Principles of communication systems 6th edition

Introduction


Signal Retrieval and Communication




Theory of systems for the conveyance of
information
Characteristics of communication systems


Uncertainty




Keep in mind: Signal retrieval problem




Noise and “information” (deterministic vs probabilistic)
Communication (only particular type of signal retrieval
problem)

Usually two resources to consider


Bandwidth vs. Power

2


Innovation in microelectronics and
signal processing have led to the
proliferation of communication systems

3


Block Diagram of a Communication
System

Keep in mind that this is only a model!
Can we make it simpler? More
complicated? Consequences?
4


Components of Block Diagram


Input transducer
 Messages




Message conversion




Analog or digital
E.g. speech  voltage variations

Transmitter
 Couple the message to the channel



Modulation, filtering, amplification, and coupling
Modulation






For the ease of radiation
To reduce noise and interference
For channel assignment
For multiplexing or transmission of several messages over a single channel
To overcome equipment limitations

5


Channel Characteristics


Channel



Signal degradation (convolutive noise)
Additive “noise”




Receiver




Sensor noise, thermal noise, interference (e.g. MUI, jammer,
…)

Demodulation, amplification

Output transducer


Loudspeaker, tape recorder, PCs, CRT, LCD, etc.
6


Channel Characteristics


Additive noise sources (usu. less troublesome)


Internal noise




Noise generated by components within a communication system,
such as resistors and solid-state active devices
Thermal noise




Shot noise




Random motion of free electrons in conductor or semiconductor excited
by thermal excitation
Random arrival of discrete charge carriers in thermionic tubes or
semiconductor junction devices

Flicker noise


Produced in semiconductors by a mechanism not well understood and is

more severe in lower frequency
7


Channel Characteristics


External noise


Noise generated from sources outside a communication
system, including atmospheric, man-made, and extraterrestrial
sources






Atmospheric noise
 Impulsive in nature, i.e. large amplitude, short-duration bursts
(how should we model it? Why model it?)
Man-made noise
 Impulsive
 Automobile and aircraft ignition noise, radio-frequency
interference (RFI), e.g. MUI
Extraterrestrial noise
 Solar and cosmic noise
8

Channel Characteristics


Convolutive noise (usu. very troublesome)


Multiple transmission paths


Diffuse type




Specular type




One or two strong reflected rays

Fading




Numerous reflected components

Random changes in attenuation within the transmission

medium

How do we model it? Why do we care?
9


10


802.11a/b/g

802.11n/ac

802.11ad

11


Traditional Cellular Network
Characteristics
•
Large frequency reuse
factor
•
Performance enhanced
by increasing spectrum
efficiency
Major Issues
•
Low system capacity

•
Poor performance for
cell edge users

MBS

MBS

MBS

MBS

MBS

MBS

MBS

MBS

MBS

12


B4G Objectives
Spectrum
Efficiency

×

Spectrum
Extension
/Utilization

×

=

Network
Density

Required capacity
2
(bps/km = bps/Hz/cell × Hz × cell/km2)

1000x
Capacity

Traffic offloading
(alternative means for communications)

Non-orthogonal multiple access

controller

Massive MIMO, advanced
receiver

Spectrum efficiency

WiFi offload, D2D, etc.
Dense urban
Shopping mall
Home/office

Current
capacity

Cellular network assists
local area radio access

Spectrum extension
Multiple access
technologies with Tx-Rx
cooperative interference
cancellation

New cellular concept for cost/energy
efficient dense deployment

Existing cellular bands
Very wide

Super wide
frequency

Hybrid access using coverage and
capacity spectrum bands

13


In a Nutshell…
Femto-BS

D2D
Relay
Characteristics
•
Wireless backhaul
•
Open access
•
Operator-deployed
Major Issues
•
Effective backhaul
design
•
Mitigating relay to
macrocell
interference

Characteristics
•
Wired backhaul
•
User-deployed
•

Closed/open/hybrid
access
Major Issues
•
Femto-to-femto
interference and
femto-to-macro
interference

Characteristics
•
Resource reuse
•
Operatorassisted
Major Issues
•
Neighbor
discovery
•
Offloading
traffic

