Collaborative Research: MLWiNS: ANN for Interference Limited Wireless Networks

合作研究：MLWiNS：干扰有限无线网络的 ANN

• 批准号: 2003082
• 负责人: Xiao Fu
• 金额: $ 18.55万
• 依托单位: Oregon State University
• 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: 2020-06-01 至 2024-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2003082&HistoricalAwards=false
• 关键词: Collaborative; Research; MLWiNS; ANN; Interference

Demand for wireless data services will continue to rise rapidly in the foreseeable future. The goal of this project is to develop new advanced solutions for wireless access networks with the objectives of increasing network throughput and maximizing overall network utilities. Long-serving model-based solutions are facing severe limitations due to delays in tracking the ever-changing radio and network environment as well as measurement inaccuracies. The main novelty of this project is to bring new tools based on artificial neural networks (ANN) to meet those challenges. In particular, this project will investigate when, how, and why ANN-based learning techniques can be applied to a wide range of wireless networking problems with realistic constraints. This project will pursue transformative solutions that aim to benefit academia and industry alike. Specifically, this project will marry supervised and unsupervised learning techniques with time-tested models of physical resources, channels, traffic, and network utilities. An important task is to exploit commonalities of ANN-based solutions for a number of subproblems to develop a set of principled, holistic solutions for the overall wireless networking problem, seeking solutions that are scalable, computationally efficient, and highly adaptive. Pertinent learnability and complexity theories backing the solutions will also be developed, in order to offer generalizable design principles. The ANN-based solutions developed are expected to be a major building block of next generation wireless access networks with associated economic benefits.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.

在可预见的将来，对无线数据服务的需求将继续迅速增长。 该项目的目的是开发新的高级解决方案，以增加网络吞吐量并最大化整体网络实用程序的目标。 由于跟踪不断变化的无线电环境和测量不正确，基于长期服务的模型解决方案正面临严重的限制。 该项目的主要新颖性是携带基于人工神经网络（ANN）的新工具，以应对这些挑战。 特别是，该项目将调查何时，以及为什么基于ANN的学习技术可以应用于具有现实约束的广泛无线网络问题。 该项目将采用旨在使学术界和行业受益的变革性解决方案。 具体而言，该项目将与经过时间考验的物理资源，渠道，流量和网络公用事业的模型结合受监督和无监督的学习技术。 一项重要的任务是利用基于ANN的解决方案的共同点，用于许多子问题，以为整体无线网络问题开发一组原则的，整体解决方案，以寻求可扩展，计算效率和高度适应性的解决方案。 还将开发出支持解决方案的相关学习性和复杂性理论，以提供可概括的设计原则。预计开发的基于ANN的解决方案将是下一代无线访问网络的主要组成部分。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影响评估标准通过评估来获得支持的。

项目成果

Identifiability-Guaranteed Simplex-Structured Post-Nonlinear Mixture Learning via Autoencoder

• DOI: 10.1109/tsp.2021.3096806
• 发表时间: 2021-06
• 期刊: IEEE Transactions on Signal Processing
• 影响因子: 5.4
• 作者: Qi Lyu;Xiao Fu

Learning to Continuously Optimize Wireless Resource in Episodically Dynamic Environment

• DOI: 10.1109/icassp39728.2021.9413503
• 发表时间: 2020-11
• 期刊: ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
• 作者: Haoran Sun;Wenqiang Pu;Minghe Zhu;Xiao Fu;Tsung-Hui Chang;Mingyi Hong

Deep Spectrum Cartography: Completing Radio Map Tensors Using Learned Neural Models

• DOI: 10.1109/tsp.2022.3145190
• 发表时间: 2021-05
• 期刊: IEEE Transactions on Signal Processing
• 影响因子: 5.4
• 作者: S. Shrestha;Xiao Fu;Min-Fong Hong

Optimal Solutions for Joint Beamforming and Antenna Selection: From Branch and Bound to Graph Neural Imitation Learning

• DOI: 10.1109/tsp.2023.3244096
• 发表时间: 2022-06
• 期刊: IEEE Transactions on Signal Processing
• 影响因子: 5.4
• 作者: S. Shrestha;Xiao Fu;Mingyi Hong

Deep Generative Model Learning For Blind Spectrum Cartography with NMF-Based Radio Map Disaggregation

• DOI: 10.1109/icassp39728.2021.9413382
• 发表时间: 2021-06
• 期刊: ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
• 作者: S. Shrestha;Xiao Fu;Min-Fong Hong