NeTS: Small: Machine Learning Meets Wireless Network Optimization: Exploring the Latent Knowledge

NeTS：小型：机器学习遇见无线网络优化：探索潜在知识

• 批准号: 1816908
• 负责人: Yu Cheng
• 金额: $ 41.07万
• 依托单位: Illinois Institute of Technology
• 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: 2018-10-01 至 2023-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1816908&HistoricalAwards=false
• 关键词: NeTS; Small; Machine; Learning; Meets

Machine learning has been widely applied in various areas including wireless networking. While the capability of machine learning in classification and pattern recognition has been widely accepted, the role it can play on fundamental research issues in wireless networks is yet to be explored. With the proliferation of heterogeneous networking, wireless network optimization has seen a tremendous increase in problem size and complexity, calling for a paradigm of efficient computation. This project aims at a pioneering study on how to exploit deep learning for significant performance gain in wireless network optimization. Innovative techniques are to be developed for extracting latent knowledge from historical optimization instances, and such knowledge will be leveraged to greatly mitigate the computation complexity in solving new optimization problems. The proposed research seamlessly integrates studies in the areas of optimization, machine learning, graph theory, and wireless networking. This interdisciplinary research will not only provide various training projects to undergraduate and graduate students, but also inspire students to pursue high-quality research with an open-minded and cross-disciplinary perspective. Outcomes from this project may directly benefit the industry with low-complexity yet efficient resource allocation algorithms in practical wireless networks. This project is expected to contribute a series of new insights and innovative methods in integrating machine learning with wireless network optimization. This study will reveal that properly trained machine learning algorithms can smartly identify critical features (in terms of a small set of critical links or transmission patterns) that lead to optimal or close-to-optimal solutions. The traditional learning framework for data classification cannot be easily tailored for exposing the latent knowledge in wireless network optimization. This project will conduct a systematic study including learning method selection, input/output design, cost function design, training set construction, and parameter tuning, to accommodate the unique needs and requirements for learning from historical optimization instances. This study will demonstrate how the learned knowledge can be exploited to significantly mitigate the computation cost in both centralized optimization and online scheduling. This study will enable people, possibly for the first time, to understand the complex relationship among the input data traffic, internal network features (link or pattern activation), and optimal resource allocation (scheduling or routing).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.

机器学习已广泛应用于包括无线网络在内的各个领域。尽管机器学习在分类和模式识别方面的能力已被广泛接受，但它在无线网络中的基本研究问题上可以发挥作用。随着异质网络的扩散，无线网络优化的问题大小和复杂性大大增加，要求有效的计算范式。该项目旨在进行一项关于如何利用深度学习以获得无线网络优化绩效增长的开创性研究。将开发创新的技术来从历史优化实例中提取潜在知识，并且将利用这些知识来大大减轻解决新优化问题的计算复杂性。提出的研究将研究无缝整合到优化，机器学习，图形论和无线网络领域。这项跨学科的研究不仅将为本科和研究生提供各种培训项目，而且还可以激发学生以开放的开放和跨学科的观点从事高质量的研究。该项目的结果可能会通过实用无线网络中的低复杂性但有效的资源分配算法直接使该行业受益。预计该项目将为将机器学习与无线网络优化整合在一起，为一系列新的见解和创新方法。这项研究将表明，经过适当训练的机器学习算法可以巧妙地识别导致最佳或近距离解决方案的关键特征（就一系列关键链接或传输模式而言）。传统的数据分类学习框架无法轻松量身定制，以揭示无线网络优化中的潜在知识。该项目将进行系统研究，包括学习方法选择，输入/输出设计，成本功能设计，训练集构建和参数调整，以适应从历史优化实例中学习的独特需求和要求。这项研究将证明如何利用学习知识，以显着减轻集中优化和在线计划中的计算成本。这项研究将使人们首次能够理解输入数据流量，内部网络功能（链接或模式激活）和最佳资源分配（调度或路由）之间的复杂关系。该奖项反映了NSF的法定任务，并被认为是通过基金会的智力和更广泛的影响来评估的支持，并被认为是值得的。

项目成果

Robust Deep Learning for Wireless Network Optimization

• DOI: 10.1109/icc40277.2020.9149445
• 发表时间: 2020-06
• 期刊: ICC 2020 - 2020 IEEE International Conference on Communications (ICC)
• 作者: Shuai Zhang;Bo Yin;Suyang Wang;Y. Cheng

A Self-Supervised Learning Approach for Accelerating Wireless Network Optimization

• DOI: 10.1109/tvt.2023.3244043
• 发表时间: 2023-06
• 期刊: IEEE Transactions on Vehicular Technology
• 影响因子: 6.8
• 作者: Shuai Zhang;O. Ajayi;Yu-long Cheng

Experience-Driven Wireless D2D Network Link Scheduling: A Deep Learning Approach

• DOI: 10.1109/icc.2019.8761818
• 发表时间: 2019-05
• 期刊: ICC 2019 - 2019 IEEE International Conference on Communications (ICC)
• 作者: Shuai Zhang;Wenlong Shen;Max Zhangt;Xianghui Cao;Yu Cheng

Age of Local Information for Fusion Freshness in Internet of Things

• DOI: 10.1109/iccc57788.2023.10233592
• 发表时间: 2023-08
• 期刊: 2023 IEEE/CIC International Conference on Communications in China (ICCC)
• 作者: Taige Chang;Xianghui Cao;Y. Cheng

Machine Learning Assisted Capacity Optimization for B5G/6G Integrated Access and Backhaul Networks

• DOI: 10.1109/infocomwkshps57453.2023.10225946
• 发表时间: 2023-05
• 期刊: IEEE INFOCOM 2023 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)
• 作者: O. Ajayi;Shuai Zhang;Yu-long Cheng