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Information Theory

7. Gaussian Channel

2020-12-24Original-language archivelegacy assets may be incomplete

Gaussian Channel

  • continuous alphet channel: input XiX_i, noise ZiZ_i, output YiY_i
  • Gaussian channel: Yi=Xi+Zi,ZiN(0,N)Y_i=X_i+Z_i,Z_i\sim\mathcal{N}(0,N)
  • Energy Constraint: 1ni=1nxi2P\frac{1}{n}\sum_{i=1}^n x_i^2\leq P
  • C=maxf(x):EX2PI(X;Y)C=\max_{f(x):EX^2\leq P}I(X;Y)
  • Gaussian channel C=12log(1+PN)C=\frac{1}{2}\log (1+\frac{P}{N}), maximum attained when XN(0,P)X\sim\mathcal{N}(0,P)
    • N=EZ2N=EZ^2
    • 信噪比:PN\frac{P}{N}
  • Guassian Noise is worest noise