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OK, for decision, I think we will do
something more interactive,
so according to Data Cobb, if it's
OK for you, we can if
anybody has any question at any time,
just put up your hand and
I will try to to come there
and try
to answer your questions.
Yeah. Um, so you
will introduce yourself.
So good luck.
Thank you.
Thanks. Um, I hope I can
just set up this.
OK, I'm sorry I
was late, it's only.
OK,
that's the wrong window, the wrong.
Are there any questions so far?
OK, I don't know why
is insisting to use my second screen.
OK, I'll just
yeah, OK, I'll just use it this way,
and I haven't got it on my screen, so
I have to look at the presentation at the
PMI. I'm sorry.
And I'm sorry for interrupt.
OK, there's also a screen.
Thanks.
Um,
so my name is Kim
Topfer. I am from Vienna.
Um, I made my amateur radio
license in 2010
and in 2006
I started at the Frankfurt,
uh, Community Wireless Network.
And since then I've been
interested in wireless communications,
high frequency engineering and of
course, digital modulations and
communications.
And, um,
I'm studying at, uh, Venice
Technical University, uh,
electrical engineering.
And so I thought, um,
because there's not that
much wireless hacking
or stuff at the physical layers.
Uh, the Congress, I thought maybe an
introduction in, um,
basic wireless stuff, digital stuff
would be interesting.
And, um, let's
start with the
era of quantum content.
Um, I've basically
divided this in a few parts
in wireless basics,
um, modulations.
We actually to, um,
analog,
although we are doing digital,
um, radio communications, we still have
to do analog stuff, um,
channel coding, the first error
correction and so on and a bit
of channel access.
And the first,
uh, start with is
the uh channel
communications uh model
which is basically
consisting of a source, uh, transmitter
or, uh, encoder and
uh, channel and receiver and uh,
I think where you actually want to
transmit your information.
So, um,
that's what you at least need
to have to have wireless communications
or any kind of communications and
channel is something very abstract.
So you can have, uh, wireless
channel, you can transmit over
basically any medium.
And, uh, you will always, almost
always have noise, different
kind of noise and interference,
interference coming from other, um,
other people, other devices,
even from yourself.
And, um,
you have to make sure that your encoder
and your decoder can actually
make the most of your channel
so you can transmit your data.
A bit about wireless channels, so
wireless channels are very
unreliable.
You can
in very small ways
like predict it, but it's
still very noisy.
You can have in
you can have interruptions
like interference.
And
for mobile wireless channels,
it's even worse because you can
because of the movement
of the different stations, you have
interference between your own station,
parts of, uh,
like buildings around you, which are
basically reflecting a signal
and interfering with the signal
you actually want to receive.
And, um, because
of different, uh, time
like time directions between the
sending and receiving,
uh.
Time, so, yeah, OK,
sorry.
We have a digital communications model
which is
used when we do digital stuff,
so we have a source like
before with Oscoda,
which is, for example, an
impact coder or any kind
of audio codec, GSM codec,
um, which does
code, which does data compression
and tries to minimize
your data.
And then we have a channel code which
tries to add redundancy
again.
So your receiver, um,
has, uh,
or it's easier for receiver to receive
a signal and to make most of your
channel
than we have got modulator
encoder where you're actually where we
are actually transmitting the signal
and sending it to
a radio channel.
And on the receiving part, it's
the same thing just the other way around.
And what I'm going to talk about is
the channel coder and modulations
and a bit of, uh, wireless
channels or wireless stuff.
First of all, when we speak about
wireless spectrum, what do we actually
mean?
And I found this great
Cassidy slide,
which shows this pretty good.
Um, we are,
um, using
this part of
the electromagnetic magnetic spectrum.
So from three megahertz to three hundred
gigahertz, of course,
most of the communication
isn't done in this
spectrum because three megahertz is
shortwave radio where you're only
transmitting a very
narrow band signals and use
usually huge antennas,
um, to get like
a few hundred kilometers or a thousand
kilometers.
