MFG) framework, an aI-aided method to make decisions. In this framework, each DSC controls its velocity to minimize the cost over a period, where the cost function is composed by the data-rate and height adjusting consumption. Meanwhile, in this model, we adopt the mean-field approximation (MFa) approach to derive the interference introduced from a large number of DSCs. Besides, the control strategy is described and explained by using the related Hamilton-Jacobi-Bellman (HJB) and Fokker-Planck-Kolmogorov (FPK) equations, respectively. Thus, a finite difference algorithm is proposed to solve the coupled partial differential equations, which can obtain the optimal altitude control strategy. The algorithm outputs show the optimal behaviors of DSCs in different environment scenarios. In additon, the simulation results verify that the proposed control strategy has better average signal to interference plus noise ratio (SINR) compared with the baseline method.
AB - Recently, drone small cells (DSCs) has been brought into significant focus, which is the one key enabler for potentially facilitating terrestrial wireless communication systems. Meanwhile, in ultra-dense unmanned networks, artificial intelligence (aI) has been a useful and efficient tool for control and management of the multi-agents. This paper investigates a downlink interference control problem in ultra-dense unmanned networks with aI-aided approach, that each DSC can adjust its altitude to increase the data-rate. This problem is formulated as a mean field game (MFG) framework, an aI-aided method to make decisions. In this framework, each DSC controls its velocity to minimize the cost over a period, where the cost function is composed by the data-rate and height adjusting consumption. Meanwhile, in this model, we adopt the mean-field approximation (MFa) approach to derive the interference introduced from a large number of DSCs. Besides, the control strategy is described and explained by using the related Hamilton-Jacobi-Bellman (HJB) and Fokker-Planck-Kolmogorov (FPK) equations, respectively. Thus, a finite difference algorithm is proposed to solve the coupled partial differential equations, which can obtain the optimal altitude control strategy. The algorithm outputs show the optimal behaviors of DSCs in different environment scenarios. In additon, the simulation results verify that the proposed control strategy has better average signal to interference plus noise ratio (SINR) compared with the baseline method.
KW - artificial intelligence
KW - downlink interference control
KW - drone small cell
KW - mean field game
KW - ultra-dense unmanned networks
UR - http://www.scopus.com/inward/record.url?scp=85079824242&partnerID=8YFLogxK
U2 - 10.1109/aCCESS.2020.2966740
DO - 10.1109/aCCESS.2020.2966740
M3 - 文章
AN - SCOPUS:85079824242
SN - 2169-3536
VL - 8
SP - 15110
EP - 15122
JO - IEEE Access
JF - IEEE Access
M1 - 8960388
ER -