Networking with massive MU-MIMO
Lin Zhong
http://recg.org
Guiding Principles
• Spectrum is scarce
• Hardware is cheap, and getting cheaper
2
Antennas
3
Omni-directional base station
Poor spatial reuse; poor power efficiency; high inter-cell interference
4
Sectored base station
Better spatial reuse; better power efficiency; high inter-cell
interference
5
Single-user beamforming base station
Better spatial reuse; best power efficiency; reduced inter-cell interference
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Multi-user MIMO base station
M: # of BS antennas
K: # of clients (K ≤ M)
Best spatial reuse; best power efficiency; reduced inter-cell interference
7
Key benefits of MU-MIMO
• High spectral efficiency
• High energy efficiency
• Low inter-cell interference
• Orthogonal to Small Cell solutions
– Centralized vs. distributed antennas
8
Why massive?
• More antennas  Higher spectral efficiency
• More antennas  Higher energy efficiency
• Simple baseband technique becomes effective
T.L. Marzetta. Noncooperative cellular wireless with unlimited numbers of base station antennas. IEEE Trans. on Wireless Comm., 2010.
9
Background: Beamforming
10
Background: Beamforming
Constructive Interference
=
11
Background: Beamforming
=
Destructive Interference
Constructive Interference
=
12
Background: Beamforming
=
Constructive Interference
Destructive Interference
=
13
Background: Beamforming
?
14
Background: Channel
Estimation
Due
PathtoEffects
environment
(Walls)and terminal
Align
For
CSI
uplink,
isphases
then
send
Uplink?
aatpilot
theBS
from
receiver
at the
the to
mobility
estimation
has
to
occur
A The
pilot
isthe
sent
fromcalculated
each
antenna
terminal
ensure
terminal
constructive
and
then
sent
calculate
back
interference
to
CSI
theatBS
BS
quickly and
periodically
+
BS
+
15
Background: Multi-user MIMO
BS
H
M: # of BS antennas
K: # of clients
K≤M
16
Multi-user MIMO: Precoding
s
s¢ = f (s)
(Kx1 matrix)
BS
(M x 1 matrix)
H
M: # of BS antennas
K: # of clients
K≤M
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Linear Precoding
s
s  W  s
(Kx1 matrix)
BS
(M x 1 matrix)
H
M: # of BS antennas
K: # of clients
K≤M
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Background: Zeroforcing Beamforming
19
Background: Zeroforcing Beamforming
20
Background: Zeroforcing Beamforming
* -1
W = c × H (H H )
*
T
21
Background: Conjugate Beamforming
22
With more antennas
23
With even more antennas
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Conjugate Multi-user
Beamforming
W = c× H
*
Conjugate approaches Zeroforcing
as M/K∞
Conjugate
W = c× H
*
vs.
Zeroforcing
* -1
W = c × H (H H )
*
T
• Trivial computation
• Nontrivial computation
• Suboptimal capacity
• Close to capacity
achieving
• Scalable
• Not scalable
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Recap
1) Estimate channels
2) Calculate weights
3) Apply linear precoding
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Scalability Challenges
1) Estimate channels
– M+K pilots, then M•K feedback
2) Calculate weights
– O(M•K2), non-parallelizable, centralized data
3) Apply linear precoding
– O(M•K), then O(M) data transport
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Argos’ Solutions
1) Estimate channels
O(M•K) → O(K)
– New reciprocal calibration method
2) Calculate weights
O(M•K2) → O(K)
– Novel distributed beamforming method
3) Apply linear precoding
O(M•K) → O(K)
– Carefully designed scalable architecture
C. Shepard et al. Argos: Practical many-antenna base stations. ACM MobiCom, 2012.
Solution: Argos Architecture
Data Backhaul
Central
Controller
Argos
Hub
Module
Argos
Hub
Module
…
Module
Argos
Hub
Module
Module
…
Module
…
Radio
Radio
…
Radio
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Argos Implementation
WARP Module
Ethernet
Central
Controller
(PC with MATLAB)
Central
Controller
Argos
Hub
Daughter
FPGA WARP Module
Cards
WARP Module
Daughter
FPGA
Radio
Power PC
Daughter
Cards
FPGA
Cards 1
Radio
Radio
Argos Hub
1 Radio
2
1
Peripherals
Hardware
Radio
FPGA
Fabric
Argos
Ethernet
Radio
Argos
and Other
I/O
Model Radio
2
FPGA
Fabric
Interconnect
Module
Interconnect
3
Peripherals
Hardware
2
Radio
Peripherals
and Other I/O Hardware
Model
Radio
Sync Pulse Module
and Other I/O
Model
3 Radio
Clock Board
4
3
Radio
Clock
Radio
Clock Board
4
Module
Clock Board
4
Distribution
…
Power PC
PowerFPGA
PC Fabric
31
16
32
Central
Controller
WARP
Module
s
Argos
Interconnects
Sync
Distribution
Argos
Hub
Clock
Distribution
Ethernet
Switch33
Experimental Setup
• Time Division Duplex (TDD)
– Uplink and Downlink use the same band
• Downlink
Listen to pilot
Send data
Calculate BF weights
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Conjugate vs. Zeroforcing
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Without considering
computation
Listen to pilot
Send data
Calculate BF weights
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Linear gains as # of BS antennas
increases
Capacity vs. M, with K = 15
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Linear gains as # of users increases
Capacity vs. K, with M = 64
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Considering computation
Listen to pilot
Send data
Calculate BF weights
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M = 64
K = 15
Zeroforcing with various hardware configurations
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Conclusion
• First many-antenna beamforming platform
– Demonstration of manyfold capacity increase
• Devised novel techniques and architecture
– Unlimited Scalability
• Simplistic conjugate beamforming works
• Need adaptive solutions
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Ongoing work
• Inter-cell interference management
• Pilot contamination
• Client grouping & scheduling
ArgosBS 1
(Outdoor)
10 GbE
10 GbE
10
GbE
Server
NetFPGA
Server
NetFPGA
10 GbE
ArgosBS 4
(Indoor)
Server
NetFPGA
10 GbE
ArgosCloud
10 GbE
10 GbE
ArgosBS 2
(Outdoor)
ArgosBS 3
(Outdoor)
A network of massive MU-MIMO base stations
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~$2,000 per antenna
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Acknowledgments
http://argos.rice.edu
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More BS antennas + MU-MIMO
Higher efficiency & lower interference
More BS antennas + MU-MIMO
Higher efficiency & lower interference