And at the upper end we've
got really high frequencies where
it's actually it's actually extremely
hard to build devices for this.
So, for example,
um,
radar devices are
in this area or even
or a bit lower,
but most of it is done between,
um, like one hundred
megahertz and 30
to 60 gigahertz.
A few examples we all know
wi fi, which is
at two point four and five
gigahertz bands,
uh, which are some bands.
Uh, and.
This spectrum bands that are actually
free for use, so you
still have to have a license for your
device, but the license
is on the device itself, but you can
freely use it for any
Wi-Fi access point you want to
do. And it's for
industrial, scientific and medical stuff.
That's what the abbreviation stands for.
Um, we are also using HITECH,
which is in the upper, uh, side
of the GSM, uh,
band, and it's
using one point eight
to ten point nine gigahertz
approximately.
UMTS supply is at
two point one gigahertz.
Uh, and LTE is in
Europe, uh, using these
for, um, frequency bands.
But, uh, these three are actually GSM
bands and um,
uh, converted to
LTE and two point
six gigahertz is, um,
the frequency
band, which is actually at the moment
mostly used for LTE
and um
three hundred, four hundred thirty
three and four and eight hundred
sixty eight megahertz are um
small free bands which are also HSM
and still Asadi for
short range devices,
um which are also
free to use for uh like weather stations,
uh telemetry data or
part of smart meters actually using this.
And what you
usually buy when you get an RF module
for your Arduino is in this frequency
bands as long as it's not two point four
gigahertz.
When we come to wireless bandwidth,
there's one
main rule in telecommunications,
um, transmitted information
always has a bandwidth.
If you haven't got if you have a signal
that has no bandwidth, you have you can't
transmit information.
So this also means
the faster you transmit information, the
higher your bandwidth will be.
And if you look at the Wi-Fi,
the new Wi-Fi specifications,
you will always see that the new
specifications have bigger channel
bandwidth and
so they can transmit faster.
And, um,
a short thing about wireless
channels, actually, two definitions
of channels, uh, telecommunications, the
first one is that the transmission
channel way where we actually have
a physical, um,
like channel where the
information gets transmitted and
the logical channel, which is
much is more or less
standardized frequency
for, um,
your transmission system.
So your wi fi has got specified
channels and you don't need to enter
your, um, exact
frequency.
And these are specified to have
optimal spectrum usage.
One of the most important
theorems in telecommunications
is the Shannon Hartley theory,
which tells the upper limit
of information rate you can actually
transmit over a channel if you
have only one transmitter
and one receiver.
So, um, this
is, uh, even if you have
perfect error correction,
you can't get over this limit.
Although, um, in wireless
communications, we can have multiple, uh,
transmitters and multiple receivers,
which we actually use when we do more
stuff like the, uh, like LTE
or, uh, the wi fi end standard.
This is sort of an
exploit. This, uh,
the theory is basically thought
for, um,
cables and it's not
designed for, um,
channels where you can wire channel
changes and where
a few centimeters of spacing of
antennas, um, is completely
different in the sense of a channel.
Um.
And this is this a
formula where we have got to see the
channel bandwidth in bits per second
because our signal bandwidth in
Hertz and beyond
the binary logarithm of one
plus s and our Westernize
the signal to noise ratio
in telecommunications, we always speak
about signal to noise ratio.
So, um,
independent of how high your
noise actually is, um, you know,
you always know how
good your quality of the your
receiving signal is if you have got to
signal to noise ratio.
And this is specified for noisy
channels, for additive
white noise, which
is the
more or less the worst case of noise you
can have because it's completely
unpredictable.
Um,
digital, um,
digital signal
processing is done in baseband
signals usually, so
if you've got an analog signal, you
usually have intermediate frequency,
which is about depends on the signal.
Depends on the, uh, depends on your
receiver transmitter is about a few
hundred kilometers kilohertz or
megahertz.
