In a level-5 autonomous driving system, the autonomous driving vehicles (AVs) are expected to sense the surroundings via analyzing a large amount of data captured by a variety of onboard sensors in near-real-time. As a result, enormous computing costs will be introduced to the AVs for processing the tasks with the deployed machine learning (ML) model, while the inference accuracy may not be guaranteed. In this context, the advent of edge intelligence (EI) and sixth-generation (6G) wireless networking are expected to pave the way to more reliable and safer autonomous driving by providing multi-access edge computing (MEC) together with ML to AVs in close proximity. To realize this goal, we propose a two-tier EI-empowered autonomous driving framework. In the autonomous-vehicles tier, the autonomous vehicles are deployed with the shallow layers by splitting the trained deep neural network model. In the edge-intelligence tier, an edge server is implemented with the remaining layers (also deep layers) and an appropriately trained multi-task learning (MTL) model. In particular, obtaining the optimal offloading strategy (including the binary offloading decision and the computational resources allocation) can be formulated as a mixed-integer nonlinear programming (MINLP) problem, which is solved via MTL in near-real-time with high accuracy. On another note, an edge-vehicle joint inference is proposed through neural network segmentation to achieve efficient online inference with data privacy-preserving and less communication delay. Experiments demonstrate the effectiveness of the proposed framework, and open research topics are finally listed.
With the great success of artificial intelligence (AI) technologies in pattern recognitions and signal processing, it is interesting to introduce AI technologies into wireless communication systems. Currently, most of studies are focused on applying AI technologies for solving old problems, e.g., wireless location accuracy and resource allocation optimization in wireless communication systems. However, It is important to distinguish new capabilities created by AI technologies and rethink wireless communication systems based on AI running schemes. Compared with conventional capabilities of wire…
In this paper, we propose a self-deployment approach for finding the optimal placement of extenders in which both the wireless back-haul and front-haul throughput of the extender are optimized. We present an artificial intelligence (AI) case based reasoning (CBR) framework that enables autonomous self-deployment in which the network can learn the environment by means of sensing and perception. New actions, i.e. extender positions, are created by problem-specific optimization and semi-supervised learning algorithms that balance exploration and exploitation of the search space. An IEEE 802.11 st…
In this work, we develop a framework that jointly decides on the optimal location of wireless extenders and the channel configuration of extenders and access points (APs) in a Wireless Mesh Network (WMN). Typically, the rule-based approaches in the literature result in limited exploration while reinforcement learning based approaches result in slow convergence. Therefore, Artificial Intelligence (AI) is adopted to support network autonomy and to capture insights on system and environment evolution. We propose a Self-X (self-optimizing and self-learning) framework that encapsulates both environ…
Toshiba Europe Ltd.（东芝欧洲有限公司）；Bristol Research and Innovation Laboratory（布里斯托尔研究与创新实验室）
Time-critical control applications typically pose stringent connectivity requirements for communication networks. The imperfections associated with the wireless medium such as packet losses, synchronization errors, and varying delays have a detrimental effect on performance of real-time control, often with safety implications. This paper introduces multi-service edge-intelligence as a new paradigm for realizing time-critical control over wireless. It presents the concept of multi-service edge-intelligence which revolves around tight integration of wireless access, edge-computing and machine le…
This paper discusses technology and opportunities to embrace artificial intelligence (AI) in the design of autonomous wireless systems. We aim to provide readers with motivation and general AI methodology of autonomous agents in the context of self-organization in real time by unifying knowledge management with sensing, reasoning and active learning. We highlight differences between training-based methods for matching problems and training-free methods for environment-specific problems. Finally, we conceptually introduce the functions of an autonomous agent with knowledge management.
