• Title/Summary/Keyword: Shannon Gauss channel

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Meeting of Gauss and Shannon at Coin Leaf in 5G Massive MIMO (5G Massive MIMO에서 가우스(Gauss)와 샤논(Shannon)이 동전 한 닢에서 만남)

  • Kim, Jeong-Su;Lee, Moon-Ho;Park, Daechul
    • The Journal of the Institute of Internet, Broadcasting and Communication
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    • v.18 no.2
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    • pp.89-103
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    • 2018
  • A genius "Prince of Mathematician" Gaussian and "Father of Communication" Shannon comes up with the creative idea of motivation to meet each other? The answer is a coin leaf. Gaussian found some creative ideas in the matter of obtaining a sum of 1 to 100. This is the same as the probability distribution curve when a coin leaf is thrown. Shannon extended the Gaussian probability distribution to define the entropy, taking the source symbol and the reciprocal logarithm to obtain the weighted average. These where the genius Gaussian and Shannon meet in the same coin leaf. This paper focuses on this point, and easily proves Gaussian distribution and Shannon entropy. As an application example, we have obtained the capacity and transition probability of Jeongju seminal vesicle, and the Shannon channel capacity is 1 when the equivalent transition probability is 1/2.

Jeju Jong-Nang Channel Code IV (제주 정낭(錠木) 채널 Code IV)

  • Park, Ju-Yong;Kim, Jeong-Su;Lee, Moon-Ho
    • The Journal of the Institute of Internet, Broadcasting and Communication
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    • v.16 no.3
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    • pp.73-80
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    • 2016
  • We had introduced the backgrounds, history and physical meanings of Jong Nang in "Jeju Jong Nang Channel Code I, II and III". In "Jeju Jong Nang Channel Code II" paper, we have introduced practical the root of digital human binary coded Jong Nang communications as the wooden gate in Korea Jeju Island custom and investigated Jong Nang gatemodels as an approximation of the AWGN model. The objective was to find a deterministic model, which was accessible to analysis the capacity. Jong Nang communications mean the normal 3 rafters placed on two vertical stones with three holes to convey the family's whereabouts that is deterministic signal. In this paper we find the capacity of deterministic signal processing about the linear deterministic signals approximately.