Generation of pseudorandom binary sequence using shot noise for optical encryption

Kumari, Shayna; Valarmathi, M. L.; Prince, Shanthi

doi:10.1109/iccsp.2016.7754537

Cited by 4 publications

(3 citation statements)

References 5 publications

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“…After calculating the value of P the decision for randomness is made such that if 1 is found to be random as the obtained value for P is 0.5271. The randomness of generated sequence is discussed in our publication [22].…”

Section: Fig 3 Simulation Layout Of Prbs Generatormentioning

confidence: 99%

Design and Simulation of Physical Layer Security for Next Generation Intelligent Optical Networks

marudhai

Prince²,

Kumari³

2022

Wireless Pers Commun

Self Cite

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With the latest technological advancements and attractive features of next generation intelligent optical networks such as high bandwidth, low power consumption, and low transmission loss, etc., they have been considered as most viable solution to satisfy promptly growing bandwidth demands. However, main optical network components bring forth a set of security challenges and reliability issues, accompanied by new vulnerabilities within the network. This paper proposes a new design for an optical encryption and decryption method for enhancing optical network security using p-i-n photodiode which generates Pseudo Random Binary Sequence (PRBS) as a shot noise fluctuations and wavelength converter based design using Semiconductor Optical Amplifier (SOA) based XOR gate which utilizes Cross-Phase Modulation (XPM). The system performance based on Bit Error Rate (BER) and Q factor are analyzed at different data rates for different link lengths up to 100 km using OptiSystem. It is observed that error free transmission with a BER of 10-12 is achieved a data rate of 10Gbps for a link length of only 30 Km for the system with PIN photodiode's shot noise being used for PRBS sequence generation. However, wavelength conversion based system enables transmission of signal at 10Gbps signal up to a link length of 90Km.

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Section: Fig 3 Simulation Layout Of Prbs Generatormentioning

confidence: 99%

Design and Simulation of Physical Layer Security for Next Generation Intelligent Optical Networks

marudhai

Prince²,

Kumari³

2022

Wireless Pers Commun

Self Cite

View full text Add to dashboard Cite

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“…The generated sequence shown in Table 1 is found to be random as the obtained value for P is 0.5271. The randomness of generated sequence is discussed in our publication [22].…”

Section: ) (T I Smentioning

confidence: 99%

Design and Simulation of Physical Layer Security for Next Generation Intelligent Optical Networks

marudhai

Prince

Kumari

2021

Preprint

Self Cite

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With the latest technological advancements and attractive features of next generation intelligent optical networks such as high bandwidth, low power consumption, and low transmission loss, etc., they have been considered as most viable solution to satisfy promptly growing bandwidth demands. However, main optical network components bring forth a set of security challenges and reliability issues, accompanied by new vulnerabilities within the network. This paper proposes a new design for an optical encryption and decryption method for enhancing optical network security using p-i-n photodiode which generates Pseudo Random Binary Sequence (PRBS) as a shot noise fluctuations and wavelength converter based design using Semiconductor Optical Amplifier (SOA) based XOR gate which utilizes Cross-Phase Modulation (XPM). The system performance based on Bit Error Rate (BER) and Q factor are analyzed at different data rates for different link lengths up to 100 km using OptiSystem. It is observed that error free transmission with a BER of 10-12 is achieved a data rate of 10Gbps for a link length of only 30 Km for the system with PIN photodiode’s shot noise being used for PRBS sequence generation. However, wavelength conversion based system enables transmission of signal at 10Gbps signal up to a link length of 90Km.

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“…Poisson distribution has the remarkable properties of initial probabilistic moments with respect to mathematical expectation m(η, α) = E 1 (η, α) = α and to dispersion D(η, α) = E 2 (η, α) = E 1 η 2 , α = α. These and other properties make it possible to use Poisson distribution in theoretical and statistical mathematics [3,4], in the study of physical phenomena [5,6], in radio engineering and nuclear physics [7,8], in informatics and information systems [9], in modeling of data transmission and networks [10,11], in economics and financial analysis [12], and in other areas up to biological studies [13][14][15][16] and research for medical physics and technics [17][18][19].…”

Section: Introductionmentioning

confidence: 99%

Poisson Twister Generator by Cumulative Frequency Technology

Deon

Menyaev

2019

Algorithms

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The widely known generators of Poisson random variables are associated with different modifications of the algorithm based on the convergence in probability of a sequence of uniform random variables to the created stochastic number. However, in some situations, this approach yields different discrete Poisson probability distributions and skipping in the generated numbers. This article offers a new approach for creating Poisson random variables based on the complete twister generator of uniform random variables, using cumulative frequency technology. The simulation results confirm that probabilistic and frequency distributions of the obtained stochastic numbers completely coincide with the theoretical Poisson distribution. Moreover, combining this new approach with the tuning algorithm of basic twister generation allows for a significant increase in length of the created sequences without using additional RAM of the computer.

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Generation of pseudorandom binary sequence using shot noise for optical encryption

Cited by 4 publications

References 5 publications

Design and Simulation of Physical Layer Security for Next Generation Intelligent Optical Networks

Design and Simulation of Physical Layer Security for Next Generation Intelligent Optical Networks

Design and Simulation of Physical Layer Security for Next Generation Intelligent Optical Networks

Poisson Twister Generator by Cumulative Frequency Technology

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