Bo Rong

The anticipated surge of Internet of Things (IoT) devices is expected to intensify the demand for mid-band spectrum resources, posing challenges to traditional spectrum sharing methods. This paper addresses the limitations of static database-assisted spectrum management frameworks and proposes a novel approach integrating high-resolution geospatial and real-time environmental data. Leveraging these inputs, the proposed framework enhances spectrum allocation accuracy, and mitigates interference more effectively, thereby increasing the access opportunities for IoT deployments. A detailed example scenario illustrates the efficacy of the proposed approach, demonstrating significant gains in spectrum sharing efficiency. It shows gains in the number of new entrants accessing the spectrum, ranging from 77% to 140%. These gains occur when moving to less conservative interference conditions and including more complex geospatial information in the propagation environment. These findings underscore the critical role of advanced spectrum sharing techniques in optimizing spectrum utilization for future IoT networks.

With the advent of sixth generation (6G) Internet of Things (IoT), integrated terrestrial network (TN) and non-terrestrial network (NTN) will play a vital role in enabling new applications and services. However, realizing the potential of 6G integrated TN-NTN requires addressing key challenges like intelligent and optimized control mechanisms for resource management, interference cancellation, and handover management. This paper explores the potential of large language models (LLMs) in intelligent network control for 6G integrated TN-NTN. LLMs can learn complex relationships and patterns from large-scale data, and then be fine-tuned on small labeled datasets, significantly reducing training time and cost. This study examines the main obstacles in the integration of 6G IoT and TN-NTN systems, and further discusses how intelligent control may effectively address those issues. Our suggested approach utilizes LLMs to create efficient anaptive control algorithms that can effectively handle the diverse, ever-changing, and decentralized characteristics of 6G integrated TN-NTN.

The Internet of Things (IoT) plays a critical role in enabling the seamless integration of disparate devices. Future IoT will rapidly expand its coverage to offer future worldwide omnipresent applications and services by merging communications in diverse spatial domains to build the space–air–ground–ocean-integrated network (SAGOI-Net). SAGOI-Net will include a significant number of battery-powered network nodes, such as satellites, unmanned vehicles, and underwater devices, due to the extremely vast geographic reach and dynamics in free space. Given the battery limitations, high-energy-efficiency communications and networking will be critical to the future system. Green SAGOI-Net seeks to not only bring ubiquitous connectivity to every corner of the globe but also to deliver more data with the same amount of energy.

Internet of vehicles (IoV) has attracted much interest recently due to its ubiquitous message exchange and content sharing among smart vehicles with the development of the mobile communication and computation technologies. In this paper, we investigate the policy for jointly communication mode selection, resource block assignment and power control in device- to-device-enabled vehicle-to-vehicle (D2D-V2V)-based IoV comm- unication networks with the purpose of guaranteeing the strict latency and reliability requirements of V2V links while maximizing the sum capacity of vehicle-to-infrastructure (V2I) links. Considering the unknown environment's dynamics as well as the continuous-valued state and action space in IoV networks, we exploit a decentralized actor-critic reinforcement learning model with a new reward function to learn the policy by interacting with the environment. Moreover, we propose an efficient transfer actor-critic learning (ETAC) approach to effectively enhance the learning efficiency and improve the learning convergence speed, in order to support reliable and delay-sensitive vehicular services in IoV networks. Simulation results show that the proposed ETAC approach can effectively reduce the generated interference in IoV networks and ensure the latency and reliability requirements of V2V link, as well as achieve the fast convergence speed and high convergence stability, compared with other existing approaches.