Reinforcement Learning for Joint Channel/Subframe Selection of LTE in the Unlicensed Spectrum

In recent years, to cope with the rapid growth in mobile data traffic, increasing the capacity of cellular networks is receiving more and more attention. To this end, offloading the current LTE-advanced or 5G system’s data traffic from licensed spectrum to the unlicensed spectrum that is used by WiFi systems, i.e., LTE-Licensed-Assisted-Access (LTE-LAA), has been extensively investigated. In the current LTE-LAA system, a Listen-Before-Talk (LBT) approach is implemented, which requires the LTE user also perform carrier sense before the transmission. However, fair LTE-WiFi coexistence is still hard to guarantee due to their unbalanced frame sizes and traffic loads. In the LTE-LAA system, the optimal channel selection and subframe number adjustment are the keys to realize efficient spectrum utilization and fair system coexistence. To this end, in this paper, we propose a reinforcement learning-based joint channel/subframe selection scheme for LTE-LAA. The proposed approach is implemented at the LTE access points with zero knowledge of the WiFi systems. The results of extensive simulations verify that the proposed approach can significantly improve the fairness and packet loss rate compared with baseline schemes.