Transcript IT-488

EC 723
Satellite Communication
Systems
Mohamed Khedr
http://webmail.aast.edu/~khedr
1
Syllabus
Week 1
Overview
Week 2
Orbits and constellations: GEO, MEO and LEO
Week 3
Satellite space segment, Propagation and
satellite links , channel modelling
Tentatively
Week 4
Satellite Communications Techniques
Week 5
Satellite Communications Techniques II
Week 6
Satellite Communications Techniques III
Satellite error correction Techniques
Week 7
Multiple Access I
Week 8
Multiple access II
Week 9
Satellite in networks I, INTELSAT systems ,
VSAT networks, GPS
Week 10
GEO, MEO and LEO mobile communications
INMARSAT systems, Iridium , Globalstar,
Odyssey
Week 11
Presentations
Week 12
Presentations
Week 13
Presentations
Week 14
Presentations
Week 15
Presentations
2
Frequency Shift Keying
Two signals are used to convey information
s1 t   A cos2f1t  1 
Constant Modulus =>
s2 t   A cos2f 2t   2 
In principle, the transmitted signal appears as
2 sinx/x functions at carrier frequencies
Each of the two states represents
a single bit of information
Each state persists for a single bit period and
then may be replaced either state
BER is: 2x BPSK BER for coherent
1
for non-coherent
BER  e
 Eb
2 No
2
3
Frequency Shift Keying
4
Other Modulations (cont.)
M-ary PSK
PSK with 2n states where n>2
Incr. spectral eff. - (More bits per Hertz)
Degraded BER compared to BPSK or QPSK
QAM - Quadrature Amplitude Modulation
Not constant envelope
Allows higher spectral eff.
Degraded BER compared to BPSK or QPSK
5
M-ary PSK
6
M-ary QAM
7
Other Modulations
OQPSK
QPSK
One of the bit streams delayed by Tb/2
Same BER performance as QPSK
MSK
QPSK - also constant envelope, continuous phase
FSK
1/2-cycle sine symbol rather than rectangular
Same BER performance as QPSK
8
Noncoherent
receivers.
(a) Quadrature
receiver using
correlators.
(b) Quadrature
receiver using
matched filters.
(c) Noncoherent
matched filter.
9
Output of
matched filter for
a rectangular RF
wave: (a)   0,
and (b)   180
degrees.
10
Noncoherent receiver for the detection of
binary FSK signals.
11
Factors that Influence Choice of
Digital Modulation Techniques
A desired modulation scheme
• Provides low bit-error rates at low SNRs
– Power efficiency
• Performs well in multipath and fading conditions
• Occupies minimum RF channel bandwidth
– Bandwidth efficiency
• Is easy and cost-effective to implement
Depending on the demands of a particular system or
application, tradeoffs are made when selecting a
digital modulation scheme.
12
Power Efficiency of Modulation
Power efficiency is the ability of the modulation technique to preserve
fidelity of the message at low power levels.
Usually in order to obtain good fidelity, the signal power needs to be
increased.
• Tradeoff between fidelity and signal power
• Power efficiency describes how efficient this tradeoff is made
E
Pow er Efficiency:  p   b
 N0

required at the receiver input for certain PER

Eb: signal energy per bit
N0: noise power spectral density
PER: probability of error
13
Bandwidth Efficiency of Modulation
Ability of a modulation scheme to accommodate data
within a limited bandwidth.
Bandwidth efficiency reflect how efficiently the
allocated bandwidth is utilized
R
Bandwidth Efficiency :  B  bps/Hz
B
R: the data rate (bps)
B: bandwidth occupied by the modulated RF signal
14
Shannon’s Bound
There is a fundamental upper bound on achievable bandwidth efficiency.
Shannon’s theorem gives the relationship between the channel
bandwidth and the maximum data rate that can be transmitted over this
channel considering also the noise present in the channel.
Shannon’s Theorem
 B max
C
S
  log 2 (1  )
B
N
C: channel capacity (maximum data-rate) (bps)
B: RF bandwidth
S/N: signal-to-noise ratio (no unit)
15
Shannon Bound
1948 Shannon demonstrated that, with
proper coding a channel capacity of

