EE359 Lecture 20 Outline

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Nov 21, 2013 (3 years and 11 months ago)

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EE359


Lecture 20 Outline


Announcements


Project due Friday at 5 pm (extension request due today).


HW 8 due Friday at 5 (no late HWs: solns posted at 5).


tbp
evals at end of class (10 bonus poits)


Must be turned in no later than Monday, Dec. 6, at exam.


2
nd

Exam next Monday, 12/6, 9:30
-
1:30, Gates B01


Review of Last Lecture+RAKE Receivers


Course Summary


EE359 Megathemes


Wireless Networks


Hot Research Topics

2
nd

Exam Announcements


2nd Exam next Monday, 12/6, 9:30
-
11:30, Gates B01


Local SCPD student take in class, others contact Joice.


Open book/notes


Covers Chapters 9
-
13 (and related prior material)


Similar format to first exam


Practice finals posted (10 bonus points)


Exam review session Thursday 5
-
6 pm TCSEQ 102


Extra OHs


My OHs: Th 7
-
8, F 4
-
5 and by appt (none today).


Rajiv: T 6
-
7, W 5
-
7, F 11
-
12, Sa 3
-
4, Email: TWTh 10
-
11pm.

Review of Last Lecture


Introduction to Spread Spectrum


Direct Sequence Spread Spectrum








ISI rejection by code autocorrelation


Maximal linear codes


Good properties


Long versus short codes

S(f)

S(f)

I(f)

S(f)
*
S
c
(f)

Info. Signal

Receiver Input

Despread Signal

I(f)
*
S
c
(f)

S(f)

a
p⡦)

p⡦)
*
S
c
(f)[
ad
⡴(
+
b

-
t


䥮漮op楧i慬

剥ce楶敲䥮pu

䑥獰re慤⁓楧i慬

br
p

(f)

Interference

Rejection

ISI

Rejection


-
1


N

1


T
c


-
T
c

1


NT
c

RAKE Receiver


Multibranch receiver


Assume h(t)

a
1
d
(t)+
a
2
d
(t
-
T
c
)+…+
a
N
d
(t
-
MT
c
)


Branches synchronized to different MP components


ISI with delay jT
c

on i
th

branch reduced by
r
s
c
((j
-
i)T
c
)








Diversity combiner can use SC, MRC, or EGC

x

x

s
c
(t)

s
c
(t
-
iT
c
)

x

s
c
(t
-
MT
c
)

Demod

Demod

Demod

y(t)

Diversity

Combiner

d
k

^

a
1
d
k
+ISI
1
+n
1

a
i
d
k
+ISI
i
+n
i

a
M
d
k
+ISI
N
+n
M

Course Summary


Signal Propagation and Channel Models


Modulation and Performance Metrics


Impact of Channel on Performance


Fundamental Capacity Limits


Flat Fading Mitigation


Diversity


Adaptive Modulation


ISI Mitigation


Equalization


Multicarrier Modulation


Spread Spectrum

Future Wireless Networks

Wireless Internet access

Nth generation Cellular

Wireless Ad Hoc Networks

Sensor Networks

Wireless Entertainment

Smart Homes/Spaces

Automated Highways

All this and more…

Ubiquitous Communication Among People and Devices


Hard Delay/Energy Constraints


Hard Rate Requirements

Design Challenges


Wireless channels are a difficult and capacity
-
limited broadcast communications medium



Traffic patterns, user locations, and network
conditions are constantly changing



Applications are heterogeneous with hard
constraints that must be met by the network



Energy, delay, and rate constraints change design
principles across all layers of the protocol stack

Signal Propagation


Path Loss


Shadowing


Multipath

d

P
r
/P
t

d=vt

Statistical Multipath Model


Random # of multipath components, each with
varying amplitude, phase, doppler, and delay


Narrowband channel


Signal amplitude varies randomly (complex Gaussian).


2
nd

order statistics (Bessel function), Fade duration, etc.


Wideband channel


Characterized by channel scattering function (
B
c
,B
d
)

Modulation Considerations


Want high rates, high spectral efficiency, high power
efficiency, robust to channel, cheap.



Linear Modulation (MPAM,MPSK,MQAM)


Information encoded in amplitude/phase



More spectrally efficient than nonlinear


Easier to adapt.


Issues: differential encoding, pulse shaping, bit mapping.



