CNS Core: Small: Next Generation Tiered Spectrum Licensing

无线移动宽带是创新和经济增长的关键驱动力。保持这种增长是具有挑战性的，需要提高无线频谱利用率的技术和监管创新，以便能够在有限的频谱资源内容纳更多的数据流量。随着5G网络及其他网络的部署，预计流量需求将继续呈指数级增长，从而带来需要创新解决方案的额外挑战。尽管在动态频谱共享方面进行了十多年的激烈研究，但频谱政策及其对频谱利用的影响仍然存在根本性的开放性问题。该项目通过提供工具和数学基础，帮助监管机构和研究界理解和设计有效的频谱共享政策，从而帮助应对这些挑战，从而使无线宽带网络能够承载比目前设想的更多的流量。此外，该项目还设计了新的教育课程和材料，为下一代劳动力设计和运营这种高性能网络做好准备。它还包括向K-12学生伸出援助之手，增加美国公民在科学和工程领域的数量和多样性，这对持续的经济增长和繁荣至关重要。该提案的智力价值在于，它开发了一种随机建模和优化方法来研究频谱许可政策的技术经济方面，重点是分层/分层许可架构。在这种方法中，收入过程和客户需求被建模为随机过程。监管机构被建模为一个实体，它试图设定频谱政策，在给定市场随机和复杂动态的不同知识水平（或缺乏知识水平）的情况下，最大限度地利用频谱。这建立了一个随机优化框架，使得比较不同的频谱监管政策作为市场的功能成为可能。此外，它还描述了在给定市场中实现最佳（最大）频谱利用率的频谱策略参数的最优值。它还为运营商提供实时随机算法，以优化其频谱组合，而无需事先了解市场/频谱动态。因此，这个框架同时抓住了监管者和经营者的视角。这项研究为监管机构和研究界提供了工具，使他们能够探索“假设”许可政策，这在目前是不可能的，除非主要通过定性和主观的案例/实证研究。该计划包括与公开可用的数据集和模拟进行广泛的验证和比较。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Wireless mobile broadband is a key driver for innovation and economic growth. Sustaining this growth is challenging and requires both technological and regulatory innovations that increase the wireless spectrum utilization, in order to be able to accommodate more data traffic within the finite spectrum resource. The exponential increase in traffic demand is expected to continue with the deployment of 5G networks and beyond, hence placing additional challenges that need innovative solutions. Despite more than a decade of intense research activity on dynamic spectrum sharing, there are still fundamental open questions regarding spectrum policies and their impact on spectrum utilization. This project helps meet these challenges, by providing tools and mathematical foundations that enable regulators, and the research community, to understand and design efficient spectrum sharing policies leading to wireless broadband networks that are more capable of carrying more traffic than what is envisioned today. In addition, the project designs new education courses and material that prepare the next generation of the workforce to design and operate such high-performance networks. It also includes activities to reach-out to K-12 students and increase the number and diversity of US citizens in science and engineering, which is essential for sustained economic growth and prosperity.The intellectual merit of this proposal is that it develops a stochastic modeling and optimization approach to study the techno-economic aspects of spectrum licensing policies, with a focus on layered/tiered licensing architectures. In this approach, the revenue processes and customer demands are modeled as stochastic processes. A regulator is modeled as an entity that attempts to set spectrum policies that maximize spectrum utilization given various levels of knowledge, or lack thereof, of the stochastic and complex dynamics of the market. This sets up a stochastic optimization framework that makes it possible to compare different spectrum regulatory policies as a function of the market. In addition, it also characterizes the optimal values of the parameters of the spectrum policies that achieve the optimal (maximum) spectrum utilization for a given market. It also provides real-time randomized algorithms for operators to optimize their spectrum portfolios without prior knowledge of the market/spectrum dynamics. Thus this framework captures both regulator and operator perspectives. The research provides tools to enable regulators, and the research community, to explore "what-if" licensing policies, which is currently not possible except through primarily qualitative and subjective case/empirical studies. The plan includes extensive validations and comparisons against publicly available data sets and simulations.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.