Eb  BW N o
Rb Eb 
S


Capacity BW log2 1    Rb
log2 1 

N o  Rb Eb
 N
 BW N o 
R E
S
since
 b b therefore
N BW N o
lim Capacity 1.443Rb
B 
Eb
No
since Rb  lim Capacity
B 
Required channel quality
Eb
Eb
Rb  1.443Rb
or
 1.6 dB for error free communications
No
No
=>we’re doing much worse
16
Tradeoff between BW Efficiency and
Power Efficiency
There is a tradeoff between bandwidth
efficiency and power efficiency
• Adding error control codes
– Improves the power efficiency
– Reduces the requires received power for a particular
bit error rate
– Decreases the bandwidth efficiency
– Increases the bandwidth occupancy
• M-ary keying modulation
– Increases the bandwidth efficiency
– Decreases the power efficiency
– More power is requires at the receiver
17
Example:
SNR for a wireless channel is 30dB and RF
bandwidth is 200kHz. Compute the
theoretical maximum data rate that can be
transmitted over this channel?
Answer:
 30 dB 
10 

S
 10
N
C  B log2 (1 
S
)  2 x105 log2 (1  1000)  1.99Mbps
N
18
Modulation Schemes Error Performance
19
M-ary PSK Error Performance
20
Operation Point Comparison
21
Union bound
Union bound
The probability of a finite union of events is upper bounded
by the sum of the probabilities of the individual events.
Let Aki denote that the observation vector z is closer to
the symbol vector s k than s i , when s iis transmitted.
Pr(
A
)P
sk,si) depends only on s i and s k .
ki
2(
Applying Union bounds yields
M
1MM
P
(
M
) 
P
(
s
,s

E
2
k
i)
M
i

1k

1
P
m
P
(sk,s

e(
i)
2
i)
k
1
k
i
2006-02-07
k

i
Lecture 5
22
Example of union bound
r
Z2
P
(
m
)

p
(
r
|m
)
d
r
e
1
r
1
2
Z1
s2
s1
Z

Z

Z
2
3
4
1
4
P
(
m
)
P
(
s
s
)

e
1
2
k,
1
Z3
k

2
Union bound:
2
A2 r
s2
2
r
s2
s1
r
s4
P
(
s
,s
)
(
r
|m
)
d
r
2
2
1
r
1
p
A
2
2006-02-07
2
s2
s1
1
s3
s4
Z4
s3
s1
1
s3
A3
s4
P
(
s
,s
)
(
r
|m
)
d
r
2
3
1
r
1
p
A
3
Lecture 5
1
s3
A4
s4
P
(
s
,s
)
(
r
|m
)
d
r
2
4
1
r
1
p
A
4
23
Upper bound based on minimum distance
P
(
s
,
s
)

Pr(
z
is
closer
s
s
, when
s
to
sent)
2
k
i
k than
i
iis
2


d
/
2
1
u
ik



exp(
)
du

Q

N

N
N
/
2
0
d
0
0


ik


dik  si sk
M
M


1
d
/
2
min


P
(
M
)

P
(
s
,
s
)

(
M

1
)
Q


E
2
k
i


M
N
/
2
i

1
k

1
0


k

i
Minimum distance in the signal space:
2006-02-07
dminmin
dik
Lecture 5
i,k
ik
24
Example of upper bound on av. Symbol
error prob. based on union bound
 2 (t )
s

