Reinforcement Learning based Joint Channel/Subframe Selection Scheme for Fair LTE-WiFi Coexistence
Yuki Kishimoto, Xiaoyan Wang, Masahiro Umehira · 2020
In recent years, to cope with the rapid growth in mobile data traffic, increasing the capacity of cellular networks is receiving much attention. To this end, offloading the current LTE-advance or the future 5G system's data traffic from licensed spectrum to unlicensed spectrum that used by WiFi system has been proposed. In the current LTE-WiFi coexistence standard, a Listen-Before-Talk (LBT) approach is adopted to make the LTE system senses the medium before a transmission. However, the channel selection and subframe adjustment issues are still open to realize fair coexistence between co-located LTE and WiFi networks. In this paper, we propose a reinforcement learning based joint channel/subframe selection scheme for fair LTE-WiFi coexistence. The proposed approach is distributedly implemented at LTE Access Points (APs) with zero knowledge of the WiFi systems. Extensive simulations have been performed, and the results verified that the proposed approach can achieve better fairness and packet loss rate compared with baseline schemes.