Integrated Approach to Airborne Laser Communication

Lasers offer tremendous advantages over RF communication systems in bandwidth and security, due to their ultra-high frequency and narrow spatial beamwidth. Atmospheric turbulence causes severe received power variations and high bit error rates (BERs) in airborne laser communication. Airborne optical communication systems require special considerations in size, complexity, power, and weight. Conventional adaptive optics systems correct for the phase only and cannot correct for strong scintillation, but here the two transmission paths are separated sufficiently so that the strong scintillation is \averaged out by incoherently summing up the two beams in the receiver. This requisite separation distance is derived for multiple geometries, turbulence conditions, and turbulence effects. Integrating multiple techniques into a system alleviates the deleterious effects of turbulence without bulky adaptive optics systems. Wave optics simulations show multiple transmitters, receiver and transmitter trackers, and adaptive thresholding significantly reduce the BER (by over 10,000 times)

An Overview on Application of Machine Learning Techniques in Optical Networks

Today's telecommunication networks have become sources of enormous amounts of widely heterogeneous data. This information can be retrieved from network traffic traces, network alarms, signal quality indicators, users' behavioral data, etc. Advanced mathematical tools are required to extract meaningful information from these data and take decisions pertaining to the proper functioning of the networks from the network-generated data. Among these mathematical tools, Machine Learning (ML) is regarded as one of the most promising methodological approaches to perform network-data analysis and enable automated network self-configuration and fault management. The adoption of ML techniques in the field of optical communication networks is motivated by the unprecedented growth of network complexity faced by optical networks in the last few years. Such complexity increase is due to the introduction of a huge number of adjustable and interdependent system parameters (e.g., routing configurations, modulation format, symbol rate, coding schemes, etc.) that are enabled by the usage of coherent transmission/reception technologies, advanced digital signal processing and compensation of nonlinear effects in optical fiber propagation. In this paper we provide an overview of the application of ML to optical communications and networking. We classify and survey relevant literature dealing with the topic, and we also provide an introductory tutorial on ML for researchers and practitioners interested in this field. Although a good number of research papers have recently appeared, the application of ML to optical networks is still in its infancy: to stimulate further work in this area, we conclude the paper proposing new possible research directions

Visible Light Communication (VLC)

Visible light communication (VLC) using light-emitting diodes (LEDs) or laser diodes (LDs) has been envisioned as one of the key enabling technologies for 6G and Internet of Things (IoT) systems, owing to its appealing advantages, including abundant and unregulated spectrum resources, no electromagnetic interference (EMI) radiation and high security. However, despite its many advantages, VLC faces several technical challenges, such as the limited bandwidth and severe nonlinearity of opto-electronic devices, link blockage and user mobility. Therefore, significant efforts are needed from the global VLC community to develop VLC technology further. This Special Issue, “Visible Light Communication (VLC)”, provides an opportunity for global researchers to share their new ideas and cutting-edge techniques to address the above-mentioned challenges. The 16 papers published in this Special Issue represent the fascinating progress of VLC in various contexts, including general indoor and underwater scenarios, and the emerging application of machine learning/artificial intelligence (ML/AI) techniques in VLC

Translational pipelines for closed-loop neuromodulation

Closed-loop neuromodulation systems have shown significant potential for addressing unmet needs in the treatment of disorders of the central nervous system, yet progress towards clinical adoption has been slow. Advanced technological developments often stall in the preclinical stage by failing to account for the constraints of implantable medical devices, and due to the lack of research platforms with a translational focus. This thesis presents the development of three clinically relevant research systems focusing on refinements of deep brain stimulation therapies. First, we introduce a system for synchronising implanted and external stimulation devices, allowing for research into multi-site stimulation paradigms, cross-region neural plasticity, and questions of phase coupling. The proposed design aims to sidestep the limited communication capabilities of existing commercial implant systems in providing a stimulation state readout without reliance on telemetry, creating a cross-platform research tool. Next, we present work on the Picostim-DyNeuMo adaptive neuromodulation platform, focusing on expanding device capabilities from activity and circadian adaptation to bioelectric marker--based responsive stimulation. Here, we introduce a computationally optimised implementation of a popular band power--estimation algorithm suitable for deployment in the DyNeuMo system. The new algorithmic capability was externally validated to establish neural state classification performance in two widely-researched use cases: Parkinsonian beta bursts and seizures. For in vivo validation, a pilot experiment is presented demonstrating responsive neurostimulation to cortical alpha-band activity in a non-human primate model for the modulation of attention state. Finally, we turn our focus to the validation of a recently developed method to provide computationally efficient real-time phase estimation. Following theoretical analysis, the method is integrated into the commonly used Intan electrophysiological recording platform, creating a novel closed-loop optogenetics research platform. The performance of the research system is characterised through a pilot experiment, targeting the modulation of cortical theta-band activity in a transgenic mouse model

Neural networks for optical channel equalization in high speed communication systems

