REU Site: Undergraduate Research Experiences in Long Range Communications with Ham Radios, Cool Algorithms, and Innovative Antennas

REU 网站：利用业余无线电、酷算法和创新天线进行远程通信的本科生研究经验

• 批准号: 1852199
• 负责人: Michael Marefat
• 金额: $ 30.7万
• 依托单位: University of Arizona
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-03-01 至 2023-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1852199&HistoricalAwards=false
• 关键词: REU; Site; Undergraduate; Research; Experiences

This Research Experiences for Undergraduates (REU) Site will provide support for eight undergraduate students each summer (24 students over 3 years) to gain research experience under the mentorship of a team of experienced faculty and advanced graduate student researchers in topics related to high frequency (HF) long range wireless communications. Long range HF communications allow wireless communication over thousands of kilometers without the need for satellites, and are of immeasurable importance to military and government, aviation, air-to-ground communications, maritime services, public safety and civil service distress communications, space and satellite operations, weather stations, FM and TV broad casting, and many other communication needs. This REU site will provide participating students a high quality, immersive, and hands-on learning experience in this exciting area. Participants will receive coaching to prepare for graduate school and professional development, and they will be mentored into becoming independent creative thinkers and researchers. Participants will gain experience in scientific dissemination and communication, including publishing in professional conferences and journals, and giving scientific presentations.Long range HF communications work by the reflection of radio waves by the ionosphere layer of the atmosphere. These communications can travel around curvature of the earth, and work without the need for significant infrastructure like satellites, cellular networks, internet, etc. The challenge with HF communications is that propagation via the ionosphere may be unreliable, the ionospheric conditions may change very rapidly, and different frequencies have different propagation characteristics via the ionosphere. Successful communication depends on the time of day, location of users, selection of frequency, modulation, antenna type, and power level. This project will generate new solutions to key problems that will enable more dependable and efficient long range HF communications. Specifically this project will investigate and develop: (i) New designs for spectrum agile radios for high frequency communications that can switch communication parameters such as modulation, coding, pulse shape, equalizer, power, etc.; (ii) Machine learning algorithms that can learn the best communication parameters for changing channel conditions; (iii) Low profile antennas that can be used in HF communications; (iv) Software and algorithms to predict network level connections that are possible ahead of time; (v) Qualitative and quantitative Ionospheric communications models; (vi) Flexible and automatic antenna tuning circuits; and (vii) Smart algorithms that can learn from their past experiences in varying Ionospheric conditions, weather situations, time and location.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.

这个研究经验的本科生（REU）网站将提供支持8个本科生每年夏天（24名学生超过3年），以获得研究经验的指导下，一个团队的经验丰富的教师和高级研究生研究人员在主题相关的高频（HF）远程无线通信。长距离HF通信允许在不需要卫星的情况下进行数千公里的无线通信，并且对于军事和政府、航空、空对地通信、海事服务、公共安全和民事服务遇险通信、空间和卫星操作、气象站、FM和TV广播以及许多其他通信需求具有不可估量的重要性。这个REU网站将为参与的学生提供高质量，身临其境，在这个令人兴奋的领域动手学习的经验。参与者将接受辅导，为研究生院和专业发展做准备，他们将被指导成为独立的创造性思想家和研究人员。参加者将获得科学传播和交流的经验，包括在专业会议和期刊上发表文章，以及进行科学演讲。远距离高频通信是通过大气层电离层反射无线电波进行的。这些通信可以在地球曲率周围传播，并且不需要诸如卫星、蜂窝网络、互联网等重要基础设施就可以工作。HF通信的挑战是经由电离层的传播可能是不可靠的，电离层条件可能变化非常快，并且不同的频率具有经由电离层的不同传播特性。成功的通信取决于一天中的时间、用户的位置、频率的选择、调制、天线类型和功率水平。 该项目将为关键问题提供新的解决方案，从而实现更可靠、更有效的远距离高频通信。具体而言，本项目将研究和开发：（i）用于高频通信的频谱捷变无线电的新设计，可以切换通信参数，如调制，编码，脉冲形状，均衡器，功率等; (ii)机器学习算法，可以学习不断变化的信道条件的最佳通信参数; ㈢可用于高频通信的低剖面天线; ㈣提前预测可能的网络一级连接的软件和算法; ㈤定性和定量电离层通信模型; ㈥灵活和自动天线调谐电路;以及（vii）智能算法，可以在不同的电离层条件、天气情况、时间和位置上从过去的经验中学习。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。

项目成果

Wiggle: Physical Challenge-Response Verification of Vehicle Platooning

• DOI: 10.1109/icnc57223.2023.10074426
• 发表时间: 2022-08
• 期刊: 2023 International Conference on Computing, Networking and Communications (ICNC)
• 作者: Connor Dickey;Quentin A. Johnson;Jingcheng Li;Ziqi Xu;Loukas Lazos;Ming Li

Machine Learning Based MIMO Equalizer for High Frequency (HF) Communications

适用于高频 (HF) 通信的基于机器学习的 MIMO 均衡器

• 发表时间: 2020
• 期刊: Proceedings of International Joint Conference on Neural Networks
• 作者: Spillane, Samuel and