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Discrete-Time Memoryless Gaussian Channel (AWGN Channel)

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Definition
InfoTheory

Consider the following discrete time additive noise channel with average input power constraint PP Yi=Xi+Zi, i=1,2,Y_{i}=X_{i}+Z_{i}, \ i=1,2,\ldotswhere Xi ⁣ ⁣ ⁣ZiX_{i}\perp\!\!\!\perp Z_{i}, i,j\forall i,j and {Zi}i=1\{Z_{i}\}_{i=1}^{\infty} are iid gaussian RVs with mean zero and variance σ2\sigma^{2}: ZiN(0,σ2), i=1,2,Z_{i}\sim\mathcal{N}(0,\sigma^{2}), \ i=1,2,\ldotswith pdf fZ(z)=12πσ2ez22σ2, zRf_{Z}(z) =\frac{1}{\sqrt{2\pi\sigma^{2}}}e^{- \frac{z^{2}}{2\sigma^{2}}}, \ z\in\mathbb{R}Since the ZiZ_{i}’s are iid, we have for any xn,ynRnx^{n},y^{n}\in\mathbb{R}^{n}, fYnXn(ynxn)=fZ(ynxn)=i=1nfZ(yixi)f_{Y^{n}|X^{n}}(y^{n}|x^{n})=f_{Z}(y^{n}-x^{n})=\prod_{i=1}^{n}f_{Z}(y_{i}-x_{i})hence the channel is memoryless with transition pdf fYX=fZ: fYX(yx)=12πσ2e(yx)22σ2, x,yRf_{Y|X}=f_{Z}: \ f_{Y|X}(y|x)= \frac{1}{\sqrt{2\pi\sigma^{2}}}e^{- \frac{(y-x)^{2}}{2\sigma^{2}}}, \ x,y\in\mathbb{R}with noise power σ2\sigma^{2} and input power constraint PP.

The Information Capacity with Input Cost is C(P)=12log2(1+Pσ2)C(P)= \frac{1}{2}\log_{2}\left(1+ \frac{P}{\sigma^{2}}\right)with “noise power” σ2\sigma^{2} and Pσ2\frac{P}{\sigma^{2}} as the “signal-to-noise ratio (SNR)”.

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