Practical Performance of MU-MIMO 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

=

Constructive Interference

=

Destructive Interference

?

3

4

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

BS

Background: Channel Estimation

+

+=

Align the phases at the receiver to ensure constructive interference

Path Effects (Walls)

5

BS

Background: Channel Estimation

Multiple users have tosend pilots orthogonally

6

Frame Structure

• Time Division Duplex (TDD)

– Uplink and Downlink use the same channel estimates

CE DownlinkComp

Channel Estimation

Computational Overhead

Uplink CE …

Coherence Time

Retrospectively Apply

Uplink

Pipeline Uplink

…

(Still Retrospective)

Downlink is Limiting Factor!

8

Background: Multi-User Beamforming

Data 1

9

Background: Multi-User Beamforming

Data

2

10

Background: Zero-forcing

Data 1

Nu

ll

Null

NullNullN

ull

11

Background: Zero-forcing

Data

2

Null

NullNull

Null

Null

12

Background: Zero-forcing

Data

2

Data 1

Data

6

Data 3

Data 4Data

5

13

Background: Scaling Up Conjugate

Data 1

14

Background: Scaling Up Conjugate

Data 1

15

Background: Scaling Up Conjugate

Data 1

16

Data 3

Data

5

Background: Scaling Up Conjugate

Data 1

Data

6

Data

2

Data 4

Conjugate vs. Zero-forcing

• Negligible Processing

• Completely Distributed

• No Latency Overhead

• Poor Spectral Efficiency

17

• O(M•K2)

• Centralized

• Substantial Overhead

• Good Spectral Efficiency

Under what scenarios, if any, does conjugate precoding outperform zero-forcing?

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

computation24

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

• Quantization

• Inversion Latency

• Data Transport– Switching Latency– Throughput

26

Performance Model

27

Conjugate vs. Zero-forcing

29

Without Considering Computation

CE TransmitComp

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Spectral Efficiency vs. # of BS antennas

K = 15

# of Base Station Antennas (M)

Spect

ral Effi

ciency

(bps/

Hz)

31

Spectral Efficiency vs. # of UsersM = 64

# of Users (K)

Spect

ral Effi

ciency

(bps/

Hz)

32

Considering Computation

CE TransmitComp

33Zeroforcing with various hardware configurations

M = 64 K = 15

10-4

10-3

10-2

10-1

0

20

40

60

80

Coherence Time (s)

Ach

ieve

d C

apac

ity (

bps/

Hz)

Conjugate

Coherence Time (s)

Ach

ieved C

ap

aci

ty (

bps/

Hz)

2 4 6 8 10 12 140

5

10

15

20

25

30

35

Number of Users

Ach

ieve

d C

apac

ity (

bps/

Hz)

Zero-Forcing

Conjugate

M = 64 Ct = 30 ms

Performance vs. # of Users

34# of Users (K)

Ach

ieved C

ap

aci

ty (

bps/

Hz)

M = 200 Ct = 30 ms

Max Multiplexing Gain vs. # of Users

# of Users (K)

Mult

iple

xin

g G

ain

(γ ·

K)

35

0 20 40 60 80 100 120 1400

20

40

60

80

100

120

140

X: 4Y: 1.253

Number of Users (K)

Mul

tiple

xing

Gai

n (

* K

)

X: 36Y: 17.27

X: 58Y: 32.39

X: 75Y: 46.86

X: 89Y: 52.82

ZF-SuperZF-Cluster

ZF-High

ZF-Mid

ZF-LowConjugate

Applicability

• Guide Base Station Design– Refine model for your implementation

• Enables adaptive precoding

36

Ramifications

1 GHz 10 GHz

ConjugateAdaptivePrecodingZero-forcing

Faster Processing

More Antennas or Higher Mobility

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 precoding 38http://argos.rice.edu

Questions?

http://argos.rice.edu

40

Frame Pipelining Schemes

CE DownlinkCom

p

Coherence Time

Comp

CE Downlink CE ……

CE

Coherence Time

CEUplink

All Downlink

All Uplink

CE DownlinkComp Uplink CE …

Coherence Time

Uplink …Optimal

Coherence Time

…(Not to Scale)