Many wireless networks, including 5G NR (New Radio) and future beyond 5G cellular systems, are expected to operate on multiple frequency bands. This paper considers the band assignment (BA) problem in dual-band systems, where the basestation (BS) chooses one of the two available frequency bands (centimeter-wave and millimeter-wave bands) to communicate with the user equipment (UE). While the millimeter-wave band might offer higher data rate, there is a significant probability of outage during which the communication should be carried on the (more reliable) centimeter-wave band. With mobility, the BA can be perceived as a sequential problem, where the BS uses previously observed information to predict the best band for a future time step. We formulate the BA as a binary classification problem and propose supervised Machine Learning (ML) solutions. We study the problem when both the BS and the UE use (i) omnidirectional antennas and (ii) both use directional antennas. In the omnidirectional case, we derive analytical benchmark solutions based on the Gaussian Process (GP) assumption for the inter-band shadow fading. In the directional case, where the labeling is shown to be complex, we propose an efficient labeling approach based on the Viterbi Algorithm (VA). We compare the performances for two channel models: (i) a stochastic channel and (ii) a ray-tracing based channel.

中文翻译：

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用于具有全向和定向天线的双频段系统中的频段分配的监督机器学习解决方案


许多无线网络，包括 5G NR（新无线电）和未来超越 5G 的蜂窝系统，预计将在多个频段上运行。本文考虑双频段系统中的频段分配（BA）问题，其中基站（BS）选择两个可用频段（厘米波和毫米波频段）之一与用户设备（UE）进行通信。虽然毫米波频段可能提供更高的数据速率，但中断的可能性很大，在此期间应在（更可靠的）厘米波带上进行通信。对于移动性，BA 可以被视为一个序列问题，其中 BS 使用先前观察到的信息来预测未来时间步的最佳频带。我们将 BA 表述为二元分类问题，并提出监督机器学习 (ML) 解决方案。我们研究当 BS 和 UE 都使用 (i) 全向天线和 (ii) 都使用定向天线时的问题。在全向情况下，我们基于带间阴影衰落的高斯过程（GP）假设得出分析基准解决方案。在定向情况下，标记很复杂，我们提出了一种基于维特比算法（VA）的有效标记方法。我们比较了两种通道模型的性能：(i) 随机通道和 (ii) 基于光线追踪的通道。