SYSC 4600 Digital Communications
Fundamental Dynamics
Fundamental Dynamics of Digital Communications
Halim Yanıkömeroğlu
Department of Systems & Computer Engineering
Carleton University
Ottawa, Canada
Fall 2014 – Halim Yanikomeroglu
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SYSC 4600 Digital Communications
Fundamental Dynamics
Outline
dB Notation
The Big Picture: OSI Model
Major impairments in communication systems
Noise (AWGN)
SNR
Main goals of digital communications
MAC, RRM, RAN
Fall 2014 – Halim Yanikomeroglu
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SYSC 4600 Digital Communications
Fundamental Dynamics
What is wrong with the below figure?
Fall 2014 – Halim Yanikomeroglu
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SYSC 4600 Digital Communications
Fundamental Dynamics
What is wrong with the below figure?
The detail is lost for the small values of the vertical axis!
Fall 2014 – Halim Yanikomeroglu
Page 4 of 28
SYSC 4600 Digital Communications
Fundamental Dynamics
What is wrong with the below figure?
The detail is lost for the small values of the vertical axis!
Want to show large and small values on the same scale?
Fall 2014 – Halim Yanikomeroglu
Page 5 of 28
SYSC 4600 Digital Communications
Fundamental Dynamics
Logarithmic versus Linear Scale
What is wrong with the below figure?
The detail is lost for the small values of the vertical axis!
Want to show large and small values on the same scale?
Use logarithmic scale (not linear scale)
Fall 2014 – Halim Yanikomeroglu
Page 6 of 28
SYSC 4600 Digital Communications
Fundamental Dynamics
dB Notation
logc(a x b) = logc(a) + logc(b)
Decibel notation:
logc(a ÷ b) = logc(a) – logc(b)
Field quantities:
Power quantities:
20 log10 (.)
10 log10 (.)
In this course: 10 log10 (.)
x  + (increased by 1,000,000 times  increased by 60 dB)
÷  - (decreased by 50 times  decreased by 17 dB)
A [U] = (10 log10 A) [dBU]
A [unitless] = (10 log10 A) [dB]
Fall 2014 – Halim Yanikomeroglu
Linear dB
5000
37
400
26
10
10
8
9
5
7
2
3
1
0
0.5
-3
0.125
-9
0.01
-20
0.0005
-33
Page 7 of 28
SYSC 4600 Digital Communications
Fundamental Dynamics
dB Notation
logc(a ÷ b) = logc(a) – logc(b)
logc(a x b) = logc(a) + logc(b)
Decibel notation:
Field quantities:
Power quantities:
20 log10 (.)
10 log10 (.)
In this course: 10 log10 (.)
x  + (increased by 1,000,000 times  increased by 60 dB)
÷  - (decreased by 50 times  decreased by 17 dB)
A [U] = (10 log10 A) [dBU]
A [unitless] = (10 log10 A) [dB]
P [W] = (10 log10P[W]) [dBW]
P [mW] = (10 log10P[mW]) [dBm]
P [dBW] = (P+30) [dBm]
Ex: 2 [W] = 3 [dBW]
Ex: 2 [mW] = 3 [dBm]
Ex: 5 [dBW] = 35 [dBm]
10 log10SNR = (10 log10(Psignal [mW] / Pnoise [mW])) [dB]
10 log10SNR = (10 log10Psignal) [dBm] – (10 log10Pnoise) [dBm]
X [dBm] – Y [dBm] = Z [dB];
Fall 2014 – Halim Yanikomeroglu
Linear dB
5000
37
400
26
10
10
8
9
5
7
2
3
1
0
0.5
-3
0.125
-9
0.01
-20
0.0005
-33
X [dBm] + Y [dB] = Z [dBm]
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SYSC 4600 Digital Communications
Fundamental Dynamics
The Big Picture: OSI Model
The Open Systems Interconnection (OSI) model is a prescription of
characterizing and standardizing the functions of a communications
system in terms of abstraction layers. [Wiki]
For example, a layer that
provides error-free
communications across a
network provides the path
needed by applications
above it, while it calls the
next lower layer to send
and receive packets that
make up the contents of
that path. Two instances
at one layer are
connected by a horizontal
connection on that layer.