We introduce "Wireless 2.0": The future generation of wireless communication networks, where the radio environment becomes controllable, programmable, and intelligent by leveraging the emerging technologies of reconfigurable metasurfaces and artificial intelligence (AI). This paper, in particular, puts the emphasis on AI-based computational methods and commence with an overview of the concept of intelligent radio environments based on reconfigurable meta-surfaces. Later we elaborate on data management aspects, the requirements of supervised learning by examples, and the paradigm of reinforceme…
Smart homes are further development of intelligent buildings and home automation, where context awareness and autonomous behaviour are added. They are based on a combination of the Internet and emerging technologies like wireless sensor nodes. These wireless sensor nodes are challenging because they consume battery power, they use network bandwidth, and they produce wireless interferences. Currently, different methods exist for handling these challenges. These methods are, however, based on adjusting the transmitter frequency and using duty-cycling in combination with sleep mode approaches. Th…
The future wireless communication system faces the bottleneck of the shortage of traditional spectrum resources and the explosive growth of the demand for wireless services. Millimeter-wave communication with spectral resources has become an effective choice for the next generation of wireless broadband cellular communication. However, the transmission path loss is large and oxygen and water molecules absorb Characteristics such as seriousness have brought great challenges to millimeter wave communication, and it is necessary to seek a technical approach different from low-frequency wireless c…
University of Maryland, Baltimore County（马里兰大学巴尔的摩县分校）
Due to the advancement in technologies, the next-generation wireless network will be very diverse, complicated, and according to the changed demands of the consumers. The current network operator methodologies and approaches are traditional and cannot help the next generation networks to utilize their resources most appropriately. The limited capability of the traditional tools will not allow the network providers to fulfill the demands of the network's subscribers in the future. Therefore, this paper will focus on machine learning, automation, artificial intelligence, and big data analytics f…
The emerging trend of ubiquitous and pervasive computing aims at embedding everyday devices such as wristwatches, smart phones, home video systems, autofocus cameras, intelligent vehicles, musical instruments, kitchen appliances etc. with microprocessors and imparts them with wireless communication capability. This advanced computing paradigm, also known as the Internet of Things or cyber-physical computing, leads internet and computing to appear everywhere and anywhere using any device and location. With maximum appreciation and due regards to the evolutionary arc, depth and scope of ceaseles…
The fifth generation (5G) wireless communication networks are currently being deployed, and beyond 5G (B5G) networks are expected to be developed over the next decade. Artificial intelligence (AI) technologies and, in particular, machine learning (ML) have the potential to efficiently solve the unstructured and seemingly intractable problems by involving large amounts of data that need to be dealt with in B5G. This article studies how AI and ML can be leveraged for the design and operation of B5G networks. We first provide a comprehensive survey of recent advances and future challenges that re…
Reverse driving a truck is a challenging task for human drivers and self-driving software due to the lack for sensors on the trailer. Self-driving and conventional trucks have an increasing need to replace the legacy communication channels between the truck and the trailer to accommodate bandwidth and latency requirements when more sensors and features are added to the trailer to support driver assist features or self-driving functions, in addition to the need of automating the tractor-trailer hitching and unhitching, which is a complex process when using wires and connectors for communication…
College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics（南京航空航天大学电子与信息工程学院）；College of Artificial Intelligence, Nanjing University of Aeronautics and Astronautics（南京航空航天大学人工智能学院）
University of Padova（帕多瓦大学）；Huawei Technologies Munich Research Center（华为技术慕尼黑研究中心）
Artificial intelligence (AI) techniques have emerged as a powerful approach to make wireless networks more efficient and adaptable. In this paper we present an ns-3 simulation framework, able to implement AI algorithms for the optimization of wireless networks. Our pipeline consists of: (i) a new geometry-based mobility-dependent channel model for V2X; (ii) all the layers of a 5G-NR-compliant protocol stack, based on the ns3-mmwave module; (iii) a new application to simulate V2X data transmission, and (iv) a new intelligent entity for the control of the network via AI. Thanks to its flexible a…
The cloud-based solutions are becoming inefficient due to considerably large time delays, high power consumption, security and privacy concerns caused by billions of connected wireless devices and typically zillions bytes of data they produce at the network edge. A blend of edge computing and Artificial Intelligence (AI) techniques could optimally shift the resourceful computation servers closer to the network edge, which provides the support for advanced AI applications (e.g., video/audio surveillance and personal recommendation system) by enabling intelligent decision making on computing at …
