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So now that we know what makes a good signal, let's say that we have got a

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negative 65 dBm signal,

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and I can sit here and tell you that that's a really good signal strength.

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That means that when I send my signal out, that it is a strong signal.

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So is that good enough?

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I mean, if I receive a signal, that's negative 65 dBm, I'll always be able to

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receive it.

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And unfortunately, saying the signal is strong is only part of the equation.

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The other part of the equation is the signal in the clear.
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The other part of the equation is the signal in the clear.

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Is it loud enough?

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For example, if you and I are sitting here talking in a really large room, we

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probably

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could be a good distance apart, use a normal volume level with our voices, and

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we could

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communicate really well with each other.

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Now add in a bunch of other people all having communications or conversations,

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I guess,

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with each other.

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That's going to add an element that we might refer to as background noise.
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That's going to add an element that we might refer to as background noise.

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So now, whether it's a bunch of people talking around us, or maybe we are in

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the furnace room,

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and the furnace is really loud, whatever the background noise is, either way,

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we probably

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are going to need to speak louder to one another.

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We might want to get closer to one another.

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And this exact same set of principles is going to apply to us in the wireless

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space.

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Because I might have a transmitter and a receiver here, and I'm going to send a

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signal that

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will degrade over time.
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will degrade over time.

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But unfortunately, this isn't the only speaker in the room.

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We have generally speaking a level of background noise in the wireless space.

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Sometimes this could be caused by other wireless devices that are far away.

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Generally speaking, this is just part of physics.

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Because there is some amount of what we might consider to be wireless radiation

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in any given

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space.

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And so, if this signal is much stronger than the noise, then we're in good
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And so, if this signal is much stronger than the noise, then we're in good

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shape.

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But if the signal isn't stronger than the noise, then we might have a problem.

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Just to draw this out, let's say that my signal is up here at this level.

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Again, maybe it's negative 65 dBm, something like that.

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And then my noise is all the way down here.

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So this gap here indicates it's a pretty good signal.

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But what happens if the noise is all the way up here?
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But what happens if the noise is all the way up here?

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It's just a really noisy environment.

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Well now, it's a lot harder to tell the difference between the signal that's

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coming in and

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the noise.

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Because we would all like to avoid a scenario where the noise is actually

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higher than the

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signal at that point, we might as well not even try to receive the signal.

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So this gap here has a name.

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Whoops, there we go.

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This gap has a name and it is the signal to noise ratio.
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This gap has a name and it is the signal to noise ratio.

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And yes, this does involve a little bit more math.

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But at least at this point, it's pretty straightforward.

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So again, we want the signal to be strong and clear.

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And as such, we want to make sure that our signal is enough above our noise

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that we can call

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it clear.

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So the stronger the signal, the more likely it is to be above the noise level.

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But if we are talking about a weaker signal, let's say we get to like negative
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But if we are talking about a weaker signal, let's say we get to like negative

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75 dBm, maybe

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this is strong enough, maybe it's not, and it might just depend on the

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background noise.

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Now this signal to noise ratio, as with all ratios, involves a division problem

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.

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But hang tight with me here because we're going to find that this actually

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changes a little

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bit.

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We take the signal and we divide it, but not by the ratio.

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It is a ratio.

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We divide it by the noise.

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So if we're working with decibels, we might have to say something like, I'm

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going to take
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going to take

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this signal of 0.001 milliwatts and divide it by point whatever this ends up

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being.

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And so this can be a pretty obnoxious math problem with all of the decimals and

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zeros and

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everything like this.

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So we don't do signal to noise with milliwatts.

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We always do signal to noise with decibels.

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And decibels, interestingly enough, from a logarithmic perspective, when we
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And decibels, interestingly enough, from a logarithmic perspective, when we

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take a division

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problem, division actually becomes subtraction.

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Meaning that I just have to take my values and decibels and subtract them from

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one another.

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So as an example, let's say that I have a signal of negative 75, let's just

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call it that.

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And then the, and that's dbm.

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And then the noise, noise is usually, usually close to negative 100, maybe like
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And then the noise, noise is usually, usually close to negative 100, maybe like

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negative 95.

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So let's just say that the noise is negative 95 dbm.

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What is the SNR?

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Well, because it's decibels, I can just take my signal and subtract my noise to

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get SNR.

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What we call the signal to noise ratio.

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That's how usually how we abbreviate it.

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So negative 75 minus negative 95.

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Again, think back to high school, middle school math, whatever that would be.
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Again, think back to high school, middle school math, whatever that would be.

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If we subtract a negative, we're adding it.

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And in the end, I can kind of just eyeball these numbers and say, all right,

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what's the

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difference between those?

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The answer here is a positive 20 db SNR.

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Notice we do not include the m here.

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It's not dbm for SNR.

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It's just decibels.

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What's a positive 20 decibel?

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Negative 75 adding 95 to it, subtracting negative becomes positive 20.
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Negative 75 adding 95 to it, subtracting negative becomes positive 20.

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And so if I do another example, if I said, hey, I'm at negative 78.

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So it's a worse signal.

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But and the noise maybe is a lot higher.

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Let's say it's negative 85.

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Well, man, at that point, it's only a positive 7 db SNR.

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So what do we consider to be good and what do we consider to be bad for a data

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network?

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We want at least a positive 10 SNR, which is pretty easy to achieve.
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We want at least a positive 10 SNR, which is pretty easy to achieve.

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Even something like this where there really high noise level, really a bad

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signal was really

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close to hitting a positive 10 SNR with a voice network.

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If we want to do voice over IP or for that matter, anything that's a little

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more sensitive,

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like real time applications, at that point we actually want it to be a lot

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higher, like 25

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decibels.

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So we do have to pay attention to the SNR in our environment.

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We have to be able to do this math to make sure we understand how to calculate
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We have to be able to do this math to make sure we understand how to calculate

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the SNR.

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And hopefully with a voice deployment, we see something a whole lot closer to

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the positive

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25.

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If we are not deploying real time applications and we just care about

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connectivity, then we

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don't need to worry nearly as much and a much weaker signal will still be clear

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enough

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to be distinguished.