The development of light detection and ranging, Radar, camera, and other advanced sensor technologies inaugurated a new era in autonomous driving. However, due to the intrinsic limitations of these ...sensors, autonomous vehicles are prone to making erroneous decisions and causing serious disasters. At this point, networking and communication technologies can greatly make up for sensor deficiencies, and are more reliable, feasible and efficient to promote the information interaction, thereby improving autonomous vehicle's perception and planning capabilities as well as realizing better vehicle control. This paper surveys the networking and communication technologies in autonomous driving from two aspects: intra- and inter-vehicle. The intra-vehicle network as the basis of realizing autonomous driving connects the on-board electronic parts. The inter-vehicle network is the medium for interaction between vehicles and outside information. In addition, we present the new trends of communication technologies in autonomous driving, as well as investigate the current mainstream verification methods and emphasize the challenges and open issues of networking and communications in autonomous driving.
The innovations provided by sixth generation wireless communication (6G) as compared to fifth generation (5G) are considered in this article based on analysis of related works. With the aim of ...achieving diverse performance improvements for the various 6G requirements, five 6G core services are identified. Two centricities and eight key performance indices (KPIs) are detailed to describe these services, then enabling technologies to fulfill the KPIs are discussed. A 6G architecture is proposed as an integrated system of the enabling technologies and is then illustrated using four typical urban application scenarios. Potential challenges in the development of 6G technology are then discussed and possible solutions are proposed. Finally, opportunities for exploring 6G are analyzed in order to guide future research.
The Metaverse can be regarded as a hypothesized iteration of the Internet, which enables people to work, play, and interact socially in a persist online 3-D virtual environment with an immersive ...experience, by generating an imaginary environment similar to the real world, including realistic sounds, images, and other sensations. The Metaverse has strict requirements for a fully-immersive experience, large-scale concurrent users, and seamless connectivity, which poses many unprecedented challenges to the sixth generation (6G) wireless system, such as ubiquitous connectivity, ultra-low latency, ultra-high capacity and reliability, and strict security. In addition, to achieve the immersive and hassle-free experience of mass users, the full coverage sensing, seamless computation, reliable caching, and persistent consensus and security should be carefully considered to integrate into the future 6G system. To this end, this article aims to depict the roadmap to the Metaverse in terms of communication and networking in 6G, including illustrating the framework of the Metaverse, revealing the strict requirements and challenges for 6G to realize the Metaverse, and discussing the fundamental technologies to be integrated in 6G to drive the implementation of the Metaverse, including intelligent sensing, digital twin (DT), space-air-ground-sea integrated network (SAGSIN), multi-access edging computing (MEC), blockchain, and the involved security issues.
As a powerful tool, the vehicular network has been built to connect human communication and transportation around the world for many years to come. However, with the rapid growth of vehicles, the ...vehicular network becomes heterogeneous, dynamic, and large scaled, which makes it difficult to meet the strict requirements, such as ultralow latency, high reliability, high security, and massive connections of the next-generation (6G) network. Recently, machine learning (ML) has emerged as a powerful artificial intelligence (AI) technique to make both the vehicle and wireless communication highly efficient and adaptable. Naturally, employing ML into vehicular communication and network becomes a hot topic and is being widely studied in both academia and industry, paving the way for the future intelligentization in 6G vehicular networks. In this article, we provide a survey on various ML techniques applied to communication, networking, and security parts in vehicular networks and envision the ways of enabling AI toward a future 6G vehicular network, including the evolution of intelligent radio (IR), network intelligentization, and self-learning with proactive exploration.
The development of mobile devices with improving communication and perceptual capabilities has brought about a proliferation of numerous complex and computation-intensive mobile applications. Mobile ...devices with limited resources face more severe capacity constraints than ever before. As a new concept of network architecture and an extension of cloud computing, Mobile Edge Computing (MEC) seems to be a promising solution to meet this emerging challenge. However, MEC also has some limitations, such as the high cost of infrastructure deployment and maintenance, as well as the severe pressure that the complex and mutative edge computing environment brings to MEC servers. At this point, how to allocate computing resources and network resources rationally to satisfy the requirements of mobile devices under the changeable MEC conditions has become a great aporia. To combat this issue, we propose a smart, Deep Reinforcement Learning based Resource Allocation (DRLRA) scheme, which can allocate computing and network resources adaptively, reduce the average service time and balance the use of resources under varying MEC environment. Experimental results show that the proposed DRLRA performs better than the traditional OSPF algorithm in the mutative MEC conditions.
