RESUMEN
An approach to adaptive optics utilizing a single-pixel camera (SPC) is proposed to maximize fiber coupling efficiency at the receiver side of an optical satellite-to-ground link perturbed by atmospheric turbulence. Using a single-pixel wavefront sensor enables operation at longer optical wavelengths, such as near and far infrared, which have advantageous propagation characteristics for free space optical communication. In this approach, a focal plane intensity image of the atmospheric-disturbed wavefront is taken via an SPC using a compressed sensing technique. An iterative speckle-based phase retrieval algorithm is then applied to infer the phase distortion corrected by a deformable mirror in a feedback loop. This computational approach to inferring the phase of the wavefront overcomes the limitations of traditional Shack-Hartman-based approaches, which are difficult to implement at high speed and at the long infrared wavelengths proposed for future optical satellite communication downlinks. It has been shown that fiber coupling efficiency is increased from less than 5% to 40%-50% in medium-to-strong turbulence scenarios with the phase retrieval algorithm proposed in this work.
RESUMEN
This publisher's note amends the author listing and affiliation section in Appl. Opt.59, 8895 (2020)APOPAI0003-693510.1364/AO.396804.
RESUMEN
A novel machine learning (ML) clustering algorithm, named light-fidelity (LiFi) Grid, is proposed to design amorphous cells of LiFi access points (APs) in order to maximize the minimum signal-to-interference-plus-noise ratio (SINR) from the viewpoint of user-centric (UC) network design. The algorithm consists of two phases. Explicitly, the first phase consists of finding clusters of user densities based on the mean-shift (MS) clustering algorithm. In contrast to some other clustering algorithms, such as K-means, MS does not need to know the number of clusters in advance. Furthermore, the combined transmission scheme is assumed in each cell. In the second phase, this paper proposes a novel clustering algorithm that addresses the problem of grouping APs based on the positions of users-UC design-in optical wireless networks (OWNs). Hence, it addresses the dynamic resource allocation problem in OWNs if APs are considered as network resources. Based on the maximization of minimum SINR metric, LiFi Grid demonstrates the superior performance relative to conventional fixed-shape cell-centric network designs. Additionally, full compatibility of the LiFi Grid clustering algorithm with the Institute of Electrical and Electronics Engineers standard 802.15.7 is also shown.