EAGER: A Novel Approach to Achieve Real-time Wireless Network Optimization

EAGER：一种实现实时无线网络优化的新方法

• 批准号: 1800650
• 负责人: Wenjing Lou
• 金额: $ 30万
• 依托单位: Virginia Polytechnic Institute and State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-04-01 至 2020-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1800650&HistoricalAwards=false
• 关键词: EAGER; Novel; Approach; Achieve; Real

Resource optimization is a fundamental problem in wireless network research. As wireless technologies become increasingly sophisticated, resource optimization problems also become more complex. For example, a major technical challenge in the design of current and future cellular networks is to solve scheduling problems involving a large number of resource units in spectral and temporal domains. Although some schedulers have been proposed to achieve optimal (or near-optimal) objectives, they typically cannot be used in the field due to excessive computation time. Instead, operators have to adopt simple schedulers and settle for performance far from optimal. This goal of this EAGER project is to tackle this fundamental problem by exploring a novel approach that could make real-time resource optimization possible. By real time, we mean that a solution to the resource optimization problem can be found in a time resolution that can be readily used in the field. Our approach is to decompose a complex resource optimization problem into a large number of extremely simple subproblems that can be matched precisely into a given Graphical Processing Unit (GPU) computing platform. By solving each small subproblem independently and all the sub-problems in parallel, it is possible to meet the stringent timing requirement for real-time scheduling. The proposed idea, if successful, has the potential to revolutionize how resource optimization is performed in the field. The proposed research activities will have a profound impact on developing new educational curricula and broadening participation in computing from underrepresented groups. New teaching materials will be developed from this research and will be used in classrooms at Virginia Tech and other universities. Special efforts to broaden participation in computing by female and underrepresented students will be made through our Wireless@VT education and research programs. Finally, the two investigators will continue their efforts to support N2 Women events at a networking conference to nurture female junior researchers to become leaders in the networking community. Some major technical challenges must be carefully addressed before we can reap the benefits of our proposed idea. In this project, the investigators plan to explore the following two thrust areas as proof-of-concept: (i) how to optimally decompose and map a resource optimization problem into a given state-of-the-art GPU platform so that the solution can be obtained in real time; (ii) how to apply the methodology for real-time optimal scheduling for next generation cellular networks as well as other (non-cellular) wireless networks. The proposed approach is interdisciplinary and involves exploring new methods and tools from the fields of computing (GPU) and optimization (decomposition) to address fundamental problems in wireless networks. Validation of the proposed ideas will be carried out on off-the-shelf low-cost GPU platform. Once the proposed idea is proven to be valid, we expect to see a new and practical methodology to achieve real-time resource optimization and significant performance improvement in the real world.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.

资源优化是无线网络研究中的一个基本问题。 随着无线技术变得越来越复杂，资源优化问题也变得更加复杂。 例如，在当前和未来的蜂窝网络设计中的一个主要技术挑战是解决涉及光谱和时间域中大量资源单位的调度问题。 尽管已经提出了一些调度程序来实现最佳（或近乎理想的）目标，但由于过度的计算时间，通常无法在现场使用它们。 取而代之的是，操作员必须采用简单的调度程序，并为远离最佳性能而定居。 这个渴望的项目的这个目标是通过探索一种可以使实时资源优化成为可能的新方法来解决这个基本问题。 到实时，我们的意思是，可以在可以在现场容易使用的时间分辨率中找到解决资源优化问题的解决方案。我们的方法是将复杂的资源优化问题分解为大量极其简单的子问题，这些子问题可以将其精确匹配到给定的图形处理单元（GPU）计算平台中。 通过独立解决每个小子问题和并行所有子问题，可以满足实时时间表的严格定时要求。拟议的想法，如果成功的话，有可能彻底改变现场的资源优化。 拟议的研究活动将对开发新的教育课程以及扩大代表性不足的群体的计算参与产生深远的影响。 这项研究将开发新的教材，并将用于弗吉尼亚理工大学和其他大学的教室。 将通过我们的无线@VT教育和研究计划进行特殊的努力，以扩大女性和代表性不足的学生的计算参与。 最后，两名调查人员将继续努力在网络会议上支持N2女性活动，以培养女性初级研究人员成为网络社区的领导者。 在我们获得提议的想法的好处之前，必须仔细解决一些主要的技术挑战。 在该项目中，调查人员计划探索以下两个推力区域作为概念证明：（i）如何将资源优化问题最佳分解和映射到给定的最先进的GPU平台中，以便可以实时获得该解决方案； （ii）如何将方法应用于下一代蜂窝网络以及其他（非细胞）无线网络的实时最佳计划。所提出的方法是跨学科的，涉及探索计算领域（GPU）和优化（分解）的新方法和工具，以解决无线网络中的基本问题。 拟议想法的验证将在现成的低成本GPU平台上进行。 一旦提出的想法被证明是有效的，我们希望看到一种新的实用方法来实现实时资源优化和现实世界中的显着绩效提高。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛的审查标准通过评估来进行评估的。

项目成果

CURT: A Real-Time Scheduling Algorithm for Coexistence of LTE and Wi-Fi in Unlicensed Spectrum

• DOI: 10.1109/dyspan.2018.8610476
• 发表时间: 2018-10
• 期刊: 2018 IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN)
• 作者: Yan Huang;Yongce Chen;Yiwei Thomas Hou;Wenjing Lou

Turbo-HB: A Novel Design and Implementation to Achieve Ultra-Fast Hybrid Beamforming

• DOI: 10.1109/infocom41043.2020.9155337
• 发表时间: 2020-07
• 期刊: IEEE INFOCOM 2020 - IEEE Conference on Computer Communications
• 作者: Yongce Chen;Yan-Cheng Huang;Chengzhang Li;Y. T. Hou;Wenjing Lou

GPU: A New Enabling Platform for Real-Time Optimization in Wireless Networks

• DOI: 10.1109/mnet.011.2000016
• 发表时间: 2020-11
• 期刊: IEEE Network
• 影响因子: 9.3
• 作者: Yan Huang;Shaoran Li;Yongce Chen;Y. T. Hou;Wenjing Lou;J. Delfeld;Vikrama Ditya