Deep Reinforcement Learning for Joint Channel Selection and Power Allocation in Cognitive Internet of Things With the development of wireless communication technology and the lack of spectrum resources, it is very meaningful to study the dynamic spectrum allocation in the cognitive Internet of Things. In this paper, the system model is firstly established. In an underlay mode, considering the interference between primary and secondary users, jointing channel selection and power allocation, aiming to maximize the spectrum efficiency of all secondary users. Different from the traditional heuristic algorithm, the underlaycognitiveradiodeepQnetwork framework (UCRDQN) based on deep reinforcement learning, is proposed to find the optimal solution efficiently. The simulation results show that the UCRDQN algorithm can achieve higher spectrum efficiency and is more stable and efficient than other algorithms.

Artificial Intelligence Enabled Internet of Things: Network Architecture and Spectrum Access The explosive growth of wireless devices motivates the development of the internetofthings (IoT), which is capable of interconnecting massive and diverse things via wireless communications. This is also called massive machine type communications (mMTC) as a part of the undergoing fifth generation (5G) mobile networks. It is envisioned that more sophisticated devices would be connected to form a hyperconnected world with the aids of the sixth generation (6G) mobile networks. To enable wireless accesses of such IoT networks, artificial intelligence (AI) can play an important role. In this article, the frameworks of centralized and distributed AIenabled IoT networks are introduced. Key technical challenges, including random access and spectrum sharing (spectrum access and spectrum sensing), are analyzed for different network architectures. Deep reinforcement learning (DRL)based strategies are introduced and neural networksbased approaches are utilized to efficiently realize the DRL strategies for system procedures such as spectrum access and spectrum sensing. Different types of neural networks that could be used in IoT networks to conduct DRL are also discussed.