Accurate Interpretation of the Online Learning Model for 6G-Enabled Internet of Things

Abstract

The 6G Internet poses intense demands for intelligent and customized designs to cope with the surging network scale, dynamically time-varying environments, diverse user requirements, and complicated manual configuration. However, traditional rule-based solutions heavily rely on human efforts and expertise, while data-driven intelligent algorithms still lack interpretability and generalization. In this paper, we propose the AIGI (AI-Generated Internet), a novel intention-driven design paradigm for the 6G Internet, which allows operators to quickly generate a variety of customized network solutions and achieve expert-free problem optimization. Driven by the diffusion modelbased learning approach, AIGI has great potential to learn the reward-maximizing trajectories, automatically satisfy multiple constraints, adapt to different objectives and scenarios, or even intelligently create novel designs and mechanisms unseen in existing network environments. Finally, we conduct a use case to demonstrate that AIGI can effectively guide the design of transmit power allocation in digital twin-based 6G networks.

Using the Linked List Algorithm to Create a Learning Environment for Online Students

“…A connected List Implementation Website will assist the lecturer perform his work and be able to communicate what he wants to explain to his pupils, and it will help students comprehend the course faster and better than the conventional style of teaching. The website will also have a page with a short description of the project's participants and the university overseeing the site and to make our website more useful, we may add other courses in the same way (Chattha et al, 2018;Verner et al, 2018;Alagumalai et al, 2020;Huang et al, 2021).…”

6G-Enabled Internet of Things: Vision, Techniques, and Open Issues

“…Huang et al [66] investigated machine learning algorithm implementations to optimize the efficiency of 6G-enabled IoT networks. They generally concentrated on the current design GBDT2NN, which summarizes the information obtained by gradient boosting decision tree (GBDT) models into a neural network (NN) models to keep the learning capacity of numeric values while also enhancing the capacity of online learning, although it loses comprehensibility.…”

Towards 6G Communications: Architecture, Challenges, and Future Directions

“…Therefore, a significant improvement in cellular networks is required in order to cope with IoT, and meet the ever increasing speed and other features such as network densification, societal automation, public safety, reliability, etc. [8], [10]. Recently, blockchain can be an efficient secure information exchange technology because of its features like decentralized architecture and elimination of central third parties, transparency with anonymity, less processing delay, etc.…”

“…The key features of the 6G network system are expected to be -extreme low latency, ultra high data rates, high energy efficiency, enhanced security, and ubiquitous network coverage across the globe [3]. Another prominent objective of the 6G network system is to satisfy the needs and requirements of Internet of Things (IoT) applications and uplift the widespread application and usage of the latest technological trends [10]. There has been a significant stress on the application of distributed ledger technology, such as blockchain, in the 6G network system as it is capable of providing privacy and security, which are the necessities of the digital world in the era of the Internet [11], [12].…”