Upload
others
View
10
Download
0
Embed Size (px)
Citation preview
Opportunistic Communication:
From Theory to Practice
David Tse
Department of EECS, U.C. Berkeley
March 9, 2005
Viterbi Conference
Fundamental Feature of Wireless Channels:
Time Variation
Time
time-varying channel
StrengthChannel
• multipath fading
• large-scale channel variations
• time-varying interference
Traditional Approach to Wireless System Design
constant channel
Time
time-varying channel
StrengthChannel
StrengthChannel
Time
Compensates for channel fluctuations.
Case Study: CDMA Systems
Two main compensating mechanisms:
1. Channel diversity:
• time diversity via coding and interleaving
• frequency diversity via Rake combining,
• macro-diversity via soft handoff
• transmit/receive antenna diversity
2. Interference management:
• power control
• interference averaging
Case Study: CDMA Systems
Two main compensating mechanisms:
1. Channel diversity:
• time diversity via coding and interleaving
• frequency diversity via Rake combining,
• macro-diversity via soft handoff
• transmit/receive antenna diversity
2. Interference management:
• power control
• interference averaging
What Drives this Approach?
constant channel
Time
time-varying channel
StrengthChannel
StrengthChannel
Time
Main application is voice, with very tight latency requirements.
Needs a consistent channel.
Opportunistic Communication: A Different View
Transmit more when and where the channel is good.
Exploits fading to achieve higher long-term throughput, but no
guarantee that the ”channel is always there”.
Appropriate for data with laxer latency requirements.
Opportunistic Communication: A Different View
Transmit more when and where the channel is good.
Exploits fading to achieve higher long-term throughput, but no
guarantee that the ”channel is always there”.
Appropriate for data with laxer latency requirements.
Opportunistic Communication: A Different View
Transmit more when and where the channel is good.
Exploits fading to achieve higher long-term throughput, but no
guarantee that the ”channel is always there”.
Appropriate for data with laxer latency requirements.
Point-to-Point Fading Channels
��
���� ��� ���
��� �������
��� �����
��� ���
Capacity-achieving strategy is waterfilling over time. (Goldsmith and
Varaiya 97)
Performance over Rayleigh Channel
� � � � ��� ���
����� ���
���
�������
����� ��� �"!� �"!$#
� � %�&�')(+*-,)�
�� ���� ���� ���
.
/
�
0
1
�
�
Performance: Low SNR
-10 -5 0 5
Full CSI
10
SNR [dB]
CSIR
C
Cawgn
0.5-15-20
3
2.5
2
1.5
1
At low SNR, capacity can be greater when there is fading.
Hitting the Peaks
(a)
(b)
(a)
(b)
Optimal AllocationNear Optimal Allocation
N0|h[m]|2
Time m
Time m
P [m]
1λ
N0|h[m]|2
N0|h[m]|2
Time m
Time m
1λ
P [m]
1λ
1λ
N0|h[m]|2
At low SNR, one can transmits only when the channel is at its peak.
Primarily a power gain.
In practice, hard to realize such gains due to difficulty in tracking the
channel when transmitting so infrequently.
Hitting the Peaks
(a)
(b)
(a)
(b)
Optimal AllocationNear Optimal Allocation
N0|h[m]|2
Time m
Time m
P [m]
1λ
N0|h[m]|2
N0|h[m]|2
Time m
Time m
1λ
P [m]
1λ
1λ
N0|h[m]|2
At low SNR, one can transmits only when the channel is at its peak.
Primarily a power gain.
In practice, hard to realize such gains due to difficulty in tracking the
channel when transmitting so infrequently.
Multiuser Opportunistic Communication
Fading Channel
MobileUser 1
User 2
User KBase Station
(Knopp and Humblet 95, T 97)
Performance
2
4
6
8
AWGNCSIRFull CSI
C [bits/s/Hz]
SNR [dB]
sum
K=16
K=4
K=2
K=1
Multiuser Diversity
Total average SNR = 0 dB.
2 4 6 8 10 12 14 160
0.5
1
1.5
2
2.5
AWGN Channel
Rayleigh Fading
Number of Users
Tot
al S
pect
ral E
ffici
eny
in b
ps/H
z
• In a large system with users fading independently, there is likely to
be a user with a very good channel at any time.
• Long term total throughput can be maximized by always serving
the user with the strongest channel.
Multiuser Diversity: A More Insightful Look
0 50 100 150 200 250 3000.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Time Slots
Req
uest
ed r
ates
in b
ps/H
z
• Independent fading makes it likely that users peak at different
times.
• In a wideband system with many users, each user operates at low
average SNR, effectively accessing the channel only when it is near
its peak.
• In the downlink, channel tracking can be done via a strong pilot
amortized between all users.
Multiuser Diversity: A More Insightful Look
0 50 100 150 200 250 3000.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Time Slots
Req
uest
ed r
ates
in b
ps/H
z
• Independent fading makes it likely that users peak at different
times.
• In a wideband system with many users, each user operates at low
average SNR, effectively accessing the channel only when it is near
its peak.
• In the downlink, channel tracking can be done via a strong pilot
amortized between all users.
Multiuser Diversity: A More Insightful Look
0 50 100 150 200 250 3000.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Time Slots
Req
uest
ed r
ates
in b
ps/H
z
• Independent fading makes it likely that users peak at different
times.
• In a wideband system with many users, each user operates at low
average SNR, effectively accessing the channel only when it is near
its peak.
