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。 随着无线技术变得日益复杂，资源优化问题也变得更加复杂。 例如，当前和未来蜂窝网络设计中的主要技术挑战是解决涉及频谱和时间域中的大量资源单元的调度问题。 尽管已经提出了一些优化器来实现最优（或接近最优）目标，但是由于过多的计算时间，它们通常不能在现场使用。 相反，运营商不得不采用简单的路由器，并满足于远远不是最佳的性能。 这个EAGER项目的目标是通过探索一种新的方法来解决这个基本问题，这种方法可以使实时资源优化成为可能。 通过真实的时间，我们的意思是可以在现场容易使用的时间分辨率中找到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，这些子问题可以精确匹配到给定的图形处理单元（GPU）计算平台。 通过独立求解每个小的子问题和并行的所有子问题，它是可能的，以满足严格的实时调度的时间要求。所提出的想法，如果成功的话，有可能彻底改变如何在现场进行资源优化。 拟议的研究活动将对制定新的教育课程和扩大代表性不足的群体参与计算产生深远影响。 新的教学材料将从这项研究中开发出来，并将在弗吉尼亚理工大学和其他大学的课堂上使用。 将通过我们的Wireless@VT教育和研究计划，特别努力扩大女性和代表性不足的学生对计算的参与。 最后，两位研究人员将继续努力，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。 在我们能够从我们提出的想法中获益之前，必须认真解决一些主要的技术挑战。 在该项目中，研究人员计划探索以下两个重点领域作为概念验证：（i）如何将资源优化问题最佳地分解和映射到给定的最先进的GPU平台，以便可以在真实的时间内获得解决方案;（ii）如何将该方法应用于下一代蜂窝网络以及其他（非蜂窝）无线网络的实时优化调度。所提出的方法是跨学科的，涉及从计算（GPU）和优化（分解）领域探索新的方法和工具，以解决无线网络中的基本问题。 所提出的想法的验证将在现成的低成本GPU平台上进行。 一旦所提出的想法被证明是有效的，我们希望看到一个新的和实用的方法，以实现实时资源优化和显着的性能改善在真实的world.This奖项反映了NSF的法定使命，并已被认为是值得的支持，通过评估使用基金会的智力价值和更广泛的影响审查标准。

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