multiuser diversity in wireless networks: smart …dntse/papers/ima810.pdf · multiuser diversity...
TRANSCRIPT
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Multiuser Diversity in Wireless Networks:
Smart Scheduling, Dumb Antennas
and Epidemic Communication
David Tse
Department of EECS, U.C. Berkeley
August 10, 2001
IMA Wireless Workshop
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Communication over Wireless Channels
• Fundamental characteristic of wireless channels: fading.
• A modern view of communication over fading channels is
emerging.
• This view has ramifications to the design of the physical and MAC
layers as well as the architecture of future wireless data networks.
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Multiuser Diversity
• Downlink scheduling for Qualcomm’s HDR (High Data Rate)
system. (Tse 99)
• Opportunistic beamforming using dumb antennas
(Viswanath, Tse and Laroia 2001)
• Multiuser diversity in mobile adhoc networks
(Grossglauser and Tse 2001 )
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Multiuser Diversity
• Downlink scheduling for Qualcomm’s HDR (High Data Rate)
system. (Tse 99)
• Opportunistic beamforming using dumb antennas
(Viswanath, Tse and Laroia 2001)
• Multiuser diversity in mobile adhoc networks
(Grossglauser and Tse 2001 )
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Multiuser Diversity
• Downlink scheduling for Qualcomm’s HDR (High Data Rate)
system. (Tse 99)
• Opportunistic beamforming using dumb antennas
(Viswanath, Tse and Laroia 2001)
• Multiuser diversity in mobile adhoc networks
(Grossglauser and Tse 2001 )
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Multiuser Diversity
• Downlink scheduling for Qualcomm’s HDR (High Data Rate)
system. (Tse 99)
• Opportunistic beamforming using dumb antennas
(Viswanath, Tse and Laroia 2001)
• Multiuser diversity in mobile adhoc networks
(Grossglauser and Tse 2001 )
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Wireless Fading Channels
Channel Quality
Time
• fast time-scale fading due to constructive and destructive
interference between multipaths;
• slow time-scale fading due to shadowing effects and varying
distances (r−α path loss).
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Qualcomm HDR’s DownLink
HDR (1xEV-DO): a wireless data system operating on IS-95 band (1.25
MHz)
Fixed Transmit Power
User 2
User 1
Base Station
Data
Measure Channel Request Rate
• HDR downlink operates on a time-division basis.
• Scheduler decides which user to serve in each time-slot.
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Downlink Multiuser Fading Channel
Fading Channel
MobileUser 1
User 2
User KBase Station
What is the sum capacity with channel state feedback?
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Information Theoretic Capacity of Downlink
(Tse 97)
2 4 6 8 10 12 14 160
0.5
1
1.5
2
2.5
Rayleigh Fading
Number of Users
Tot
al s
pect
al e
ffici
eny
in b
ps/H
z
Each user undergoes independent Rayleigh fading with average received
signal-to-noise ratio SNR = 0dB.
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To Fade or Not to Fade?
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
Sum Capacity of fading channel much larger than non-faded channel!
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Multiuser Diversity
0 200 400 600 800 1000 1200 1400 1600 1800 20000
500
1000
1500
time slots
requ
este
d ra
te (
kbps
)
symmetric channels
• 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.
(uplink multiuser diversity: Knopp and Humblet 95)
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Fairness and Delay
1000 1500 2000 25000
500
1000
1500
2000
2500
time slots
requ
este
d ra
te (
kbps
)
asymmetric channels
tc
Challenge is to exploit multiuser diversity while sharing the benefits
fairly and timely to users with asymmetric channel statistics.
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Hitting the Peaks
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 within a latency
time-scale tc.
• In a large system, at any time there is likely to be a user whose
channel is near its peak.
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Hitting the Peaks
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 within a latency
time-scale tc.
• In a large system, at any time there is likely to be a user whose
channel is near its peak.
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Proportional Fair Scheduler
At time slot t, given
1) users’ average throughputs T1(t), T2(t), . . . , TK(t) in a past window.
2) current requested rates R1(t), R2(t), . . . , RK(t)
transmit to the user k∗ with the largest
Rk(t)
Tk(t).
Average throughputs Tk(t) can be updated by an exponential filter.
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Comments
• If users have symmetric channel statistics, this reduces to the
greedy policy of transmitting to the mobile with the highest
requested rate.
• If channels have different statistics, competition for resource is
made fair by normalization
• feedback is built into the metric Rk(t)/Tk(t) to provide a fair
bandwidth allocation over the time-scale tc.
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Comparison with Round-Robin Policy
Round-Robin Policy
• Give same number of time slots to all the users in a round-robin
fashion, regardless of their channel conditions.
Proportional fair policy:
• Give roughly the same number of time slots to all users, but try to
transmit to a user when its channel condition is near its peak.
• Resource fair, but not necessarily performance fair.
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Proportional Fairness
Under stationary assumptions, long-term average throughputs
T ∗1 , . . . , T ∗K of the scheduler maximizes
X
k
log Tk
among all schedulers.
