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Combatting Inter-cell Interference in MU-MIMO Networks

Hang Yu, Oscar Bejarano and Lin Zhong

ECE Department, Rice University

1

Guiding principlesβ€’ Spectrum is precious

β€’ Hardware is getting cheaper and more efficient

2

0

200

400

600

800

1000

1200

2002 2004 2006 2008 2010 2012 2014

2x2 MIMO

SISO

Pow

erco

nsu

mp

tio

n (

mW

)

Mobile devices are embracing more antennas

3

1-2 antennas 1-2 antennas 1-4 antennas

How to best use client antennas?

4

MobiCom’11: beamforming to achieve power efficiency

5

This work: inter-cell interference

6

This work: inter-cell interference

7

Key insight: clients and APs can coordinately cancel inter-cell interference

8

Example of two cells

Client3

AP1

Client2

Client1

Client4

AP2

1 2 1 2

1 2 3

1

1 2

1

9

Cell 1 Cell 2

A single AP delivers two streams

Client3

AP1

Client2

Client1

Client4

AP2

1 2 1 2

1 2 3

1

1 2

1

10

Cell 1 Cell 2

Coordinated interference cancellation delivers three streams

Client3

AP1

Client2

Client1

Client4

AP2

1 2 1 2

1 2 3

1

1 2

1

11

Coordinated interference cancellation delivers three streams

Client3

AP1

Client2

Client1

Client4

AP2

1 2 1 2

1 2 3

1

1 2

1

12

Coordinated interference cancellation delivers three streams

Client3

AP1

Client2

Client1

Client4

AP2

1 2 1 2

1 2 3

1

1 2

1

13

Coordinated interference cancellation delivers three streams

Client3

AP1

Client2

Client1

Client4

AP2

1 2 1 2

1 2 3

1

1 2

1

14

How to achieve coordinated interference cancellation with low overhead?

15

Why is it hard?

β€’ Coordination can be expensive

β€’ Optimizing beamforming weights requires full channel knowledge

16

Key idea: two-step optimization

17

Transmitting/receiving data streams

Cancelling inter-cell interference

or

18

Antenna usage optimization:To communicate data or cancel interference?

Optimized antenna usage

Transmitting Cancelling

19

Client3

AP1

Client2

Client1

Client4

AP2

1 2 1 2

1 2 3

1

1 2

1

Algorithm recursively applies to arbitrary MU-MIMO networks

20

Key property:

Only # of antennas required to optimize the antenna usage

21

𝐰 , = ?

22

Beamforming weight optimization

Key property:

Partial channel knowledge required to optimize the beamforming weights

23

Overview of CoaCa

β€’ Coordinated optimization of AP and Client antennas

β€’ Interleave 802.11ac channel sounding to achieve coordinated interference cancellation