E

E
,
i

1
,...,
4
i
i
s
d i , k  2 Es
ik
Es
d min  2 Es
s2
d1, 2
d 2,3
s3
 Es
s1
d 3, 4
d1, 4
 1 (t )
Es
s4
 Es
2006-02-07
Lecture 5
25
Summary of Useful Formulas
26
Summary of Digital Communications -1
Legend of variables mentioned in this section:
M = modulation size. (Ex: 2, 4, 16, 64)
Bw = Bandwidth in Hertz
 = Roll-off factor (from 0 to 1)
Gc = Coding Gain (convert from dB to linear to use in formulas)
Ov = Channel Overhead (convert from % to fraction : 0 to1)
BER = Bit Error Rate
27
Summary of Digital Communications - 2
•
Bits per Symbol:
Bs  Log2 M
1
Rs 
BW
1
•
Symbol Rate [symbol/second]:
•
1 
Gross Bit Rate [bps]: RG  Bs Rs  Log2 M 
 BW
1  
•
Net Data Rate [bps]:
 1 
Ri  RG (1  Ov)  Log2 M 
 BW (1  Ov)
1  
28
BER Calculation as a Function of Modulation
Scheme and Eb/No Available
• Equations given on next slide are used to calculate the bit error
rate (BER) given the bit energy by spectral noise ratio (Eb/No) as
input.
• These functions are used in their direct form for the bit error rate
calculations. Excel and some scientific calculators provide the
solution for the “erfc” function.
• The formulas provided can be inverted by numerical methods to
obtain the Eb/No required as a function of the BER.
• Also possible to draw the graphic and obtain the “inverse” by
graphical inspection.
29
BER Calculation as a Function of Modulation
Scheme and Eb/No Available - 2
Modulation
Schem e
Coh-PSK
Coh-DPSK
Coh-QPSK
Ncoh-QPSK(Dif)
Coh-8-PSK
Ncoh-8PSK(Dif)
16-QAM
32-QAM
64-QAM
256-QAM
Coh-4FSK
Theoretical BER Calculation
BER = 0.5*ERFC(SQRT((Eb/No)))
BER = ERFC(SQRT((Eb/No)))-0.5*(ERFC(SQRT((Eb/No))))^2
BER = ERFC(SQRT((Eb/No)))-0.25*(ERFC(SQRT((Eb/No))))^2
BER = ERFC(SQRT(2*(Eb/No))*SIN(PI()/4))
BER = ERFC(SQRT(3*(Eb/No))*SIN(PI()/8))
BER = ERFC(SQRT(2*3*(Eb/No))*SIN(PI()/(2*8)))
BER = ((1-1/K)/(LOG(K)/LOG(2)))*ERFC(SQRT(3*(LOG(K)/LOG(2))/(K^2-1)*(Eb/No)))
Where K = 4
BER = ((1-1/K)/(LOG(K)/LOG(2)))*ERFC(SQRT(3*(LOG(K)/LOG(2))/(K^2-1)*(Eb/No)))
Where K = 6
BER = ((1-1/K)/(LOG(K)/LOG(2)))*ERFC(SQRT(3*(LOG(K)/LOG(2))/(K^2-1)*(Eb/No)))
Where K = 8
BER = ((1-1/K)/(LOG(K)/LOG(2)))*ERFC(SQRT(3*(LOG(K)/LOG(2))/(K^2-1)*(Eb/No)))
Where K = 16
BER = 0.5*ERFC(SQRT((Eb/No)/2))
30
Maximum Likelihood (ML) Detection:
Concepts
31
Likelihood Principle
Experiment:
Pick Urn A or Urn B at random
Select a ball from that Urn.
The ball is black.
What is the probability that the selected
Urn is A?
32
Likelihood Principle (Contd)
Write out what you know!
P(Black | UrnA) = 1/3
P(Black | UrnB) = 2/3
P(Urn A) = P(Urn B) = 1/2
We want P(Urn A | Black).
Gut feeling: Urn B is more likely than Urn A (given
that the ball is black). But by how much?
This is an inverse probability problem.
Make sure you understand the inverse nature of the
conditional probabilities!
Solution technique: Use Bayes Theorem.
33
Likelihood Principle (Contd)
Bayes manipulations:
P(Urn A | Black) =
P(Urn A and Black) /P(Black)
Decompose the numerator and denomenator in terms of the
probabilities we know.
P(Urn A and Black) = P(Black | UrnA)*P(Urn A)
P(Black) = P(Black| Urn A)*P(Urn A) + P(Black|
UrnB)*P(UrnB)
We know all these values Plug in and crank.
P(Urn A and Black) = 1/3 * 1/2
P(Black) = 1/3 * 1/2 + 2/3 * 1/2 = 1/2
P(Urn A and Black) /P(Black) = 1/3 = 0.333
Notice that it matches our gut feeling that Urn A is less likely, once we
34
have seen black.
Likelihood Principle
Way of thinking…
Hypotheses: Urn A or Urn B ?
Observation: “Black”
Prior probabilities: P(Urn A) and P(Urn B)
Likelihood of Black given choice of Urn: {aka forward probability}
P(Black | Urn A) and P(Black | Urn B)
Posterior Probability: of each hypothesis given evidence
P(Urn A | Black)
{aka inverse probability}
Likelihood Principle (informal): All inferences depend ONLY on
The likelihoods P(Black | Urn A) and P(Black | Urn B), and
The priors P(Urn A) and P(Urn B)
Result is a probability (or distribution) model over the space of
possible hypotheses.
35
Maximum Likelihood (intuition)
Recall:
P(Urn A | Black) = P(Urn A and Black)
/P(Black) =
P(Black | UrnA)*P(Urn A) / P(Black)
P(Urn? | Black) is maximized when P(Black |
Urn?) is maximized.
Maximization over the hypotheses space (Urn A or Urn B)
P(Black | Urn?) = “likelihood”
=> “Maximum Likelihood” approach to maximizing
posterior probability
36
Maximum Likelihood (ML): mechanics
Independent Observations (like Black): X1, …, Xn
