Regulation Aware Spectrum Sharing in 5G and B5G Wireless Networks

该提案探讨了频谱共享在5G和超5G（B5 G）系统中的作用。人们普遍认为，对移动的通信服务的需求增长将超过频谱的可用性。在这方面，在移动的网络运营商之间共享频谱或从控股公司租赁频谱以使频谱与需求最佳匹配是越来越有吸引力的解决方案。虽然这一概念已在认知无线电的背景下进行了探索，但在本提案中，我们探讨了频谱共享和新兴技术趋势（如大规模多输入多输出（MIMO）系统和网络切片）的相互作用，从而实现应用程序和频谱使用的虚拟化。 这种合作关系的长期目标是为TELUS可能必须在完全不同的频谱许可模式下运营的未来做好准备。其目的是了解这种许可制度将施加哪些限制，以及TELUS的运营将如何做出反应。在这里，我们提出了实现这一目标的初步步骤，分析新兴技术与合作模式之间的相互作用，以了解5G（以及超越5G -B5 G）网络背景下的可能性、监管限制和新的分析工具以及利用共享频谱的技术。 在追求这些目标，我们提出了三个推力：分析大规模MIMO系统使用频谱共享透镜，并提出信号处理和资源分配方案，以鼓励频谱共享，提供频谱共享的经济分析，最后，使用机器学习技术，特别是强化学习，以评估系统的性能和经济价值有限的信息系统的状态。

This proposal explores the role of spectrum sharing in 5G and beyond-5G (B5G) systems. It is well-accepted that growth in demand for mobile communication services will outstrip the availability of spectrum. In this regard, sharing spectrum amongst mobile network operators, or leasing spectrum from a holding company, to best match spectrum to demand is an increasingly appealing solution. While this notion has been explored in the context of cognitive radio, in this proposal we explore the interplay of spectrum sharing and emerging technology trends such as massive multiple-input, multiple-output (MIMO) systems and network slicing, allowing for virtualization of applications and spectrum usage. The long-term goal of this partnership is to prepare for a future where TELUS may have to operate under a completely different spectrum licensing paradigm. The aim is to understand what constraints such a licensing regime would impose and how TELUS operations would have to change in response. Here we propose initial steps toward that goal, analyzing the interplay between emerging technologies and cooperation modes to understand what is possible in the context of 5G (and beyond-5G -B5G) networks, the regulatory constraints and new tools to analyze and techniques to exploit shared spectrum. In pursuing these goals, we propose three thrusts: analyzing massive MIMO systems using a spectrum sharing lens and proposing signal processing and resource allocation schemes to encourage spectrum sharing, providing economic analyses of spectrum sharing and, finally, using machine learning techniques, especially reinforcement learning, to evaluate system performance and economic value with limited information about the state of the system.