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CIF: Small: Machine Learning for Wireless Propagation Channels

CIF：小型：无线传播通道的机器学习

基本信息

批准号：
2008443
负责人：
Andreas Molisch
金额：
$ 50万
依托单位：
University of Southern California
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2020
资助国家：
美国
起止时间：
2020-10-01 至 2024-09-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2008443&HistoricalAwards=false
关键词：
CIF Small Machine Learning Wireless

项目摘要

Wireless communications is an essential part of modern society, and with the introduction of 5G, will become even more important. One of the key challenges of wireless communications is that the signals are distorted on their way from the transmitter to the receiver, i.e., by the propagation channel. In order to make communications efficient, the system needs to know and correct these distortions at the receiver. The current way of getting this information is through sending special, known signals, from which the distortion information can be extracted. However this is highly inefficient as useful (payload) data cannot be sent during this time. These challenges are further exacerbated if the distortion information needs to be known at the transmitter, or when prediction of what the distortions will be in the future is needed, or in a different frequency band, or at different locations. This project explores the use of powerful identification and prediction techniques in Machine Learning (ML) to make such predictions. The project team will combine knowledge about the physics of propagation channels and extensive measurement data to train the ML algorithms together with novel ML training approaches to tackle this problem.Importantly, the structure of wireless channels is very different from structures of other types of data (images, documents, etc.) to which ML has been applied. Thus application of ML to wireless channels cannot simply apply modern training algorithms (e.g. deep learning) known from image classification and other well-known ML applications, but rather requires incorporation of the special properties of wireless propagation and suitable rethinking of the ML approaches. The project will thus develop new methodologies, and provide new insights, both from the perspective of ML and that of propagation channels and their impact on system design. The project will also establish an extensive database of channel measurement and ray tracing data that will be used to train and evaluate the proposed algorithms which will be made available to other US researchers.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.

无线通信是现代社会不可或缺的一部分，随着5G的引入，无线通信将变得更加重要。无线通信的关键挑战之一是信号在从发送器到接收器的途中发生失真，即受到传播信道的影响。为了使通信高效，系统需要知道并在接收方纠正这些失真。目前获取这些信息的方法是通过发送特殊的已知信号，从中可以提取失真信息。然而，这是非常低效的，因为在这段时间内不能发送有用的(有效负载)数据。如果需要在发射机上知道失真信息，或者当需要预测将来的失真将是什么时，或者在不同的频带或在不同的位置，这些挑战进一步加剧。这个项目探索了机器学习(ML)中强大的识别和预测技术的使用来做出这样的预测。项目组将结合传播信道的物理知识和大量的测量数据来训练ML算法和新的ML训练方法来解决这个问题。重要的是，无线信道的结构与其他类型的数据(图像、文档等)的结构非常不同。已对其应用ML的。因此，将ML应用于无线信道不能简单地应用从图像分类和其他众所周知的ML应用中获知的现代训练算法(例如，深度学习)，而是需要结合无线传播的特殊特性和对ML方法的适当反思。因此，该项目将从ML和传播渠道及其对系统设计的影响的角度开发新的方法，并提供新的见解。该项目还将建立一个广泛的通道测量和射线跟踪数据数据库，用于培训和评估建议的算法，并将提供给其他美国研究人员。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

期刊论文数量（4）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Toward Moderate Overparameterization: Global Convergence Guarantees for Training Shallow Neural Networks

DOI：
10.1109/jsait.2020.2991332
发表时间：
2019-02
期刊：
IEEE Journal on Selected Areas in Information Theory
影响因子：
0
作者：
Samet Oymak;M. Soltanolkotabi
通讯作者：
Samet Oymak;M. Soltanolkotabi

Reinforcement Learning Empowered Massive IoT Access in LEO-based Non-Terrestrial Networks

强化学习支持基于 LEO 的非地面网络中的大规模物联网访问

DOI：
10.1109/ictc55196.2022.9952919
发表时间：
2022
期刊：
13th International Conference on Information and Communication Technology Convergence (ICTC
影响因子：
0
作者：
Leel, Ju-Hyung;Selvam, Dheeraj Panneer;Molisch, Andreas F.;Kim, Joongheon
通讯作者：
Kim, Joongheon

Outlier-Robust Sparse Estimation via Non-Convex Optimization

通过非凸优化的异常值稳健稀疏估计

DOI：
发表时间：
2022
期刊：
Conference on Neural Information Processing Systems
影响因子：
0
作者：
Cheng, Yu;Diakonikolas, Ilias;Ge, Rong;Gupta, Shivam;Kane, Daniel M.;Soltanolkotabi, Mahdi
通讯作者：
Soltanolkotabi, Mahdi

Data augmentation for deep learning based accelerated MRI reconstruction with limited data