Solve derivative of least squares matrix equation

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SUMMARY

The discussion focuses on deriving the optimal transform matrix T for a MIMO communication system, where the received signal is expressed as r = Hs + n. The least squares solution is sought to minimize the error D = ||s - s_hat||^2, leading to the equation dD/dT = 0. A critical observation is that the matrix T cancels out in the expression for D, resulting in D = 0, which implies a need for reevaluation of the approach due to the noise affecting s_hat. Participants also seek recommendations for literature on matrix algebra relevant to this topic.

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beyondlight
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Homework Statement



I am designing a MIMO communication system, with input signal s, channel H and transform matrix T. The received signal is corrupted by noise.

Homework Equations


[/B]
The received signal is r = Hs+n

And then it is transformed (compressed) by:

y = Tr

And then its estimate s_hat is computed:

s_hat = inv(TH)*y = inv(H)inv(T)THs + inv(H)inv(T)Tn

Set C = inv(H)inv(T)Tn
I want to find an optimum T based on the least squares solution:

D = norm(s-s_hat)^2
dD/dT = 0

The Attempt at a Solution


[/B]
D= (s-s_{hat})^{H}(s-s_{hat})=0
D = (s-H^{-1}T^{-1}{THs})^{H}(s-H^{-1}T^{-1}THs)
D = ||s|| -s^{H}H^{-1}T^{-1}{THs}-s^{H}C-s^{H}H^{H}T^{H}(T^{-1})^{H}(H^{-1})^{H}s+(s^{H}H^{H}T^{H}(T^{-1})^{H}(H^{-1})^{H}s)(H^{-1}T^{-1}{THs})-sH^{H}T^{H}(T^{-1})^{H}(H^{-1})^{H}C-C^{H}s-C^{H}H^{-1}T^{-1}{THs}+C^{H}CHow do I find the derivative dD/dT? Suppose that I do find it, how then do I proceed to obtain T alone on one side of the equation?

I would also like to get some ideas on which book covers this kind of matrix algebra.
 
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beyondlight said:

Homework Statement



I am designing a MIMO communication system, with input signal s, channel H and transform matrix T. The received signal is corrupted by noise.

Homework Equations


[/B]
The received signal is r = Hs+n

And then it is transformed (compressed) by:

y = Tr

And then its estimate s_hat is computed:

s_hat = inv(TH)*y = inv(H)inv(T)THs + inv(H)inv(T)Tn

Set C = inv(H)inv(T)Tn
I want to find an optimum T based on the least squares solution:

D = norm(s-s_hat)^2
dD/dT = 0

The Attempt at a Solution


[/B]
D= (s-s_{hat})^{H}(s-s_{hat})=0
D = (s-H^{-1}T^{-1}{THs})^{H}(s-H^{-1}T^{-1}THs)
D = ||s|| -s^{H}H^{-1}T^{-1}{THs}-s^{H}C-s^{H}H^{H}T^{H}(T^{-1})^{H}(H^{-1})^{H}s+(s^{H}H^{H}T^{H}(T^{-1})^{H}(H^{-1})^{H}s)(H^{-1}T^{-1}{THs})-sH^{H}T^{H}(T^{-1})^{H}(H^{-1})^{H}C-C^{H}s-C^{H}H^{-1}T^{-1}{THs}+C^{H}CHow do I find the derivative dD/dT? Suppose that I do find it, how then do I proceed to obtain T alone on one side of the equation?

I would also like to get some ideas on which book covers this kind of matrix algebra.

The matrix ##T## disappears from your expression for ##D## as you have written it:
##H^{-1} T^{-1} T H = H^{-1} H = I## (the unit matrix), because ##T^{-1}T = I## and ##H^{-1}H = I##. So, what you have written is, basically, ##D = (s-s)^H (s-s)##, which is just 0 for all ##H, T##.
 
Ray Vickson said:
The matrix ##T## disappears from your expression for ##D## as you have written it:
##H^{-1} T^{-1} T H = H^{-1} H = I## (the unit matrix), because ##T^{-1}T = I## and ##H^{-1}H = I##. So, what you have written is, basically, ##D = (s-s)^H (s-s)##, which is just 0 for all ##H, T##.
But since s_hat is corrupted by noise then this will not be exactly true?
 
beyondlight said:
But since s_hat is corrupted by noise then this will not be exactly true?

I am just going by what you wrote. Perhaps what you wrote is not appropriate.
 

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