CAREER: Generalizing Deep Learning for Wireless Communication

该奖项的全部或部分资金来自《2021年美国救援计划法案》(公共法律117-2)。下一代(XG)蜂窝、车载(V2X)、机载、毫米波(MmWave)网络中的无线信道因时变损伤而瘫痪，限制了其在实践中的应用。当前技术在发送器处采用空间多路复用，这在统计上非平稳的xG信道下计算昂贵且脆弱，这也使接收器复杂化。这些方法充其量只能实现适度的误码率，不足以支持移动AR/VR/XR、空中通信和4K/8K HDR视频流服务等高数据速率无线应用。这项职业研究概括了基于深度学习(DL)的无线收发器的架构，该架构将在所有类型的无线通道中始终以低误码率运行，特别是在未来的xG通道中表现优于最先进水平。总体而言，预计它将在所有类型的通道和应用程序中实现3-5个数量级的可靠性改进。教育计划的重点是一个网络学习平台，用互动元素、多媒体和适应性内容来增强传统教科书，以促进自学。此外，设想了一个扩展的现实平台，用于虚拟实验室体验，目前限制了工程教育的实践方面。总体而言，该教育计划扩大了学生的参与范围，使其超越了国际学生联合会所在机构的界限。该项目在四个基本领域扩展了深度学习(DL)对实用无线收发机的理解和适用性：1)可靠性：它采取数学原则性的方法来理解用于无线通信的深度学习模型的一般性，其能够适应变化的无线环境而不损害可靠性；2)一般性：当前技术经常导致过度优化的模型，当暴露于信道状态的非平稳变化时，这些模型是脆弱的。这项研究通过创新自适应算法对信道状态进行精确的时空分解，并对波形进行预调节以实现无差错通信；3)复杂性：所提出的方法的低计算复杂度将使下行收发机易于重新配置，并且具有保证的差错性能；以及4)适应性：数据依赖、逆模型设计和转移学习将确保下行模型能够在不牺牲可靠性和复杂性的情况下快速适应短暂的信道状态。最后，通过收发信机结构的原型硬件实现，验证了本文的研究成果，并通过大量的空中实验进行了验证。总体而言，发射器和接收器共同工作，以适应任何和所有平衡模型复杂性和错误性能的信道条件。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。

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).Wireless channels in Next Generation (xG) cellular, vehicular (V2X), air-borne, millimeter wave (mmWave) networks are crippled by time-varying impairments that limit their utility in practice. Current art employs spatial multiplexing at the transmitter which is computationally expensive and brittle under statistically non-stationary xG channels, complicating the receiver as well. At best, these methods are only able to achieve a modest error rate that is inadequate to support high data rate wireless applications like mobile AR/VR/XR, aerial communications and 4K/8K HDR video streaming services. This CAREER research generalizes the architecture of a Deep Learning (DL) based wireless transceiver that will consistently operate with low error rate in all types of wireless channels, but especially outperform the state of the art in future xG channels. Overall, it is envisioned to achieve 3-5 orders of magnitude improvement in reliability across all types of channels and applications. The education plan focuses on a web-learning platform that augments traditional textbooks with interactive elements, multimedia and adaptive content to promote self-learning. Further, an extended reality platform is envisioned for virtual laboratory experience that currently limits the hands-on aspect of engineering education. Collectively, the education plan broadens the participation of students beyond the boundaries of the PI’s home institution. This project expands the understanding and applicability of deep learning (DL) for practical wireless transceivers in four fundamental areas: 1) Reliability: It takes a mathematically principled approach towards understanding the generality of DL models for wireless communications that can adapt to changing wireless environment without compromising reliability; 2) Generality: Current art often lead to over-optimized models that are brittle when exposed to non-stationary changes in the channel state. This research takes a holistic approach by innovating adaptive algorithm for accurate spatio-temporal decomposition of the channel state and pre-condition the waveform for error free communications; 3) Complexity: Low computational complexity of the proposed methods will make DL transceivers easy to reconfigure with minimal to no retraining and operate with guaranteed error performance; and 4) Adaptability: Data dependent, inverse model design and transfer learning will ensure the DL models can adapt quickly to ephemeral channel states without compromising on reliability and complexity. Finally, the research is made practical by prototype hardware implementation of the transceiver architecture and validated with extensive over-the-air experimentation. Overall, the transmitter and receiver work together to adapt in any and all channel conditions that balances model complexity and error performance.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.