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Opportunistic Communication:
Smart Scheduling and Dumb Antennas
David Tse
Department of EECS, U.C. Berkeley
January 28, 2002
Intel
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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 not only the physical
layer but to the design of the entire wireless network.
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Opportunistic Communication
Smart Scheduling
Downlink scheduling for Qualcomms HDR (High Data Rate) system.
(Tse 99)
Dumb Antennas
Opportunistic beamforming using multiple transmit antennas
(Viswanath, Tse and Laroia 2001)
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Wireless Fading Channels
Channel Quality
Time
fading due to constructive and destructive interference between
multiple signal paths;
Rayleigh fading: superposition of many small paths
Rician fading: many small paths plus one dominant path
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Qualcomm HDRs 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
Totalspectalefficienyi
n
bps/Hz
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
TotalSpectralEfficien
yin
bps/Hz
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
requested
rate
(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.
effective SNR at time t = max1kK
|hk(t)|2.
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Multiuser Diversity
0 200 400 600 800 1000 1200 1400 1600 1800 20000
500
1000
1500
time slots
requestedr
ate(
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.
effective SNR at time t = max1kK
|hk(t)|2.
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Multiuser Diversity
Diversity in wireless systems arises from independent signal paths.
Traditional forms of diversity includes time, frequency andantennas.
Multiuser diversity arises from independent fading channels across
different users.
Fundamental difference: Traditional diversity modes pertain to
point-to-point links, while multiuser diversity provides
network-wide benefit.
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Multiuser Diversity
Diversity in wireless systems arises from independent signal paths.
Traditional forms of diversity includes time, frequency andantennas.
Multiuser diversity arises from independent fading channels across
different users.
Fundamental difference: Traditional diversity modes pertain to
point-to-point links, while multiuser diversity provides
network-wide benefit.
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Fairness and Delay
1000 1500 2000 25000
500
1000
1500
2000
2500
time slots
requested
rate
(k
bps)
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
requestedr
ate
(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
requestedr
ate
(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), . . . , T K(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 with
time constant tc.
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Comments
If users have symmetric channel statistics, this reduces to the
greedy policy of transmitting to the mobile with the highestrequested 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-robinfashion, 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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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
totalthrough
put(kbps)
mobile environment
fixed environment
round robin
average SNR = 0dB
timescale 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
requested
rate
ofa
user(kbps)
time slots
mobile environment
0 1000 2000 30000
200
400
600
800
1000
1200
1400
1.6 sec
time slots
requested
rate
ofa
user(kbps)
fixed environment
Channel varies faster and has more dynamic range in mobile
environments.
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Throughput of Scheduler: Asymmetric Users
(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 purposely induce
them?
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Dumb Antennas
Received signal at user k:
(t)h1k(t) +
1 (t) exp(j(t))h2k(t)
x(t).
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Slow Fading Environment: Before
0 500 1000 1500 2000 2500 300080
100
120
140
160
180
200
220
Time Slots
SupportableRate
User 1
User 2
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After
0 500 1000 1500 2000 2500 300050
0
50
100
150
200
250
300
350
Time Slots
SupportableRate
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
AverageThr
oughputinbps/Hz
Opp. BF
Coherent BF
Consider first a slow fading environment when channels of the
users are fixed (but random).
Dumb antennas can approach the performance of true
beamforming when there are many users in the systems.
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Opportunistic versus True Beamforming
If the gains h1k and h2k are known at the transmitter, then true
beamforming can be performed:
=
| h1k |2
| h1k |2 + | h2k |2
= h1k h2k
Dumb antennas randomly sweep out a beam and opportunistically
sends data to the user closest to the beam.
Opportunistic beamforming 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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Opportunistic versus True Beamforming
If the gains h1k and h2k are known at the transmitter, then true
beamforming can be performed:
=
| h1k |2
| h1k |2 + | h2k |2
= h1k h2k
Dumb antennas randomly sweep out a beam and opportunistically
sends data to the user closest to the beam.
Opportunistic beamforming 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 achievethroughputs
Tk 1
KRbfk
, 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 1 2 3 4 5 6 7
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
with opp. bf.
without opp. bf.
Channel Amplitude
Density
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
totalthroughp
ut(kbps)
mobile
fixed
fixed but with opp. beamforming
latency timescale 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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Space Time Codes
Space time codes: intelligent use of transmit diversity to improve
reliability of point-to-point links.
For 2 transmit antennas, Alamouti scheme is the best space-time
code.
Let us compare smart and dumb antennas in terms of both
performance and complexity.
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Comparison: Performance
Slow Fading:
Alamouti: diversity gain
dumb antennas: diversity gain plus 3 dB power gain
Fast Fading:
Alamouti: reduces channel fluctuations and thereby reduces the
multiuser diversity gain.
dumb antennas: keeps the fluctuations the same in Rayleigh
fading and increases the fluctuations in Rician fading.
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Comparison: Performance
Slow Fading:
Alamouti: diversity gain
dumb antennas: diversity gain plus 3 dB power gain
Fast Fading:
Alamouti: reduces channel fluctuations and thereby reduces the
multiuser diversity gain.
dumb antennas: keeps the fluctuations the same in Rayleigh
fading and increases the fluctuations in Rician fading.
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Comparison: Complexity
Alamouti:
requires two separate pilots to estimate the multi-antenna channel.
special encoder/decoder.
Dumb Antennas:
only requires a single pilot to estimate the overall channel SNR.
no special encoder/decoder.
In fact the mobiles are completely oblivious to the existence ofmultiple transmit antennas.
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Comparison: Complexity
Alamouti:
requires two separate pilots to estimate the multi-antenna channel.
special encoder/decoder.
Dumb Antennas:
only requires a single pilot to estimate the overall channel SNR.
no special encoder/decoder.
In fact the mobiles are completely oblivious to the existence ofmultiple transmit antennas.
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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. Multiuser diversity allows interference avoidance.
Dumb antennas provides opportunistic nulling for users in other
cells.
Particularly important in interference-limited systems with no soft
handoff.
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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. Multiuser diversity allows interference avoidance.
Dumb antennas provides opportunistic nulling for users in other
cells.
Particularly important in interference-limited systems with no soft
handoff.
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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. Multiuser diversity allows interference avoidance.
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
Role of transmit antennas is to provide further link 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
Role of transmit antennas is to provide further link 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
Role of transmit antennas is to provide further link 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
Role of transmit antennas is to provide further link 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.
Role of transmit antennas is to amplify the fluctuations.
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.
Role of transmit antennas is to amplify the fluctuations.
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.
Role of transmit antennas is to amplify the fluctuations.
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.
Role of transmit antennas is to amplify the fluctuations.
Exploits more relaxed latency requirements of data as well as MAC
layer packet scheduling mechanisms.
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A Broader Perspective
Efforts on increasing wireless capacity has been on boosting
spectral efficiency of point-to-point links.
Rely on sophisticated physical layer signal processing techniques:
smart antennas, interference suppression, etc.....
Future progress will come from taking a broader network
perspective.
Recommended