CAREER: Advancing Space Optical Communication Systems Via Hybrid Model-Based and Learning-Based Frameworks

职业：通过基于模型和基于学习的混合框架推进空间光通信系统

基本信息

批准号：
2114779
负责人：
Zouheir Rezki
金额：
$ 50万
依托单位：
University of California-Santa Cruz
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2020
资助国家：
美国
起止时间：
2020-10-01 至 2025-05-31
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2114779&HistoricalAwards=false
关键词：
CAREER Advancing Space Optical Communication

项目摘要

Space optical communication (SOC) can provide orders-of-magnitude higher data rates than its Radio-Frequency (RF) communication counterpart and promises to be a key technology for space communication networks. However, limitations to developing SOC yet to be overcome include: 1) the atmospheric channel is dynamic and not well-understood, preventing its statistical characterization; 2) the traditional communication system design ignores full use of relevant information from real-time data; 3) the complexity of the systems required to achieve the performance gain of SOC (over RF) would increase rapidly, hence SOC engineering solutions must incorporate the complexity as a constraint. To address these challenges, this research develops a systematic design framework which combines model-based and data-driven design paradigms for SOC and where the system 1) models and predicts the long-term dynamics of the atmospheric channel, and 2) proactively adapts its communication and networking strategy to the dynamics of the environment, thereby maximizing end-to-end system performances in terms of data rates, energy efficiency, spectrum efficiency, and link reliability. The proposed approach will demonstrate how SOC can be a reliable platform that complements existing technologies to fulfill the requirements of easy deployment, high data rates, and affordable complexity of future systems. Potential benefits of the project include deploying broadband internet via optical drones in poor countries, thus enabling access to information and education; ensuring connectivity between aircraft, thus improving the safety, reliability, and efficiency of air travel; and enhancing the reliability of space exploratory missions, thus increasing our potential for discovery. The research effort will be integrated with the principal investigator's educational career goal of promoting undergraduate research, encouraging enrollment of high-school students in STEM and recruiting underrepresented students by working with the institution's existing diversity recruitment and support programs.Current communication systems are either difficult to deploy at large scale or limited by the RF spectrum licensing burdens. This project's contributions are significant because they show, via a mix of theoretical and practical frameworks, how SOC can be a reliable platform that complements and enhances existing technologies. The first objective of the research is to derive the performance limits of SOC, which describe the best error probability and channel capacity that a well-designed system can achieve in various relevant settings such as multiple access and relay channels, and accounting for atmospheric impairments. To mitigate the atmospheric effects, a sharp statistical channel model will be devised. The work will encompass deep-space, near-earth and space system networks. While deep space communication is well described via the Poisson channel model, the effects of the atmospheric attenuation and pointing error could be captured via statistical models. Depending on the communication scenarios, an input-dependent or an input-independent Gaussian noise could also be incorporated. The methodology to undertake this objective is based on applying tools from information and communication theories along with a non-parametric statistical channel learning approach. The second objective is to develop powerful machine learning techniques to perform signal classification, estimate parameters of the atmosphere, determine the mapping between input and output data and infer probability distributions in order to design communication systems that can efficiently perform without relying heavily on channel models. Block structure Deep Neural Network (DNN)-based as well as end-to-end DNN-based designs, for point-to-point and multiuser settings, will be considered. The methodology to undertake this objective relies mainly on designing SOC auto-encoders with gradient-free optimization techniques and block structure DNN-based channel estimation, signal classification, and detection.This project is jointly funded by the Communications, Circuits and Sensing Systems (CCSS) Program and the Established Program to Stimulate Competitive Research (EPSCoR).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.

空间光学通信（SOC）可以比其广播频率（RF）通信对应物更高的数据速率，并承诺成为太空通信网络的关键技术。但是，尚未克服的发展SOC的局限性包括：1）大气渠道是动态的，而不是被良好理解的，从而阻止了其统计特征； 2）传统通信系统设计忽略了实时数据中相关信息的充分使用； 3）实现SOC性能增益（超过RF）所需的系统的复杂性将迅速增加，因此SOC工程解决方案必须将复杂性纳入约束。 To address these challenges, this research develops a systematic design framework which combines model-based and data-driven design paradigms for SOC and where the system 1) models and predicts the long-term dynamics of the atmospheric channel, and 2) proactively adapts its communication and networking strategy to the dynamics of the environment, thereby maximizing end-to-end system performances in terms of data rates, energy efficiency, spectrum efficiency, and link reliability.拟议的方法将证明SOC如何成为一个可靠的平台，该平台可以补充现有技术，以满足易于部署，高数据速率和未来系统的负担得起的复杂性的要求。该项目的潜在好处包括通过贫穷国家通过光学无人机部署宽带互联网，从而可以获取信息和教育；确保飞机之间的连通性，从而提高航空旅行的安全性，可靠性和效率；并提高空间探索任务的可靠性，从而增加了我们的发现潜力。研究工作将与首席研究者的教育职业融合，即促进本科研究，鼓励通过与机构的现有多样性招聘和支持计划合作，鼓励入学高中生，并招募了代表性不足的学生。该项目的贡献很重要，因为它们通过理论和实用框架的混合来表明SOC如何成为一个可靠的平台，可以补充和增强现有技术。该研究的第一个目的是得出SOC的性能限制，该限制描述了精心设计的系统在各种相关设置（例如多次访问和中继渠道）中可以实现的最佳误差概率和渠道容量，并考虑了大气障碍。为了减轻大气效应，将设计一个尖锐的统计通道模型。这项工作将包括深空，近地和太空系统网络。虽然通过泊松通道模型很好地描述了深空通信，但可以通过统计模型捕获大气衰减和指向误差的效果。根据通信方案，也可以合并与输入有关的或独立的高斯噪声。实现此目标的方法基于应用信息和通信理论中的工具以及非参数统计渠道学习方法。第二个目标是开发强大的机器学习技术来执行信号分类，估计大气的参数，确定输入和输出数据之间的映射以及推断概率分布，以设计可以有效执行的通信系统，而无需大量依赖通道模型。将考虑基于点对点和多用户设置的基于块结构深神经网络（DNN）以及基于端到端DNN的设计。实现这一目标的方法主要依赖于设计SOC自动编码器，并使用无梯度的优化技术和基于DNN结构的基于DNN结构的渠道估计，信号分类和检测。该项目由通信，电路和传感系统（CCSS）计划（CCSS）计划（CCSS）计划和既定的DEARTORE DELSER（EPSCOR）的启发（EPSCOR）的既定为dects forty the ns decors。通过使用基金会的智力优点和更广泛影响的评论标准进行评估。