Solve derivative of least squares matrix equation

Click For Summary

Homework Help Overview

The discussion revolves around optimizing a transform matrix T in the context of a MIMO communication system. The original poster is attempting to derive the least squares solution for the estimation of the input signal s, given the received signal r, which is affected by noise.

Discussion Character

  • Exploratory, Assumption checking, Problem interpretation

Approaches and Questions Raised

  • The original poster attempts to find the derivative of a least squares matrix equation and questions how to isolate T in the resulting expression. Some participants question the validity of the expressions used, particularly regarding the impact of noise on the estimation process.

Discussion Status

Participants are exploring the implications of the original poster's derivation and questioning the assumptions made about the matrix T and its role in the least squares formulation. There is a recognition of potential issues with the expressions provided, particularly in relation to the noise affecting the estimate s_hat.

Contextual Notes

There is an ongoing discussion about the appropriateness of the mathematical expressions used, particularly concerning the simplification that leads to the matrix T seemingly disappearing from the equation. Participants are also seeking resources for further reading on matrix algebra relevant to this problem.

beyondlight
Messages
64
Reaction score
0

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.
 
Physics news on Phys.org
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.
 

Similar threads

Replies
2
Views
2K
  • · Replies 3 ·
Replies
3
Views
2K
  • · Replies 24 ·
Replies
24
Views
3K
  • · Replies 6 ·
Replies
6
Views
2K
  • · Replies 10 ·
Replies
10
Views
2K
Replies
6
Views
3K
  • · Replies 1 ·
Replies
1
Views
1K
  • · Replies 3 ·
Replies
3
Views
2K
Replies
1
Views
2K
  • · Replies 9 ·
Replies
9
Views
2K