Nonlinear modulation (FSK)


Information encoded in frequency



More robust to channel and amplifier nonlinearities


Linear Modulation in AWGN


ML detection induces decision regions


Example: 8PSK




P
s

depends on


# of nearest neighbors


Minimum distance
d
min
(depends on
g
s
)


Approximate expression



s
M
M
s
Q
P
g
b
a

d
min

Linear Modulation in Fading


In fading
g
s

and therefore
P
s

random


Metrics:
outage
,
average
P
s

, combined
outage and average.

P
s

P
s(target)

Outage

P
s

T
s

T
s

s
s
s
s
s
d
p
P
P
g
g
g
)
(
)
(


Moment Generating

Function Approach


Simplifies average
P
s

calculation


Uses alternate Q function representation


P
s

reduces to MGF of
g
s

distribution



Closed form or simple numerical calculation
for general fading distributions


Fading greatly increases average
P
s

.

Doppler Effects


High doppler causes channel phase to
decorrelate between symbols



Leads to an irreducible error floor for
differential modulation


Increasing power does not reduce error



Error floor depends on B
d
T
s


Delay spread exceeding a symbol time
causes ISI (self interference).




ISI leads to irreducible error floor


Increasing signal power increases ISI power



ISI requires that T
s
>>T
m

(R
s
<<B
c
)

ISI Effects

Tm

0

Capacity of Flat Fading Channels


Three cases


Fading statistics known


Fade value known at receiver


Fade value known at receiver and transmitter


Optimal Adaptation


Vary rate and power relative to channel


Optimal power adaptation is water
-
filling


Exceeds AWGN channel capacity at low SNRs


Suboptimal techniques come close to capacity

Variable
-
Rate Variable
-
Power MQAM

Uncoded

Data Bits

Delay


Point

Selector

M(
g
)
-
Q䅍


Modulator

Power: S(
g
)

T漠䍨慮C敬

g
(t)

g
⡴(

log
2

M(
g
⤠䉩瑳

佮O瑨


g
⤠P潩湴o

BSPK

4
-
QAM

16
-
QAM

Goal: Optimize S(
g
) and M(
g
) to maximize EM(
g
)

Optimal Adaptive Scheme


Power Water
-
Filling




Spectral Efficiency

S
S
K
K
K
(
)
g
g
g
g
g
g







1
1
0
0
0
else
g

1
0
g
1
g
K
g
k

g

R
B
p
d
K
K









log
(
)
.
2
g
g
g
g
g
Equals Shannon capacity with an effective power loss of
K
.

Practical Constraints


Constellation restriction


Constant power restriction


Constellation updates.


Estimation error.


Estimation delay.

Diversity


Send bits over independent fading paths


Combine paths to mitigate fading effects.



Independent fading paths


Space, time, frequency, polarization diversity.



Combining techniques


Selection combining (SC)


Equal gain combining (EGC)


Maximal ratio combining (MRC)

Diversity Performance


Maximal Ratio Combining (MRC)


Optimal technique (maximizes output SNR)


Combiner SNR is the sum of the branch SNRs.


Distribution of SNR hard to obtain.


Can use MGF approach for simplified analysis.


Exhibits 10
-
40 dB gains in Rayleigh fading.



Selection Combining (SC)


Combiner SNR is the maximum of the branch SNRs.


Diminishing returns with # of antennas.


CDF easy to obtain, pdf found by differentiating.


Can get up to about 20 dB of gain.


Multiple Input Multiple

Output (MIMO)Systems


MIMO systems have multiple (
M
) transmit and
receiver antennas




With perfect channel estimates at TX and RX,
decomposes to
M

indep. channels


M
-
fold capacity increase over SISO system


Demodulation complexity reduction



Beamforming alternative:


Send same symbol on each antenna (diversity gain)


Diversity versus capacity tradeoff

Digital Equalizers


Equalizer mitigates ISI


Typically implemented as FIR filter.



