Lecture 5

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MIMO Channel Equalisation MATLAB Programming Conclusions Overview 1 MIMO Channel Equalisation 2 MATLAB Programming 3 Conclusions Dr. Waleed Al-Hanafy Digital Signal Processing (ECE407) — <strong>Lecture</strong> no. 5 Linear Equalisation of MIMO Channels
MIMO Channel Equalisation MATLAB Programming Conclusions System Model v s y ˜s H W H y = Hs+v (1) y — noisy N r-dimensional received vector s — N t-dimensional transmit vector ∈ S N is assumed to be a spatially-uncorrelated and uniformly distributed complex random vector process with zero-mean and variance σ 2 s (i.e. R ss = E [ ss H] = σ 2 sI Nt v — noise vector with dimension N r ×1 drawn from CN ( 0,σ 2 v) , or equivalently R vv = E [ vv H] = σ 2 vI Nr H — N r ×N t flat-fading channel with entries h ij are assumed i.i.d. complex Gaussian random variables with zero-mean and unit-variance E [ |h ij| 2] = 1, i.e., h ij ∈ CN (0,1) Dr. Waleed Al-Hanafy Digital Signal Processing (ECE407) — <strong>Lecture</strong> no. 5 Linear Equalisation of MIMO Channels
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Lecture 5

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