Collaborative Research: MLWiNS: Deep Neural Networks Meet Physical LayerCommunications -- Learning with Knowledge of Structure

合作研究：MLWiNS：深度神经网络满足物理层通信——利用结构知识进行学习

• 批准号: 2003059
• 负责人: Lingjia Liu
• 金额: $ 27.01万
• 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: 2020
• 资助国家: 美国
• 起止时间: 2020-08-01 至 2024-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2003059&HistoricalAwards=false
• 关键词: Collaborative; Research; MLWiNS; Deep; Neural

Machine learning techniques are expected to be the main driving force for performance gains in future wireless networks. In practice, the difficulty in applying learning algorithms in communication systems comes from a paradigm shift in the methodology: From conventional model-based analytical solutions to data-driven empirical-result-based approaches. While learning algorithms offer better flexibility and efficiency in using computational resources, the lack of guarantees in the performance, robustness and security raise issues when adopting into engineering designs. The goal of this project is to combine learning-based data-driven approaches with conventional model-based analysis, to design novel learning algorithms that can utilize the structural knowledge of engineering systems, thus reaping the advantages of both methods.This project focuses on the physical layer of wireless networks, where analytical solutions, clearly defined performance benchmarks, as well as clearly isolated modeling deficiencies are usually present. They key challenge is to utilize the analytical results whenever possible, and focus the learning power on those parts of the system where the conventional approach fails, including non-linear, non-Gaussian, and non-stationary elements. Three problem areas are identified to test such new hybrid design methods, including 1) symbol-detection for MIMO fading interference channels, 2) massive MIMO with low resolution analog-to-digital converters, and 3) MIMO-OFDM waveform analysis in non-linear radio channels.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.

机器学习技术有望成为未来无线网络性能提升的主要驱动力。在实践中，在通信系统中应用学习算法的困难来自于方法论的范式转变：从传统的基于模型的分析解决方案到基于数据驱动的实验结果的方法。虽然学习算法在使用计算资源方面提供了更好的灵活性和效率，但在性能，鲁棒性和安全性方面缺乏保证，在采用工程设计时会出现问题。该项目的目标是将联合收割机基于学习的数据驱动方法与传统的基于模型的分析方法相结合，设计新的学习算法，可以利用工程系统的结构知识，从而获得这两种方法的优势。他们面临的主要挑战是尽可能利用分析结果，并将学习能力集中在传统方法失败的系统部分，包括非线性，非高斯和非平稳元素。 确定了三个问题领域来测试这种新的混合设计方法，包括1）MIMO衰落干扰信道的符号检测，2）具有低分辨率模数转换器的大规模MIMO，3）MIMO-OFDM信号在非线性环境下的波形分析该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查进行评估，被认为值得支持的搜索.

项目成果

Harnessing Tensor Structures – Multi-Mode Reservoir Computing and Its Application in Massive MIMO

利用张量结构 — 多模式储层计算及其在大规模 MIMO 中的应用

• DOI: 10.1109/twc.2022.3164203
• 发表时间: 2022
• 期刊: IEEE Transactions on Wireless Communications
• 影响因子: 10.4
• 作者: Zhou, Zhou;Liu, Lingjia;Xu, Jiarui
• 通讯作者: Xu, Jiarui

Real-Time Machine Learning for Multi-User Massive MIMO: Symbol Detection Using Multi-Mode StructNet

• DOI: 10.1109/twc.2023.3268945
• 发表时间: 2023-12
• 期刊: IEEE Transactions on Wireless Communications
• 影响因子: 10.4
• 作者: Lianjun Li;Jiarui Xu;Lizhong Zheng;Lingjia Liu

Reservoir Computing Meets Extreme Learning Machine in Real-Time MIMO-OFDM Receive Processing

储层计算在实时 MIMO-OFDM 接收处理中遇到极限学习机

• DOI: 10.1109/tcomm.2022.3141399
• 发表时间: 2022
• 期刊: IEEE Transactions on Communications
• 影响因子: 8.3
• 作者: Li, Lianjun;Liu, Lingjia;Zhou, Zhou;Yi, Yang
• 通讯作者: Yi, Yang

Learning with Knowledge of Structure: A Neural Network-Based Approach for MIMO-OFDM Detection

利用结构知识进行学习：基于神经网络的 MIMO-OFDM 检测方法

• DOI: 10.1109/ieeeconf51394.2020.9443477
• 发表时间: 2020
• 期刊: and Computers
• 作者: Zhou, Zhou;Jere, Shashank;Zheng, Lizhong;Liu, Lingjia
• 通讯作者: Liu, Lingjia