And, um, in
digital systems we use,
uh, zero hertz
intermediate frequency,
um, which is complex because
computers can easily,
um.
Can easily calculate
with complex.
Signals and,
um,
when we get to modulations,
it will be a bit clearer.
When we sent this
hurts, we, of course, have the left part,
which are actually negative
frequencies, which is a bit odd.
But if you think about
a channel where we have got a frequency
here and the starting and end frequency
here, um, you
still have the left part of the channel.
So if we, um,
generate we generate a complex
representation of the signal.
We still have the, uh, negative
and positive parts of the frequency
of the frequency of frequency of
the whole channel.
Um,
this is, uh, typical receiver
of, uh, software defined radio,
a system which is just
a band pass filter at the front
or after the antenna.
Then you've got the low noise amplifier,
which is a really good amplifier,
which amplifies your received
signal and adds a
very or every
amplifier adds noise and his amplifier
has very low noise.
And then you've got two mixers
which are locked over the same local
oscillator, but with
a different face of that and
uh, with zero,
um, degrees face offset and
ninety degrees face offset.
So we generate our,
um, real part of
the complex representation
and our imaginary part of the complex
representation.
And after this we
already have the analog digital
converters which um.
Yeah.
Just convert your analog signal into
digital and then the
digital signal processing, and because
we do all the signal processing and
most of the filtering software,
it's very easy to
adapt, um,
digital wireless
communication systems.
So, for example, your Wi-Fi
with your phone, the
base of your phone usually can
transmit or receive different
modulations or a lot of
different types of
telecommunication standard like GSM
a lot, and also
to UMTS and LTE,
LTE in the same baseband chip.
And this is because the signal
processing is all done in software.
And, uh, if you have a new
signal send out, you trust you
basically just to
firmware update.
And the other way around,
if you want to send signals, if
you want to modulate signals,
it's just you
have a signal processor, your digital
analog converter, and
again, makes us switch to converting
to your designated frequency
and then your power amplifier.
So it's the same set up
just the other way around.
Then about baseband modulation,
the first question is, why do we actually
modulate data we could just
send like rectangular
impulses over our wi fi channel
or wireless channel,
but, um, rectangular
puzzles have very high
bandwidth and are very inefficient.
So they would just use
most of the spectrum and
you would need a lot of power or you can
actually not that easily
transmits rectangular pulses.
You also you always have to modulate
them.
Um, and what we also
want to do is to match the channel
characteristics.
So if we have got in a noisy
channel and a small, uh,
signal to noise ratio, we want to use
a modulation that
is more robust, um, for this
noise. And if we have a very good
channel, for example, if your Wi-Fi
access point is just a few meters apart
from your laptop, you can use
modulations which are high in bandwidth
and, uh, which just
as a lot of throughput.
There are a few modulations I picked,
which mostly used, um,
the first one is české
amplitude shifting.
Um, I want
I haven't got a slide for this because
it's actually quite easy.
You just take one or two,
um, amplitude
levels and just
modulated with your amplitude.
So if you have only got one, you're
basically doing one of king.
So if you've got a signal, you, for
example, receive
a one. And if you have got no signal,
you're, for example, receive zero
frequency shift. King is, uh,
doing something similar
just with two frequencies and is
a bit similar, similar to
a form modulation,
analog modulation.
We have two, um,
frequencies and
two modulating one depends
depending on what we actually want to
send with two
frequencies.
Then we've got Festschrift King
with us, which is a face modulation
and is commonly commonly
used, um,
this is PPK Binary Face
with King, which is the easiest
form. We have the
caution complex.
Um.
At the complex,
um.
I only know the drama, what it's the
German, it's even
the complex
plane.
Thanks.
We've got a complex
plane
and we've got two
symbols here.
Um, and
these are spaced 180
degrees apart. So we got we
have got a positive symbol
and a negative symbol,
um, and which is
with this modulation, we have got
a wireless bandwidth of about,
uh, the symbol. Right.
