CAREER: Development of Learning Frameworks for Nonlinear Massive MIMO Systems

职业：非线性大规模 MIMO 系统学习框架的开发

• 批准号: 2146436
• 负责人: Duy Nguyen
• 金额: $ 50万
• 依托单位: San Diego State University Foundation
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-15 至 2027-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2146436&HistoricalAwards=false
• 关键词: CAREER; Development; Learning; Frameworks; Nonlinear

The need for enabling massive connectivity using energy- and cost-efficient massive multiple-input multiple-output (MIMO) wireless transceivers, mobile devices, sensors, and actuators has motivated engineers to use low-cost and non-customized hardware. However, these hardware components are highly susceptible to generating nonlinear distortions. Moreover, each component distorts the signals of interest in its own way with a possibly unknown nonlinear transfer function that needs to be compensated by signal processing techniques. Studies of massive MIMO have demonstrated its tolerance to hardware impairments. In addition, community research efforts have also focused on new performance frontiers in signal processing for nonlinear massive MIMO. This CAREER project is framed into these efforts by developing novel learning frameworks for signal processing in nonlinear massive MIMO systems in which structured optimization of their performance is not feasible or too complex to implement. The project aims to construct a holistic approach to estimate, detect, optimize, and adapt in nonlinear MIMO systems with the development of model-based and model-free machine learning, deep learning, deep reinforcement learning, and meta-learning frameworks. The outcomes of the project can transform the area of MIMO signal processing and propel massive MIMO into the next stage of development. Moreover, the project will integrate research and education activities through the development of undergraduate and graduate level courses and an integrated wireless testbed for research and training. The project also aims to broaden the participation of underrepresented minorities and women in research and educational activities in electrical engineering with an emphasis on communications and signal processing.This project will develop new learning frameworks to resolve the aggregation of nonlinearities in wireless transceivers and to optimize massive MIMO performance in highly dynamic and ill-defined operational environments with possibly unknown distortions. The project is organized into four interconnected thrusts: i) Learning to estimate and detect with machine learning and deep learning by incorporating the domain knowledge of nonlinear MIMO for devising efficient model-based and data-driven learning models, ii) Learning to optimize signaling designs with deep learning and deep reinforcement learning, focusing on optimizing pilot and signaling designs for model-based and model-free nonlinear MIMO systems, iii) Learning to adapt with offline and online meta-learning, focusing on developing learning models that will enable fast adaptation to a newly observed nonlinear MIMO system configuration with only few training samples, and iv) Integrating research into coursework development for teaching wireless communications and signal processing to undergraduate/graduate students with hands-on experiments using built-in software-defined radio and off-the-shelf wireless testbeds. Through the development of learning to estimate, detect, optimize, and adapt frameworks, this project will promote a better understanding of signal processing in nonlinear MIMO systems and will have broad applications in other learning-based wireless systems.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.

使用节能且具有成本效益的大规模多输入多输出 (MIMO) 无线收发器、移动设备、传感器和执行器来实现大规模连接的需求促使工程师使用低成本和非定制的硬件。然而，这些硬件组件非常容易产生非线性失真。此外，每个组件都会以自己的方式扭曲感兴趣的信号，并具有可能未知的非线性传递函数，需要通过信号处理技术进行补偿。大规模 MIMO 的研究已经证明了其对硬件损伤的容忍度。此外，社区研究工作还集中在非线性大规模 MIMO 信号处理的新性能前沿。该职业项目通过开发用于非线性大规模 MIMO 系统中信号处理的新颖学习框架来构建这些工作，在该系统中，对其性能进行结构化优化是不可行的或太复杂而难以实现。该项目旨在通过开发基于模型和无模型的机器学习、深度学习、深度强化学习和元学习框架，构建一种整体方法来估计、检测、优化和适应非线性 MIMO 系统。该项目的成果可以改变MIMO信号处理领域，并推动大规模MIMO进入下一发展阶段。此外，该项目将通过开发本科和研究生课程以及用于研究和培训的综合无线测试平台来整合研究和教育活动。该项目还旨在扩大代表性不足的少数族裔和妇女对电气工程研究和教育活动的参与，重点是通信和信号处理。该项目将开发新的学习框架，以解决无线收发器中非线性的聚合问题，并优化大规模在高度动态和不明确的操作环境中（可能存在未知失真）中的 MIMO 性能。该项目分为四个相互关联的主旨：i) 通过结合非线性 MIMO 领域知识，学习使用机器学习和深度学习进行估计和检测，以设计高效的基于模型和数据驱动的学习模型，ii) 学习优化信令设计通过深度学习和深度强化学习，专注于优化基于模型和无模型非线性 MIMO 系统的导频和信令设计，iii) 学习适应离线和在线元学习，专注于开发能够实现快速适应的学习模型到新观察到的非线性 MIMO 系统配置只有很少的训练样本，以及 iv) 将研究整合到课程开发中，通过使用内置软件定义无线电和现成的无线测试台进行动手实验，向本科生/研究生教授无线通信和信号处理。通过开发学习估计、检测、优化和适应框架，该项目将促进人们更好地理解非线性 MIMO 系统中的信号处理，并将在其他基于学习的无线系统中得到广泛的应用。该奖项反映了 NSF 的法定使命和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，该项目被认为值得支持。

项目成果

Variational Bayes Inference for Data Detection in Cell-Free Massive MIMO

用于无细胞大规模 MIMO 数据检测的变分贝叶斯推理

• DOI: 10.1109/ieeeconf56349.2022.10051916
• 发表时间: 2022-10
• 期刊: and Computers
• 作者: Nguyen, Ly V.;Ngo, Hien Quoc;Tran, Le;Swindlehurst, A. Lee;Nguyen, Duy H.
• 通讯作者: Nguyen, Duy H.

Leveraging Deep Neural Networks for Massive MIMO Data Detection

利用深度神经网络进行大规模 MIMO 数据检测

• DOI: 10.1109/mwc.013.2100652
• 发表时间: 2023-02
• 期刊: IEEE Wireless Communications
• 影响因子: 12.9
• 作者: Nguyen, Ly V.;Nguyen, Nhan T.;Tran, Nghi H.;Juntti, Markku;Swindlehurst, A. Lee;Nguyen, Duy H.
• 通讯作者: Nguyen, Duy H.

A Variational Bayesian Perspective on MIMO Detection with Low-Resolution ADCs

使用低分辨率 ADC 进行 MIMO 检测的变分贝叶斯视角

• DOI: 10.1109/ieeeconf56349.2022.10052059
• 发表时间: 2022-10-31
• 期刊: 2022 56th Asilomar Conference on Signals, Systems, and Computers
• 作者: Ly V. Nguyen;A. L. Swindlehurst;D. Nguyen
• 通讯作者: D. Nguyen