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

空间光通信（SOC）可以提供比其射频通信（RF）通信更高数量级的数据速率，有望成为空间通信网络的关键技术。然而，发展有机碳还需要克服的限制包括：1)大气通道是动态的，不太清楚，无法进行统计表征；2)传统的通信系统设计忽略了充分利用实时数据中的相关信息；3)实现SOC （RF）性能增益所需的系统复杂性将迅速增加，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的设计框架，该框架结合了基于模型和数据驱动的SOC设计范式，其中系统1)建模并预测大气通道的长期动态，2)主动调整其通信和网络策略以适应环境的动态，从而最大化端到端系统在数据速率、能源效率、频谱效率和链路可靠性方面的性能。所提出的方法将展示SOC如何成为一个可靠的平台，补充现有技术，以满足未来系统易于部署、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；确保飞机之间的互联互通，从而提高航空旅行的安全性、可靠性和效率；提高太空探索任务的可靠性，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，该目标是促进本科研究，鼓励高中生注册STEM，并通过与该机构现有的多元化招聘和支持项目合作，招募代表性不足的学生。当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。这个项目的贡献是重要的，因为它们通过理论和实践框架的结合，展示了SOC如何成为一个补充和增强现有技术的可靠平台。该研究的第一个目标是推导SOC的性能极限，该极限描述了一个设计良好的系统在各种相关设置（如多址和中继信道）中可以实现的最佳错误概率和信道容量，并考虑到大气损伤。为了减轻大气的影响，将设计一个精确的统计信道模型。这项工作将包括深空、近地和空间系统网络。虽然通过泊松信道模型可以很好地描述深空通信，但可以通过统计模型捕获大气衰减和指向误差的影响。根据通信场景的不同，也可以加入依赖输入或不依赖输入的高斯噪声。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，并推断概率分布，以便设计能够有效执行而不严重依赖信道模型的通信系统。将考虑基于块结构深度神经网络（DNN）以及端到端基于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使用无梯度优化技术和基于块结构dnn的信道估计、信号分类和检测来设计SOC自编码器。该项目由通信、电路和传感系统（CCSS）计划和促进竞争研究的既定计划（EPSCoR）共同资助。该奖项反映了美国国家科学基金会的法定使命，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。

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.