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

空间光通信(SOC)可以提供比射频(RF)通信高一个数量级的数据速率，有望成为空间通信网络的关键技术。然而，发展SOC的局限性包括：1)大气信道是动态的，不被很好地理解，阻碍了其统计特性；2)传统的通信系统设计忽视了对实时数据的相关信息的利用；3)实现SOC(射频)性能增益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作为约束。为了应对这些挑战，本研究开发了一个系统设计框架，该框架结合了基于模型和数据驱动的SOC设计范例，其中系统1)对大气信道的长期动态进行建模和预测，2)主动调整其通信和联网策略以适应环境的动态，从而在数据速率、能量效率、频谱效率和链路可靠性方面最大化端到端系统的性能。建议的方法将展示SOC如何成为一个可靠的平台，对现有技术进行补充，以满足未来系统的易部署、高数据速率和负担得起的复杂性的要求。该项目的潜在好处包括通过光学无人机在贫穷国家部署宽带互联网，从而使人们能够获得信息和教育；确保飞机之间的连接，从而提高航空旅行的安全性、可靠性和效率；以及增强空间探索任务的可靠性，从而增加我们发现的潜力。这项研究工作将与首席研究员的教育职业目标相结合，即促进本科生研究，鼓励高中生进入STEM招生，并通过与该机构现有的多元化招聘和支持计划合作，招收代表不足的学生。目前的通信系统要么难以大规模部署，要么受到射频频谱许可负担的限制。该项目的贡献是重大的，因为它们通过理论和实践框架的组合展示了SOC如何成为补充和增强现有技术的可靠平台。研究的第一个目标是推导出SOC的性能极限，它描述了一个设计良好的系统在各种相关环境下可以达到的最佳差错概率和信道容量，例如多址接入和中继信道，并考虑了大气损伤。为了减轻大气影响，将设计一个锐化的统计信道模型。这项工作将包括深空、近地和空间系统网络。深空通信可以用泊松信道模型来描述，而大气衰减和指向误差的影响可以通过统计模型来捕捉。根据通信场景的不同，还可以加入依赖于输入的或独立于输入的高斯噪声。实现这一目标的方法是基于应用信息和通信理论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，以便设计能够在不严重依赖信道模型的情况下高效执行的通信系统。将考虑基于块结构深度神经网络(DNN)以及基于端到端DNN的设计，用于点对点和多用户设置。实现这一目标的方法主要依赖于设计具有无梯度优化技术的SOC自动编码器和基于DNN的块结构信道估计、信号分类和检测。该项目由通信、电路和传感系统(CCSS)计划和既定的激励竞争研究计划(EPSCoR)联合资助。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。

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.