How can I simplify this Convolution through a limit change?

In summary, the convolution y[n] = x[n] * h[n] for discrete signals x[n] = \alpha^nu[n] and h[n] = \beta^nu[n] where \alpha \neq \beta can be simplified to y[n] = \beta^n \sum_{k=0}^n(\frac{\alpha}{\beta})^k , n\geq0, which can be further simplified to (\frac{\beta^{n+1} - \alpha^{n+1}}{\beta - \alpha})u[n] , \alpha \neq \beta. The restriction on n≥0 is due to the fact that u[n] = 0 when n <
  • #1
ElijahRockers
Gold Member
270
10

Homework Statement




Compute the convolution y[n] = x[n] * h[n]

if discrete signals:
[itex] x[n] = \alpha^nu[n][/itex]
[itex] h[n] = \beta^nu[n][/itex]

Where [itex]\alpha \neq \beta[/itex].

Homework Equations



[itex]y[n] = \sum_{k=-\infty}^{\infty} h[n-k]x[k][/itex]

The Attempt at a Solution



I plugged the two equations into the above convolution formula and got

[itex]y[n] = \sum_{k=-\infty}^{\infty} \beta^{h-k}u[n-k]\alpha^ku[k][/itex]

Simplifying, I get

[itex]y[n] = \beta^n \sum_{k=-\infty}^{\infty} (\frac{\alpha}{\beta})^ku[n-k]u[k][/itex]

And that's as far as I understand. The solution is supposed to be:

[itex]y[n] = \beta^n \sum_{k=0}^n(\frac{\alpha}{\beta})^k , n\geq0[/itex]
[itex]y[n] = (\frac{\beta^{n+1} - \alpha^{n+1}}{\beta - \alpha})u[n] , \alpha \neq \beta[/itex]

But I'm not sure how to get there. I see that the change in limits somehow facilitates this, but I just can't wrap my head around how to arrive at that, especially the part where α≠β.

I see that for each n, k goes through all possible values and sums each result, but I don't see why the lower limit for the solution is 0, or the upper limit is n.

There's obviously something fundamental about convolutions that I'm not getting.
 
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  • #2
ElijahRockers said:

Homework Statement




Compute the convolution y[n] = x[n] * h[n]

if discrete signals:
[itex] x[n] = \alpha^nu[n][/itex]
[itex] h[n] = \beta^nu[n][/itex]

Where [itex]\alpha \neq \beta[/itex].

Homework Equations



[itex]y[n] = \sum_{k=-\infty}^{\infty} h[n-k]x[k][/itex]

The Attempt at a Solution



I plugged the two equations into the above convolution formula and got

[itex]y[n] = \sum_{k=-\infty}^{\infty} \beta^{h-k}u[n-k]\alpha^ku[k][/itex]

Simplifying, I get

[itex]y[n] = \beta^n \sum_{k=-\infty}^{\infty} (\frac{\alpha}{\beta})^ku[n-k]u[k][/itex]

And that's as far as I understand. The solution is supposed to be:

[itex]y[n] = \beta^n \sum_{k=0}^n(\frac{\alpha}{\beta})^k , n\geq0[/itex]
[itex]y[n] = (\frac{\beta^{n+1} - \alpha^{n+1}}{\beta - \alpha})u[n] , \alpha \neq \beta[/itex]

But I'm not sure how to get there. I see that the change in limits somehow facilitates this, but I just can't wrap my head around how to arrive at that, especially the part where α≠β.

I see that for each n, k goes through all possible values and sums each result, but I don't see why the lower limit for the solution is 0, or the upper limit is n.

There's obviously something fundamental about convolutions that I'm not getting.

Are the equations [itex] x[n] = \alpha^nu[n], \; y[n] = \beta^nu[n][/itex] supposed to hold for all integer n from -∞ to +∞, or are they supposed to hold only for integer n ≥ 0? (The answer makes a BIG difference.)
 
  • #3
Ray Vickson said:
Are the equations [itex] x[n] = \alpha^nu[n], \; y[n] = \beta^nu[n][/itex] supposed to hold for all integer n from -∞ to +∞, or are they supposed to hold only for integer n ≥ 0? (The answer makes a BIG difference.)

Why? The u(n) says n > = 0 only, does it not?
.
 
  • #4
Z transforms, anyone? Hint: anu[n] → 1/(1 - az-1).
 
  • #5
ElijahRockers said:

Homework Statement




Compute the convolution y[n] = x[n] * h[n]

if discrete signals:
[itex] x[n] = \alpha^nu[n][/itex]
[itex] h[n] = \beta^nu[n][/itex]

Where [itex]\alpha \neq \beta[/itex].

Homework Equations



[itex]y[n] = \sum_{k=-\infty}^{\infty} h[n-k]x[k][/itex]

The Attempt at a Solution



I plugged the two equations into the above convolution formula and got

[itex]y[n] = \sum_{k=-\infty}^{\infty} \beta^{h-k}u[n-k]\alpha^ku[k][/itex]

Simplifying, I get

[itex]y[n] = \beta^n \sum_{k=-\infty}^{\infty} (\frac{\alpha}{\beta})^ku[n-k]u[k][/itex]

And that's as far as I understand. The solution is supposed to be:

[itex]y[n] = \beta^n \sum_{k=0}^n(\frac{\alpha}{\beta})^k , n\geq0[/itex]
[itex]y[n] = (\frac{\beta^{n+1} - \alpha^{n+1}}{\beta - \alpha})u[n] , \alpha \neq \beta[/itex]

But I'm not sure how to get there. I see that the change in limits somehow facilitates this, but I just can't wrap my head around how to arrive at that, especially the part where α≠β.

