Abstract
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 different 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.
Highlights
To fulfill the constantly changing requirements of the services in different massive Internet of Things (IoT) applications, the networks are expected to be deeply integrated with artificial intelligence (AI) to autonomously adjust the network configuration, architecture, resources, and other parameters according to the different scenarios, which are beyond the abilities of 5G networks
In order to fulfill the different requirements of the constantly emerging new services in massive IoT, 6G networks are expected to be deeply integrated with AI and to be able to autonomously adjust the network configuration, architecture, and other parameters to achieve the best match between the networks and the services
In order to realize the future intelligent wireless network to well satisfy the service requirements of the massive IoT application scenarios, it is necessary to embed the essential capabilities of AI into the wireless system
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. To fulfill the constantly changing requirements of the services in different massive IoT applications, the networks are expected to be deeply integrated with AI to autonomously adjust the network configuration, architecture, resources, and other parameters according to the different scenarios, which are beyond the abilities of 5G networks. 6G networks are expected to have endogenous intelligence, which can well support the massive IoT application scenarios. The autonomous decision-making schemes in 6G networks are expected to support the constantly changing service requirements and meet the requirements of the low latency in massive IoT. M. Peng et al [31] proposed an extreme-intelligent and extreme-concise system architecture of radio access networks to fulfill the requirements of ultra-high data rates and ultra-low latency in 6G networks. It can be seen that our proposed framework is more suitable for the massive IoT scenarios
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