[Wiki]
Fall 2014 – Halim Yanikomeroglu
http://www.hill2dot0.com/wiki/index.php?title=OSI_reference_model
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SYSC 4600 Digital Communications
Fundamental Dynamics
The Big Picture: OSI Model
The physical layer defines the means of transmitting raw bits rather
than logical data packets over a physical link connecting network
nodes. The bit stream may be grouped into code words or symbols
and converted to a physical signal that is transmitted over a hardware
transmission medium.
The physical layer provides
an electrical, mechanical,
and procedural interface to
the transmission medium.
The shapes and properties
of the electrical connectors,
the frequencies to broadcast
on, the modulation scheme
to use and similar low-level
parameters, are specified
here. [Wiki]
http://baluinfo.com/networking/basic-networking-part-2/
Fall 2014 – Halim Yanikomeroglu
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SYSC 4600 Digital Communications
Fundamental Dynamics
Imprecise Terminology
Often used synonymously in industry:
Digital Communications (SYSC 4600)
Transmission Technologies
Physical Layer
But they have slightly different meanings
Fall 2014 – Halim Yanikomeroglu
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SYSC 4600 Digital Communications
Fundamental Dynamics
Digital Communications Block Diagram
Digital Communications, Sklar
Fall 2014 – Halim Yanikomeroglu
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SYSC 4600 Digital Communications
Fundamental Dynamics
Major Impairments in Communication Systems: A Simple Picture
noise
Transmitter
Channel
Receiver
interference
Fall 2014 – Halim Yanikomeroglu
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SYSC 4600 Digital Communications
Fundamental Dynamics
Major Impairments in Communication Systems: A Simple Picture
noise
Transmitter
Noise: always present
Fall 2014 – Halim Yanikomeroglu
Channel
Receiver
interference
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SYSC 4600 Digital Communications
Fundamental Dynamics
Major Impairments in Communication Systems: A Simple Picture
noise
Transmitter
Noise: always present
Channel
Receiver
interference
Channel
Ideal channel (AWGN channel)
does not distort (change the shape of) the transmitted signal
introduces attenuation and delay
Fall 2014 – Halim Yanikomeroglu
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SYSC 4600 Digital Communications
Fundamental Dynamics
Major Impairments in Communication Systems: A Simple Picture
noise
Transmitter
Noise: always present
Channel
Receiver
interference
Channel
Ideal channel (AWGN channel)
does not distort (change the shape of) the transmitted signal
introduces attenuation and delay
Non-idealities in channel
Distortion channel: distorts; may introduce self-interference
Fading channel: ideal channel with a time-varying impulse response
Fall 2014 – Halim Yanikomeroglu
Page 16 of 28
SYSC 4600 Digital Communications
Fundamental Dynamics
Major Impairments in Communication Systems: A Simple Picture
noise
Transmitter
Noise: always present
Channel
Receiver
interference
Channel
Ideal channel (AWGN channel)
does not distort (change the shape of) the transmitted signal
introduces attenuation and delay
Non-idealities in channel
Distortion channel: distorts; may introduce self-interference
Fading channel: ideal channel with a time-varying impulse response
Interference (interference channel)
Major source of interference: other-user interference (co-channel interference)
Occurs mainly in wireless channels
Can be handled via signal processing, beamforming, RRM, …
Fall 2014 – Halim Yanikomeroglu
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SYSC 4600 Digital Communications
Fundamental Dynamics
Additive White Gaussian Noise (AWGN)
AWGN is a channel model in which the only impairment to
communication is noise
AWGN: A linear addition of white noise with a constant spectral
density and a Gaussian distribution of amplitude. [Wiki]
The model does not account for channel impairments. However, it
produces simple and tractable mathematical models which are useful
for gaining insight into the underlying behavior of a system before
these other phenomena are considered. [Wiki]
Gaussian noise: Noise amplitude is a Gaussian distributed random
variable (central limit theorem).
White noise: An idealized noise process with a power spectral density
independent of frequency.
Fall 2014 – Halim Yanikomeroglu
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SYSC 4600 Digital Communications
Fundamental Dynamics
Additive White Gaussian Noise (AWGN)
Pnoise= k T B F = N0 B F
k:
T:
N 0:
B:
F:
Boltzmann’s constant = 1.38 x 10-23 J/K
Temperature in degrees Kelvin (generally taken as 290oK)
Noise power spectral density (constant)
Bandwidth (signal bandwidth)
Noise figure
N0 = k T = -174 dBm/Hz
White noise power spectral density
SN(f)
N0/2
Ex: 200 KHz channel (LTE resource block)
F = 7 dB  Pnoise = -114 dBm
Broadband signal  Pnoise increases
Fall 2014 – Halim Yanikomeroglu
f
Infinite total power (?)
