slides - Argos

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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
6
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
17
Linear Precoding
s
s  W  s
(Kx1 matrix)
BS
(M x 1 matrix)
H
M: # of BS antennas
K: # of clients
K≤M
18
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
24
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
26
Recap
1) Estimate channels
2) Calculate weights
3) Apply linear precoding
27
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
28
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
30
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
34
Conjugate vs. Zeroforcing
35
Without considering
computation
Listen to pilot
Send data
Calculate BF weights
36
Linear gains as # of BS antennas
increases
Capacity vs. M, with K = 15
37
Linear gains as # of users increases
Capacity vs. K, with M = 64
38
Considering computation
Listen to pilot
Send data
Calculate BF weights
39
M = 64
K = 15
Zeroforcing with various hardware configurations
40
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
41
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
42
43
~$2,000 per antenna
44
Acknowledgments
http://argos.rice.edu
45
More BS antennas + MU-MIMO
Higher efficiency & lower interference
More BS antennas + MU-MIMO
Higher efficiency & lower interference
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