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

机器学习已经广泛应用于包括无线网络在内的各个领域。虽然机器学习在分类和模式识别方面的能力已被广泛接受，但它在无线网络基础研究问题上的作用还有待探索。随着异构网络的发展，无线网络优化问题的规模和复杂性急剧增加，需要一种高效的计算范式。该项目旨在对如何利用深度学习在无线网络优化中实现显著性能提升进行开创性研究。创新的技术将被开发用于从历史优化实例中提取潜在的知识，并且这些知识将被利用来大大降低解决新的优化问题的计算复杂性。拟议的研究无缝集成了优化，机器学习，图论和无线网络领域的研究。这种跨学科的研究不仅将提供各种培训项目，本科生和研究生，但也激励学生追求高质量的研究与开放的思想和跨学科的角度。该项目的成果可能会直接受益于实际无线网络中的低复杂度，但有效的资源分配算法的行业。该项目有望在将机器学习与无线网络优化相结合方面提供一系列新的见解和创新方法。这项研究将揭示，经过适当训练的机器学习算法可以智能地识别导致最佳或接近最佳解决方案的关键特征（就一小部分关键链路或传输模式而言）。传统的数据分类的学习框架不能很容易地定制暴露在无线网络优化的潜在知识。该项目将进行系统的研究，包括学习方法选择，输入/输出设计，成本函数设计，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。这项研究将展示如何学习的知识可以被利用，以显着降低集中式优化和在线调度的计算成本。这项研究将使人们，可能是第一次，了解输入数据流量，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的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。

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.