Criterion for coefficient choice


Minimize P
b

(Hard to solve for)


Eliminate ISI (Zero forcing, enhances noise)


Minimize MSE (balances noise increase with ISI removal)



Channel must be learned through training and
tracked during data transmission.

n(t)

c(t)

+

d(t)=
S
d
n
p(t
-
nT)

g*(
-
t)

H
eq
(z)

d
n

^

y
n

Multicarrier Modulation


Divides bit stream into N substreams


Modulates substream with bandwidth B/N


Separate subcarriers


B/N<B
c

flat fading (no ISI)


FDM has substreams completely separated


OFDM overlaps substreams


More spectrally efficient


Substreams separated in receiver


Efficient FFT Implementation


One modulator and demodulator


FFT performs frequency translation


Cyclic prefix eliminates ISI between blocks

Fading Across Subcarriers


Compensation techniques


Frequency equalization (noise enhancement)


Precoding (channel inversion)


Coding across subcarriers


Adaptive loading (power and rate)


Practical Issues for OFDM


Peak
-
to
-
average power ration


System imperfections

Direct Sequence

Spread Spectrum


Bit sequence modulated by
chip

sequence




Spreads bandwidth by large factor (K)


Despread by multiplying by s
c
(t) again (s
c
(t)=1)


Mitigates ISI and narrowband interference


ISI mitigation a function of code autocorrelation


Must synchronize to incoming signal

s(t)

s
c
(t)


T
b
=KT
c


T
c

S(f)

S
c
(f)


1/
T
b


1/
T
c

S(f)
*
S
c
(f)

2

RAKE Receiver


Multibranch receiver


Branches synchronized to different MP components








These components can be coherently combined


Use SC, MRC, or EGC

x

x

s
c
(t)

s
c
(t
-
iT
c
)

x

s
c
(t
-
NT
c
)

Demod

Demod

Demod

y(t)

Diversity

Combiner

d
k

^

Megathemes of EE359




The wireless vision poses great technical challenges


The wireless channel greatly impedes performance


Low fundamental capacity.


Channel is randomly time
-
varying.


ISI must be compensated for.


Hard to provide performance guarantees (needed for multimedia).


We can compensate for flat fading using diversity or adapting.


MIMO channels promise a great capacity increase.



A plethora of ISI compensation techniques exist


Various tradeoffs in performance, complexity, and implementation.

Wireless Network Design


Broadcast and Multiple Access Channels


Spectral Reuse


Cellular System Design


Ad
-
Hoc Network Design


Networking Issues

Broadcast and Multiple

Access Channels

Broadcast (BC):


One Transmitter


to Many Receivers.

Multiple Access (MAC):


Many Transmitters


to One Receiver.

R
1

R
2

R
3

x

h
1
(t)

x

h
21
(t)

x

h
3
(t)

x

h
22
(t)

7C29822.033
-
Cimini
-
9/97

Bandwidth Sharing


Dedicated channel assignment



Frequency Division



Time Division



Code Division


Hybrid Schemes

Code Space

Time

Frequency

Code Space

Time

Frequency

Code Space

Time

Frequency

Multiple Access SS



Interference between users mitigated by code
cross correlation







In downlink, signal and interference have
same received power



In uplink, “close” users drown out “far” users
(near
-
far problem)

)
(
)
2
cos(
5
.
5
.
)
(
)
(
5
.
5
.
))
(
2
cos(
)
2
cos(
)
(
)
(
)
(
)
2
(
cos
)
(
)
(
)
(
ˆ
12
2
1
0
2
1
2
2
1
1
1
2
2
0
2
2
2
1
1
1
t
r
t
p
a
a
t
p
p
t
t
a
p
a
c
T
c
c
c
c
c
c
T
c
c
f
d
d
dt
t
s
t
s
d
d
dt
t
f
t
f
t
s
t
s
t
s
t
f
t
s
t
s
t
x
b
b











a

a

Multiuser Detection


In all CDMA systems and in TD/FD/CD
cellular systems, users interfere with each other.



In most of these systems the interference is
treated as noise.


Systems become interference
-
limited


Often uses complex mechanisms to minimize impact
of interference (power control, smart antennas, etc.)



Multiuser detection exploits the fact that the
structure of the interference is known


Interference can be detected and subtracted out


Better have a darn good estimate of the interference

RANDOM ACCESS TECHNIQUES

7C29822.038
-
Cimini
-
9/97

Random Access


Dedicated channels wasteful for data


use statistical multiplexing


Techniques


Aloha


Carrier sensing


Collision detection or avoidance


Reservation protocols


PRMA


Retransmissions used for corrupted data


Poor throughput and delay characteristics under
heavy loading


Hybrid methods

BASE

STATION

Cellular System Design


Frequencies, timeslots, or codes reused at
spatially
-
separate locations


Efficient system design is interference
-
limited


Base stations perform centralized control functions


Call setup, handoff, routing, adaptive schemes, etc.

