热点论文带您领略未来通信热点技术的最新进展——图书馆前沿文献专题推荐服务（58）
2022-03-01
在上一期前沿文献中推荐中，介绍了通信新材料技术领域的最新进展，包括：具有集成传感能力的可重构智能表面、智能反射面辅助无线通信的教程、多用户MISO无线通信中的信道估计，以及可抵御分布式窃听攻击的安全时空调制毫米波无线链路。
在本期的文献推荐中，关注点着眼于未来通信热点技术的研究进展，选取了：由放电感应位移电流实现的范式转变完全自供电长距离无线传感解决方案、面向 6G 的整体网络虚拟化和无处不在的网络智能、对支持毫米波波束成形的无人机通信和网络的研究，以及面向6G绿色通信的AI模型四篇文献，供相关领域的科研人员参考。
领域一 由放电感应位移电流实现的范式转变完全自供电长距离无线传感解决方案
A paradigm shift fully self-powered long-distance wireless sensing solution enabled by discharge-induced displacement current
HAOYU WANG, etc.
SCIENCE ADVANCES, 2021
The rapid development of the Internet of Things depends on wireless devices and their network. Traditional wireless sensing and transmission technology still requires multiple modules for sensing, signal modulation, transmission, and power, making the whole system bulky, rigid, and costly. Here, we proposed a paradigm shift wireless sensing solution based on the breakdown discharge–induced displacement current. Through that, we can combine the abovementioned functional modules in a single unit of self-powered wireless sensing e-sticker (SWISE), which features a small size (down to 9 mm by 9 mm) and long effective transmission distance (>30 m) when compared to existing wireless sensing technologies. Furthermore, SWISEs have functions of multipoint motion sensing and gas detection in fully self-powered manner. This work proposes a solution for flexible self-powered wireless sensing platforms, which shows great potential for implantable and wearable electronics, robotics, health care, infrastructure monitoring, human-machine interface, virtual reality, etc.
https://www.science.org/doi/10.1126/sciadv.abi6751
领域二 面向 6G 的整体网络虚拟化和无处不在的网络智能
Holistic Network Virtualization and Pervasive Network Intelligence for 6G
Xuemin Shen, etc.
IEEE Communications Surveys & Tutorials, 2021
In this tutorial paper, we look into the evolution and prospect of network architecture and propose a novel conceptual architecture for the 6th generation (6G) networks. The proposed architecture has two key elements, i.e., holistic network virtualization and pervasive artificial intelligence (AI). The holistic network virtualization consists of network slicing and digital twin, from the aspects of service provision and service demand, respectively, to incorporate service-centric and user-centric networking. The pervasive network intelligence integrates AI into future networks from the perspectives of networking for AI and AI for networking, respectively. Building on holistic network virtualization and pervasive network intelligence, the proposed architecture can facilitate three types of interplay, i.e., the interplay between digital twin and network slicing paradigms, between model-driven and data-driven methods for network management, and between virtualization and AI, to maximize the flexibility, scalability, adaptivity, and intelligence for 6G networks. We also identify challenges and open issues related to the proposed architecture. By providing our vision, we aim to inspire further discussions and developments on the potential architecture of 6G.
https://ieeexplore.ieee.org/document/9651548
领域三 对支持毫米波波束成形的无人机通信和网络的研究
A Survey on Millimeter-Wave Beamforming Enabled UAV Communications and Networking
Zhenyu Xiao, etc.
IEEE Communications Surveys & Tutorials, 2021
Unmanned aerial vehicles (UAVs) have found widespread commercial, civilian, and military applications. Wireless communication has always been one of the core technologies for UAV. However, the communication capacity is becoming a bottleneck for UAV to support more challenging application scenarios. The heavily-occupied sub-6 GHz frequency band is not sufficient to meet the ultra high-data-traffic requirements. The utilization of the millimeter-wave (mmWave) frequency bands is a promising direction for UAV communications, where large antenna arrays can be packed in a small area on the UAV to perform three-dimensional (3D) beamforming. On the other hand, UAVs serving as aerial access points or relays can significantly enhance the coverage and quality of service of the terrestrial mmWave cellular networks. In this paper, we provide a comprehensive survey on mmWave beamforming enabled UAV communications and networking. The technical potential of and challenges for mmWave-UAV communications are presented first. Then, we provide an overview on relevant mmWave antenna structures and channel modeling. Subsequently, the technologies and solutions for UAV-connected mmWave cellular networks and mmWave-UAV ad hoc networks are reviewed, respectively. Finally, we present open issues and promising directions for future research in mmWave beamforming enabled UAV communications and networking.
https://ieeexplore.ieee.org/document/9366805
领域四 面向6G绿色通信的AI模型
AI Models for Green Communications Towards 6G
Bomin Mao, etc.
IEEE Communications Surveys & Tutorials, 2021
Green communications have always been a target for the information industry to alleviate energy overhead and reduce fossil fuel usage. In the current 5G and future 6G eras, there is no doubt that the volume of network infrastructure and the number of connected terminals will keep exponentially increasing, which results in the surging energy cost. It becomes growing important and urgent to drive the development of green communications. However, there is no doubt that 6G will have increasingly stringent and diversified requirements for Quality of Service (QoS), security, flexibility, and intelligence, all of which challenge the improvement of energy efficiency. Moreover, the dynamic energy harvesting process, which will be widely adopted in 6G, further complicates the power control and network management. To address these challenges and reduce human intervention, Artificial Intelligence (AI) has been extensively recognized and acknowledged as the only solution. Academia and industry have conducted extensive research to alleviate energy demand, improve energy efficiency, and manage energy harvesting in various communication scenarios. In this paper, we present main considerations for green communications and survey related research on AI-based green communications. We focus on how AI techniques are adopted to manage networks and improve energy efficiency towards the green era. We analyze how Machine Learning (ML) techniques including state-of-the-art Deep Learning (DL) can cooperate with conventional AI methods and mathematical models to reduce the algorithm complexity and improve the accuracy rate in 6G. Finally, we discuss the existing problems and envision the open research issues of AI models towards green 6G.
https://ieeexplore.ieee.org/document/9627726
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