RESUMO
A unique automatic receiver signal distribution strategy is proposed for private optical networks based on the concept of non-orthogonality. A non-orthogonal signal waveform can compress the spectral bandwidth, which not only fits a signal in a bandwidth limited scenario, but also enables the compression ratio information for labelling. Depending on a unique value of spectral compression, an end user destination can be correlated. A network edge node will rely on deep learning to intelligently identify each raw signal and forward it to corresponding end users with no sophisticated digital signal pre-processing. In this case, signal identification and distribution are faster while computationally intensive signal compensation and detection will be shifted to each end user since the receiver is highly dynamic and user-defined in private optical networks. An intelligent signal classifier will be trained considering various fiber transmission factors such as transmission distance, training dataset size and launch power. At the end, a universal classifier is obtained, which can be used to identify signals in a system for any fiber transmission distance and launch power.
RESUMO
Luminescent solar concentrators (LSCs) have recently emerged as a promising receiver technology in free-space optical communications due to their inherent ability to collect light from a wide field-of-view and concentrate it into small areas, thus leading to high optical gains. Several high-speed communication systems integrating LSCs in their detector blocks have already been demonstrated, with the majority of efforts so far being devoted to maximising the received optical power and the system's field-of-view. However, LSCs may pose a severe bottleneck on the bandwidth of such communication channels due to the comparably slow timescale of the fluorescence events involved, a situation further aggravated by the inherent reabsorption in these systems, and yet, an in-depth study into such dynamic effects remains absent in the field. To fill this gap, we have developed a comprehensive analytical solution that delineates the fundamental bandwidth limits of LSCs as optical detectors in arbitrary free-space optical links, and establishes their equivalence with simple RC low-pass electrical circuits. Furthermore, we demonstrate a time-domain Monte Carlo simulation platform, an indispensable tool in the multiparameter optimisation of LSC-based receiver systems. Our work offers vital insight into LSC system dynamic behaviour and paves the way to evaluate the technology for a wide range of applications, including visible light communications, high-speed video recording, and real-time biological imaging, to name a few.
RESUMO
Visible light communication (VLC) is a wireless technology that relies on optical intensity modulation and is potentially a game changer for internet-of-things (IoT) connectivity. However, VLC is hindered by the low penetration depth of visible light in non-transparent media. One solution is to extend operation into the "nearly (in)visible" near-infrared (NIR, 700-1000 nm) region, thus also enabling VLC in photonic bio-applications, considering the biological tissue NIR semitransparency, while conveniently retaining vestigial red emission to help check the link operativity by simple eye inspection. Here, we report new far-red/NIR organic light-emitting diodes (OLEDs) with a 650-800 nm emission range and external quantum efficiencies among the highest reported in this spectral range (>2.7%, with maximum radiance and luminance of 3.5 mW/cm2 and 260 cd/m2, respectively). With these OLEDs, we then demonstrate a "real-time" VLC setup achieving a data rate of 2.2 Mb/s, which satisfies the requirements for IoT and biosensing applications. These are the highest rates ever reported for an online unequalised VLC link based on solution-processed OLEDs.