Multi-user free-space optical communication (FSOC) is beginning to draw a significant attention for its ability to support increased system network capacity while using single receiving photodiode and satisfying size, weight, and power (SWaP) constraints imposed by space- and aerial-based mobile communication. Despite these advantages, support of multi-user capabilities cause increased system complexity due to accommodating heterogenous users communications with varying transmission and data rate requirements. Machine learning (ML) has recently been considered as a promising approach for introducing cognition into the network to mitigate some of the complexity. A cognitive method based on unsupervised ML was derived for estimating the number of users communicating and sharing time and bandwidth resources with a single-node receiver. A weighted clustering approach was introduced and experimentally validated when users received with similar amplitude information that results in underestimation. Obtained results confirmed that the proposed methodology was able to accurately differentiate the number of simultaneously transmitting users with accuracy greater than 92%— even in the presence of moderate atmospheric turbulence. An experimental analysis was conducted to determine data size and receiver sampling rate requirements for accurate estimation. Furthermore, an empirical model was derived to evaluate the effect of preamble signal length given a particular sampling rate on the accuracy of the estimation. The model was validated for up-to four users.

Mobile free space optical (FSO) nodes have been characterized as a way to resolve movement and misalignment incompatibilities that are inherent in optical nodes. By introducing multibranch angular diversity mobility to FSO nodes, connection reliability is enhanced. In this paper, receiving branches are assumed to be circularly placed on a single plane, and transmitting branches are positioned on a different platform. A coverage of 360 in this unbalanced branch configuration is achieved with the deployment of diversity combining, wherein the transmitter follows a transmit selection diversity rule. Channel turbulence is modeled as weak lognormal with spatially correlated fading samples. On the receiver side, selection combining (SC), equal gain combining (EGC), maximum ratio combining (MRC), and threshold generalized SC (T-GSC) schemes serve as candidates for combining purposes. Statistical discussion relative to branch signals and combiner output is presented, and bit error performance and outage probability are numerically evaluated. When compared with MRC, T-GSC was found to have lower estimation error. The T-GSC scheme additionally demonstrated overall superior performance over SC and EGC for mobile FSO.

A significant challenge for free-space optical (FSO) links is the restrictive alignment requirements, especially when the transceivers are moving. For moderate distances and rapid unpredictable motion, the receiver's field of view and the positioning system's dynamics become factors. We explore the use of adaptive transmitter power and beam divergence to improve the likelihood of maintaining a mobile FSO link by using Gaussian beam propagation theory and link budgets. We calculate the allowable misalignment between the transceivers' optical axes as a function of power, divergence, and transceiver distance. The maximum allowable error is independent of the distance, except when the field of view is a limiting factor. Certain combinations of divergence and power, while suboptimal for one distance, provide a relaxed misalignment limit for many distances. Based on the calculations, we make initial suggestions for system design.

Radiofrequency (RF) communications offer reliable but low data rates and energy-inefficient satellite links, while free-space optical (FSO) promises high bandwidth but struggles with disturbances imposed by atmospheric effects. A hybrid RF/FSO architecture aims to achieve optimal reliability along with high data rates for space communications. Accurate prediction of dynamic ground-to-satellite FSO link availability is critical for routing decisions in low-earth orbit constellations. In this paper, we propose a system leveraging ubiquitous RF links to proactively forecast FSO link degradation prior to signal drops below threshold levels. This enables pre-calculation of rerouting to maximally maintain high data rate FSO links throughout the duration of weather effects. We implement a supervised learning model to anticipate FSO attenuation based on the analysis of RF patterns. Through the simulation of a dense lower earth orbit (LEO) satellite constellation, we demonstrate the efficacy of our approach in a simulated satellite network, highlighting the balance between predictive accuracy and prediction duration. An emulated cloud attenuation model is proposed to provide insight into the temporal profiles of RF signals and their correlation to FSO channel dynamics. Our investigation sheds light on the trade-offs between prediction horizon and accuracy arising from RF beacon numbers and proximity.

A fiber-bundle approach for beam steering at the transmitter was recently proposed and investigated that allowed tracking of the receiver without the use of mechanical devices for mobile free-spaced optical (FSO) platforms experiencing misalignment due to movement and atmospheric turbulence. This paper investigates different transmitter configurations along with a general pointing, acquisition and tracking (PAT) algorithm on key parameters including power availability at the receiver, switching time, link recapturing time, and coverage area at the receiver plane. The alignment control algorithm with different ways to track receiver is also evaluated on these key parameters. The algorithm performance versus transmitted power, switching time and the threshold power for beam steering was examined. The investigation finds that a combination of switching combined with splitting of the light between fibers is a suitable solution to provide the best trade-off between link up time and the coverage area at the receiver, and thus is a good general purpose design choice, and the optical tracking system with target location information demands fewer efforts to recover and maintain the dynamic link than the one without the awareness of the receiver location information.

Multi-point free-space optical communication (FSOC) has recently received extensive interest as a valuable solution for providing increased capacity in the upcoming communication networks. Diverse optical multiple access control (O-MAC) techniques have proved to successfully support multi-user FSOC. However, to adaptively and automatically select the most fitting O-MAC technique or combination thereof for signal demodulation, the number of transmitting users should be known a priori at the receiver side. In this letter, a technique based on histogram peak detection and unsupervised machine learning (e.g., k-mean, k-medoid, hierarchical, and fuzzy clustering), is proposed and experimentally demonstrated for number of users detection. The proposed methodology relies on amplitude information of received signals. Results show that when multiple users— signals are received with equal amplitude, under-estimation of user number was observed; hence, a modified approach—a weighted clustering—was employed and experimentally validated. The work reported herein demonstrated the ability of the proposed methodology to simultaneously and accurately detect multiple transmitting users in real time and under severe atmospheric turbulence.