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.

中文翻译：

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非授权频谱中LTE联合信道/子帧选择的强化学习

近年来，为了应对移动数据流量的快速增长，增加蜂窝网络的容量越来越受到重视。为此，将当前 LTE-advanced 或 5G 系统的数据流量从许可频谱卸载到 WiFi 系统使用的非许可频谱，即 LTE 许可辅助接入 (LTE-LAA)，已被广泛研究。目前的LTE-LAA系统采用LBT(Listen-Before-Talk)方式，要求LTE用户在传输前也进行载波侦听。然而，由于帧大小和流量负载不平衡，仍然难以保证公平的 LTE-WiFi 共存。在LTE-LAA系统中，最优信道选择和子帧数调整是实现高效频谱利用和公平系统共存的关键。为此，在本文中，我们提出了一种基于强化学习的 LTE-LAA 联合信道/子帧选择方案。所提出的方法是在 LTE 接入点实施的，对 WiFi 系统的了解为零。大量仿真的结果证实，与基线方案相比，所提出的方法可以显着提高公平性和丢包率。