practical performance of mu- mimo precoding in many-antenna base stations clayton shepard narendra...
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Practical Performance of MU-MIMO Precoding in Many-Antenna Base Stations
Clayton Shepard Narendra Anand Lin Zhong
Background: Many-Antennas
• More antennas = more capacity
• Traditional approaches don’t scale
2
Background: Beamforming
=
Constructive Interference
=
Destructive Interference
?
3
4
Due to environment and terminal mobility estimation has to occur quickly and periodically
BS
Background: Channel Estimation
+
+=
Align the phases at the receiver to ensure constructive interference
Path Effects (Walls)
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BS
Background: Channel Estimation
Multiple users have tosend pilots orthogonally
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Frame Structure
• Time Division Duplex (TDD)
– Uplink and Downlink use the same channel estimates
CE DownlinkComp
Channel Estimation
Computational Overhead
Uplink CE …
Coherence Time
Retrospectively Apply
Uplink
Pipeline Uplink
…
(Still Retrospective)
Downlink is Limiting Factor!
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Background: Multi-User Beamforming
Data 1
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Background: Multi-User Beamforming
Data
2
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Background: Zero-forcing
Data 1
Nu
ll
Null
NullNullN
ull
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Background: Zero-forcing
Data
2
Null
NullNull
Null
Null
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Background: Zero-forcing
Data
2
Data 1
Data
6
Data 3
Data 4Data
5
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Background: Scaling Up Conjugate
Data 1
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Background: Scaling Up Conjugate
Data 1
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Background: Scaling Up Conjugate
Data 1
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Data 3
Data
5
Background: Scaling Up Conjugate
Data 1
Data
6
Data
2
Data 4
Conjugate vs. Zero-forcing
• Negligible Processing
• Completely Distributed
• No Latency Overhead
• Poor Spectral Efficiency
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• O(M•K2)
• Centralized
• Substantial Overhead
• Good Spectral Efficiency
Under what scenarios, if any, does conjugate precoding outperform zero-forcing?
Performance Factors
• Environmental– Complex, and constantly changing
• Design– Straightforward and Static
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Performance Factors
• Environmental– Channel Coherence– Precoder Spectral Efficiency
• Design– Number of Antennas– Hardware Capability
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Environmental Factor: Channel Coherence
• Coherence Time– Increases frequency of channel
estimation
• Coherence Bandwidth– Increases coherence bandwidth
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Env. Factor: Precoder Spectral Efficiency
• Real-world performance, neglecting overhead
• Performance Depends on:– User Orthogonality– Propagation Effects– Noise– Interference
• Can be modeled, but impossible to capture everything
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Design Factor: Number of Antennas
• Number of Base Station Antennas (M)– Increases amount of computation
• Number of User Antennas (K)– Increases channel estimation and
computation24
Design Factor: Hardware Capability
• Conjugate has negligible computational cost
• Zero-forcing requires:– Bi-Directional Data Transport– Large Matrix Inversions
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Zero-forcing Hardware Factors
• Channel Bandwidth
• Quantization
• Inversion Latency
• Data Transport– Switching Latency– Throughput
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Performance Model
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Conjugate vs. Zero-forcing
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Without Considering Computation
CE TransmitComp
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Spectral Efficiency vs. # of BS antennas
K = 15
# of Base Station Antennas (M)
Spect
ral Effi
ciency
(bps/
Hz)
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Spectral Efficiency vs. # of UsersM = 64
# of Users (K)
Spect
ral Effi
ciency
(bps/
Hz)
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Considering Computation
CE TransmitComp
33Zeroforcing with various hardware configurations
M = 64 K = 15
10-4
10-3
10-2
10-1
0
20
40
60
80
Coherence Time (s)
Ach
ieve
d C
apac
ity (
bps/
Hz)
Conjugate
Coherence Time (s)
Ach
ieved C
ap
aci
ty (
bps/
Hz)
2 4 6 8 10 12 140
5
10
15
20
25
30
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Number of Users
Ach
ieve
d C
apac
ity (
bps/
Hz)
Zero-Forcing
Conjugate
M = 64 Ct = 30 ms
Performance vs. # of Users
34# of Users (K)
Ach
ieved C
ap
aci
ty (
bps/
Hz)
M = 200 Ct = 30 ms
Max Multiplexing Gain vs. # of Users
# of Users (K)
Mult
iple
xin
g G
ain
(γ ·
K)
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0 20 40 60 80 100 120 1400
20
40
60
80
100
120
140
X: 4Y: 1.253
Number of Users (K)
Mul
tiple
Gai
n (
* K
)
X: 36Y: 17.27
X: 58Y: 32.39
X: 75Y: 46.86
X: 89Y: 52.82
ZF-SuperZF-Cluster
ZF-High
ZF-Mid
ZF-LowConjugate
Applicability
• Guide Base Station Design– Refine model for your implementation
• Enables adaptive precoding
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Ramifications
1 GHz 10 GHz
ConjugateAdaptivePrecodingZero-forcing
Faster Processing
More Antennas or Higher Mobility
Conclusions
• Accurate model of real-world precoding performance
– Separates unpredictable environmental factors from deterministic design
• Conjugate can outperform zerforcing
• Useful for guiding design and enabling adaptive precoding 38http://argos.rice.edu
Questions?
http://argos.rice.edu
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Frame Pipelining Schemes
CE DownlinkCom
p
Coherence Time
Comp
CE Downlink CE ……
CE
Coherence Time
CEUplink
All Downlink
All Uplink
CE DownlinkComp Uplink CE …
Coherence Time
Uplink …Optimal
Coherence Time
…(Not to Scale)