In the upcoming sixth-generation (6G) era, the demand for constructing a wide-area time-sensitive Internet of Things (IoT) keeps increasing. As conventional cellular technologies are hard to be directly used for wide-area time-sensitive IoT, it is beneficial to use non-terrestrial infrastructures including satellites and unmanned aerial vehicles (UAVs), where a non-terrestrial network (NTN) can be built under the cell-free architecture. Driven by the time-sensitive requirements and uneven distribution of IoT devices, the NTN is required to be empowered by mobile edge computing (MEC) while providing oasis-oriented on-demand coverage for the devices. Nevertheless, communication and MEC systems are coupled with each other under the influence of complex propagation environment in the MEC-empowered NTN, which makes it hard to orchestrate the resources. In this paper, we propose a process-oriented framework to design the communication and MEC systems in a time-division manner. Under this framework, the large-scale channel state information (CSI) is used to characterize the complex propagation environment with an affordable cost, where a non-convex latency minimization problem is formulated. After that, the approximated problem is given and it can be decomposed into subproblems. These subproblems are further solved in an iterative way. Simulation results demonstrate the superiority of the proposed process-oriented scheme over other algorithms. These results also indicate that the payload deployments of UAVs should be appropriately predesigned to improve the efficiency of resource use. Furthermore, the results imply that it is advantageous to integrate NTN with MEC for wide-area time-sensitive IoT.
The non-terrestrial networks (NTNs) are recognized as a key component to provide cost-effective and high-capacity ubiquitous connectivity in the future wireless communications. In this paper, we investigate the secure transmission in a terahertz (THz)-empowered reconfigurable intelligent surface (RIS)-assisted NTN (T-RANTN), which is composed of a low-Earth orbit satellite transmitter, an RIS-installed high-altitude platform (HAP) and two unmanned aerial vehicle (UAV) receivers, only one of which is trustworthy. An approximate ergodic secrecy rate (ESR) expression is derived when the atmospher…
Active RIS-Empowered Covert Satellite-Terrestrial Communications
College of Computer Science and Technology, Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University（吉林大学计算机科学与技术学院、教育部符号计算与知识工程重点实验室）；Auburn University（奥本大学）；School of Electrical and Computer Engineering, University of Sydney（悉尼大学电气与计算机工程学院）；Graduate School of Information Sciences, Tohoku University（东北大学情报科学研究院）
xApp Empowered Resource Management for Non-Terrestrial Users in 5G O-RAN Networks
School of Electronic and Electrical Engineering, University of Leeds（利兹大学电子与电气工程学院）；College of Electronics Engineering, Ninevah University（尼尼微大学电子工程学院）
In this paper, we propose a novel algorithm for energy-efficient, low-latency dynamic mobile edge computing (MEC), in the context of beyond 5G networks endowed with Reconfigurable Intelligent Surfaces (RISs). In our setting, new computing requests are continuously generated by a set of devices and are handled through a dynamic queueing system. Building on stochastic optimization tools, we devise a dynamic learning algorithm that jointly optimizes the allocation of radio resources (i.e., power, transmission rates, sleep mode and duty cycle), computation resources (i.e., CPU cycles), and RIS ref…
Next-generation wireless networks require massive connectivity and ubiquitous coverage, for which non-terrestrial networks (NTNs) are a promising enabler. However, NTNs, especially non-geostationary satellites bring about challenges such as increased handovers (HOs) due to the moving coverage area of the satellite on the ground. Accordingly, in this work, we compare the conventional measurement-based HO triggering mechanism with other alternatives such as distance, elevation angle, and timer-based methods in terms of the numbers of HOs, ping-pong HOs, and radio link failures. The system-level …
Beijing Institute of Technology（北京理工大学）；University of Southampton（南安普顿大学）；Imperial College London（帝国理工学院）；Princeton University（普林斯顿大学）