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Throughput of HDR Scheduler: Symmetric Users
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 environment
fixed environment
round robin
average SNR = 0dBtime−scale tc = 1.6sec
latency
Mobile environment: 3 km/hr, Rayleigh fading
Fixed environment: 2Hz Rician fading with Efixed/Escattered = 5.
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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.
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Throughput of Scheduler: Cellular Network
(Jalali, Padovani and Pankaj 2000)
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Inducing Randomness
• Scheduling algorithm exploits the nature-given channel fluctuations
by hitting the peaks.
• If there are not enough fluctuations, why not induce them
artificially?
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Dumb Antennas
���������
� �������
����������
� ����� ������������ !#"
$ �����
� � �����
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Slow Fading Environment: Before
0 500 1000 1500 2000 2500 300080
100
120
140
160
180
200
220
Time Slots
Sup
port
able
Rat
e
User 1
User 2
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After
0 500 1000 1500 2000 2500 3000−50
0
50
100
150
200
250
300
350
Time Slots
Sup
port
able
Rat
e
User 1
User 2
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Opportunistic Beamforming: Slow Fading
0 5 10 15 20 25 30 350.8
0.9
1
1.1
1.2
1.3
1.4
1.5
Number of Users
Ave
rage
Thr
ough
put i
n bp
s/H
z Opp. BF
Coherent BF
• Dumb antennas randomly sweep out a beam and opportunistically
sends data to the user closest to the beam.
• Can approach the performance of true beamforming when there
are many users in the systems, but with much less feedback and
channel measurements.
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Asymptotic Result
Assume that the slow fading states of each user are i.i.d. randomly
generated (but fixed for all time).
In a large system of K users, with high probability, the users achieve
throughputs
Tk =1
KRbf
k , k = 1, . . . K
.
where Rbfk is the rate user k gets when it is perfectly beamformed to.
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Opportunistic Beamforming: 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
2 antenna, Ricean
Rayleigh
1 antenna, Ricean
Channel Amplitude
Den
sity
Improves performance in fast fading Rician environments by spreading
the fading distribution.
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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.
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Comparison to Space Time Codes
• No special multi-antenna encoder or decoder nor MIMO channel
estimation.
• In fact the mobiles are completely oblivious to the existence of
multiple transmit antennas.
• Antennas are truly dumb, but yet can surpass performance of
space time codes.
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Cellular System: Opportunistic Nulling
• In a cellular systems, users are scheduled when their channel is
strong and the interference from adjacent base-stations is weak.
• Dumb antennas provides opportunistic nulling for users in other
cells.
• Particularly important in interference-limited systems with no soft
handoff.
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Traditional CDMA Downlink Design
• orthogonalize users (via spreading codes)
• Makes individual point-to-point links reliable by averaging:
• interleaving
• multipath combining,
• soft handoff
• transmit/receive antenna diversity
• Important for voice with very tight latency requirements.
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Traditional CDMA Downlink Design
• orthogonalize users (via spreading codes)
• Makes individual point-to-point links reliable by averaging:
• interleaving
• multipath combining,
• soft handoff
• transmit/receive antenna diversity
• Important for voice with very tight latency requirements.
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Traditional CDMA Downlink Design
• orthogonalize users (via spreading codes)
• Makes individual point-to-point links reliable by averaging:
• interleaving
• multipath combining,
• soft handoff
• transmit/receive antenna diversity
• Important for voice with very tight latency requirements.
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Downlink Design: Modern View
• Shifts from the point-to-point view to a multiuser network view.
• Wants large and fast fluctuations of both channel and interference
so that we can ride the peaks.
• Exploits more relaxed latency requirements of data as well as MAC
layer packet scheduling mechanisms.
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Downlink Design: Modern View
• Shifts from the point-to-point view to a multiuser network view.
• Wants large and fast fluctuations of both channel and interference
so that we can ride the peaks.
• Exploits more relaxed latency requirements of data as well as MAC
layer packet scheduling mechanisms.
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Downlink Design: Modern View
• Shifts from the point-to-point view to a multiuser network view.
• Wants large and fast fluctuations of both channel and interference
so that we can ride the peaks.
• Exploits more relaxed latency requirements of data as well as MAC
layer packet scheduling mechanisms.
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Channel Quality
Time
So far we have exploited randomness from fast fading.
How about the slow fading?
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Talk Outline
• Downlink scheduling for Qualcomm’s HDR (High Data Rate)
system. (Tse 99)
• Opportunistic beamforming using dumb antennas
(Viswanath, Tse and Laroia 2001)
• Multiuser diversity in mobile adhoc networks
(Grossglauser and Tse 2001 )
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All-Wireless Ad-Hoc Networks
source A
transmission
interference
transmissiondestination B
source B
destination A
• no wireline infrastructure
• nodes can be relays for other nodes
• packets can go through multiple hops
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Scalability of Ad-Hoc Networks
−1 −0.8 −0.6 −0.4 −0.2 0 0.2 0.4 0.6 0.8 1−1
−0.8
−0.6
−0.4
−0.2
0
0.2
0.4
0.6
0.8
1
x
y
n=1000, θ=0.41
Point-to-point: Suppose each node has a stream of traffic for a
particular destination node.
