Conference Papers

Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506

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Author: search.filters.author.Sripati, U.Subject: search.filters.subject.Distribution functions

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    Performance analysis of Free Space Optical links encoded using Luby Transform codes
    (2012) Prakash, G.; Kulkarni, M.; Sripati, U.; Kalyanpur, M.N.
    Free Space Optical (FSO) communication is an emerging transmission technique to transmit high data rates without using cables. This technology is expected to revolutionize the present communication system architectures both in the terrestrial and the in -space architecture. Atmospheric effects can significantly degrade the performance of FSO systems. This reduces the SNR and leads to impaired performance. FSO channels can be modeled using Gamma-Gamma, Weibull, Log-Normal, K distribution functions. Error control codes can help to mitigate atmospheric turbulence induced signal fading in free space optical communication links. Luby Transform codes belong to a class of error control codes called Fountain codes and are meant for erasure channels. In this paper, we propose encoding FSO links with Luby Transform (LT) codes for error channels. Decoding is done using belief propagation with Log Likelihood Ratio and results are obtained for different modulation schemes under different channel distributions. © 2012 IEEE.
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    Using RBF neural networks and kullback-leibler distance to classify channel models in Free Space Optics
    (2012) Prakash, G.; Kulkarni, M.; Sripati, U.
    Free Space Optical (FSO) communication systems offer a license free and cost effective access performance. FSO systems provide virtually unlimited bandwidth. Since the laser beams used in these systems are spatially confined, the links are very secure. However FSO links perform well only in clear weather conditions. Clouds, fog, aerosols, and turbulence drastically affect the performance of FSO systems and lead to fluctuations in both the intensity and phase of the received signal. FSO links can suffer from data packet corruption and erasure. Various statistical models have been proposed to describe the atmospheric turbulence channels. The choice of the appropriate model for varying level of turbulence is dependent on the atmospheric parameters. In this paper we classify the channels using Radial Basis Function Neural Networks to decide the best fit. We also use Kullback-Leibler distance as a measure between the reference distribution and the distribution of observed data. © 2012 IEEE.