Web 3.0 enables user-generated contents and user-selected authorities. With decentralized wireless edge computing architectures, Web 3.0 allows users to read, write, and own contents. A core technology that enables Web 3.0 goals is blockchain, which provides security services by recording content in a decentralized and transparent manner. However, the explosion of on-chain recorded contents and the fast-growing number of users cause increasingly unaffordable computing and storage resource consumption. A promising paradigm is to analyze the semantic information of contents that can convey preci…
The emergence of Multi-Access Edge Computing (MEC) technology aims at extending cloud computing capabilities to the edge of the wireless access networks. MEC provides real-time, high-bandwidth, low-latency access to radio network resources, allowing operators to open their networks to a new ecosystem and value chain. Moreover, it will provide a new insight to the design of future 5th Generation (5G) wireless systems. This paper describes five key technologies, including Network Function Vitalization (NFV), Software Defined Networking (SDN), Network Slicing, Information Centric Networking (ICN)…
Machine learning (ML) and artificial intelligence (AI) have recently made a significant impact on improving the operations of wireless networks and establishing intelligence at the edge. In return, rare efforts were made to explore how adapting, optimizing, and arranging wireless networks can contribute to implementing ML/AI at the edge. This article aims to address this void by setting a vision on how wireless networking researchers can leverage their expertise to return the favor to edge learning. It will review the enabling technologies, summarize the inaugural works on this path, and shed …
Old Dominion University（欧道明大学）；Kyung Hee University（庆熙大学）；Virginia Tech（弗吉尼亚理工学院暨州立大学）
Explainable artificial intelligence (XAI) twin systems will be a fundamental enabler of zero-touch network and service management (ZSM) for sixth-generation (6G) wireless networks. A reliable XAI twin system for ZSM requires two composites: an extreme analytical ability for discretizing the physical behavior of the Internet of Everything (IoE) and rigorous methods for characterizing the reasoning of such behavior. In this paper, a novel neuro-symbolic explainable artificial intelligence twin framework is proposed to enable trustworthy ZSM for a wireless IoE. The physical space of the XAI twin …
University of Glasgow（格拉斯哥大学）；Technology Innovation Institute（技术创新研究院）；Ajman University（阿治曼大学）；Khalifa University（哈利法大学）；Carleton University（卡尔顿大学）；CentraleSupelec（中央理工-高等电力学院）；University Paris-Saclay（巴黎萨克雷大学）
New technological advancements in wireless networks have enlarged the number of connected devices. The unprecedented surge of data volume in wireless systems empowered by artificial intelligence (AI) opens up new horizons for providing ubiquitous data-driven intelligent services. Traditional cloudcentric machine learning (ML)-based services are implemented by centrally collecting datasets and training models. However, this conventional training technique encompasses two challenges: (i) high communication and energy cost and (ii) threatened data privacy. In this article, we introduce a comprehe…
Swarm Intelligence for Next-Generation Wireless Networks: Recent Advances and Applications
School of Engineering, Deakin University（迪肯大学工程学院）；Center for Artificial Intelligence Research and Optimization, Torrens University Australia（澳大利亚托伦斯大学人工智能研究与优化中心）；School of Electrical and Data Engineering, University of Technology Sydney（悉尼科技大学电气与数据工程学院）；School of Biomedical Convergence Engineering, Pusan National University（釜山国立大学生物医学融合工程学院）
Old Dominion University（奥多明尼昂大学）；University of Stavanger（斯塔万格大学）；Virginia Commonwealth University（弗吉尼亚联邦大学）；Norwegian University of Science and Technology（挪威科技大学）；eKare, Inc.（eKare公司）
With the rapid development and integration of artificial intelligence (AI) methods in next-generation networks (NextG), AI algorithms have provided significant advantages for NextG in terms of frequency spectrum usage, bandwidth, latency, and security. A key feature of NextG is the integration of AI, i.e., self-learning architecture based on self-supervised algorithms, to improve the performance of the network. A secure AI-powered structure is also expected to protect NextG networks against cyber-attacks. However, AI itself may be attacked, i.e., model poisoning targeted by attackers, and it r…
Internet of Things (IoT) connectivity has a prominent presence in the 5G wireless communication systems. As these systems are being deployed, there is a surge of research efforts and visions towards 6G wireless systems. In order to position the evolution of IoT within the 6G systems, this paper first takes a critical view on the way IoT connectivity is supported within 5G. Following that, the wireless IoT evolution is discussed through multiple dimensions: time, space, intelligence, and value. We also conjecture that the focus will broaden from IoT devices and their connections towards the eme…
GenAINet: Enabling Wireless Collective Intelligence via Knowledge Transfer and Reasoning
Artificial Intelligence and Digital Science Research Center, Technology Innovation Institute（人工智能与数字科学研究中心、技术创新研究所）；G Research Center, Khalifa University（6G研究中心、哈利法大学）；Centre for Wireless Communications, University of Oulu（无线通信研究中心、奥卢大学）；CRAN, Université de Lorraine and CNRS（CRAN、洛林大学和国家科学研究中心）