I see that for each n, k goes through all possible values and sums each result, but I don't see why the lower limit for the solution is 0, or the upper limit is n.

There's obviously something fundamental about convolutions that I'm not getting.

It seems to me that you're almost there. You just need to take into account the values of ##k## for which ##u[k]## and ##u[n-k]## are nonzero, and change the limits on the sum accordingly. Assuming the definition
$$u[k] = \begin{cases}
1 & \textrm{ if }k \geq 0 \\
0 & \textrm{ otherwise} \\
\end{cases}$$
then for what values of ##k## is ##u[n-k]## nonzero? And then what about the product ##u[k]u[n-k]##?
 
  • #6
u[n-k] = 1 when k is ≤ n
u[k] is 1 when k ≥ 0

ok, so k only matters from 0≤k≤n, so i see where the limits are... and 1 times 1 is 1...

ok. its clicking.

but why is the restriction on n≥0?

Also, I used a formula for the sum when A/B ≠ 1 to get

[itex]\beta^n(\frac{1-(\frac{\alpha}{\beta})^{n+1}}{1-\frac{\alpha}{\beta}})[/itex]

But... is it just a matter of simplifying this to get the expression given as the solution?
 
  • #7
ElijahRockers said:
u[n-k] = 1 when k is ≤ n
u[k] is 1 when k ≥ 0

ok, so k only matters from 0≤k≤n, so i see where the limits are... and 1 times 1 is 1...

ok. its clicking.

but why is the restriction on n≥0?
This is because if ##n < 0##, there are no values of ##k## which satisfy ##0 \leq k \leq n##, or putting it another way, ##u(k)u(n-k) = 0## for all ##k##. Therefore ##y[n] = 0## if ##n < 0##. This is why there is a ##u[n]## in the expression for the solution.
Also, I used a formula for the sum when A/B ≠ 1 to get

[itex]\beta^n(\frac{1-(\frac{\alpha}{\beta})^{n+1}}{1-\frac{\alpha}{\beta}})[/itex]

But... is it just a matter of simplifying this to get the expression given as the solution?
Yes. Try multiplying through by ##\beta^n##, and clearing denominators.
 
  • #8
Ok, after grinding my gears for a bit, I get why n>0 for the first expression, duh, the sum upper limit can't be less than the lower limit,

but where does the u[n] come from in the second expression?
 
  • #9
By the way, as you noted, your expression is invalid if ##\alpha = \beta##. But there is a valid solution if ##\alpha = \beta##. For completeness, you might consider finding a separate formula for that special case.
 
  • #10
jbunniii said:
By the way, as you noted, your expression is invalid if ##\alpha = \beta##. But there is a valid solution if ##\alpha = \beta##. For completeness, you might consider finding a separate formula for that special case.

That's part B), already figured that one out,

y[n] = (n+1)αn...

but the solution has u[n] also, just like in part A)... not sure where either of those comes from.
 
  • #11
ElijahRockers said:
That's part B), already figured that one out,

y[n] = (n+1)αn...

but the solution has u[n] also, just like in part A)... not sure where either of those comes from.
See the remark I made above regarding why the expression only holds for ##n \geq 0##:
This is because if ##n < 0##, there are no values of ##k## which satisfy ##0 \leq k \leq n##, or putting it another way, ##u(k)u(n-k) = 0## for all ##k##. Therefore ##y[n] = 0## if ##n < 0##. This is why there is a ##u[n]## in the expression for the solution.
 
  • #12
Ok, that makes some kind of sense... it's kind of hard to visualize what's actually happening but I see where you are coming from.

Thanks for your help!
 

1. What is a Convolution in scientific terms?

Convolution is a mathematical operation that combines two functions to create a third function that is a modified version of the original ones. It is commonly used in signal processing and image processing to analyze the relationships between two signals or images.

2. How can I simplify Convolution through a limit change?

To simplify Convolution through a limit change, you can use the property of linearity of the integral. This means that you can split the integral into multiple integrals and then change the limits of integration. This can help to simplify complex Convolution equations and make them easier to solve.

3. What is the importance of simplifying Convolution?

Simplifying Convolution can help to make complex mathematical operations more manageable and easier to solve. It can also help to reduce the computational complexity of algorithms used in signal and image processing, making them more efficient.

4. Can Convolution be simplified in all cases?

While Convolution can be simplified through a limit change in many cases, there are some cases where it may not be possible to do so. This is because Convolution involves combining two functions, and the complexity of the functions may make it difficult to simplify the equation.

5. Are there any alternatives to simplifying Convolution through a limit change?

Yes, there are other methods for simplifying Convolution, such as using the properties of Convolution, using the Fourier Transform, or using the Convolution theorem. It is important to choose the most appropriate method based on the specific equation and the desired outcome.

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