The increasingly huge amount of device connections will transform the Internet of Things (IoT) into the massive IoT. The use cases of massive IoT consist of the smart city, digital agriculture, smart traffic, etc., in which the service requirements are different and even constantly changing. To fulfill the different requirements, the networks must be able to automatically adjust the network configuration, architectures, resource allocations, and other network parameters according to the

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... t scenarios to match the different service requirements in massive IoT, which are beyond the abilities of the fifth generation (5G) networks. Moreover, the sixth generation (6G) networks are expected to have endogenous intelligence, which can well support the massive IoT application scenarios. In this paper, we first propose the framework of the 6G self-evolving networks, in which the autonomous decision-making is one of the vital parts. Then, we introduce the autonomous decision-making methods and analyze the characteristics of the different methods and mechanisms for 6G networks. To prove the effectiveness of the proposed framework, we consider one of the typical scenarios of massive IoT and propose an artificial intelligence (AI)-based distributed decision-making algorithm to solve the problem of the offloading policy and the network resource allocation. Simulation results show that the proposed decision-making algorithm with the self-evolving networks can improve the quality of experience (QoE) compared with the lower training.