Collaborative Research: SWIFT: Dynamic Spectrum Sharing via Stochastic Optimization

合作研究：SWIFT：通过随机优化实现动态频谱共享

• 批准号: 2229468
• 负责人: Vahid Tarokh
• 金额: $ 11.78万
• 依托单位: Duke University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-10-01 至 2025-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2229468&HistoricalAwards=false
• 关键词: Collaborative; Research; SWIFT; Dynamic; Spectrum

The next generation of communication networks must support extremely complex systems and challenging applications such as smart city/home, smart manufacturing, autonomous vehicles, and virtual and augmented reality. These new applications should be more accessible worldwide and lead to wider harmonization, lower broadband costs, and reduce the digital divide. At the same time, radar sensing is becoming more pervasive in areas with increasing demands for the 5G spectrum. These ever-increasing demands on spectral resources require the use of intelligent spectrum scheduling techniques. Meanwhile, several new technologies like reconfigurable intelligent surface (RIS) and dual-function unmanned aerial vehicles (UAVs) provide additional design degrees of freedom. This project develops the general mathematical framework that is needed to integrate and optimize these new technologies for resilient coexistence over a shared spectrum. Current spectrum allocation between communication and radar users, designed by regulatory bodies, aims to avoid interference between users at all times. This conservative approach is not suited in the wake of increasing demand for throughput and dual communication and sensing functionality and results in a reduced capacity. This inefficiency is exacerbated by the introduction of additional design degrees of freedom and the corresponding constraints related to the characteristics and dynamics of new technologies like RIS modules and UAVs. This project develops a new paradigm in which spectrum sharing moves from hard deterministic constraints to stochastic schemes with a desired low probability of harmful interference. This enables leveraging recent advances in stochastic programming to derive resilient solutions. In this approach, the constraints of optimization are random variables that have to satisfy the bounds given by interference limits with a desired high probability. These stochastic constraints may be time-varying to account for uncertainty in the dynamic environment. A key component to enable this development is an accurate characterization of the mutual interference and performance tradeoffs among coexisting radar and communication nodes. his project develops the general mathematical framework that is needed to integrate and optimize these new technologies for resilient coexistence over a shared spectrum.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.

下一代通信网络必须支持极其复杂的系统和具有挑战性的应用，如智能城市/家庭、智能制造、自动驾驶汽车以及虚拟和增强现实。这些新的应用程序应该在全世界更容易获得，并导致更广泛的协调，降低宽带成本，缩小数字鸿沟。与此同时，雷达传感在对5G频谱需求不断增长的地区变得越来越普遍。这些对频谱资源不断增长的需求需要使用智能频谱调度技术。与此同时，一些新技术，如可重构智能表面（RIS）和双功能无人机（UAV）提供了额外的设计自由度。该项目开发了集成和优化这些新技术所需的通用数学框架，以便在共享频谱上实现弹性共存。目前通信和雷达用户之间的频谱分配由监管机构设计，旨在始终避免用户之间的干扰。这种保守的方法不适合随着对吞吐量以及双重通信和感测功能的需求增加而出现的情况，并且导致容量降低。由于引入了额外的设计自由度以及与RIS模块和无人机等新技术的特性和动态相关的相应约束，这种低效率更加严重。该项目开发了一种新的范例，其中频谱共享从硬确定性约束移动到具有所需的低有害干扰概率的随机方案。这使得利用随机规划的最新进展来获得弹性解决方案。在这种方法中，优化的约束条件是随机变量，必须满足所需的高概率的干扰限制给出的界限。这些随机约束可以是时变的，以考虑动态环境中的不确定性。实现这一发展的一个关键组成部分是共存的雷达和通信节点之间的相互干扰和性能权衡的准确表征。他的项目开发了集成和优化这些新技术所需的通用数学框架，以实现共享频谱上的弹性共存。该奖项反映了NSF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。