Coexistence Mechanism of LTE and WiFi Based on Reinforcement Learning in Heterogeneous Networks

The coexistence mechanism of LTE-U (long term evolution - unlicensed) and WiFi in 5G heterogeneous wireless networks is discussed. Q learning based – almost blank subframe (QL-ABS) configuration mechanism is proposed for LTE-U. In the mechanism, the queuing theory is used to model the heterogeneous network where LTE-U and WiFi coexist, and the network delay performance is used to represent the input state of Q-learning. The simulation results show that the proposed mechanism can generate a more reasonable almost blank subframe configuration strategy for LTE-U under multiple services and different load conditions through autonomous learning process, and therefore has better online learning performance. Compared with the traditional methods, the proposed mechanism better solves the problem of coexistence between LTE-U and existing WiFi network in the unlicensed frequency bands, and improves the overall delay and online performances of the networks.