Practical Performance of MUMIMO 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
=
Destructive Interference
Constructive Interference
?
=
3
Background: Channel
Estimation
Due
tothe
environment
andreceiver
terminalto
Path
Effects
(Walls)
Align
phases
at the
mobility
estimation has
to occur
ensure constructive
interference
quickly and periodically
+
BS
=
+
4
Background: Channel
Estimation
Multiple users have to
send pilots orthogonally
BS
5
Frame Structure
• Time Division Duplex (TDD)
– Uplink and Downlink use the same channel
estimates
(Still Retrospective)
Coherence Time
Channel
Estimation
CE
Uplink
Comp
Downlink
Retrospectively
Apply
Uplink
CE
…
…
Pipeline Uplink
Computational
Overhead
6
Downlink is Limiting Factor!
Background: Multi-User
Beamforming
8
Background: Multi-User
Beamforming
9
Background: Zero-forcing
10
Background: Zero-forcing
11
Background: Zero-forcing
12
Background: Scaling Up
Conjugate
13
Background: Scaling Up
Conjugate
14
Background: Scaling Up
Conjugate
15
Background: Scaling Up
Conjugate
16
Conjugate vs. Zero-forcing
• Negligible Processing
• O(M•K2)
• Completely
Distributed
• Centralized
• Substantial Overhead
• No Latency Overhead
• Poor Spectral
Efficiency
• Good Spectral
Efficiency
17
Under what scenarios, if any, does
conjugate precoding outperform zeroforcing?
Performance Factors
• Environmental
– Complex, and constantly changing
• Design
– Straightforward and Static
19
Performance Factors
• Environmental
– Channel Coherence
– Precoder Spectral Efficiency
• Design
– Number of Antennas
– Hardware Capability
20
Environmental Factor: Channel
Coherence
• Coherence Time
– Increases frequency of channel estimation
• Coherence Bandwidth
– Increases coherence bandwidth
21
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
22
23
Design Factor: Number of
Antennas
• Number of Base Station Antennas (M)
– Increases amount of computation
• Number of User Antennas (K)
– Increases channel estimation and
computation
24
Design Factor: Hardware
Capability
• Conjugate has negligible computational
cost
• Zero-forcing requires:
– Bi-Directional Data Transport
– Large Matrix Inversions
25
Zero-forcing Hardware Factors
• Channel Bandwidth
• Inversion Latency
• Quantization
• Data Transport
– Switching Latency
– Throughput
26
Performance Model
27
Conjugate vs. Zero-forcing
Without Considering
Computation
CE
Comp
Transmit
29
Spectral Efficiency vs. # of BS
antennas
Spectral Efficiency (bps/Hz)
K = 15
# of Base Station Antennas (M)
30
Spectral Efficiency vs. # of Users
Spectral Efficiency (bps/Hz)
M = 64
# of Users (K)
31
Considering Computation
CE
Comp
Transmit
32
M = 64
K = 15
80
Achieved Capacity
Capacity (bps/Hz)
(bps/Hz)
Achieved
Conjugate
60
40
20
0
-4
10
-3
-2
10
10
CoherenceTime
Time
Coherence
(s)
-1
10
(s)
Zeroforcing with various hardware
33
Performance vs. # of Users
M = 64
Ct = 30 ms
Achieved
(bps/Hz)
Capacity (bps/Hz)
Achieved Capacity
35
30
25
20
15
10
Zero-Forcing
Conjugate
5
0
2
4
6
8
10
# of Users
(K)
Number
of Users
12
14
34
Max Multiplexing Gain vs. # of Users
M = 200 Ct = 30 ms
140
ZF-Super
ZF-Cluster
ZF-High
ZF-Mid
ZF-Low
Conjugate
(γ · K)
Gain
Multiplexing
Multiplexing Gain
( * K)
120
100
80
60
X: 75
Y: 46.86
X: 89
Y: 52.82
X: 58
Y: 32.39
40
X: 36
Y: 17.27
20
X: 4
Y: 1.253
0
0
20
40
80
60
Number of Users (K)
# of Users (K)
100
120
140
35
Applicability
• Guide Base Station Design
– Refine model for your implementation
• Enables adaptive precoding
36
Ramifications
More Antennas
Faster or Higher
MobilityProcessing
Zero-forcing
1 GHz
Adaptive
Precoding
Conjugate
10 GHz
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
http://argos.rice.edu
38
precoding
Questions?
http://argos.rice.edu
Frame Pipelining Schemes
Coherence Time
All
Downlink
CE
Comp
Downlink
CE
Comp
Coherence Time
All
Uplink
CE
Downlink
CE
…
…
CE
…
Coherence Time
Uplink
(Not to Scale)
Coherence Time
Optimal
CE
Uplink
Comp
Downlink
Uplink
CE
…
…
40