论文标题
消除NB-IOT干扰LTE系统：基于机器学习的稀疏方法
Eliminating NB-IoT Interference to LTE System: a Sparse Machine Learning Based Approach
论文作者
论文摘要
窄带互联网（NB-iot）是一种具有竞争性的5G技术，用于大规模的机器型通信方案，但与此同时，将窄带干扰（NBI）引入了现有的宽带传输，例如增强的移动宽带（EMBB）方案中的长期演化（LTE）系统。为了促进无线异质网络中的谐波和公平共存，消除NB-iot对LTE系统的干扰很重要。在本文中，为准确的NBI恢复而制定了一种新型的基于机器学习的框架和稀疏组合优化问题，可以使用所提出的迭代稀疏学习算法有效地解决，称为稀疏交叉透明度最小化（SCEM）。为了进一步提高恢复精度和收敛速率，将正则化引入了称为正则化SCEM的增强算法中的损耗函数。此外，利用NBI的空间相关性，该框架扩展到多输入多输出系统。仿真结果表明，所提出的方法有效消除NB-iot对LTE系统的干扰，并显着胜过最先进的方法。
Narrowband internet-of-things (NB-IoT) is a competitive 5G technology for massive machine-type communication scenarios, but meanwhile introduces narrowband interference (NBI) to existing broadband transmission such as the long term evolution (LTE) systems in enhanced mobile broadband (eMBB) scenarios. In order to facilitate the harmonic and fair coexistence in wireless heterogeneous networks, it is important to eliminate NB-IoT interference to LTE systems. In this paper, a novel sparse machine learning based framework and a sparse combinatorial optimization problem is formulated for accurate NBI recovery, which can be efficiently solved using the proposed iterative sparse learning algorithm called sparse cross-entropy minimization (SCEM). To further improve the recovery accuracy and convergence rate, regularization is introduced to the loss function in the enhanced algorithm called regularized SCEM. Moreover, exploiting the spatial correlation of NBI, the framework is extended to multiple-input multiple-output systems. Simulation results demonstrate that the proposed methods are effective in eliminating NB-IoT interference to LTE systems, and significantly outperform the state-of-the-art methods.
