In this paper, we consider an Internet-of-Things (IoT) network that supports two types of devices, namely static devices (SDs) and dynamic devices (DDs), with multiple channels. The notion of power-domain non-orthogonal multiple access (NOMA) is applied so that DDs can not only dynamically change channels, but also transmit signals with a high power to reduce collisions with SDs. Thus, learning algorithms are needed for DDs to select channels. To this end, multiarmed bandit (MAB) algorithms can be used. However, in general, it is difficult to analyze the performance of MAB algorithms in the setting due to the interaction of DDs. Thus, in this paper, instead of analyzing MAB algorithms, we formulate an evolutionary game of DDs in NOMA-based IoT networks and analyze the evolutionary game. It is shown that the game has a unique evolutionary stable state (ESS) in terms of the channel selection probability. The performance according to the ESS can be regarded as a baseline performance, because it is the best performance achieved by the DDs competing for multiple channel resources. Thus, the resulting baseline performance can be used to see if a learning algorithm including MAB can achieve a better performance by avoiding competition between DDs.

中文翻译：

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基于 NOMA 的物联网网络中动态设备的演化博弈

在本文中，我们考虑支持两种类型设备的物联网 (IoT) 网络，即静态设备 (SD) 和动态设备 (DD)，具有多个通道。应用了功率域非正交多址 (NOMA) 的概念，因此 DD 不仅可以动态改变信道，还可以传输高功率信号以减少与 SD 的冲突。因此，DDs 需要学习算法来选择通道。为此，可以使用多臂老虎机 (MAB) 算法。但是，一般来说，由于 DD 的交互，很难分析 MAB 算法在设置中的性能。因此，在本文中，我们不是分析 MAB 算法，而是在基于 NOMA 的物联网网络中制定 DD 的演化博弈并分析演化博弈。结果表明，该游戏在信道选择概率方面具有独特的进化稳定状态（ESS）。根据 ESS 的性能可以被视为基线性能，因为它是 DD 争夺多个信道资源所达到的最佳性能。因此，由此产生的基线性能可用于查看包含 MAB 的学习算法是否可以通过避免 DD 之间的竞争来实现更好的性能。