Enhanced LBT Mechanism for LTE-Unlicensed Using Reinforcement Learning
Enhanced LBT Mechanism for LTE-Unlicensed Using Reinforcement Learning
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DOI：
10.1109/ccece.2018.8447665
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发表时间：
2018-05
期刊：
影响因子：
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通讯作者：
Meesam Haider;M. Erol-Kantarci
中科院分区：
文献类型：
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作者：
Meesam Haider;M. Erol-Kantarci
在过去的十年中，连接设备的数量一直在大幅增长。这些连接的设备从传统的智能手机到电器、太阳能电池板、转换器、电动汽车和可穿戴设备。满足他们的连接需求是增加压力的无线网络已经与服务的带宽饥渴的移动的用户。LTE免许可（LTE-U）旨在利用免许可频谱来卸载移动的用户（LTE用户）流量，增加容量，从而在需求膨胀的时代改善用户/设备体验。同时，WiFi是在未授权频谱上运行的主导技术。因此，LTE-U需要确保WiFi用户的性能不会随着LTE用户卸载其流量而降低。在本文中，我们提出了一种基于Q学习的媒体接入方法，以增强LTE-U的先听后说（LBT）机制。基于Q学习的LBT通过在LTE-U用户尝试接入未授权频段时增强WiFi用户的性能来帮助解决共存问题。我们的结果表明，与标准LBT实现相比，提出的基于Q学习的LBT将WiFi用户的端到端延迟减少了几十秒。它还将WiFi流量的交付成功率提高了71%。
The amount of connected devices has been growing tremendously over the past decade. These connected devices range from the traditional smart phones to electrical appliances, solar panels, converters, electric vehicles and wearables. Satisfying their connectivity demand is adding pressure to the wireless networks which are already pressed with serving their bandwidth-hungry mobile users. LTE Unlicensed (LTE-U) aims to exploit the unlicensed spectrum to offload mobile user (LTE users) traffic, increase capacity, and hence improve the us $e$ r/device experience in an era of inflated demand. Meanwhile, WiFi is the dominant technology operating at the unlicensed spectrum. Therefore, LTE-U needs to ensure the performance of WiFi users do not degrade as LTE users offload their traffic. In this paper, we propose a Q-learning based medium access approach to enhance the Listen Before Talk (LBT) mechanism of LTE-U. Q-learning based LBT helps with the co-existence issue by enhancing the performance of WiFi users at times when LTE-U users try to access the unlicensed bands. Our results show that the proposed Q-learning based LBT reduces the end-to-end delay of WiFi users in the order of several tens of seconds in comparison to the standard LBT implementation. It also increases the delivery success rate of WiFi traffic by up to 71%.