论文标题
通过人工神经网络选择5G新广播的继电器选择
Relay Selection for 5G New Radio Via Artificial Neural Networks
论文作者
论文摘要
毫米波提供了一个宽带宽带的替代频带，以更好地实现增强的移动宽带（EMBB）和超级可靠和低缓慢通信（URLLC）的支柱技术，用于5G-新无线电（5G-NR）。当使用MMWave频带时，继电器站有助于无线电访问网络中的基站覆盖（RAN）作为一种有吸引力的技术。但是，继电器的选择导致最强的链接成为促进使用MMWave行驶的关键技术。继电器选择的另一种方法是利用现有的操作数据，并应用适当的人工神经网络（ANN）和深度学习算法来减轻MMWave频段的严重褪色。在本文中，我们使用具有多层感知的ANN应用分类技术来预测多个传输链接的路径损失，并在一定的损耗水平上进行基础，从而执行有效的继电器选择，这也建议将切换到适当的路径。将具有多层感知的ANN与其他ML算法进行比较，以证明5G-NR中继电器选择的有效性。
Millimeter-wave supplies an alternative frequency band of wide bandwidth to better realize pillar technologies of enhanced mobile broadband (eMBB) and ultra-reliable and lowlatency communication (uRLLC) for 5G - new radio (5G-NR). When using mmWave frequency band, relay stations to assist the coverage of base stations in radio access network (RAN) emerge as an attractive technique. However, relay selection to result in the strongest link becomes the critical technology to facilitate RAN using mmWave. A alternative approach toward relay selection is to take advantage of existing operating data and apply appropriate artificial neural networks (ANN) and deep learning algorithms to alleviate severe fading in mmWave band. In this paper, we apply classification techniques using ANN with multilayer perception to predict the path loss of multiple transmitted links and base on a certain loss level, and thus execute effective relay selection, which also recommends the handover to an appropriate path. ANN with multilayer perceptions are compared with other ML algorithms to demonstrate the effectiveness for relay selection in 5G-NR.
