TY - JOUR
T1 - Artificial Intelligence to Manage Network Traffic of 5G Wireless Networks
AU - Fu, Yu
AU - Wang, Sen
AU - Wang, Cheng-Xiang
AU - Hong, Xuemin
AU - McLaughlin, Stephen
PY - 2018/11/29
Y1 - 2018/11/29
N2 - The deployment of 5G wireless communication systems is projected to begin in 2020. With new scenarios, new technologies, and new network architectures, the traffic management for 5G networks will present significant technical challenges. In recent years, AI technologies, especially ML technologies, have demonstrated significant success in many application domains, suggesting their potential to help solve the problem of 5G traffic management. In this article, we investigate the new characteristics of 5G wireless network traffic and discuss challenges they present for 5G traffic management. Potential solutions and research directions for the management of 5G traffic, including distributed and lightweight ML algorithms and a novel AI assistant content retrieval algorithm framework, are discussed.
AB - The deployment of 5G wireless communication systems is projected to begin in 2020. With new scenarios, new technologies, and new network architectures, the traffic management for 5G networks will present significant technical challenges. In recent years, AI technologies, especially ML technologies, have demonstrated significant success in many application domains, suggesting their potential to help solve the problem of 5G traffic management. In this article, we investigate the new characteristics of 5G wireless network traffic and discuss challenges they present for 5G traffic management. Potential solutions and research directions for the management of 5G traffic, including distributed and lightweight ML algorithms and a novel AI assistant content retrieval algorithm framework, are discussed.
U2 - 10.1109/MNET.2018.1800115
DO - 10.1109/MNET.2018.1800115
M3 - Article
SN - 0890-8044
VL - 32
SP - 58
EP - 64
JO - IEEE Network
JF - IEEE Network
IS - 6
ER -