argos clayton w. shepard hang yu, narendra anand, li erran li, thomas marzetta, richard yang, lin...

Argos

Clayton W. ShepardHang Yu, Narendra Anand, Li

Erran Li, Thomas Marzetta, Richard Yang,

Lin Zhong

Practical Many-Antenna Base Stations

2

Motivation

• Spectrum is scarce

• Hardware is cheap

3

MU-MIMO Theory

• More antennas = more capacity

–Multiplexing

– Power

– Orthogonality

4

Data

2

Data 1

Data

6

Data 3

Data 4Data

5

5

Why not?

• Nothing scales with the number of antennas

– CSI Acquisition

– Computation

– Data Transportation

Background: Beamforming

6

=

Constructive Interference

=

Destructive Interference

?

7

Due to environment and terminal mobility estimation has to occur quickly and periodically

BS

The CSI is then calculated at the terminal and sent back to the BSA pilot is sent from each BS antenna

Background: Channel Estimation

+

+=

Align the phases at the receiver to ensure constructive interferenceFor uplink, send a pilot from theterminal then calculate CSI at BS

Path Effects (Walls)

Tx

Rx

Tx

Rx

Tx

Rx

Uplink?

Tx

Rx

Measured channels are not reciprocal due to differences in the Tx and Rx hardware!

8

Background: Multi-User Beamforming

Data 1

9


Data

2

10

Background: Null Steering

Data 1

Nu

ll

Null

NullNullN

ull

11

Background: Null Steering

Data

2

Null

NullNull

Null

Null

12


Data

2

Data 1

Data

6

Data 3

Data 4Data

5

13

Background: Scaling Up

Data 1

14


Data 1

15


Data 1

16

Data 3

Data

5


Data 1

Data

6

Data

2

Data 4

17

Background: Linear Precoding

• Calculate beamweights– Every antenna has a beamweight for

each terminal

• Multiply symbols by weight, then add together: sWs

18

Recap

1) Acquire CSI

2) Calculate Weights

3) Apply Linear Precoding

19

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

20

Argos’ Solutions

1) Acquire CSI– New reciprocal calibration method

2) Calculate Weights– Novel distributed beamforming method

3) Apply Linear Precoding– Carefully designed scalable

architecture

O(M•K) → O(K)

O(M•K2) → O(K)*

O(M•K) → O(K) *

21

Solutions

• Reciprocal Calibration

• Distributed Beamforming

• Scalable Architecture

22

Channel Reciprocity

• Pilot transmission source?– Basestation– Terminal

• Base station pilot transmission – Requires feedback – M pilots (M ≥ K)

• Terminal pilot transmission– No feedback– K pilots

Channel Reciprocity

23

TxC

RxC

TxA

RxA

TxB

RxB

Channel Estimation

Transmission

Tx/Rx Chain differences

require calibration

Can we do this without terminal

involvement?

TxA+RxC-TxC-RxA

TxB+RxC-TxC-RxB

TxA+C+RxC-TxC-C-RxA

Key Idea

24

=

= Any constant phase shift results in same beampattern!

Channel Reciprocity

25

TxC

RxC

TxA

RxA

TxB

RxB

Channel Estimation

Transmission

TxA+RxC-TxC-RxA

TxB+RxC-TxC-RxB

TxA-RxA

TxB-RxB

• Find phase difference between A and B

• Tx from A:• Phase offset = TxA-RxA

• Tx from B: • (TxB-RxB )

Internal Reciprocal Calibration

26

TxA

RxA

TxB

RxB

TxA+C+RxB TxB+C+RxA TxA+RxB - TxB-

RxA

add phase difference + (TxA+RxB - TxB-RxA) = TxA-

RxA

27

Solutions




28

Problems with Existing Methods

• Central data dependency

• Transport latency causes capacity loss

• Can not scale– Becomes exorbitantly expensive then

infeasible

29

Conjugate Beamforming

• Requires global power scaling by constant:

• Where, e.g.:

• This creates a central data dependency

30

BS

Good Channel

Conjugate Beamforming Power

Okay Channel

Bad Channel

31

BS

High Power

Conjugate Beamforming Power

Normal Power

Low Power

32

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

33

BS

High Power

Distributed Conjugate Beamforming

High Power

High Power

34

Solutions




?

• Scalable– Support

thousands of BS antennas

• Cost-effective– Cost scales

linearly with # of antennas

35

Architectural Design Goals…

Data

36

Linear Precoding…

M

K

K

K

K

…

…

…

…

37

Scalable Linear Precoding…

M

K

K

K

K

…

…

…

…

38

Scalable Linear Precoding…

M

K

K

K

K

Common Databus!

…

…

…

…

39

MUBF Linear Precoding: Uplink

…

M

K

…

K

K

K

…

…

…

40

Scalable Linear Precoding

…

…

…

…

…

M

K

Constant Bandwidth!

K

K

K

41

Partition Ramifications

• CSI and weights are computed and applied locally at each BS radio– No overhead for additional BS radios

• No central data dependency– No latency from data transport– Constant data rate common bus (no

switching!)

• Unlimited scalability!

42

How do we design it?

• Daisy-chain (series)– Unreliable– Large end to end latency

• Token-ring | Interconnected– Not amenable to linear precoding– Variable Latency– Routing overhead

…

…

…

• Flat structure– Unscalable– Expensive, with large fixed cost

43

Solution: Argos Architecture

Central Controller

Argos Hub

Argos Hub

Argos Hub

Module

Module

Module

Module

Module

…

…

…

Data Backhaul

ModuleRadio Radio Radio…

44

Argos Implementation

Central Controller

Argos Hub

Module

Module

Module

…

Central Controller

(PC with MATLAB)

Sync Pulse

Ethernet

Clock Distribution

Argos Hub Argos

Interconnect

WARP Module

FPGA

Power PC

FPGA Fabric

Hardware Model

Peripherals and Other

I/O

Clock Board

Daughter

Cards

Radio 4

Radio 3

Radio 2

Radio 1

WARP Module

FPGA

Power PC

FPGA Fabric

Hardware Model


I/O

Clock Board

Daughter

Cards

Radio 4

Radio 3

Radio 2

Radio 1

16

………

Eth

ern

et

WARP Module

FPGA

Power PC

FPGA Fabric

Hardware Model


I/O

Clock Board

Daughter

Cards

Radio 4

Radio 3

Radio 2

Radio 1

ArgosInterconne

ct

46

WARP Module

s

Central Controll

er

Argos Hub

Clock Distributio

n

Ethernet

Switch

SyncDistributio

n

Argos Interconnects

47

System Performance

48

Linear Gains as # BS Ant. Increases

Capacity vs. M, with K = 15

49

Linear Gains as # of Users Increases

Capacity vs. K, with M = 64

50

Scaling # of Users with 16 BS Ant.

Capacity vs. K, with M = 16

51

Zero-forcing is not always better!

Capacity vs. K, with M = 16 | Low Power

52

Calibration is stable for hours!

53

Conclusion

• First many-antenna beamforming platform– Real-world demonstration of manyfold

capacity increase

• Devised novel techniques and architecture– Unlimited Scalability

http://recg.rice.edu

http://argos.rice.edu

argos clayton w. shepard hang yu, narendra anand, li erran li, thomas marzetta, richard yang, lin...

Documents