Future wireless networks are facing new challenges. These new challenges require new solutions and strategies of the network deployment, management, and operation. Many driving factors are decisive in the re-definition and re-design of the future wireless network architecture. In the previously published paper "5G Wireless Network - MyNET and SONAC", MyNET and SONAC, a future network architecture, are described. This paper elaborates MyNET platform with more details. The design principles of MyNET architecture, the development of MyNET platform, the functions of MyNET platform, the automation …
Fifth-generation (5G) communication, which has many more features than fourth-generation communication, will be officially launched very soon. A new paradigm of wireless communication, the sixth-generation (6G) system, with the full support of artificial intelligence is expected to be deployed between 2027 and 2030. In beyond 5G, there are some fundamental issues, which need to be addressed are higher system capacity, higher data rate, lower latency, and improved quality of service (QoS) compared to 5G system. This paper presents the vision of future 6G wireless communication and its network a…
University of Oulu（奥卢大学）；CentraleSupélec（中央理工-高等电力学院）；Université Paris-Saclay（巴黎-萨克雷大学）
Fueled by the availability of more data and computing power, recent breakthroughs in cloud-based machine learning (ML) have transformed every aspect of our lives from face recognition and medical diagnosis to natural language processing. However, classical ML exerts severe demands in terms of energy, memory and computing resources, limiting their adoption for resource constrained edge devices. The new breed of intelligent devices and high-stake applications (drones, augmented/virtual reality, autonomous systems, etc.), requires a novel paradigm change calling for distributed, low-latency and r…
Pinching Antennas Meet AI in Next-Generation Wireless Networks
Department of Electrical and Computer Engineering（电气与计算机工程系）；Department of Computer Science（计算机科学系）；University of Manchester（曼彻斯特大学）；University of British Columbia（不列颠哥伦比亚大学）；School of Electronics and Computer Science（电子与计算机科学学院）
Edge Artificial Intelligence for 6G: Vision, Enabling Technologies, and Applications
The Hong Kong University of Science and Technology（香港科技大学）；Peng Cheng Laboratory（鹏城实验室）；ShanghaiTech University（上海科技大学）；Huawei Technologies Co., Ltd.（华为技术有限公司）；Tsinghua University（清华大学）
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…
Physical-AI: From Channel Awareness to Environmental Intelligence in 6G Wireless Networks
Augmented Cognition Meta-communications ERC Research Center, Korea University, Korea（增强认知元通信ERC研究中心，韩国大学，韩国）；Department of Computer Science, Munster Technological University (MTU)（计算机科学系，穆斯特技术大学（MTU））；School of Computing, Faculty of Engineering and Computing, Dublin City University, Ireland（计算学院，工程与计算学院，都柏林城市大学，爱尔兰）；Department of Electrical and Computer Engineering of the Democritus University, Greece（德米特里大学电气与计算机工程系，希腊）；Department of Electrical and Computer Engineering, The University of Hong Kong, Hong Kong（电气与计算机工程系，香港大学，香港）；Department of Electronic Engineering, Kyung Hee University, Yongin-si, Gyeonggi-do 17104, South Korea（电子工程系， Kyung Hee大学， Yongin-si, Gyeonggi-do 17104, 韩国）
Industry 5.0 vision, a step toward the next industrial revolution and enhancement to Industry 4.0, envisioned the new goals of resilient, sustainable, and human-centric approaches in diverse emerging applications, e.g., factories-of-the-future, digital society. The vision seeks to leverage human intelligence and creativity in nexus with intelligent, efficient, and reliable cognitive collaborating robots (cobots) to achieve zero waste, zerodefect, and mass customization-based manufacturing solutions. However, the vision requires the merging of cyber-physical worlds through utilizing Industry 5.…
The 5G Phase-2 and beyond wireless systems will focus more on vertical applications such as autonomous driving and industrial Internet-of-things, many of which are categorized as ultra-Reliable Low-Latency Communications (uRLLC). In this article, an alternative view on uRLLC is presented, that information latency, which measures the distortion of information resulted from time lag of its acquisition process, is more relevant than conventional communication latency of uRLLC in wireless networked control systems. An AI-assisted Situationally-aware Multi-Agent Reinforcement learning framework for…
Chongqing University of Posts and Telecommunications（重庆邮电大学）；University of Guelph（圭尔夫大学）；Hosei University（法政大学）
Emerging six generation (6G) is the integration of heterogeneous wireless networks, which can seamlessly support anywhere and anytime networking. But high Quality-of-Trust should be offered by 6G to meet mobile user expectations. Artificial intelligence (AI) is considered as one of the most important components in 6G. Then AI-based trust management is a promising paradigm to provide trusted and reliable services. In this article, a generative adversarial learning-enabled trust management method is presented for 6G wireless networks. Some typical AI-based trust management schemes are first revi…
With the gradual deployment of 5G and the continuous popularization of edge intelligence (EI), the explosive growth of data on the edge of the network has promoted the rapid development of 6G and ubiquitous intelligence (UbiI). This article aims to explore a new method for modeling latency guarantees for UbiI in 6G given 6G's extremely stochastic nature in terahertz (THz) environments, THz channel tail behavior, and delay distribution tail characteristics generated by the UBiI random component, and to find the optimal solution that minimizes the end-to-end (E2E) delay of UbiI. In this article,…
