So now that we know what makes a good signal, let's say that we have got a
negative 65 dBm signal,
and I can sit here and tell you that that's a really good signal strength.
That means that when I send my signal out, that it is a strong signal.
So is that good enough?
I mean, if I receive a signal, that's negative 65 dBm, I'll always be able to
receive it.
And unfortunately, saying the signal is strong is only part of the equation.
The other part of the equation is the signal in the clear. WEBVTT X-TIMESTAMP-MAP=LOCAL:00:00:00.000,MPEGTS:900000
The other part of the equation is the signal in the clear.
Is it loud enough?
For example, if you and I are sitting here talking in a really large room, we
probably
could be a good distance apart, use a normal volume level with our voices, and
we could
communicate really well with each other.
Now add in a bunch of other people all having communications or conversations,
I guess,
with each other.
That's going to add an element that we might refer to as background noise. WEBVTT X-TIMESTAMP-MAP=LOCAL:00:00:00.000,MPEGTS:900000
That's going to add an element that we might refer to as background noise.
So now, whether it's a bunch of people talking around us, or maybe we are in
the furnace room,
and the furnace is really loud, whatever the background noise is, either way,
we probably
are going to need to speak louder to one another.
We might want to get closer to one another.
And this exact same set of principles is going to apply to us in the wireless
space.
Because I might have a transmitter and a receiver here, and I'm going to send a
signal that
will degrade over time. WEBVTT X-TIMESTAMP-MAP=LOCAL:00:00:00.000,MPEGTS:900000
will degrade over time.
But unfortunately, this isn't the only speaker in the room.
We have generally speaking a level of background noise in the wireless space.
Sometimes this could be caused by other wireless devices that are far away.
Generally speaking, this is just part of physics.
Because there is some amount of what we might consider to be wireless radiation
in any given
space.
And so, if this signal is much stronger than the noise, then we're in good WEBVTT X-TIMESTAMP-MAP=LOCAL:00:00:00.000,MPEGTS:900000
And so, if this signal is much stronger than the noise, then we're in good
shape.
But if the signal isn't stronger than the noise, then we might have a problem.
Just to draw this out, let's say that my signal is up here at this level.
Again, maybe it's negative 65 dBm, something like that.
And then my noise is all the way down here.
So this gap here indicates it's a pretty good signal.
But what happens if the noise is all the way up here? WEBVTT X-TIMESTAMP-MAP=LOCAL:00:00:00.000,MPEGTS:900000
But what happens if the noise is all the way up here?
It's just a really noisy environment.
Well now, it's a lot harder to tell the difference between the signal that's
coming in and
the noise.
Because we would all like to avoid a scenario where the noise is actually
higher than the
signal at that point, we might as well not even try to receive the signal.
So this gap here has a name.
Whoops, there we go.
This gap has a name and it is the signal to noise ratio. WEBVTT X-TIMESTAMP-MAP=LOCAL:00:00:00.000,MPEGTS:900000
This gap has a name and it is the signal to noise ratio.
And yes, this does involve a little bit more math.
But at least at this point, it's pretty straightforward.
So again, we want the signal to be strong and clear.
And as such, we want to make sure that our signal is enough above our noise
that we can call
it clear.
So the stronger the signal, the more likely it is to be above the noise level.
But if we are talking about a weaker signal, let's say we get to like negative WEBVTT X-TIMESTAMP-MAP=LOCAL:00:00:00.000,MPEGTS:900000
But if we are talking about a weaker signal, let's say we get to like negative
75 dBm, maybe
this is strong enough, maybe it's not, and it might just depend on the
background noise.
Now this signal to noise ratio, as with all ratios, involves a division problem
.
But hang tight with me here because we're going to find that this actually
changes a little
bit.
We take the signal and we divide it, but not by the ratio.
It is a ratio.
We divide it by the noise.
So if we're working with decibels, we might have to say something like, I'm
going to take WEBVTT X-TIMESTAMP-MAP=LOCAL:00:00:00.000,MPEGTS:900000
going to take
this signal of 0.001 milliwatts and divide it by point whatever this ends up
being.
And so this can be a pretty obnoxious math problem with all of the decimals and
zeros and
everything like this.
So we don't do signal to noise with milliwatts.
We always do signal to noise with decibels.
And decibels, interestingly enough, from a logarithmic perspective, when we WEBVTT X-TIMESTAMP-MAP=LOCAL:00:00:00.000,MPEGTS:900000
And decibels, interestingly enough, from a logarithmic perspective, when we
take a division
problem, division actually becomes subtraction.
Meaning that I just have to take my values and decibels and subtract them from
one another.
So as an example, let's say that I have a signal of negative 75, let's just
call it that.
And then the, and that's dbm.
And then the noise, noise is usually, usually close to negative 100, maybe like WEBVTT X-TIMESTAMP-MAP=LOCAL:00:00:00.000,MPEGTS:900000
And then the noise, noise is usually, usually close to negative 100, maybe like
negative 95.
So let's just say that the noise is negative 95 dbm.
What is the SNR?
Well, because it's decibels, I can just take my signal and subtract my noise to
get SNR.
What we call the signal to noise ratio.
That's how usually how we abbreviate it.
So negative 75 minus negative 95.
Again, think back to high school, middle school math, whatever that would be. WEBVTT X-TIMESTAMP-MAP=LOCAL:00:00:00.000,MPEGTS:900000
Again, think back to high school, middle school math, whatever that would be.
If we subtract a negative, we're adding it.
And in the end, I can kind of just eyeball these numbers and say, all right,
what's the
difference between those?
The answer here is a positive 20 db SNR.
Notice we do not include the m here.
It's not dbm for SNR.
It's just decibels.
What's a positive 20 decibel?
Negative 75 adding 95 to it, subtracting negative becomes positive 20. WEBVTT X-TIMESTAMP-MAP=LOCAL:00:00:00.000,MPEGTS:900000
Negative 75 adding 95 to it, subtracting negative becomes positive 20.
And so if I do another example, if I said, hey, I'm at negative 78.
So it's a worse signal.
But and the noise maybe is a lot higher.
Let's say it's negative 85.
Well, man, at that point, it's only a positive 7 db SNR.
So what do we consider to be good and what do we consider to be bad for a data
network?
We want at least a positive 10 SNR, which is pretty easy to achieve. WEBVTT X-TIMESTAMP-MAP=LOCAL:00:00:00.000,MPEGTS:900000
We want at least a positive 10 SNR, which is pretty easy to achieve.
Even something like this where there really high noise level, really a bad
signal was really
close to hitting a positive 10 SNR with a voice network.
If we want to do voice over IP or for that matter, anything that's a little
more sensitive,
like real time applications, at that point we actually want it to be a lot
higher, like 25
decibels.
So we do have to pay attention to the SNR in our environment.
We have to be able to do this math to make sure we understand how to calculate WEBVTT X-TIMESTAMP-MAP=LOCAL:00:00:00.000,MPEGTS:900000
We have to be able to do this math to make sure we understand how to calculate
the SNR.
And hopefully with a voice deployment, we see something a whole lot closer to
the positive
25.
If we are not deploying real time applications and we just care about
connectivity, then we
don't need to worry nearly as much and a much weaker signal will still be clear
enough
to be distinguished.