Question: How does the throughput per source-destination pair scale
with the number of nodes n per unit area?
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Mobile Ad-hoc Network Model
• Mobility model: Nodes move randomly and independently on a
disk of unit area.
• Channel model: path loss factor of r−α at distance r, with α > 2
(slow fading);
• Communication model: a packet is successfully received if
signal-to-interference ratio is greater than a prescribed threshold.
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Baseline: Fixed Nodes Case
Suppose nodes are randomly placed but immobile.
Gupta and Kumar:
The best achievable throughput per source-destination pair decreases to
zero like O( 1√n
).
This result holds even allowing optimal and centralized scheduling,
power control, relaying and routing.
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What’s the Problem?
−1 −0.8 −0.6 −0.4 −0.2 0 0.2 0.4 0.6 0.8 1−1
−0.8
−0.6
−0.4
−0.2
0
0.2
0.4
0.6
0.8
1
x
y
n=1000, θ=0.41
• communication confined to nearest neighbors to allow dense
spatial reuse.
• a typical route has order√
n relay nodes
• each packet is transmitted of the order√
n times.
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Mobility Can Help!
Main result: (Grossglauser and Tse 00)
A long-term throughput of O(1) per S-D pair can be achieved when
nodes are mobile......
......if one is willing to wait.
Throughput is averaged over the time-scale of mobiliy.
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Mobility Can Help!
Main result: (Grossglauser and Tse 00)
A long-term throughput of O(1) per S-D pair can be achieved when
nodes are mobile.......
......if one is willing to wait.
Throughput is averaged over the time-scale of mobiliy.
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Mobility Can Help!
Main result: (Grossglauser and Tse 00)
A long-term throughput of O(1) per S-D pair can be achieved when
nodes are mobile.......
......if one is willing to wait.
Throughput is averaged over the time-scale of mobiliy.
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Idea #1
Communicate only when the source and destination are nearest
neighbors to each other.
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Direct Communication Does Not Work
sender
receiverarea
O(1/n)
• The source and destination are nearest neighbors only O(1/n) of
the time
• In fact, can show S-D throughput is at most O
�n− 1
1+α/2
�for any
policy that does not use relays.
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Multiuser Diversity via Relaying
Source Destination
Point- to - Point Link
Source
as RelaysOther Nodes
Destination
DownLink UpLink
• Multiuser diversity created artificially using all other nodes as
relays.
• Channel variation comes from slow rather than fast time-scale
fading.
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Phase I: Source to Relays
t=2
t=1
t=0
source
• At each time slot, source relays a packet to nearest neighbor
• Different packets are distributed to different relay nodes.
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Phase II: Relays to Destination
destination D
relay
not a relay
• Steady state: all nodes have packets destined for D
• Each relay node forwards packets to D only when it gets close.
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Phase I and II Staggered
phase 1 phase 2
Destination D
n−2 relay nodes
n−1 routes
Source S
Direct transmission
• O(1) throughput from S to D
• Communication is confined to nearest neighbors, but each packet
goes through at most two hops.
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Network Capacity
• The above discussion pertains to a single source-destination pair.
• We have to show that every S-D pair can follow the same strategy
simultaneously.
• O(n) simultaneous nearest neighbor communication is possible,
due to power law decay of the received power from a randomly
located node.
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Discussion
• Throughput is gained at the expense of delays, at the time-scale
of significant changes in network topology.
• The result is extreme in the delay-throughput tradeoff, but
suggests that mobility should be exploited in designing scalable
architectures for delay-tolerant applications.
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Conclusions: Three Lessons Learnt
• Hitting the peaks versus averaging of the fading channel.
• Data versus voice.
• A system view towards wireless network design.
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Conclusions: Three Lessons Learnt
• Channel variation can be exploited (even exaggerated) rather than
mitigated:
Hitting the peaks versus averaging.
• Data versus voice.
• A system view towards wireless network design.
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Conclusions: Three Lessons Learnt
• Channel variation can be exploited (even exaggerated) rather than
mitigated:
Hitting the peaks versus averaging.
• Data versus voice.
• A system view towards wireless network design.
![Page 60: Multiuser Diversity in Wireless Networks: Smart …dntse/papers/ima810.pdf · Multiuser Diversity in Wireless Networks: Smart Scheduling, Dumb Antennas and Epidemic Communication](https://reader031.vdocuments.site/reader031/viewer/2022021821/5b0daa6b7f8b9abc0a8e035c/html5/thumbnails/60.jpg)
Conclusions: Three Lessons Learnt
• Channel variation can be exploited (even exaggerated) rather than
mitigated:
Hitting the peaks versus averaging.
• Data versus voice.
• A system view towards wireless network design.