Among the LTE-A communication techniques, Device-to-Device (D2D) communication which is defined to directly route data traffic between spatially closely located mobile user equipments (UEs), holds ...great promise in improving energy efficiency, throughput, delay, as well as spectrum efficiency. As a combination of ad-hoc and centralized communication mechanisms, D2D communication enables researchers to merge together the long-term development achievements in previously disjoint domains of ad-hoc networking and centralized networking. To help researchers to have a systematic understanding of the emerging D2D communication, we provide in this paper a comprehensive survey of available D2D related research works ranging from technical papers to experimental prototypes to standard activities, and outline some open research problems which deserve further studies.
Recently, the 5G is widely deployed for supporting communications of high mobility nodes including train, vehicular and unmanned aerial vehicles (UAVs) largely emerged as the main components for ...constructing the wireless heterogeneous network (HetNet). To further improve the radio utilization, the Time Division Duplex (TDD) is considered to be the potential full-duplex communication technology in the high mobility 5G network. However, the high mobility of users leads to the high dynamic network traffic and unpredicted link state change. A new method to predict the dynamic traffic and channel condition and schedule the TDD configuration in real-time is essential for the high mobility environment. In this paper, we investigate the channel model in the high mobility and heterogeneous network and proposed a novel deep reinforcement learning based intelligent TDD configuration algorithm to dynamically allocate radio resources in an online manner. In the proposal, the deep neural network is employed to extract the features of the complex network information, and the dynamic Q-value iteration based reinforcement learning with experience replay memory mechanism is proposed to adaptively change TDD Up/Down-link ratio by evaluated rewards. The simulation results show that the proposal achieves significant network performance improvement in terms of both network throughput and packet loss rate, comparing with conventional TDD resource allocation algorithms.
The end-to-end quality of service (QoS) and quality of experience (QoE) guarantee is quite important for network optimization. The current 5G and conceived 6G network in the future with ultra high ...density, bandwidth, mobility and large scale brings urgent requirement of high efficient end-to-end optimization methods. The conventional network optimization methods without learning and intelligent decision ability are hard to handle the high complexity and dynamic scenarios of 6G. Recently, machine learning based QoS and QoE aware network optimization algorithms emerge as a hot research area and attract much attention, which is widely acknowledged as the potential solution for end-to-end optimization in 6G. However, there are still many critical issues of employing machine learning in networks, especially in 6G. In this paper, we give a comprehensive survey on the recent machine learning based network optimization methods to guarantee the end-to-end QoS and QoE. To easy to follow, we introduce the investigated works following the end-to-end transmission flow from network access, routing to network congestion control and adaptive steaming control. Then we discuss some open issues and potential future research directions.
Space-air-ground-sea integrated network (SAGSIN), which integrates satellite communication networks, aerial networks, terrestrial networks, and marine communication networks, has been widely ...envisioned as a promising network architecture for 6G. In consideration of its cooperation characteristics of multi-layer networks, open communication environment, and time-varying topologies, SAGSIN faces many unprecedented security challenges, and there have been a number of researches related to SAGSIN security performed over the past few years. Based on such observation, we provide in this paper a detailed survey of recent progress and ongoing research works on SAGSIN security in the aspects of security threats, attack methodologies, and defense countermeasures. To the best of our knowledge, we are the first to present the state-of-the-art of security for SAGSIN, since existing surveys focused either on a certain segment or on several segments of the integrated network, and little can be found on the full coverage network. In addition to reviewing existing works on SAGSIN security, we also present some discussions on cross-layer attacks and security countermeasure in SAGSIN, and identify new challenges ahead and future research directions.
For future networks in the 6G, it will be important to maintain a ubiquitous connection, bring processing heavy applications to remote areas, and analyze big amounts of data to efficiently provide ...services. To achieve such goals, the literature has utilized satellite networks to reach areas far away from the network core, and there has even been research into equipping such satellites with edge cloud servers to provide computation offloading to remote devices. However, analyzing the big data created by these devices is still a problem. One could transfer the data to a central server, but this has a high transmission cost. One could process the data through distributed machine learning, but such a technique is not as efficient as centralized learning. Thus, in this paper, we analyze the learning costs behind centralized and distributed learning and propose a hybrid solution that adaptively uses the advantages of both in a cloud server-equipped satellite network. Our proposal can identify the best learning strategy for each device based on the current scenario. Results show that the proposal is not only efficient in solving machine learning tasks, but it is also dynamic to react to different configurations while maintaining top performance.