8C32810.44
-
Cimini
-
7/98

Design Issues


Reuse distance


Cell size


Channel assignment strategy


Interference management


Power adaptation


Smart antennas


Multiuser detection


Dynamic resource allocation

Dynamic Resource Allocation

Allocate resources as user and network conditions change


Resources:


Channels


Bandwidth


Power


Rate


Base stations


Access



Optimization criteria


Minimize blocking (voice only systems)


Maximize number of users (multiple classes)


Maximize “revenue”


Subject to some minimum performance for each user


BASE

STATION

Ad
-
Hoc Networks


Peer
-
to
-
peer communications


No backbone infrastructure or centralized control


Routing can be multihop.


Topology is dynamic.


Fully connected with different link SINRs


Open questions


Fundamental capacity


Optimal routing


Resource allocation (power, rate, spectrum, etc.) to meet QoS

Power Control


Assume each node has an SIR constraint





Write the set of constraints in matrix form



If
r
F
<1



愠a湩n略⁳潬畴楯n



Power control algorithms


Centralized or distributed

i
j
i
j
ij
i
i
ii
i
P
G
P
G
SIR
g








0
P
0,
u
P
F
I






u
F
I
P
-1
*


P
1

P
2

P
*

Feasible Region

Iterative Algorithm

Power control for random channels more complicated

Wireless Networks with
Energy
-
Constrained Nodes



Limited node processing/communication capabilities


Nodes can cooperate in transmission and reception.


Intelligence must be “in the network”


Data flows to centralized location.


Low per
-
node rates but 10s to 1000s of nodes


Data highly correlated in time and space.

Energy
-
Constrained Nodes


Each node can only send a
finite

number of bits.


Energy minimized by sending each bit very slowly.


Introduces a delay versus energy tradeoff for each bit.



Short
-
range networks must consider both transmit
and processing energy.


Sophisticated techniques not necessarily energy
-
efficient.


Sleep modes save energy but complicate networking.



Changes
everything
about the network design:


Bit allocation must be optimized across
all
protocols.


Delay vs. throughput vs. node/network lifetime tradeoffs.


Optimization of node cooperation.

NETWORK ISSUES

8C32810.53
-
Cimini
-
7/98

Higher Layer

Networking Issues


Architecture


Mobility Management


Identification/authentication


Routing


Handoff


Control


Reliability and Quality
-
of
-
Service

Wireless Applications and QoS

Wireless Internet access

Nth generation Cellular

Wireless Ad Hoc Networks

Sensor Networks

Wireless Entertainment

Smart Homes/Spaces

Automated Highways

All this and more…

Applications have hard delay constraints, rate requirements,

and energy constraints that must be met

These requirements are collectively called QoS

Challenges to meeting QoS


Wireless channels are a difficult and capacity
-
limited broadcast communications medium



Traffic patterns, user locations, and network
conditions are constantly changing



No single layer in the protocol stack can
guarantee QoS: cross
-
layer design needed



It is impossible to guarantee that hard constraints
are always met, and average constraints aren’t
necessarily good metrics.

Crosslayer Design


Application


Network



Access


Link


Hardware


Delay Constraints

Rate Requirements

Energy Constraints

Mobility

Optimize and adapt across design layers

Provide robustness to uncertainty

Schedule dedicated resources

4G


Is 4G an evolution, an alternative, or a supplement
to 3G, or something more?



What services should 4G support?





Research challenges associated with 4G:


Air interface


Flexible QoS


Support for heterogeneous services


Cross
-
layer design

Promising Research Areas


Link Layer


Wideband air interfaces and dynamic spectrum management


Practical MIMO techniques (modulation, coding, imperfect CSI)


Cellular Systems


How to use multiple antennas


Multihop routing


Variable QoS


Ad Hoc Networks


How to use multiple antennas


Cross
-
layer design


Sensor networks


Energy
-
constrained communication


Cooperative techniques




Information Theory


Capacity of ad hoc networks


Imperfect CSI


Incorporating delay: Rate distortion theory for networks

The End


Thanks!!!


Have a great winter break