It is anticipated that 6G wireless networks will accelerate the convergence of the physical and cyber worlds and enable a paradigm-shift in the way we deploy and exploit communication networks. Machine learning, in particular deep learning (DL), is expected to be one of the key technological enablers of 6G by offering a new paradigm for the design and optimization of networks with a high level of intelligence. In this article, we introduce an emerging DL architecture, known as the transformer, and discuss its potential impact on 6G network design. We first discuss the differences between the t…
Rotatable Antenna-Empowered Wireless Networks: A Tutorial
School of Microelectronics, South China University of Technology（华南理工大学微电子学院）；Department of Electronic and Electrical Engineering, Southern University of Science and Technology (SUSTech)（南方科技大学电子与电气工程系）；Department of Electrical and Computer Engineering, University of Waterloo（滑铁卢大学电气与计算机工程系）；State Key Laboratory of CNS/ATM and the School of Interdisciplinary Science, Beijing Institute of Technology（北京理工大学神经系统/原子交通控制国家重点实验室及交叉科学研究院）；National Key Laboratory of Wireless Communications, University of Electronic Science and Technology of China（电子科技大学无线通信国家重点实验室）；Department of Electronic Engineering, Shanghai Jiao Tong University（上海交通大学电子工程系）；School of Electronic and Information Engineering, South China University of Technology（华南理工大学电子与信息工程学院）
This paper investigates computation offloading in blockchain-empowered Internet of Things (IoT), where the task data uploading link from sensors to a base station (BS) is protected by intelligent reflecting surface (IRS)-assisted physical layer security (PLS). After receiving task data, the BS allocates computational resources provided by mobile edge computing (MEC) servers to help sensors perform tasks. Existing blockchain-based computation offloading schemes usually focus on network performance improvements, such as energy consumption minimization or latency minimization, and neglect the Gas…
Secure Task Offloading and Resource Allocation Design for Multi-Layer Non-Terrestrial Networks
Interdisciplinary Centre for Security, Reliability and Trust (SnT), University of Luxembourg（卢森堡大学安全、可靠与信任跨学科中心）；Centre for Wireless Innovation (CWI), Queen’s University Belfast（贝尔法斯特女王大学无线创新中心）
Driven by the unprecedented high throughput and low latency requirements in next-generation wireless networks, this paper introduces an artificial intelligence (AI) enabled framework in which unmanned aerial vehicles (UAVs) use non-orthogonal multiple access (NOMA) and mobile edge computing (MEC) techniques to service terrestrial mobile users (MUs). The proposed framework enables the terrestrial MUs to offload their computational tasks simultaneously, intelligently, and flexibly, thus enhancing their connectivity as well as reducing their transmission latency and their energy consumption. To t…
Physics-Informed Digital Twins for Channel Estimation and Traffic Prediction of Non-Terrestrial Networks
University of Waterloo（滑铁卢大学）；National Mobile Communications Research Laboratory, School of Information Science and Engineering, Southeast University（东南大学信息科学与工程学院）
University of Piraeus（比雷埃夫斯大学）；RapidMiner GmbH（RapidMiner有限公司）；Nokia Standardization Department（诺基亚标准化部）；Université Paris-Saclay（巴黎萨克雷大学）；CNRS（法国国家科学研究中心）；CentraleSupélec（巴黎中央理工-高等电力学院）
Terahertz (THz) wireless networks are expected to catalyze the beyond fifth generation (B5G) era. However, due to the directional nature and the line-of-sight demand of THz links, as well as the ultra-dense deployment of THz networks, a number of challenges that the medium access control (MAC) layer needs to face are created. In more detail, the need of rethinking user association and resource allocation strategies by incorporating artificial intelligence (AI) capable of providing "real-time" solutions in complex and frequently changing environments becomes evident. Moreover, to satisfy the ul…
Lightweight Task-Oriented Semantic Communication Empowered by Large-Scale AI Models
Beijing Key Laboratory of Network System Architecture and Convergence, School of Information and Communication Engineering, Beijing University of Posts and Telecommunications（北京网络系统架构与收敛重点实验室，信息与通信工程学院，北京邮电大学）；Beijing Laboratory of Advanced Information Networks, School of Information and Communication Engineering, Beijing University of Posts and Telecommunications（北京先进信息网络实验室，信息与通信工程学院，北京邮电大学）；Department of Electrical and Computer Engineering and Institute for Data Science and Computing, University of Miami（电气与计算机工程系和数据科学与计算研究所，迈阿密大学）；Dept. of Information Systems Technology and Design, Singapore University of Technology and Design（信息系统技术与设计系，新加坡科技设计大学）
