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

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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.