24

Channel sounding in 802.11ac

Client1

Client2

AP

25

AP sends NDP-A

Client1

Client2

NDP-AAP

26

AP sends NDP

Client1

Client2

NDP-AAP NDP

27

Client1 reports its channel

Client1

Client2

NDP-AAP NDP

BF-R

𝐇1β†’1

28

AP polls Client2

Client1

Client2

NDP-AAP NDP

BF-R

BF-P𝐇1β†’1

29

Client2 reports its channel

Client1

Client2

NDP-AAP NDP

BF-R

BF-P

BF-R

𝐇1β†’1 𝐇1β†’2

30

AP transmits to both clients

Client1

Client2

NDP-AAP NDP

BF-R

BF-P

BF-R

DATA𝐇1β†’1 𝐇1β†’2

31

Example of two cells

32

Client3

AP1

Client2

Client1

Client4

AP2

1 2 1 2

1 2 3

1

1 2

1

Timeline of CoaCa

Client1

Client3

Client4

AP1

AP2

33

AP1 sends NDP-A

Client1

Client3

Client4

NDP-AAP1

AP2

34

AP1 sends NDP-A

Client1

Client3

Client4

NDP-AAP1

AP2

35

# of ant

# of ant

# of ant

# of ant

AP1 sends NDP

Client1

Client3

Client4

NDP-AAP1 NDP

AP2

36

# of ant

# of ant

# of ant

# of ant

Clients estimate channels from AP1

Client1

Client3

Client4

NDP-AAP1 NDP

AP2

𝐇1β†’1

𝐇1β†’3

𝐑1β†’4

37

# of ant

# of ant

# of ant

# of ant

AP2 sounds the channel

Client1

Client3

Client4

NDP-AAP1 NDP

AP2 NDP-A NDP

𝐇1β†’1

𝐇1β†’3

𝐑1β†’4

𝐇2β†’1

𝐇2β†’3

𝐑2β†’4

38

# of ant

# of ant

# of ant

# of ant

# of ant

# of ant

# of ant

# of ant

AP2 sounds the channel

Client1

Client3

Client4

NDP-AAP1 NDP

AP2 NDP-A NDP

𝐇1β†’1

𝐇1β†’3

𝐑1β†’4

𝐇2β†’1

𝐇2β†’3

𝐑2β†’4

39

Antenna usage optimization

# of ant

# of ant

# of ant

# of ant

# of ant

# of ant

# of ant

# of ant

AP1 polls Client1

Client1

Client3

Client4

NDP-AAP1 NDP

AP2 NDP-A NDP

𝐇1β†’1

𝐇1β†’3

𝐑1β†’4

𝐇2β†’1

𝐇2β†’3

𝐑2β†’4

BF-P

Compute 𝐯1

40

Antenna usage optimization

# of ant

# of ant

# of ant

# of ant

# of ant

# of ant

# of ant

# of ant

Client1 reports its channel vector

Client1

Client3

Client4

NDP-AAP1 NDP

AP2 NDP-A NDP

BF-R𝐇1β†’1

𝐇1β†’3

𝐑1β†’4

𝐇2β†’1

𝐇2β†’3

𝐑2β†’4

𝐇1β†’1𝐯1BF-P

Compute 𝐯1

41

Antenna usage optimization

# of ant

# of ant

# of ant

# of ant

# of ant

# of ant

# of ant

# of ant

AP2 polls Client4

Client1

Client3

Client4

NDP-AAP1 NDP

AP2 NDP-A NDP

BF-R

BF-P

𝐇1β†’1

𝐇1β†’3

𝐑1β†’4

𝐇2β†’1

𝐇2β†’3

𝐑2β†’4

𝐇1β†’1𝐯1BF-P

Compute 𝐯1

42

Antenna usage optimization

# of ant

# of ant

# of ant

# of ant

# of ant

# of ant

# of ant

# of ant

Client4 reports to both APs

Client1

Client3

Client4

NDP-AAP1 NDP

AP2 NDP-A NDP

BF-R

BF-P

BF-R

𝐇1β†’1

𝐇1β†’3

𝐑1β†’4

𝐇2β†’1

𝐇2β†’3

𝐑2β†’4

𝐇1β†’1𝐯1BF-P

Compute 𝐯1

𝐑2β†’4

𝐑1β†’4

43

Antenna usage optimization

# of ant

# of ant

# of ant

# of ant

# of ant

# of ant

# of ant

# of ant

Client3 overhears Client4

Client1

Client3

Client4

NDP-AAP1 NDP

Overhear

AP2 NDP-A NDP

BF-R

BF-P

BF-R

𝐇1β†’1

𝐇1β†’3

𝐑1β†’4

𝐇2β†’1

𝐇2β†’3

𝐑2β†’4

𝐇1β†’1𝐯1BF-P

Compute 𝐯1

𝐑1β†’4

𝐑2β†’4

𝐑1β†’4

44

Antenna usage optimization

# of ant

# of ant

# of ant

# of ant

# of ant

# of ant

# of ant

# of ant

Beamforming weight of Client3

AP1

1 2

AP2

1 2

Client1

1 2 3

Client2

1

Client3

1 2 Client4

1

𝐯3 = 𝐑1β†’4βŠ₯ 𝐇1β†’3

βŠ₯

𝐇1β†’3

𝐑1β†’4

45

Channel alignment by Client3

AP1

1 2

Client3

1 2 Client4

1

𝐯3 = 𝐑1β†’4βŠ₯ 𝐇1β†’3

βŠ₯

𝐇1β†’3

𝐑1β†’4

46

Channel alignment by Client3

AP1