Hypothesis 
Likelihood Function: L() = P(X1, …, Xn | ) = i P(Xi | )
{Independence => multiply individual likelihoods}
Log Likelihood LL() = i log P(Xi | )
Maximum likelihood: by taking derivative and setting to zero
and solving for 
P
Maximum A Posteriori (MAP): if non-uniform prior
probabilities/distributions
Optimization function
37
OFDM
38
Motivation
• High bit-rate wireless applications in a multipath radio
environment.
• OFDM can enable such applications without a high
complexity receiver.
• OFDM is part of WLAN, DVB, and BWA standards
and is a strong candidate for some of the 4G wireless
technologies.
39
What is OFDM?
Modulation technique
Requires channel coding
Solves multipath problems
Transmitter:
Info
Source
Source
coding
e.g. Audio
Channel
coding /
interleaving
0110
OFDM
modulation
I/Q-mod.,
upconverter
01101101
Receiver:
Info
Sink
I/Q
PSD
Source
decoding
Decoding /
deinterleaving
RF
Radiochannel
PSD
f
Down*
converter,
-fc
I/Q-demod.
OFDM demodulation
I/Q
f
fc
RF
40
Multipath Transmission
• Fading due to constructive and destructive addition of
multipath signals.
• Channel delay spread can cause ISI.
• Flat fading occurs when the symbol period is large compared
to the delay spread.
• Frequency selective fading and ISI go together.
41
Multipath Propagation
Reflections from
walls, etc.
Time dispersive
channel
p (tImpulse
) (PDP)
response:
t [ns]
Problem with high rate
data transmission:
inter-symbol-
Multipath Radio Channel
Delay Spread
• Power delay profile conveys the multipath delay spread
effects of the channel.
• RMS delay spread quantifies the severity of the ISI
phenomenon.
• The ratio of RMS delay spread to the data symbol period
determines the severity of the ISI.
43
Inter-Symbol-Interference
Transmitted
signal:
Received
Signals:
Line-of-sight:
Reflected:
The symbols
add up on
 the
Distortion!
channel
Delays
Multipath Radio Channel
Concept of parallel transmission (1)
Channel impulse
response
Time
1 Channel (serial)
Channels are transmitted
at different frequencies
(sub-carriers)
2 Channels
8 Channels
In practice: 50 … 8000
Channels (sub-carriers)
OFDM Technology
The Frequency-Selective Radio Channel
Power response [dB]
20
15
10
5
0
-5
-10
Frequency
Interference of reflected (and LOS) radio
waves
Frequency-dependent fading
Multipath Radio Channel
Concept of parallel transmission (2)
Channel impulse
response
Time
Frequency
1 Channel (serial)
Frequency
2 Channels
Channel
transfer function
Signal is
“broadband”
Frequency
8 Channels
Frequency
Channels are
“narrowband”
OFDM Technology
Concept of an OFDM signal
Ch.1
Ch.2
Ch.3
Ch.4
Ch.5
Ch.6
Ch.7
Ch.8
Ch.9
Conventional multicarrier techniques
Ch.10
frequency
Ch.2 Ch.4 Ch.6
Ch.8 Ch.10
Ch.1 Ch.3 Ch.5
Ch.7 Ch.9
Saving of bandwidth
50% bandwidth saving
Orthogonal multicarrier techniques
Implementation and System Model
frequency
A Solution for ISI channels
• Conversion of a high-data rate stream into several low-rate
streams.
• Parallel streams are modulated onto orthogonal carriers.
• Data symbols modulated on these carriers can be recovered
without mutual interference.
• Overlap of the modulated carriers in the frequency domain different from FDM.
49
OFDM
• OFDM is a multicarrier block transmission system.
• Block of ‘N’ symbols are grouped and sent parallely.
• No interference among the data symbols
sent in a block.
50
OFDM Mathematics
N 1
s(t )   X k e
j 2 f k t
t os]
k 0
Orthogonality Condition
T
 g (t ).g
1
*
2
(t )dt  0
0
In our case
T
e
j 2 f p t
.e
 j 2 f qt
dt  0
0
For p q
Where fk=k/T
51
Transmitted Spectrum
52
Spectrum of the modulated data symbols
Rectangular Window
of duration T0
Has a sinc-spectrum
with zeros at 1/ T0
Magnitude
T0
Other carriers are
put in these zeros
 sub-carriers are
orthogonal
Frequency
N sub-carriers:
sBB,k (t )  w(t  kT )
N 1
j 2 if ( t  kT )
x
e
 i ,k
i 0
resembles
IDFT!
53
OFDM terminology
• Orthogonal carriers referred to as subcarriers {fi,i=0,....N-1}.
• OFDM symbol period {Tos=N x Ts}.
• Subcarrier spacing f = 1/Tos.
54
OFDM and FFT
• Samples of the multicarrier signal can be obtained using
the IFFT of the data symbols - a key issue.
• FFT can be used at the receiver to obtain the data symbols.
• No need for ‘N’ oscillators,filters etc.
• Popularity of OFDM is due to the use of IFFT/FFT which
have efficient implementations.
55
OFDM Signal
s(t ) 