• In the downlink, channel tracking can be done via a strong pilot
amortized between all users.
1x EV-DO’s DownLink
Fixed Transmit Power
User 2
User 1
Base Station
Data
Measure Channel Request Rate
Information theory suggests that resource should be scheduled in a
channel-dependent way.
Challenge is to exploit multiuser diversity while sharing the benefits
fairly and timely to users.
1x EV-DO’s DownLink
Fixed Transmit Power
User 2
User 1
Base Station
Data
Measure Channel Request Rate
Information theory suggests that resource should be scheduled in a
channel-dependent way.
Challenge is to exploit multiuser diversity while sharing the benefits
fairly and timely to users.
Symmetric Users
0 200 400 600 800 1000 1200 1400 1600 1800 20000
500
1000
1500
time slots
requ
este
d ra
te (
kbps
)
symmetric channels
Serving the best user at each time is also fair in terms of long-term
throughputs.
Asymmetric Users
1000 1500 2000 25000
500
1000
1500
2000
2500
time slots
requ
este
d ra
te (
kbps
)
asymmetric channels
tc
• Want to serve each user when it is near its peak.
• A peak should be defined with respect to a latency time-scale tc.
Asymmetric Users
1000 1500 2000 25000
500
1000
1500
2000
2500
time slots
requ
este
d ra
te (
kbps
)
asymmetric channels
tc
• Want to serve each user when it is near its peak.
• A peak should be defined with respect to a latency time-scale tc.
Asymmetric Users
1000 1500 2000 25000
500
1000
1500
2000
2500
time slots
requ
este
d ra
te (
kbps
)
asymmetric channels
tc
• Want to serve each user when it is near its peak.
• A peak should be defined with respect to a latency time-scale tc.
Proportional Fair Scheduling
(T 99)
Schedule the user with the highest ratio Rk/Tk, where
Rk = current requested rate of user k
Tk = average throughput in past tc time slots
Performance
2 4 6 8 10 12 14 160
100
200
300
400
500
600
700
800
900
1000
1100
Low mobility environment
Fixed environment
Number of users
Tot
al th
roug
hput
(kb
ps)
High mobility environment
latency time scale tc = 1.6s
Average SNR = 0dB
Fixed environment: 2Hz Rician fading with Efixed/Escattered = 5.
Low Mobility environment: 3 km/hr, Rayleigh fading
High mobility environment: 30 km/hr, Rayleigh fading
Channel Dynamics
0 1000 2000 30000
200
400
600
800
1000
1200
1400
1.6 sec
requ
este
d ra
te o
f a u
ser
(kbp
s)
time slots
mobile environment
0 1000 2000 30000
200
400
600
800
1000
1200
1400
1.6 sec
time slots
requ
este
d ra
te o
f a u
ser
(kbp
s)
fixed environment
Channel varies faster and has more dynamic range in mobile
environments.
Inducing Randomness
• Scheduling algorithm exploits the nature-given channel fluctuations
by hitting the peaks.
• If there are not enough fluctuations, why not purposely induce
them?
Dumb Antennas
(Viswanath, T and Laroia 02)
���������
� �������
����������
� ����� ������������ !#"
$ �����
� � �����
The information-bearing signal at each of the transmit antennas are
multiplied by a random complex gain.
Inducing Randomness
transmission times
opportunisticbeamforming
t
Strength
Channel
t
Strength
Channel
user 1
user 2
afteropportunisticbeamforming
Strength
Channel
t
Strength
Channel
t
before
Slow Fading: Opportunistic Beamforming
user 2
user 1
• Dumb antennas create a beam in random time-varying direction.
• In a large system, there is likely to be a user near the beam at any
one time.
• By transmitting to that user, close to true beamforming
performance is achieved.
Fast Fading
0 0.5 1 1.5 2 2.5 30
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
2
Rayleigh
2 antenna, Ricean
1 antenna, Ricean
Channel Amplitude
Den
sity
Improves performance in fast fading Rician environments by spreading
the fading distribution.
Overall Performance Improvement
2 4 6 8 10 12 14 160
100
200
300
400
500
600
700
800
900
1000
1100
number of users
tota
l thr
ough
put (
kbps
)
mobile
fixed
fixed but with opp. beamforming
latency time−scale tc = 1.6s
average SNR = 0 dB
Mobile environment: 3 km/hr, Rayleigh fading
Fixed environment: 2Hz Rician fading with Efixed/Escattered = 5.
Smart vs Dumb Antennas
• Space-time codes increase reliability of point-to-point links but
decreases multiuser diversity gains.
• Dumb antennas add fluctuations to point-to-point links but
increases multiuser diversity gains.
Smart vs Dumb Antennas
• Space-time codes increase reliability of point-to-point links but
decreases multiuser diversity gains.
• Dumb antennas add fluctuations to point-to-point links but
increases multiuser diversity gains.
Conclusions
• Implementation of a new point of view has to obey system
constraints.
• The new point of view impacts rest of the system design and
suggests new research problems.
• Interplay between theory and system is what makes
communications research so fun!
Conclusions
• Implementation of a new point of view has to obey system
constraints.
• The new point of view impacts rest of the system design and
suggests new research problems.
• Interplay between theory and system is what makes
communications research so fun!
Conclusions
• Implementation of a new point of view has to obey system
constraints.
• The new point of view impacts rest of the system design and
suggests new research problems.
• Interplay between theory and system is what makes
communications research so fun!