1 2

Client3

1 2 Client4

1

𝐯3 = 𝐑1β†’4βŠ₯ 𝐇1β†’3

βŠ₯

𝐑1β†’4𝐇1β†’3

𝐑1β†’4

47

Channel alignment by Client3

AP1

1 2

Client3

1 2 Client4

1

𝐯3 = 𝐑1β†’4βŠ₯ 𝐇1β†’3

βŠ₯

𝐑1β†’4

𝐰1

𝐇1β†’3

𝐑1β†’4

48

Channel alignment by Client3

AP1

1 2

Client3

1 2 Client4

1

𝐯3 = 𝐑1β†’4βŠ₯ 𝐇1β†’3

βŠ₯

𝐰1

𝐇1β†’3(1) 𝐇1β†’3(2)𝐇1β†’3

𝐑1β†’4𝐇1β†’3𝐯3

49

AP2 polls Client3

Client1

Client3

Client4

NDP-AAP1 NDP

Overhear

AP2 NDP-A NDP

BF-R

BF-P

BF-R

𝐇1β†’1

𝐇1β†’3

𝐑1β†’4

𝐇2β†’1

𝐇2β†’3

𝐑2β†’4

𝐇1β†’1𝐯1

Compute 𝐯3

BF-P

BF-P

Compute 𝐯1

𝐑1β†’4

𝐑2β†’4

𝐑1β†’4

50

Antenna usage optimization

# of ant

# of ant

# of ant

# of ant

# of ant

# of ant

# of ant

# of ant

Client3 reports its channel vector

Client1

Client3

Client4

NDP-AAP1 NDP

Overhear

AP2 NDP-A NDP

BF-R

BF-P

BF-R

BF-R

𝐇1β†’1

𝐇1β†’3

𝐑1β†’4

𝐇2β†’1

𝐇2β†’3

𝐑2β†’4

𝐇1β†’1𝐯1

𝐇2β†’3𝐯3

Compute 𝐯3

BF-P

BF-P

Compute 𝐯1

𝐑1β†’4

𝐑2β†’4

𝐑1β†’4

51

Antenna usage optimization

# of ant

# of ant

# of ant

# of ant

# of ant

# of ant

# of ant

# of ant

Client3 reports its channel vector

Client1

Client3

Client4

NDP-AAP1 NDP

Overhear

AP2 NDP-A NDP

BF-R

BF-P

BF-R

𝐇1β†’1

𝐇1β†’3

𝐑1β†’4

𝐇2β†’1

𝐇2β†’3

𝐑2β†’4

𝐇1β†’1𝐯1

Compute 𝐯3

BF-P

BF-P

Compute 𝐯1

𝐑1β†’4

𝐑2β†’4

𝐑1β†’4

52

BF-R

𝐇2β†’3𝐯3

Beamforming weight optimizationAntenna usage optimization

# of ant

# of ant

# of ant

# of ant

# of ant

# of ant

# of ant

# of ant

Both APs transmit simultaneously

Client1

Client3

Client4

NDP-AAP1 NDP

Overhear

AP2 NDP-A NDP

BF-R

BF-P

BF-R

DATA

DATA

𝐇1β†’1

𝐇1β†’3

𝐑1β†’4

𝐇2β†’1

𝐇2β†’3

𝐑2β†’4

𝐇1β†’1𝐯1

Compute 𝐯3

BF-P

BF-P

Compute 𝐯1

𝐑1β†’4

𝐑2β†’4

𝐑1β†’4

53

Beamforming weight optimizationAntenna usage optimization

BF-R

𝐇2β†’3𝐯3

# of ant

# of ant

# of ant

# of ant

# of ant

# of ant

# of ant

# of ant

No additional frames

Client1

Client3

Client4

NDP-AAP1 NDP

Overhear

AP2 NDP-A NDP

BF-R

BF-P

BF-R

DATA

DATA

𝐇1β†’1

𝐇1β†’3

𝐑1β†’4

𝐇2β†’1

𝐇2β†’3

𝐑2β†’4

𝐇1β†’1𝐯1

Compute 𝐯3

BF-P

BF-P

Compute 𝐯1

𝐑1β†’4

𝐑2β†’4

𝐑1β†’4

54

Beamforming weight optimizationAntenna usage optimization

BF-R

𝐇2β†’3𝐯3

# of ant

# of ant

# of ant

# of ant

# of ant

# of ant

# of ant

# of ant

Will APs/clients acquire enough channel knowledge?

55

56

Theorem 1:

At most, a AP needs:

channel knowledge from its served clients, or

clients it interferes with and holds the cancellation responsibility

57

Theorem 1:

At most, a AP needs:

channel knowledge from its served clients,

clients it interferes with and holds the cancellation responsibility

58

Theorem 1:

At most, a AP needs:

channel knowledge from its served clients,

or clients it interferes with and holds the cancellation responsibility

Clients report necessary channels

Client1

Client3

Client4

NDP-AAP1 NDP

Overhear

AP2 NDP-A NDP

BF-R

BF-P

BF-R

DATA

DATA

𝐇1β†’1

𝐇1β†’3

𝐑1β†’4

𝐇2β†’1

𝐇2β†’3

𝐑2β†’4

𝐇1β†’1𝐯1

Compute 𝐯3

BF-P

BF-P

Compute 𝐯1

𝐑1β†’4

𝐑2β†’4

𝐑1β†’4

59

BF-R

𝐇2β†’3𝐯3

# of ant

# of ant

# of ant

# of ant

# of ant

# of ant

# of ant

# of ant

Theorem 2:

At most, a client needs:

channel knowledge from clients in the same cell;

if ordered properly, channel knowledge from previous clients.

