热点论文与带您领略6G网络技术的最新发展趋势 ——图书馆前沿文献专题推荐服务（22）

2020-10-09

 

       在上一期前沿文献中推荐中，关注点落在了与通信性能密切相关的射频开关、天线阵列、功放介质以及波束成形器件等方面，选取了相关文献进行探讨。
       讨论完射频，天线等基础器件和材料研究的通用性，本期针对6G通信系统演进，进一步讨论机器学习技术在6G网络中的应用，以及自治网络中可被信赖的深度学习的演技方法，用于分布式网络管理的区块链技术等，针对以上方向选取了最新的文献，推送给相关领域的科研人员，用以参考。
 
6G Architecture to Connect the Worlds
Volker Ziegler, etc.
IEEE Access,2019,8: 173508 - 173520
The post-pandemic future will offer tremendous opportunity and challenge from transformation of the human experience linking physical, digital and biological worlds: 6G should be based on a new architecture to fully realize the vision to connect the worlds. We explore several novel architecture concepts for the 6G era driven by a decomposition of the architecture into platform, functions, orchestration and specialization aspects. With 6G, we associate an open, scalable, elastic, and platform agnostic het-cloud, with converged applications and services decomposed into micro-services and serverless functions, specialized architecture for extreme attributes, as well as open service orchestration architecture. Key attributes and characteristics of the associated architectural scenarios are described. At the air-interface level, 6G is expected to encompass use of sub-Terahertz spectrum and new spectrum sharing technologies, air-interface design optimized by AI/ML techniques, integration of radio sensing with communication, and meeting extreme requirements on latency, reliability and synchronization. Fully realizing the benefits of these advances in radio technology will also call for innovations in 6G network architecture as described.  
Blockchain-Empowered Framework for Decentralized Network Management in 6G
Taras Maksymyuk, etc.
IEEE Communications Magazine, 2020, 58(9): 86 - 92
Mobile network evolution beyond 5G requires a complete rethink of spectrum management. To fulfill unprecedented performance expectations, future 6G networks require fine-grained spectrum sharing in terms of volume, time, and usage area. In this article, we study a novel direction for blockchain integration into the mobile network infrastructure. In particular, we discuss the potential benefits and challenges of the proposed architecture in terms of spectrum and infrastructure sharing. The key implementation aspects of blockchain for 6G, such as a tokenization model for spectrum and infrastructure, the distributed ledger structure, and feasible consensus algorithms, are studied in detail. Finally, we implement three types of smart contracts for service provisioning with semi-persistent, dynamic, and intelligent spectrum trading and analyze the number of transactions for each type. The simulation results show that the average throughput in the case of intelligent spectrum trading is 7 percent higher than that of the semi-persistent trading and 4 percent lower than that of the dynamic trading. From the economic perspective of operators, intelligent trading provides 19 percent more profit than semi-persistent trading and 8 percent less profit than intelligent trading. Intelligent trading also has much lower overhead in the blockchain than dynamic trading, while being very close to the lowest overhead of semi-persistent trading.  
Machine Learning for 6G Wireless Networks: Carry-Forward-Enhanced Bandwidth, Massive Access, and Ultrareliable/Low Latency
Jun Du, etc
IEEE Vehicular Technology Magazine, 2020
To satisfy the expected plethora of demanding services, the future generation of wireless networks (6G) has been mandated as a revolutionary paradigm to carry forward the capacities of enhanced broadband, massive access, and ultrareliable and lowlatency service in 5G wireless networks to a more powerful and intelligent level. Recently, the structure of 6G networks has tended to be extremely heterogeneous, densely deployed, and dynamic. Combined with tight quality of service (QoS), such complex architecture will result in the untenability of legacy network operation routines. In response, artificial intelligence (AI), especially machine learning (ML), is emerging as a fundamental solution to realize fully intelligent network orchestration and management. By learning from uncertain and dynamic environments, AI-/ML-enabled channel estimation and spectrum management will open up opportunities for bringing the excellent performance of ultrabroadband techniques, such as terahertz communications, into full play. Additionally, challenges brought by ultramassive access with respect to energy and security can be mitigated by applying AI-/ML-based approaches. Moreover, intelligent mobility management and resource allocation will guarantee the ultrareliability and low latency of services. Concerning these issues, this article introduces and surveys some state-of-the-art techniques based on AI/ML and their applications in 6G to support ultrabroadband, ultramassive access, and ultrareliable and lowlatency services. Trustworthy Deep Learning in 6G-Enabled Mass Autonomy: From Concept to Quality-of-Trust Key Performance Indicators
Chen Li , etc.
IEEE Vehicular Technology Magazine,2020
Mass autonomy promises to revolutionize a wide range of engineering, service, and mobility industries. Coordinating complex communication among hyperdense autonomous agents requires new artificial intelligence (AI)-enabled orchestration of wireless communication services beyond 5G and 6G mobile networks. In particular, safety and mission-critical tasks will legally require both transparent AI decision processes and quantifiable quality-of-trust (QoT) metrics for a range of human end users (consumer, engineer, and legal). We outline the concept of trustworthy autonomy for 6G, including essential elements such as how explainable AI (XAI) can generate the qualitative and quantitative modalities of trust. We also provide XAI test protocols for integration with radio resource management and associated key performance indicators (KPIs) for trust. The research directions proposed will enable researchers to start testing existing AI optimization algorithms and develop new ones with the view that trust and transparency should be built in from the design through the testing phase.