I am studying the zero forcing as a simple linear receiver for MIMO detection. according to following notes: http://www.ee.ic.ac.uk/bruno.clerckx/All.pdf.

The zero forcing can be understood as the following two ways.

  1. For a single stream $\mathbf{q}$, we can project the received $\mathbf{y}$ onto a subspace orthogonal to channel matrix $\mathbf{H}$ without $\mathbf{h}_q$, then apply the matched filter to the projected signal or,
  2. We can just work on all streams in one shot with the pseudo inverse of the channel matrix, i.e., $(\mathbf{H}^T\mathbf{H})^{-1}\mathbf{H}^T$.

I can understand that the second approach certainly cancels the interference as $(\mathbf{H}^T\mathbf{H})^{-1}$ exists if columns of $\mathbf{H}$ are independent, and obviously $(\mathbf{H}^T\mathbf{H})^{-1}\mathbf{H}^T\mathbf{H} = \mathbf{I}$ such that $(\mathbf{H}^T\mathbf{H})^{-1}\mathbf{H}^T\mathbf{y}=\mathbf{x} + (\mathbf{H}^T\mathbf{H})^{-1}\mathbf{H}^T\mathbf{n}$.

It seems to me that according to (2), a decorrelator for the $q^{th}$ stream is just the $q^{th}$ row of the pseudo-inverse of the matrix $\mathbf{H}$.

My question is, can we prove, or conclude (2) from (1)?

The following is the MATLAB script implemented according to the methods described in the above-mentioned notes.

%zero forcing

randn('seed', 0);
m=2; n=2;

x = randn(n,1)+1j*randn(n,1);

H = randn(m,n)+1j*randn(m,n);


%zeero forcing filter

G0 = inv(H'*H)*H';


% transform the y into the null space of the 

% subspace spanned by H1m (H w/o h1)

[U, S, Vc]=svd(H1m);

% Q1 spans null space of the subspace spanned by H1m (H w/o h1)

% (actually it is null space of H1m')

% i.e., Q1'*y projects y into the null space of H1m

% y1 = Q1'*y = Q1'*h1*x1 + Q1'*n + zero (cancelled interference)


% (Q1'*h1)' is the match filter to the transformed y1

% g1=h1'*(Q1*Q1'), note that Q1*Q1' is not orthogonal but Q1'*Q1=I


% normalization



%snr for stream 1



%snr1 - 1/nv(1,1)
  • $\begingroup$ There is a couple hundred slides, can you specify which slides you are talking about? $\endgroup$
    – Engineer
    Feb 21 '20 at 1:57
  • $\begingroup$ The approach (1) is from page 154-155 of the slides. $\endgroup$
    – Hao xue
    Feb 22 '20 at 1:13

It seems like you understand your question already but are unsure it is correct? Let the decorrelator be $\mathbf{D}=(\mathbf{H}^T\mathbf{H})^{-1}\mathbf{H}^T$, then the symbols post-decorrelator are $\mathbf{z}=\mathbf{D}\mathbf{y}=\mathbf{D}(\mathbf{Hx}+\mathbf{n})$. The first stream, $z_1$, is computed by taking the first row of $\mathbf{D}$, call the row vector $\mathbf{d}_1$ (first row of $\mathbf{D}$), and performing the operation: $z_1=\mathbf{d}_1(\mathbf{Hx}+\mathbf{n})$. You get $z_1=x_1 + \text{filtered noise}$ because $\mathbf{d}_1\mathbf{H}=[1, 0, ..., 0]$.

  • $\begingroup$ Thanks for your answer. I understand D is able to cancel the interference. My question is how to relate these two approaches, e.g., how to prove the following fact, if I form a matrix by putting the single-stream decorrelators implemented in (1) together in a row-by-row manner, this matrix is actually the pseudo inverse of H, i.e., the D you mentioned? $\endgroup$
    – Hao xue
    Feb 22 '20 at 1:25
  • $\begingroup$ There’s nothing to prove, it’s just matrix multiplication $\endgroup$
    – Engineer
    Feb 22 '20 at 2:49
  • $\begingroup$ please let me rephrase, how can i prove, that the g_1=(Q_1'*h_1)'*Q_1' in my matlab script, after normalization, is the d_1 you mentioned? Note that Q_1 spans the null space of the subspace spanned by H1m' (H' w/o h_1') $\endgroup$
    – Hao xue
    Feb 22 '20 at 3:11
  • $\begingroup$ Its the inverse because just taking it row-by-row. There is nothing to prove. Once you have $\mathbf{D}$, you can either do your approach #1 (each stream individually) or approach #2 (treat as a bank of filters) $\endgroup$
    – Engineer
    Feb 22 '20 at 12:37

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