[论文解读] A Comprehensive Survey of Spectrum Sharing Schemes from a Standardization and Implementation Perspective

Mohammad Parvini, Amir Hossein Zarif+6 more|arXiv (Cornell University)|Mar 21, 2022

Advanced MIMO Systems Optimization被引用 10

一句话总结

本文全面综述了授权频段和非授权频段中的频谱共享方案，分析了5G及以后网络在标准化工作和实施挑战方面的进展。论文评估了LSA、SAS、LTE-U、LAA、MulteFire和NR-U等技术，强调了人工智能/机器学习在智能频谱共享中的整合，并指出了部署过程中面临的关键技术、监管和经济障碍。

ABSTRACT

As the services and requirements of next-generation wireless networks become increasingly diversified, it is estimated that the current frequency bands of mobile network operators (MNOs) will be unable to cope with the immensity of anticipated demands. Due to spectrum scarcity, there has been a growing trend among stakeholders toward identifying practical solutions to make the most productive use of the exclusively allocated bands on a shared basis through spectrum sharing mechanisms. However, due to the technical complexities of these mechanisms, their design presents challenges, as it requires coordination among multiple entities. To address this challenge, in this paper, we begin with a detailed review of the recent literature on spectrum sharing methods, classifying them on the basis of their operational frequency regime that is, whether they are implemented to operate in licensed bands (e.g., licensed shared access (LSA), spectrum access system (SAS), and dynamic spectrum sharing (DSS)) or unlicensed bands (e.g., LTE-unlicensed (LTE-U), licensed assisted access (LAA), MulteFire, and new radio-unlicensed (NR-U)). Then, in order to narrow the gap between the standardization and vendor-specific implementations, we provide a detailed review of the potential implementation scenarios and necessary amendments to legacy cellular networks from the perspective of telecom vendors and regulatory bodies. Next, we analyze applications of artificial intelligence (AI) and machine learning (ML) techniques for facilitating spectrum sharing mechanisms and leveraging the full potential of autonomous sharing scenarios. Finally, we conclude the paper by presenting open research challenges, which aim to provide insights into prospective research endeavors.

研究动机与目标

• 分析下一代无线网络在授权和非授权频段中频谱共享技术的当前状态。
• 通过分析技术和监管要求，弥合标准化框架与实际厂商实现之间的差距。
• 评估人工智能和机器学习在实现自主且高效的频谱共享机制中的作用。
• 识别在干扰管理、移动性、定价模型以及异构共享方案间网络协调方面尚未解决的技术研究挑战。

提出的方法

• 根据频段类型对频谱共享方案进行分类：授权频段（如LSA、SAS、DSS）和非授权频段（如LTE-U、LAA、MulteFire、NR-U）。
• 回顾3GPP、IEEE以及监管机构（如FCC、ISED）在频谱接入框架方面的标准化活动。
• 分析传统蜂窝网络为支持动态频谱共享和非授权频段操作所需的技术修改。
• 评估在非授权频段中LBT（先听后发）协议的实现复杂性，以及NR-U中波束成形带来的挑战。
• 评估人工智能/机器学习在频谱共享中的应用，包括智能干扰管理、功率控制和动态资源分配。
• 识别基站（eNB/gNB）、频谱接入系统（SAS）和现有用户之间在互操作性和协调方面的挑战。

实验结果

研究问题

• RQ1 标准化频谱共享框架（如LSA、SAS、DSS）在技术可行性、可扩展性和监管合规性方面如何比较？
• RQ2 将传统蜂窝网络适配为支持授权和非授权频段动态频谱共享的关键实施挑战是什么？
• RQ3 人工智能和机器学习在多大程度上可以提升频谱共享机制的性能和自主性？
• RQ4 哪些未解决的技术和经济障碍（如移动性管理、定价模型和干扰协调）阻碍了大规模部署？
• RQ5 在真实世界拥塞和波束成形限制下，非授权频段共享方案（如LTE-U、NR-U）的性能如何？

主要发现

• 授权频段的频谱利用率估计仅为10–20%，表明存在显著的资源闲置，共享接入具有巨大潜力。
• LSA和SAS框架在现有用户参与度、静态干扰管理以及缺乏标准化移动性协议方面面临挑战。
• 非授权频段共享（如LTE-U、LAA、NR-U）受Wi-Fi拥塞和LBT机制的制约，波束成形引入了隐性节点和暴露节点问题。
• 动态频谱共享（DSS）需要eNB和gNB之间的软件升级和网络同步，带来了资本支出（CAPEX）和信令开销的挑战。
• 人工智能和机器学习在优化干扰管理和功率控制方面展现出潜力，但其全部潜能尚未被充分探索，仍需进一步研究。
• 厂商锁定和互操作性缺乏仍是主要障碍，尤其对新进入者和小型运营商而言。