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CAREER: Generalizing Deep Learning for Wireless Communication

职业：将深度学习推广到无线通信

基本信息

批准号：
2144980
负责人：
Aveek Dutta
金额：
$ 55.7万
依托单位：
SUNY at Albany
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：
2022-07-01 至 2027-06-30
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2144980&HistoricalAwards=false
关键词：
CAREER Generalizing Deep Learning Wireless

项目摘要

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.

该奖项全部或部分由2021年美国救援计划法案（公法117-2）资助。下一代（xG）蜂窝、车载（V2X）、机载、毫米波（mmWave）网络中的无线信道受到时变损害的影响，限制了它们在实践中的实用性。现有技术在发射机处采用空间复用，这在统计上非平稳的xG信道下在计算上是昂贵且脆弱的，也使接收机复杂化。这些方法充其量只能实现适度的错误率，不足以支持高数据速率无线应用，如移动的AR/VR/XR、空中通信和4K/8 K HDR视频流服务。这项CAREER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信道中以低错误率持续运行，但在未来的xG信道中表现尤其出色。总体而言，可以设想在所有类型的通道和应用中实现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，以互动元素、多媒体和适应性内容补充传统教科书，以促进自学。此外，一个延展实境平台的虚拟实验室的经验，目前限制了动手方面的工程教育。总的来说，教育计划扩大了学生的参与范围，超越了PI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）对实际无线收发器的理解和适用性：1）可靠性：它采用数学原理方法来理解无线通信DL模型的通用性，可以适应不断变化的无线环境而不影响可靠性; 2）通用性：当前技术经常导致过度优化的模型，其在暴露于信道状态的非平稳变化时是脆弱的。本研究通过创新自适应算法采取整体方法，用于信道状态的精确时空分解，并为无差错通信预处理波形; 3）复杂性：所提出的方法的低计算复杂性将使DL收发器易于重新配置，具有最小的重新训练或没有重新训练，并以保证的差错性能进行操作;以及4）适应性：数据依赖、逆模型设计和迁移学习将确保DL模型可以快速适应短暂的信道状态，而不会影响可靠性和复杂性。最后，通过收发器架构的原型硬件实现，并通过广泛的空中实验进行了验证，使研究成为现实。总的来说，发射机和接收机协同工作，以适应任何和所有的信道条件，平衡模型的复杂性和错误性能。该奖项反映了NSF的法定使命，并已被认为是值得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的支持。