We actually use and we can transmit
one bit possible
if we enhance this.
Um, we have,
uh, Kupinski where we have
got, um, four symbols,
which is actually not much different
than this, which just,
um. Beeps Okay.
Uh, and Autobytel or
um, spaced 90 degrees
apart and which
it with each symbol we can transmit
to bits.
And the nice thing about this is,
um, it has got the same
bandwidth as BAPS Care
and um
the same uh
error probabilities.
So uh, it's
usually more efficient to use Kupinski
because you don't need more power and you
have got the same signal.
Uh, you only need the same signal levels
at the receiving part.
And this is because, um, these
are two pairs of symbols
which are orthogonal.
Um, then we have got MPC,
we can just add more symbols,
which means that the spacing between the
symbols, um, gets lower.
So we need more actually
more, um, power of the
signal at the receiving end to transmit
this or to have lower
or lower errors,
a low error rate.
Um.
The next thing is
credit amplitude modulation,
which is a combination
of amplitude
and phase modulation,
and it's just, um,
uh, the symbols
in grid arrangement
and you can have any signs of
grid, for example, you can have, uh,
two hundred sixty five
of fifty six, um,
any number of symbols you want
to have and which each
with each symbol you transmit.
In this case
it's for four bits.
So it's 16
curium.
Um, then we come to channel coding,
what we usually wants to
do is, uh, if we've got our,
um, binary stream, which we want
to transmit, uh,
the stream is, um,
can have like a
lot of zeros, uh,
large number of ones after
another. And we want
to have, uh, basically
a random data stream we don't
want to have and modulated signals.
And for this we are using
um usually um, um,
sort of random sequence.
And this is, for example, the generator
for DBS, which
is, uh, fifteen bits of
feedback, shifting register
and the output is here and just
gets excited with
your input stream.
And the receiver also
needs to notice so
he can actually add and uh
and unscramble
the received, um, bitstream.
Um.
Due to feeding and interference, we
usually want to have forward error
correction, and you will
very unlikely find
a wireless system which hasn't got
forward error correction.
And there are two types of
codes which are used
or which are also used,
for example, DVD or any,
um, when you want to transmit
or store data.
And these are black codes
and codes.
And when you do wireless stuff,
they or digital wireless transmissions,
they usually get combined,
um, in an
inner and outer kode.
Um, the inner code
is usually the code that's
at the end of your
sending.
Um so
which is
at the basically
just before transmitting or before
modulating the data.
And the other code is
um your
um code which is
used after your, you
usually randomize your bitstream
um decoder.
It is K divided by NDS,
which is the
number of inputs, item input
bits and, and a number
of. OK, I've got a typo here.
Um
is the number of input bits and and it's
the number of output bits.
So for example,
uh code rate of
one half means that
you're actually doubling your bitrate.
The simplest codes, parity,
parity, check codes, um,
and usually do this
with, um, generate a matrixes
and
for example, if you use
if you, um, at
a parity beat to every fight,
um.
Uh, then you have,
uh, Kudret of
eight, divided by nine, for example, and
the distance between the different code
words used, uh, uh, determines
what your distance,
uh, an error correction is.
So, um, if you use,
um, a few code
words, but, uh, huge code
words, then you can usually, um.
Have high error correction
and an example for this is
having code.
I read someone read Solomon, quote, is
very often used is,
for example, firstly use that
as a transmissions,
um, where it is
also used with the convolutional kode
and is a non
binary code where we actually,
um, use the code word as,
um, coefficients to,
uh, finish field.
And it's also used
by devious activity,
um, the digital audio
broadcasting and ATC,
the American Digital Television
Television Standard.
Um, then we have
got low density Perry to check
codes, which are actually
not new, but, uh, were
forgotten for a long time and
used, for example, that Alti
and DVC, as far
as I remember,
if I remember correctly,
um.
And are
basically parity checks over huge
block sizes.
Um, so you.
Of.