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SYSC 4600 Digital Communications
Fundamental Dynamics
SNR, SINR
Signal-to-Noise Ratio: Defined at the receiver front end
SNR = (signal power) ∕ (noise power)
SNR = Psignal ∕ Pnoise
SNR = (bit energy) ∕ (noise power spectral density)
SNR = Eb ∕ N0
Signal-to-Interference-plus-Noise Ratio:
SINR = Psignal ∕ (Pinterference+ Pnoise)
Classical view: Threat interference as noise  business as usual
(use the theory developed for AWGN channel)
Modern view: Can we exploit the structure in the interference signal?
Fall 2014 – Halim Yanikomeroglu
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SYSC 4600 Digital Communications
Fundamental Dynamics
Wireless Channel: Fading Signal
SNR
AWGN channel:
Fading channel:
Fall 2014 – Halim Yanikomeroglu
Ps: fixed
 SNR: fixed
Ps: variable  SNR: variable
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SYSC 4600 Digital Communications
Fundamental Dynamics
Main Goal of Digital Communications
Transmitter
Channel
SNR
Receiver
noise
Main Goal: For a given fixed SNR or an SNR distribution what
operations should take place at transmitter and receiver to improve
the performance?
Performance: Some meaningful metric
User metrics: (ultimately) eye, ear, feeling, smell, …
MOS (mean opinion scores)  frame error rate (FER)  packet error
rate (PER)  symbol error rate (SER)  bit error rate (BER) 
maximize SNR
resort to better transmission and/or reception techniques
Fall 2014 – Halim Yanikomeroglu
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SYSC 4600 Digital Communications
Fundamental Dynamics
Main Goal of Digital Communications
SNR=10 dB
Fall 2014 – Halim Yanikomeroglu
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SYSC 4600 Digital Communications
Fundamental Dynamics
Main Goal of Digital Communications
noise
TX
Channel
+
RX
• How do you send information (reliably) through a channel?
• For a given channel (medium), design TX and RX for best performance
• Best? Maximize/minimize SER, BER, SNR, mutual information, …
• Network metrics may be different than link metrics:
number of users, outage, sum (aggregate) rate, revenue, …
Fall 2014 – Halim Yanikomeroglu
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SYSC 4600 Digital Communications
Fundamental Dynamics
Main Goal of Digital Communications
Transmitter
Channel
SNR
Receiver
noise
For a given fixed SNR (or an SNR distribution) what operations
should take place at transmitter and receiver to improve the
performance?
Pulse shaping
Modulation, demodulation
Channel coding, decoding
Diversity
Equalization
…
Fall 2014 – Halim Yanikomeroglu
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SYSC 4600 Digital Communications
Fundamental Dynamics
Channel Capacity
Channel capacity, Shannon capacity, information-theoretic capacity
C = log2(1+SNR), bits per second per Hertz
Non-constructive existence theorem
Developments
Shannon’s original formulation: 1948
Block codes, convolutional codes, …
Turbo codes (1993)
Low-density parity check (LDPC) codes (1963, 1996)
Polar codes (2008)
Fall 2014 – Halim Yanikomeroglu
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SYSC 4600 Digital Communications
Fundamental Dynamics
Bandwidth vs Rate
T: Pulse duration,
W: Bandwidth
R: Rate  R = 1/T
Inverse relation between T and W
Direct relation between R and W
Narrow pulses (high rates)  Large bandwidth
Fall 2014 – Halim Yanikomeroglu
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SYSC 4600 Digital Communications
Fundamental Dynamics
MAC, RRM, RAN
Want SNR ↑ ?  PS ↑ and/or Pn ↓ (limited control on Pn)
Want SINR ↑ ?  PS ↑ and/or PI ↓ and/or Pn ↓(limited control on Pn)
How can we increase PS ?
How can we decrease PI ?
Answer:
Medium Access Control (MAC) [layer 2]
Radio Resource Management (RRM) [layer 2]
Radio Access Network (RAN)
How do we compute PS ?  Propagation modeling
Fall 2014 – Halim Yanikomeroglu
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