In this paper, a new and simple rate-adaptive transmission scheme for free-space optical (FSO) communication systems with intensity modulation and direct detection (IM/DD) over atmospheric turbulence channels is analyzed. This scheme is based on the joint use of repetition coding and variable silence periods, exploiting the potential time-diversity order (TDO) available in the turbulent channel as well as allowing the increase of the peak-to-average optical power ratio (PAOPR). Here, repetition coding is firstly used in order to accommodate the transmission rate to the channel conditions until the whole time diversity order available in the turbulent channel by interleaving is exploited. Then, once no more diversity gain is available, the rate reduction can be increased by using variable silence periods in order to increase the PAOPR. Novel closed-form expressions for the average bit-error rate (BER) as well as their corresponding asymptotic expressions are presented when the irradiance of the transmitted optical beam follows negative exponential and gamma-gamma distributions, covering a wide range of atmospheric turbulence conditions. Obtained results show a diversity order as in the corresponding rate-adaptive transmission scheme only based on repetition codes but providing a relevant improvement in coding gain. Simulation results are further demonstrated to confirm the analytical results. Here, not only rectangular pulses are considered but also OOK formats with any pulse shape, corroborating the advantage of using pulses with high PAOPR, such as gaussian or squared hyperbolic secant pulses. We also determine the achievable information rate for the rate-adaptive transmission schemes here analyzed.
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Sci Rep
December 2024
Climate and Global Dynamics Laboratory, NSF National Center for Atmospheric Research, 1850 Table Mesa Drive, Boulder, CO, 80305, USA.
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View Article and Find Full Text PDFJ Aerosol Sci
November 2024
National Institute for Occupational Safety and Health, Centers for Disease Control and Prevention, Cincinnati, OH, 45226, United States.
Trace measurement of aerosol chemical composition in workplace atmospheres requires the development of high-throughput aerosol collectors that are compact, hand-portable, and can be operated using personal pumps. We describe the design and characterization of a compact, high flow, Turbulent-mixing Condensation Aerosol-in-Liquid Concentrator (TCALC) that allows direct collection of aerosols as liquid suspensions, for off-line chemical, biological, or microscopy analysis. The TCALC unit, measuring approximately 12 × 16 × 18 cm, operates at an aerosol sample flowrate of up to 10 L min, using rapid mixing of a hot flow saturated with water vapor and a cold aerosol sample flow, thereby promoting condensational growth of aerosol particles.
View Article and Find Full Text PDFNature
December 2024
National Oceanography Centre, Southampton, UK.
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View Article and Find Full Text PDFPhys Rev E
November 2024
Department of Mechanical Engineering, Indian Institute of Technology Kanpur, Uttar Pradesh 208106, India.
The hydrodynamic and thermal interactions between neighboring vapor bubbles on hot surfaces play a crucial role in heat transport and flow characteristics. To investigate these interactions, we conducted numerical simulations of saturated vapor bubbles in a two-dimensional square enclosure filled with liquid water. The water was heated at the bottom and cooled at the top to replicate boiling at 100^{∘}C and normal atmospheric pressure.
View Article and Find Full Text PDFSensors (Basel)
December 2024
Beijing Urban Construction Survey and Design Institute, Beijing 100101, China.
Interferometric Synthetic Aperture Radar (InSAR) is a widely used remote sensing technology for Earth observation, enabling the detection and measurement of ground deformation through the generation of interferograms. However, phase noise remains a critical factor that degrades interferogram quality. To address this issue, this study proposes MOMFNet, a deep learning approach for InSAR phase filtering based on multi-objective multi-kernel feature extraction that leverages multi-objective multi-kernel feature extraction.
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