Um, a very large, um,
matrix, and you
usually can only solve this matrix by,
um, iterating over the
elements and this
is the.
Uh, blockquote
error correcting code, which is
very close to the final limit, or is
the
error correcting code, which is most
always
which comes to the sun limits
like, uh, very
close.
OK, um, two computer codes,
these are not blackouts, but are,
um, codes where we
take our bitstream or input bitstream
and convolve it mathematically.
So, um, for example,
this is a, uh, simple coda
with, uh, lengths of three or
we have two, uh, elements where we
save the last bit and
for every bit that, um.
Comes at the input we shift
this one and two
exclusive or
operations on the output and we have got
two outputs, so that means
that we have got a half hour
rate codec.
And you can do this for any
number of raids, for
example, for you can also
have for pots if you really want high.
Error correcting possibilities,
but.
Means that you also
have a lot of redundancy and,
um.
Usually this gets used as
a base code or mother code,
and then we have got punctuation, which
means we actually
leave bits out,
which we don't transmit and receive
Edzard again. So we are
doing or we are making actively
errors.
Um, but we don't have to
transmit the whole, um,
bitstream we generate.
This is usually decoded
with the Iturbi algorithm,
and the reason why it is
these codes were actually forgotten
is because the Viterbi algorithm
is quite complex
to solve for hire a
length of code.
For example, WPT uses
code length of seven.
And you can,
uh, if you take this,
uh, you can actually do a state machine
out of it.
And you have got
the state so far to,
um.
For two
elements here, you have got
two bids for the state
and what we are
doing at the Riverside side is
we try to.
We try to think what,
uh, uh, what's the, uh,
state machine at the US at the moment
doing?
And, uh, at this
diagram, uh.
Uh, this is,
uh, for example, zero, and
if we go down, we actually transmitted
the one, so.
If we at first.
Um, received these symbols,
um, and understand this one,
we can actually recalculates
the path finished state
machine that the sender was actually
using and
the receiver tries to find the path
with lowest distance or with lowest
error.
The second
type of quotes which are coming close
to the limit to barcodes,
which are actually, um,
parallel, um, concatenated,
uh, criminal records, uh, this is
a bit of, uh, mixture of
blockquote and convoluted codes
we use here.
Um, this is actually fundi from the
original paper about this.
Uh, we use, uh, to convolutional
codes.
And used them as
parity with us,
we use them to add information, but
we are still using the bitstream, which
is on the input side.
And
low density parity check records are
better than this, but this comes also
very close.
And after.
Um, after
our forward error correction, you
you usually do data
interleaving because wireless
channels have
usually got past errors.
So you've got a small time
amount where you can away, uh, signal
to noise ratio is really bad and you have
got a lot of errors.
And then you can all again have,
um, a time span where
you receive signals
without any errors and.
Um, convolutional codes
are bad at, um,
uh, when it comes to,
uh, a lot of errors after another.
So what we are doing is we are
interleaving. The signal and this is the
interleave of tubes.
There are different approaches
to interleave us, for example, that you
can have a permutation matrix
like Altius or Convolutional
interleave, where you've got
these storage elements and you,
um, which each with each, but
you switch one of the positions.
So you're actually doing,
um.
Like, randomizing
your packets over a specific
span.
So if we if you've got,
um, for example,
10 bites in a
single row, which
which could not be decoded after
to the
Adira
interleaving at the receiver side,
did these 10 bytes
spread around across
the whole input stream
and then your blockhouse and your
computer codes have it easier to
decode them again?
Um, then we come to channel access
to a channel access we have got is,
uh, Simplex are broadcasting,
um, which is like
every radio station does.
You've got one sender and,
uh.
You can have many,
many receivers
you don't need synchronization to and
you can't have any synchronization,
and because you have you don't have a
back channel to your
to your transmitter.
Um.
Often this is also used if you've got,
for example, a few wireless nodes
which are only transmitting
at a very low
rate of very
low time of year,
actually time slots.