N 1
 ( X
n  k 0
n,k
e j 2
g k (t )  
0
k
fk 
Tos
g k (t  nTos ))
fk t
t os]
Otherwise
K=0,..........N-1
56
By sampling the low pass equivalent signal at a rate N times
higher than the OFDM symbol rate 1/Tos, OFDM frame
can be expressed as:
N 1
m
Fn (m)   X n,k gk (t  nTos ) t  (n  )Tos
N
k 0

Fn (m)    X n,k e
 k 0
N 1
j 2 k
m
N
m = 0....N-1

  N .IDFT  X n,k 

57
Interpretation of IFFT&FFT
IFFT at the transmitter & FFT at the receiver
Data symbols modulate the spectrum and the
time domain symbols are obtained using the
IFFT.
Time domain symbols are then sent on the
channel.
FFT at the receiver to obtain the data.
58
Interference between OFDM Symbols
• Transmitted Signal
OS1
OS2
OS3
• Due to delay spread ISI occurs
Delay Spread
IOSI
• Solution could be guard interval between OFDM symbols
59
Cyclic Prefix
• Zeros used in the guard time can alleviate interference
between OFDM symbols (IOSI problem).
• Orthogonality of carriers is lost when multipath channels
are involved.
• Cyclic prefix can restore the orthogonality.
60
Cyclic Prefix
• Convert a linear convolution channel into a circular
convolution channel.
• This restores the orthogonality at the receiver.
• Energy is wasted in the cyclic prefix samples.
61
Cyclic Prefix Illustration
Tg
Tos
OS 1
OS 2
Cyclic Prefix
OS1,OS2 - OFDM Symbols
Tg
- Guard Time Interval
Ts
- Data Symbol Period
Tos
- OFDM Symbol Period - N * Ts
62
Guard interval (2) - Cyclic extension
63
Design of an OFDM System
Data rate;
modulation
order
Channel
impulse
response
Channel
Parameters
are needed
Guard
interval
length
x(4 … 10)
FFT
symbol
length
Nr. of
carriers
Other constraints:
•Nr. of carriers should match FFT size
and data packet length
•considering coding and modulation schemes
Introduction
Spectral Shaping by Windowing
OFDM System Design
OFDM Symbol Configuration
Not all FFT-points can be used for data carriers
Lowpass filters for AD- and DA-conversion
• oversampling required
Transfer function of
transmitter/receiver
–fs/2
–N/2, …
useable sub-carriers
DC
useable sub-carriers
…, –1, 0, 1, …
Design of an OFDM System
fs/2
…, N/2–1
frequency
sub-carrier
index i
Advantages of OFDM
Solves the multipath-propagation problem
Simple equalization at receiver
Computationally efficient
For broadband systems more efficient than SC
Supports several multiple access schemes
TDMA, FDMA, MC-CDMA, etc.
Supports various modulation schemes
Adaptability to SNR of sub-carriers is possible
Elegant framework for MIMO-systems
All interference among symbols is removed
67
Problems of OFDM (Research Topics)
time domain signal (baseband)
0.2
Synchronization issues:
Time synchronization
• Find start of symbols
Frequency synchr.
0.1
0
-0.1
-0.2
• Find sub-carrier
positions
imaginary
real
0
20
40
60
80
100
120
sample nr.
140
160
180
200
amplitude
Non-constant power envelope
Linear amplifiers needed
Channel estimation:
To retrieve data
Channel is time-variant
frequency
f frequency offset
OFDM Technology
OFDM Transmitter
X0
Serial
Input
to
Symbols Parallel
x0
Parallel
to
Serial
and
add CP
IFFT
XN-1
Add
CP
xN-1
RF Section
DAC
Windowing
69
OFDM Receiver
x0
ADC
and
Remove
CP
Serial to
Parallel
X0
Parallel
to Serial
and
Decoder
FFT
xN-1
Output
Symbols
XN-1
70
Synchronization
• Timing and frequency offset can influence performance.
• Frequency offset can influence orthogonality of subcarriers.
• Loss of orthogonality leads to Inter Carrier Interference.
71
Peak to Average Ratio
• Multicarrier signals have high PAR as compared to single
carrier systems.
• PAR increases with the number of subcarriers.
• Affects power amplifier design and usage.
72
Peak to Average Power Ratio
73
74
75