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。保持这一增长具有挑战性，需要技术和监管创新来提高无线频谱利用率，以便能够在有限的频谱资源中容纳更多数据流量。随着5G网络及以后的部署，流量需求的指数级增长预计将继续下去，因此会带来额外的挑战，需要创新的解决方案。尽管在动态频谱共享方面进行了十多年的密集研究，但关于频谱政策及其对频谱利用率的影响，仍然存在一些基本的开放问题。该项目通过提供工具和数学基础来帮助应对这些挑战，使监管机构和研究团体能够了解和设计高效的频谱共享政策，从而使无线宽带网络能够承载比目前预想的更多的流量。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此类高性能网络做好准备。它还包括接触K-12学生和增加美国公民在科学和工程领域的数量和多样性的活动，这对持续的经济增长和繁荣至关重要。这项建议的智力优势是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/分级许可架构。在该方法中，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，该实体试图设置频谱政策，在给定或缺乏对市场随机和复杂动态的各种级别的知识的情况下，最大化频谱利用率。这建立了一个随机优化框架，使作为市场函数的不同频谱监管政策的比较成为可能。此外，它还刻画了在给定市场中实现最优(最大)频谱利用率的频谱策略参数的最佳值。它还为运营商提供实时随机算法，以优化他们的频谱组合，而无需事先了解市场/频谱动态。因此，这一框架抓住了监管者和运营商的视角。这项研究提供了工具，使监管者和研究界能够探索“假设”许可政策，这目前是不可能的，除非主要通过定性和主观的案例/经验研究。该计划包括广泛的验证和与公开可用的数据集和模拟的比较。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势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.