CAREER: Development of Learning Frameworks for Nonlinear Massive MIMO Systems

对于使用节能和经济高效的大规模多输入多输出（MIMO）无线收发器、移动的设备、传感器和致动器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，每个分量以其自身的方式使感兴趣的信号失真，其中可能未知的非线性传递函数需要通过信号处理技术来补偿。大规模MIMO的研究已经证明了其对硬件损伤的耐受性。此外，社区研究工作也集中在非线性大规模MIMO信号处理的新性能前沿。该CAREER项目通过开发用于非线性大规模MIMO系统中的信号处理的新型学习框架来构建这些努力，其中结构化优化其性能是不可行的或太复杂而无法实现。该项目旨在构建一种整体方法来估计，检测，优化和适应非线性MIMO系统，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。该项目的成果可以改变MIMO信号处理领域，并推动大规模MIMO进入下一个发展阶段。此外，该项目将通过开发本科生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民族和妇女参与电气工程领域的研究和教育活动，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的非线性聚合问题，并在高度动态和不明确的操作环境中优化大规模MIMO性能，可能存在未知的失真。该项目分为四个相互关联的重点：ii）学习利用深度学习和深度强化学习来优化信令设计，专注于优化基于模型和无模型的非线性MIMO系统的导频和信令设计，iii）学习适应离线和在线元学习，专注于开发学习模型，该学习模型将使得能够快速适应新观察到的非线性MIMO系统配置，仅具有很少的训练样本，以及iv）将研究融入课程开发中，为本科生/研究生教授无线通信和信号处理，并使用内置的软件定义无线电和离线无线电进行实践实验。货架无线测试台。通过开发学习估计、检测、优化和适应框架，该项目将促进对非线性MIMO系统中信号处理的更好理解，并将在其他基于学习的无线系统中得到广泛应用。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标准进行评估，被认为值得支持。

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.