So there is no
interaction between the transmitters
and the receivers.
Um.
Then we can have TDMA,
which is a time division, multiple
access, where we use one
channel and divide it into
different time spans for
different, um, uh,
centers or for different transmitters.
For example, um, this
is each one of these boxes
is a time slot.
And, uh, in each of these time
spans, a different transmitter
sends, uh, this
is usually used almost
always used at mobile communications
and for example, tacts,
LTE, GSM,
UMTS, um,
all using this.
And because we
have um,
this time splits were the second
or the next transmitter,
um has to
send as fast as possible
after the uh the last one
has entered.
Um you have uh
you need to have exact timings
and add wireless
or at uh.
Um, mobile communications, where
where you have got base stations, usually
base base stations, tell the
clients when they should send.
Um,
we can also have, um, multiple
channels where we send
simultaneously, which is frequency
division, multiple access,
uh, where we have an uplink and
downlink channel, uh,
which is based by duplex offset.
And both channels can
be active at the same time.
And if you have got a good analog
filtering, you can receive
at the same time that you transmit.
So to us actually full duplex,
um, possible over wireless.
And um,
most of the mobile networks
use this.
Um, for example,
if you combine this with TDMA, then
the uplinked channels
get each client gets a single,
um.
At times in a single time slot
and.
The base station basically
owns the downlinked channel.
And there's also called division multiple
access, if we want to
transmit simultaneously, we on
we usually need to have something
that is orthogonal.
Um, for example, um.
Frequency division, multiple access is
using different frequencies so
we can distinguish them at code
division, multiple access for use the
same frequency and we transmit at the
same time.
And it's only because of
digital coding, um,
that we can distinguish
two signals from each other.
And this is, for example, used at
such Gallileo and
CDMA and, uh,
UMTS.
OK, and thanks
for your attentions at any.
And the questions.
Everything's OK.
OK, so thanks.
Who was the next?
OK, so OK.
Yeah, um, so, um,
probably most of the people in this room
understand a lot of the of the digital
and what you explained and the coding and
the error correction in your
eyes. If you rate well,
do not even know if this question
actually if you have to compare the
analog or the real transmission stuff to
get it to the antenna and to get it
like into the air compared with the
digital stuff, how would you rate it?
Like the ratio is
is most of the work on the on
the transmission, on the antenna, on the
amplifying, on the on the
signal processing or is it on the digital
end.
Um, well this depends, just depends
a lot on your signal or on your
um what you actually
want to do. If you want to have a
wireless signal, a wireless transmission,
um which is um
possible to adapt to a lot of uh
or if you want to have a system,
um, which can
do a lot of stuff, for example,
for um, if you have a software
defined radio, you usually want
to have, uh, large bandwidth where you
can choose which signal you actually
want to use, then, um,
analog stuff gets much complicated,
much more complicated.
Um, also
if you have got um,
for example, uh,
very different
thing, uh, very difficult.
Uh.
For example, mobile communications
for mobile communications, you need
really good amplifiers,
you need to do a lot of
frequency engineering
so you can go to your cell
sites in the right direction, in the
right.
Uh, um.
Sizes in the red.
Yeah, OK,
I hope you
yes, OK.
I thanks for the talk.
I'm a radiometer myself, and I want to
get into this stuff.
What's your recommendation on free
software? Do you recommend starting this
new radio? Is that too complex
or what's your recommendation?
Which which software should I look into?
Um, I started with
Radio Free a few years ago, and
I've done a few
a bit of radio projects.
Um.
The problem was the four
people which are good at
coding or at
writing software is that
radio is mostly
C++. If you want if you really
want to go into the details,
um, if you just
want to experiment with different kinds,
it can get
very difficult.
That's the first thing you
need to get into it.
So it's a very steep learning
curve, but it's very nice.
Um, yeah.
It's a very nice software development
kit.
And also,
yeah, OK, we
have questions,
no, any other question,
you know. OK, thank you.
OK, thanks.