### Slides - Argos

```Practical Many-Antenna Base
Argos Stations
Clayton W. Shepard
Hang Yu, Narendra Anand, Li Erran Li,
Thomas Marzetta, Richard Yang, Lin
Zhong
Motivation
• Spectrum is scarce
• Hardware is cheap
2
MU-MIMO Theory
• More antennas = more capacity
– Multiplexing
– Power
– Orthogonality
3
4
Why not?
• Nothing scales with the number of antennas
– CSI Acquisition
– Computation
– Data Transportation
5
Background: Beamforming
=
Destructive Interference
Constructive Interference
?
=
6
Background: Channel
Estimation
Measured
Due
PathtoEffects
environment
channels
(Walls)and
are not
terminal
The
Align
CSI
the
is
phases
then
send
aat
pilot
the from
atantenn
the
the to
mobility
reciprocal
due
tocalculated
differences
has
to
AFor
pilot
is estimation
sent
from
each
BSoccur
terminal
ensure
terminal
constructive
and
then
sent
calculate
interference
to
CSI
theatBS
BS
quickly
in
the Tx
and
and
periodically
Rxback
hardware!
Tx
R
x
+
Tx
BS
R
x
=
Tx
+
R
x
Tx
R
x
7
Background: Multi-User
Beamforming
8
Background: Multi-User
Beamforming
9
Background: Null Steering
10
Background: Null Steering
11
Background: Multi-User
Beamforming
12
Background: Scaling Up
13
Background: Scaling Up
14
Background: Scaling Up
15
Background: Scaling Up
16
Background: Linear Precoding
• Calculate beamweights
– Every antenna has a beamweight for each
terminal
• Multiply symbols by weight, then add
together: s   W  s
17
Recap
1) Acquire CSI
2) Calculate Weights
3) Apply Linear Precoding
18
Scalability Challenges
1) Acquire CSI
– 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
19
Argos’ Solutions
1) Acquire CSIO(M•K) → O(K)
– New reciprocal calibration method
2) Calculate WeightsO(M•K2) → O(K)*
– Novel distributed beamforming method
O(M•K) → O(K) *
3) Apply Linear Precoding
– Carefully designed scalable architecture
20
Solutions
• Reciprocal Calibration
• Distributed Beamforming
• Scalable Architecture
21
Channel Reciprocity
• Pilot transmission source?
– Basestation
– Terminal
• Base station pilot transmission
– Requires feedback
– M pilots (M ≥ K)
• Terminal pilot transmission
– No feedback
– K pilots
22
Channel Reciprocity
TxA
RxA
TxB
Tx
TxAA+C+Rx
+RxC-Tx
C-Tx
C- CC-Rx
RxA A
Can
Tx/Rx
we Chain
do this
differences
without terminal
require
involvement?
calibration
TxB+RxC-TxCRxB
TxC
RxC
Channel Estimation
RxB
Transmission
23
=
Key Idea
Any constant phase shift results
in same beampattern!
=
24
Channel Reciprocity
TxA
TxATx
+Rx
A-Rx
C-Tx
A CRxA
RxA
TxB
TxC
TxBTx
+Rx
B-Rx
C-Tx
B CRxB
RxC
Channel Estimation
RxB
Transmission
25
Internal Reciprocal Calibration
TxA
• Find phase difference between A
and B
TxA+C+RxB
TxB+C+RxA
RxA
TxA+RxB - TxBRxA
TxB
RxB
• Tx from A:
• Phase offset = TxA-RxA
• Tx from B: + (TxA+RxB - TxBRxBA)) =
• (TxB-Rx
TxA26
RxA
Solutions
• Reciprocal Calibration
• Distributed Beamforming
• Scalable Architecture
27
Problems with Existing Methods
• Central data dependency
• Transport latency causes capacity loss
• Can not scale
– Becomes exorbitantly expensive then
infeasible
28
Conjugate Beamforming
• Requires global power scaling by
constant:
• Where, e.g.:
• This creates a central data dependency
29
Conjugate Beamforming Power
Good Channel
BS
Okay Channel
30
Conjugate Beamforming Power
Low Power
High Power
BS
Normal Power
31
Distributed Conjugate
Beamforming
• Scale power at each antenna:
• Maximizes utilization of every radio
– More appropriate for real-world deployments
• Quickly approaches optimal as K
increases
– Channels are independent and uncorrelated
32
Distributed Conjugate
Beamforming
High Power
High Power
BS
High Power
33
Solutions
• Reciprocal Calibration
• Distributed Beamforming
• Scalable Architecture
34
Architectural Design Goals
• Scalable
Data
– Support
thousands of BS
antennas
?
– Cost scales
linearly with # of
antennas
…
• Cost-effective
35
Linear Precoding
…
…
K
…
K
…
K
…
K
M
36
Scalable Linear Precoding
…
…
K
…
K
…
K
…
K
M
37
Scalable Linear Precoding
…
…
K
…
K
…
K
K
…
Common
Databus!
M
38
K
K
…
K
…
…
…
…
K
M
39
Scalable Linear Precoding
Constant
Bandwidth!
K
K
…
K
…
…
…
…
K
M
40
Partition Ramifications
• CSI and weights are computed and
applied locally at each BS radio
• No central data dependency
– No latency from data transport
– Constant data rate common bus (no
switching!)
• Unlimited scalability!
41
How do we design it?
• Daisy-chain (series)
…
– Unreliable
– Large end to end latency
• Flat structure
…
– Unscalable
– Expensive, with large fixed
cost
• Token-ring | Interconnected
…
– Not amenable to linear precoding
– Variable Latency
42
Solution: Argos Architecture
Data Backhaul
Central
Controller
Argos
Hub
Module
Argos
Hub
Module
…
Module
Argos
Hub
Module
Module
…
Module
…
…
43
Argos Implementation
WARP Module
Ethernet
Central
Controller
(PC with MATLAB)
Central
Controller
Argos
Hub
Daughter
FPGA WARP Module
Cards
WARP Module
Daughter
FPGA
Power PC
Daughter
Cards
FPGA
Cards 1
Argos Hub
2
1
Peripherals
Hardware
FPGA
Fabric
Argos
Ethernet
Argos
and Other
I/O
2
FPGA
Fabric
Interconnect
Module
Interconnect
3
Peripherals
Hardware
2
Peripherals
and Other I/O Hardware
Model
Sync Pulse Module
and Other I/O
Model
Clock Board
4
3
Clock
Clock Board
4
Module
Clock Board
4
Distribution
…
Power PC
PowerFPGA
PC Fabric
44
16
45
Central
Controller
WARP
Module
s
Argos
Interconnects
Sync
Distribution
Argos
Hub
Clock
Distribution
Ethernet
Switch46
System Performance
47
Linear Gains as # BS Ant.
Increases
Capacity vs. M, with K = 15
48
Linear Gains as # of Users
Increases
Capacity vs. K, with M = 64
49
Scaling # of Users with 16 BS
Ant.
Capacity vs. K, with M = 16
50
Zero-forcing is not always
better!
Capacity vs. K, with M = 16 | Low
Power
51
Calibration is stable for hours!
52
Conclusion
• First many-antenna beamforming platform
– Real-world demonstration of manyfold capacity
increase
• Devised novel techniques and architecture
– Unlimited Scalability
http://argos.rice.edu
http://recg.rice.e
du
53
```