La demande future de bande passante pour les données dépassera les capacités des systèmes de communication optique actuels, qui approchent de leurs limites en raison des limitations de la bande passante électrique des composants de l’émetteur. L’interférence intersymbole (ISI) due à cette limitation de bande est le principal facteur de dégradation pour atteindre des débits de données élevés. Dans ce mémoire, nous étudions plusieurs techniques de réseaux neuronaux (NN) pour combattre les limites physiques des composants de l’émetteur pilotés à des débits de données élevés et exploitant les formats de modulation avancés avec une détection cohérente. Notre objectif principal avec les NN comme égaliseurs de canaux ISI est de surmonter les limites des récepteurs optimaux conventionnels, en fournissant une complexité évolutive moindre et une solution quasi optimale. Nous proposons une nouvelle architecture bidirectionnelle profonde de mémoire à long terme (BiLSTM), qui est efficace pour atténuer les graves problèmes d’ISI causés par les composants à bande limitée. Pour la première fois, nous démontrons par simulation que notre BiLSTM profonde proposée atteint le même taux d’erreur sur les bits(TEB) qu’un estimateur de séquence à maximum de vraisemblance (MLSE) optimal pour la modulation MDPQ. Les NN étant des modèles pilotés par les données, leurs performances dépendent fortement de la qualité des données d’entrée. Nous démontrons comment les performances du BiLSTM profond réalisable se dégradent avec l’augmentation de l’ordre de modulation. Nous examinons également l’impact de la sévérité de l’ISI et de la longueur de la mémoire du canal sur les performances de la BiLSTM profonde. Nous étudions les performances de divers canaux synthétiques à bande limitée ainsi qu’un canal optique mesuré à 100 Gbaud en utilisant un modulateur photonique au silicium (SiP) de 35 GHz. La gravité ISI de ces canaux est quantifiée grâce à une nouvelle vue graphique des performances basée sur les écarts de performance de base entre les solutions optimales linéaires et non linéaires classiques. Aux ordres QAM supérieurs à la QPSK, nous quantifions l’écart de performance BiLSTM profond par rapport à la MLSE optimale à mesure que la sévérité ISI augmente. Alors qu’elle s’approche des performances optimales de la MLSE à 8QAM et 16QAM avec une pénalité, elle est capable de dépasser largement la solution optimale linéaire à 32QAM. Plus important encore, l’avantage de l’utilisation de modèles d’auto-apprentissage comme les NN est leur capacité à apprendre le canal pendant la formation, alors que la MLSE optimale nécessite des informations précises sur l’état du canal.The future demand for the data bandwidth will surpass the capabilities of current optical communication systems, which are approaching their limits due to the electrical bandwidth limitations of the transmitter components. Inter-symbol interference (ISI) due to this band limitation is the major degradation factor to achieve high data rates. In this thesis, we investigate several neural network (NN) techniques to combat the physical limits of the transmitter components driven at high data rates and exploiting the advanced modulation formats with coherent detection. Our main focus with NNs as ISI channel equalizers is to overcome the limitations of conventional optimal receivers, by providing lower scalable complexity and near optimal solution. We propose a novel deep bidirectional long short-term memory (BiLSTM) architecture, that is effective in mitigating severe ISI caused by bandlimited components. For the first time, we demonstrate via simulation that our proposed deep BiLSTM achieves the same bit error rate (BER) performance as an optimal maximum likelihood sequence estimator (MLSE) for QPSK modulation. The NNs being data-driven models, their performance acutely depends on input data quality. We demonstrate how the achievable deep BiLSTM performance degrades with the increase in modulation order. We also examine the impact of ISI severity and channel memory length on deep BiLSTM performance. We investigate the performances of various synthetic band-limited channels along with a measured optical channel at 100 Gbaud using a 35 GHz silicon photonic(SiP) modulator. The ISI severity of these channels is quantified with a new graphical view of performance based on the baseline performance gaps between conventional linear and nonlinear optimal solutions. At QAM orders above QPSK, we quantify deep BiLSTM performance deviation from the optimal MLSE as ISI severity increases. While deep BiLSTM approaches the optimal MLSE performance at 8QAM and 16QAM with a penalty, it is able to greatly surpass the linear optimal solution at 32QAM. More importantly, the advantage of using self learning models like NNs is their ability to learn the channel during the training, while the optimal MLSE requires accurate channel state information

Integrated Approach to Free Space Optical Communications in Strong Turbulence

The propagation of a free space optical communication signal through atmospheric turbulence experiences random fluctuations in intensity, including signal fades which negatively impact the communications link performance. This research develops an analytical probability density function (PDF) to model the best case scenario of using multiple independent beams to reduce the intensity fluctuations. The PDF was further developed to account for partially correlated beams, such as would be experienced by beams having finite separation. The PDF was validated with results obtained from digital simulations as well as lab experiments. The research showed that as the number of transmitted beams increases the probability of fade decreases. While fade probability is reduced by adding more beams, using more than four transmitters does little to improve the overall performance. Additionally, the use of pulse position modulation (PPM) provided significant improvement over traditional fixed threshold on/off keying with the impact of signal fading reduced. Combining PPM with multiple transmitters produced the best overall bit error rate results