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Из курса от партнера Princeton University

Networks Illustrated: Principles without Calculus

40 оценки

Princeton University

Networks Illustrated: Principles without Calculus

40 оценки

What makes WiFi faster at home than at a coffee shop? How does Google order its search results from the trillions of webpages on the Internet? Why does Verizon charge $15 for every GB of data we use? Is it really true that we are connected in six social steps or less? These are just a few of the many intriguing questions we can ask about the social and technical networks that form integral parts of our daily lives. This course is about exploring the answers, using a language that anyone can understand. We will focus on fundamental principles like “sharing is hard”, “crowds are wise”, and “network of networks” that have guided the design and sustainability of today’s networks, and summarize the theories behind everything from the social connections we make on platforms like Facebook to the technology upon which these websites run. Unlike other networking courses, the mathematics included here are no more complicated than adding and multiplying numbers. While mathematical details are necessary to fully specify the algorithms and systems we investigate, they are not required to understand the main ideas. We use illustrations, analogies, and anecdotes about networks as pedagogical tools in lieu of detailed equations. All the features of this course are available for free. It does not offer a certificate upon completion.

Из урока

Power Control in Cellular Networks

How is it possible that we can all communicate effectively without disrupting each other's calls, messages, or Internet usage? In this lesson, we will take a look at some of the methods that have been developed for letting us "share" the air over which our phones communicate.

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Christopher Brinton
Lecturer
Electrical Engineering
Mung Chiang
Professor
Electrical Engineering

Many people will argue that there's always a need for more capacity, and

cellular networks are certainly a prime example of that.

Because every time an improvement is made and we can fit more people into the same

network. Using a more sophisticated technology,

that capacity quickly gets eaten up. So we were having this problem in this

time period, with amps, in around the 1990s.

Where the network was then once again bursting at the seams.

So there were many, many millions of mobile subscribers by this time, and we

needed to find a way to do more with what we currently had.

And make spectrum sharing more efficient. So engineers began experimenting with a

fundamentally different type of signal. And what do we really mean by that?

Well if we look at the way that signals were going through networks at this time.

They were all traversing networks in their exact electrical forms, which we

call an analog signal. A good analogy to analog signal is just

speech, which is really what we're transmitting here, we're transmitting a

speech wave form. So it goes up when you speak higher with

more intensity, and goes down when you speak lower, higher, lower, and so on.

You can view this as a speech waveform. And so from the time that it was sent

from your phone to the time that it got to the receiver it would look exactly

like this anywhere in the network. With the rise of computers, engineers

realized that they can more efficiently deal with these signals if they use a

different type of signal. So, rather than having it traverse in

this analog form, they said, let's first convert it to a bunch of ones and zeroes,

which is really computer code. And each of these ones and zeroes are

called bits, and a bit is, just as we said, a 0 or a 1.

It's just on, off type of computer logic. And so, this is called a digital signal.

This is a type of digital signal, and digital signals are completely discreet,

in both time and value. So, it can only take the value of either

a 1 or a 0. So this would take the value of a 1, then

1, this takes a value of 0, this takes a value of 1.

And so on whereas the analog signal has many values that it takes in between

infinite number of values that it takes when it's rising up to the top here.

And coming back down, and then rising back up, going back down, and so on.

So, while we hear analog signals like speech waveform.

When it comes back out it's shot out as an analog signal.

Computers can only hear and process, digital signals.

So computers don't like to hear analog signals.

They only like to hear digital signals. So as we started to use computers in

networks, we started to use digital technology and that gave enormous

capacity advantages as well. So the transition from analog to digital,

really marked the migration from 1G to 2G.

When we started to have digital phones, and phones that had computers in them and

so on, we started to get more features than just the standard call.

So in 1990 a Motorola phone looked something like this, still only used for

calling. So you could dial someone's number, and

then you can call them and hear them on the other end and speak.

By 2000 a phone looked more like this, so you'd actually have a digital readout

screen. Which is was a [UNKNOWN] digital

technology as well as features like texting.

So once we hit 2G that's when they started to see people being able to text.

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