D2D
backhaul
Relay

MBS

Femto-BS

Pico-BS

Macrocells: 20-40 watts
(large footprint)
Pico-BS

Characteristics
•
Wired backhaul
•
Operator-deployed
•
Open access
Major Issues
•
Offloading traffic from macro to picocells
•
Mitigate interference

14


Systems Analysis Techniques


Time and frequency domain analyses




Looking at things from different perspective

Modulation and communication theories




Modulation theory employs time and frequency domain
analyses to analyze and design systems for modulating
and demodulating of information-bearing signals
Analysis of interfering signals on system performance,
and design of systems to counteract their effects are
part of communication theory, which makes use of
modulation theory
15


Probabilistic Approaches to System
Optimization


As seen earlier, proper modeling of (additive and
convolutive) noise (incl. interference) is important


Probabilistic models are often used




Why?

Design


Optimal design is crucial




Many “optimal” design are not optimal – depends on perspective

How do we do it? (We are engineers, this is important!)


Statistical signal detection and estimation theory




Wiener optimum filter, matched filter, adaptive filter, and many more…

Information theory and coding


Shannon says it can be done, but didn’t tell us how it can be done

16

Signal and Linear System
Analysis


Signal Model and Classifications


Deterministic signals




Completely specified function of time: predictable, no
uncertainty. E.g.
=
x ( t ) A cos ω0t , − ∞ < t < ∞,
where A and ω0 are constants

Random/Stochastic signals




Take on random values at any given time instant and
characterized by pdf: not completely predictable, with
uncertainty. E.g. x(n) = value of a die shown when tossed at time
index n
If the signal is random, how do we describe (model) it?
2

Signal Model and Classifications


Periodic signal




A signal x(t) is periodic iff there exists a constant T0,
such that x(t + T0) = x(t), ∀t. The smallest such T0 is
called fundamental period or simply period

Aperiodic signal


Cannot find a finite T0 such that x(t + T0) = x(t), ∀t

3


Signal Model and Classifications


Phasor signal and spectra


A special periodic function
j (ω0t +θ )
=

x ( t ) Ae
=
Ae jθ e jω0t ,

x ( t )  rotating phasor, Ae jθ  phasor, A, θ ∈ 


Why use this complex signal?





Key part of modulation theory
Construction signal for almost any signal
Easy mathematical analysis for signal
Phase carries time delay information

4


Signal Model and Classifications


More on phasor signal



Information is contained in A and t (given a fixed f0 or ω0)
The related real sinusoidal function

x ( t ) A cos (ω0=
t + θ ) Re { x ( t )} , or=
x ( t ) A cos (ω0=
t +θ )
=


1
1
x ( t ) + x * ( t )
2
2

In vector form graphically

5


Signal Model and Classifications


Frequency-domain representation


Line spectra

x ( t )

x (t )

Single-sided (SS) amplitude and phasor vs double-sided (DS):

6


Signal Model and Classifications


Singular functions

Unit impulse function: δ ( t )
1. Definition

∫ δ ( t ) dt = 1
x ( 0 ) ∫ δ ( t ) dt = x ( 0 )
t

⇒ ∫ x ( t ) δ ( t ) dt =
t

t

2. Sifting property:
Defines a precise sample point of x ( t ) at time t (or t0 if δ ( t - t0 ))
=
x ( t0 )

∫ x ( t ) δ ( t − t ) dt
t

0

3. Basic function for linearly constructing a time signal
=
x (t )
4. Some properties
1
δ ( at=
δ (t ) ;
)
a

∫τ x (τ ) δ ( t − τ ) dτ
δ ( t=
) δ ( −t ) : even function

7


Signal Model and Classifications
5. What is δ ( t ) precisely? Some intuitive ways of realizing it:
E.g. 1

1

, t < ε,
 lim
δ ( t ) =  ε →0 2ε
0, otherwise

E.g. 2

πt 
 1
δ ( t ) = lim ε  sin 
ε →0
ε 
 πt

2

⇒ Any signal having unit area and zero width in the limit as some
parameter goes to zero is a suitable representation

8


Signal Model and Classifications
Unit step function: u ( t )
Definition
u (t )
=

δ ( λ ) d λ; δ (t )
∫=
t

−∞

du ( t )
dt

9