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On Evolutionary Game of Dynamic Devices in NOMA-Based IoT Networks
IEEE Transactions on Cognitive Communications and Networking  (IF4.341),  Pub Date : 2021-03-17, DOI: 10.1109/tccn.2021.3066191
Jinho Choi

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.