6G Visions:Mobile Ultra-Broadband,Super Internet-of-Things,and Artificial Intelligence

With a ten-year horizon from concept to reality, it is time now to start thinking about what will the sixth-generation(6G) mobile communications be on the eve of the fifth-generation(5G) deployment. To pave the way for the development of 6G and beyond, we provide 6G visions in this paper. We first introduce the state-of-the-art technologies in 5G and indicate the necessity to study 6G. By taking the current and emerging development of wireless communications into consideration, we envision 6G to include three major aspects, namely, mobile ultra-broadband, super Internet-of-Things(IoT), and artificial intelligence(AI). Then, we review key technologies to realize each aspect. In particular, teraherz(THz) communications can be used to support mobile ultra-broadband, symbiotic radio and satellite-assisted communications can be used to achieve super IoT, and machine learning techniques are promising candidates for AI. For each technology, we provide the basic principle, key challenges, and state-of-the-art approaches and solutions.

A Throughput-Aware Joint Vehicle Route and Access Network Selection Approach Based on SMDP

In intelligent transportation system(ITS), the interworking of vehicular networks(VN) and cellular networks(CN) is proposed to provide high-data-rate services to vehicles. As the network access quality for CN and VN is location related, mobile data offloading(MDO), which dynamically selects access networks for vehicles, should be considered with vehicle route planning to further improve the wireless data throughput of individual vehicles and to enhance the performance of the entire ITS. In this paper, we investigate joint MDO and route selection for an individual vehicle in a metropolitan scenario. We aim to improve the throughput of the target vehicle while guaranteeing its transportation efficiency requirements in terms of traveling time and distance. To achieve this objective, we first formulate the joint route and access network selection problem as a semi-Markov decision process(SMDP). Then we propose an optimal algorithm to calculate its optimal policy. To further reduce the computation complexity, we derive a suboptimal algorithm which reduces the action space. Simulation results demonstrate that the proposed optimal algorithm significantly outperforms the existing work in total throughput and the late arrival ratio.Moreover, the heuristic algorithm is able to substantially reduce the computation time with only slight performance degradation.

基于大数据的无线通信技术

随着各类丰富的移动应用普及,可以预见,未来的无线通信系统将需要满足更为苛刻的服务要求,为无线通信技术的发展带来挑战.另一方面,大数据在众多领域的应用取得了令人瞩目的成功.大数据如何赋能无线通信成为学术界和工业界关注的焦点.过去5年,我们承担了国家自然科学基金"无线通信大数据基础理论与技术研究"重点项目群,致力于研究基于大数据的无线通信技术,探索使用大数据突破无线通信发展瓶颈的新理论和新方法.我们将这类在无线通信中产生、能够服务于无线通信的大数据定义为无线大数据.本文是对项目群的研究和实践结果的总结.具体地,我们首先分析了无线大数据的来源和特征,然后构建了与之匹配的无线知识学习范式、智能无线网络框架以及跨地域、跨机构、多用途、一体化的无线大数据平台,最后提出并验证了基于大数据的无线传播环境分析、无线传输以及无线接入技术,从而为基于大数据的无线通信技术的进一步发展提供基础理论支撑和应用范式.

Large Intelligent Surface/Antennas (LISA): Making Reflective Radios Smart

Large intelligent surface/antennas(LISA),a two-dimensional artificial structure with a large number of reflective-surface/antenna elements,is a promising reflective radio technology to construct programmable wireless environments in a smart way.Specifically,each element of the LISA adjusts the reflection of the incident electromagnetic waves with unnatural properties,such as negative refraction,perfect absorption,and anomalous reflection,thus the wireless environments can be software-defined according to various design objectives.In this paper,we introduce the reflective radio basics,including backscattering principles,backscatter communication,reflective relay,the fundamentals and implementations of LISA technology.Then,we present an overview of the state-of-the-art research on emerging applications of LISA-aided wireless networks.Finally,the limitations,challenges,and open issues associated with LISA for future wireless applications are discussed.

Realizing Intelligent Spectrum Management for Integrated Satellite and Terrestrial Networks

Nowadays both satellite and terrestrial networks are expanding rapidly to meet the ever-increasing demands for higher throughput,lower latency,and wider coverage.However,spectrum scarcity places obstacles in the sustainable development.To accommodate the expanding network within a limited spectrum,spectrum sharing is deemed as a promising candidate.Particularly,cognitive radio(CR)has been proposed in the literature to allow satellite and terrestrial networks to share their spectrum dynamically.However,the existing CR-based schemes are found to be impractical and inefficient because they neglect the difficulty in obtaining the accurate and timely environment perception in satellite communications and only focus on link-level coexistence with limited interoperability.In this paper,we propose an intelligent spectrum management framework based on software defined network(SDN)and artificial intelligence(AI).Specifically,SDN transforms the heterogenous satellite and terrestrial networks into an integrated satellite and terrestrial network(ISTN)with reconfigurability and interoperability.AI is further used to make predictive environment perception and to configure the network for optimal resource allocation.Briefly,the proposed framework provides a new paradigm to integrate and exploit the spectrum of satellite and terrestrial networks.

Wireless big data:transforming heterogeneous networks to smart networks

In HetNets(Heterogeneous Networks),each network is allocated with xed spectrum resource and provides service to its assigned users using speci c RAT(Radio Access Technology).Due to the high dynamics of load distribution among di erent networks,simply optimizing the performance of individual network can hardly meet the demands from the dramatically increasing access devices,the consequent upsurge of data trac,and dynamic user QoE(Quality-of-Experience).The deployment of smart networks,which are supported by SRA(Smart Resource Allocation)among di erent networks and CUA(Cognitive User Access)among di erent users,is deemed a promising solution to these challenges.In this paper,we propose a frame-work to transform HetNets to smart networks by leveraging WBD(Wireless Big Data),CR(Cognitive Radio)and NFV(Network Function Virtualization)techniques.CR and NFV support resource slicing in spectrum,physical layers,and network layers,while WBD is used to design intelligent mechanisms for resource mapping and trac prediction through powerful AI(Arti cial Intelligence)methods.We analyze the characteristics of WBD and review possible AI methods to be utilized in smart networks.In particular,the potential of WBD is revealed through high level view on SRA,which intelligently maps radio and network resources to each network for meeting the dynamic trac demand,as well as CUA,which allows mobile users to access the best available network with manageable cost,yet achieving target QoS(Quality-of-Service)or QoE.