19,462 research outputs found

    Mobile Communication Networks and Digital Television Broadcasting Systems in the Same Frequency Bands – Advanced Co-Existence Scenarios

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    The increasing demand for wireless multimedia services provided by modern communication systems with stable services is a key feature of advanced markets. On the other hand, these systems can many times operate in a neighboring or in the same frequency bands. Therefore, numerous unwanted co-existence scenarios can occur. The aim of this paper is to summarize our results which were achieved during exploration and measurement of the co-existences between still used and upcoming mobile networks (from GSM to LTE) and digital terrestrial television broadcasting (DVB) systems. For all of these measurements and their evaluation universal measurement testbed has been proposed and used. Results presented in this paper are a significant part of our activities in work package WP5 in the ENIAC JU project “Agile RF Transceivers and Front-Ends for Future Smart Multi-Standard Communications Applications (ARTEMOS)”

    Comb-based WDM transmission at 10 Tbit/s using a DC-driven quantum-dash mode-locked laser diode

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    Chip-scale frequency comb generators have the potential to become key building blocks of compact wavelength-division multiplexing (WDM) transceivers in future metropolitan or campus-area networks. Among the various comb generator concepts, quantum-dash (QD) mode-locked laser diodes (MLLD) stand out as a particularly promising option, combining small footprint with simple operation by a DC current and offering flat broadband comb spectra. However, the data transmission performance achieved with QD-MLLD was so far limited by strong phase noise of the individual comb tones, restricting experiments to rather simple modulation formats such as quadrature phase shift keying (QPSK) or requiring hard-ware-based compensation schemes. Here we demonstrate that these limitations can be over-come by digital symbol-wise phase tracking algorithms, avoiding any hardware-based phase-noise compensation. We demonstrate 16QAM dual-polarization WDM transmission on 38 channels at an aggregate net data rate of 10.68 Tbit/s over 75 km of standard single-mode fiber. To the best of our knowledge, this corresponds to the highest data rate achieved through a DC-driven chip-scale comb generator without any hardware-based phase-noise reduction schemes

    Principles of Neuromorphic Photonics

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    In an age overrun with information, the ability to process reams of data has become crucial. The demand for data will continue to grow as smart gadgets multiply and become increasingly integrated into our daily lives. Next-generation industries in artificial intelligence services and high-performance computing are so far supported by microelectronic platforms. These data-intensive enterprises rely on continual improvements in hardware. Their prospects are running up against a stark reality: conventional one-size-fits-all solutions offered by digital electronics can no longer satisfy this need, as Moore's law (exponential hardware scaling), interconnection density, and the von Neumann architecture reach their limits. With its superior speed and reconfigurability, analog photonics can provide some relief to these problems; however, complex applications of analog photonics have remained largely unexplored due to the absence of a robust photonic integration industry. Recently, the landscape for commercially-manufacturable photonic chips has been changing rapidly and now promises to achieve economies of scale previously enjoyed solely by microelectronics. The scientific community has set out to build bridges between the domains of photonic device physics and neural networks, giving rise to the field of \emph{neuromorphic photonics}. This article reviews the recent progress in integrated neuromorphic photonics. We provide an overview of neuromorphic computing, discuss the associated technology (microelectronic and photonic) platforms and compare their metric performance. We discuss photonic neural network approaches and challenges for integrated neuromorphic photonic processors while providing an in-depth description of photonic neurons and a candidate interconnection architecture. We conclude with a future outlook of neuro-inspired photonic processing.Comment: 28 pages, 19 figure
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