标题： 基于机器学习的密集LTE-U/Wi-Fi共存场景下的频谱共享 显示英文标题

标题： Machine Learning enabled Spectrum Sharing in Dense LTE-U/Wi-Fi Coexistence Scenarios

摘要： 机器学习（ML）技术在复杂工程问题中的应用已被证明是一种有吸引力且高效解决方案。 ML已被成功应用于图像识别、自动化工业操作等几个实际任务中。 ML技术在解决非线性问题方面的潜力影响了这项工作，该工作旨在将已知的ML技术应用于无线频谱共享，并在Wi-Fi和LTE在非授权频谱中的共享中开发新的技术。 在本工作中，我们关注由LTE-U论坛开发的LTE-Unlicensed（LTE-U）规范，该规范使用占空比方法实现公平共存。 该规范建议当共信道Wi-Fi基本服务集（BSSs）的数量从一个增加到两个或更多时，减少LTE-U基站（BS）的占空比。 然而，在不解码Wi-Fi数据包的情况下，实时检测信道上运行的Wi-Fi BSS数量是一个具有挑战性的问题。 在本工作中，我们展示了一种新颖的基于ML的方法，该方法通过利用LTE-U关闭期间观察到的能量值来解决这个问题。 与需要在LTE-U基站配备完整的Wi-Fi接收器来解码整个Wi-Fi数据包相比，在LTE-U基站关闭期间仅观察能量值相对较为简单。 我们通过实时实验实现了并验证了所提出的基于ML的方法，并证明了一个和多个Wi-Fi接入点传输之间的能量分布存在明显的模式。 与现有的自相关（AC）和能量检测（ED）方法相比，所提出的基于ML的方法结果具有更高的准确性（所有情况下接近99%）。 显示英文摘要

摘要： The application of Machine Learning (ML) techniques to complex engineering problems has proved to be an attractive and efficient solution. ML has been successfully applied to several practical tasks like image recognition, automating industrial operations, etc. The promise of ML techniques in solving non-linear problems influenced this work which aims to apply known ML techniques and develop new ones for wireless spectrum sharing between Wi-Fi and LTE in the unlicensed spectrum. In this work, we focus on the LTE-Unlicensed (LTE-U) specification developed by the LTE-U Forum, which uses the duty-cycle approach for fair coexistence. The specification suggests reducing the duty cycle at the LTE-U base-station (BS) when the number of co-channel Wi-Fi basic service sets (BSSs) increases from one to two or more. However, without decoding the Wi-Fi packets, detecting the number of Wi-Fi BSSs operating on the channel in real-time is a challenging problem. In this work, we demonstrate a novel ML-based approach which solves this problem by using energy values observed during the LTE-U OFF duration. It is relatively straightforward to observe only the energy values during the LTE-U BS OFF time compared to decoding the entire Wi-Fi packet, which would require a full Wi-Fi receiver at the LTE-U base-station. We implement and validate the proposed ML-based approach by real-time experiments and demonstrate that there exist distinct patterns between the energy distributions between one and many Wi-Fi AP transmissions. The proposed ML-based approach results in a higher accuracy (close to 99\% in all cases) as compared to the existing auto-correlation (AC) and energy detection (ED) approaches.