## 1 Introduction  
In recent years, the number of Internet-of-Things (IoT) devices has increased. The International Data Corporation (IDC) predicts that more than 40 billion IoT devices will generate 79 zettabytes (ZB;10\({}^{21}\) bytes) by 2025 [1]. One of the goals of Beyond 5G is to support massive machine-type communications (mMTC). 6G-IoT aims to support the transmission of 10 million connected devices per square kilometer [2]. Various companies and alliances have developed unique low-power wide-area (LPWA) systems and have offered their own devices or network services. The wireless intelligent utility network (Wi-SUN) spreads across more than 40 countries, Sigfox in more than 70 countries, and LoRa in more than 160 countries, and they have been leading the IoT world [3]. Network congestion would become unavoidable in a \"massive\" IoT environment. Each country has regulations on available frequencies band and communication channels for IoT devices. For example, the 915 MHz band is assigned in the US, the 433 MHz and 868 MHz band in the EU, and the 920 MHz band in Japan. In addition, the duty ratio, which indicates the communication frequency, is set to 1% or less or 10% or less. Efficient use of frequency resources is essential to ease congestion while complying with regulations. This fact has motivated many researchers to discuss dynamic spectrum access, where IoT devices can access channels dynamically to improve spectrum efficiency.  
There are mainly two categories of resource allocation methods: centralized ones and decentralized ones. Regarding the centralized methods, references [4] propose time-slotted channel hopping (TSCH) based channel allocation methods that have been adopted in the IEEE 802.15.4e standard [5] to improve the IoT network performance. Reference [6] proposes a resource allocation method based on deep Q learning performed at the gateway (GW) side. Although the centralized methods can adequately manage the operation of end nodes, increasing the number of nodes brings the burden on the controlling server. In addition, synchronization and constant connection between GW and IoT devices are required, resulting in high power consumption of the end device. Therefore, the centralized methods are unsuitable for avoiding collisions in massive IoT networks.  
The IoT device decides the access channels in the autonomous decentralized resource allocation method. Reference [7] proposed a decentralized channel assignment method for cognitive radio networks. In [8], a reinforcement learning method was adopted to dynamically select channels in a complicated communication environment. In [9], a deep learning-based method was proposed to determine the transmission schedule and power. References [10,11] treated channel access by a single user as a multi-armed bandit (MAB) problem [12]. Reference [10] proposed a channel selection protocol based on optimization and reference [11] analyzed the average communication performance of competitive users. Reference [13] formulated the channel access problem as multi-player MAB (MP-MAB) problems. In [14], the channel assignment problem is also formulated as an MP-MAB problem. The proposed MAB-based channel assignment method in this work is implemented on a single-board computer supported by IEEE 802.15.4g/ 4e communication. The performance is evaluated using 30 IoT devices, verifying that the MAB methods are efficient for the channel assignment in dynamic IoT systems. However, a massive heterogeneous scenario is not well considered. In [15], a distributed learning technique based on bandit algorithms is proposed for LoRa devices to select their access selection. In [16], a distributed channel selection method based on TOW dynamics is proposed for fully decentralized networks. Both reference [15] and reference [16] implement and evaluate their proposed methods on the practical IoT devices.