60

Theorem 2:

At most, a client needs:

channel knowledge from clients in the same cell;

if ordered properly, channel knowledge from previous clients.

61

Theorem 2:

At most, a client needs:

channel knowledge from clients in the same cell;

if ordered properly, channel knowledge from previous clients.

62

Clients report in the optimal order

Client1

Client3

Client4

NDP-AAP1 NDP

Overhear

AP2 NDP-A NDP

BF-R

BF-P

BF-R

BF-R

DATA

DATA

𝐇1β†’1

𝐇1β†’3

𝐑1β†’4

𝐇2β†’1

𝐇2β†’3

𝐑2β†’4

𝐇1β†’1𝐯1

𝐇2β†’3𝐯3

Compute 𝐯3

BF-P

BF-P

Compute 𝐯1

𝐑1β†’4

𝐑2β†’4

𝐑1β†’4

63

# of ant

# of ant

# of ant

# of ant

# of ant

# of ant

# of ant

# of ant

Will CoaCa APs/clients interoperate with unmodified 802.11ac clients?

64

Clients passively report channels

65

Client1

Client3

Client4

NDP-AAP1 NDP

Overhear

AP2 NDP-A NDP

BF-R

BF-P

BF-R

BF-R

DATA

DATA

𝐇1β†’1

𝐇1β†’3

𝐑1β†’4

𝐇2β†’1

𝐇2β†’3

𝐑2β†’4

𝐇1β†’1𝐯1

𝐇2β†’3𝐯3

Compute 𝐯3

BF-P

BF-P

Compute 𝐯1

𝐑1β†’4

𝐑2β†’4

𝐑1β†’4

# of ant

# of ant

# of ant

# of ant

# of ant

# of ant

# of ant

# of ant

Clients do not collide with APs

66

Client1

Client3

Client4

NDP-AAP1 NDP

Overhear

AP2 NDP-A NDP

BF-R

BF-P

BF-R

BF-R

DATA

DATA

𝐇1β†’1

𝐇1β†’3

𝐑1β†’4

𝐇2β†’1

𝐇2β†’3

𝐑2β†’4

𝐇1β†’1𝐯1

𝐇2β†’3𝐯3

Compute 𝐯3

BF-P

BF-P

Compute 𝐯1

𝐑1β†’4

𝐑2β†’4

𝐑1β†’4

Fake client ID

# of ant

# of ant

# of ant

# of ant

# of ant

# of ant

# of ant

# of ant

Correctness of the two-step optimization

Client1

Client3

Client4

NDP-AAP1 NDP

Overhear

AP2 NDP-A NDP

BF-R

BF-P

BF-R

DATA

DATA

𝐇1β†’1

𝐇1β†’3

𝐑1β†’4

𝐇2β†’1

𝐇2β†’3

𝐑2β†’4

𝐇1β†’1𝐯1

Compute 𝐯3

BF-P

BF-P

Compute 𝐯1

𝐑1β†’4

𝐑2β†’4

𝐑1β†’4

67

Beamforming weight optimizationAntenna usage optimization

BF-R

𝐇2β†’3𝐯3

# of ant

# of ant

# of ant

# of ant

# of ant

# of ant

# of ant

# of ant

Interoperability with unmodified 802.11ac clients

Client1

Client3

Client4

NDP-AAP1 NDP

Overhear

AP2 NDP-A NDP

BF-R

BF-P

BF-R

DATA

DATA

𝐇1β†’1

𝐇1β†’3

𝐑1β†’4

𝐇2β†’1

𝐇2β†’3

𝐑2β†’4

𝐇1β†’1𝐯1

Compute 𝐯3

BF-P

BF-P

Compute 𝐯1

𝐑1β†’4

𝐑2β†’4

𝐑1β†’4

68

Beamforming weight optimizationAntenna usage optimization

BF-R

𝐇2β†’3𝐯3

# of ant

# of ant

# of ant

# of ant

# of ant

# of ant

# of ant

# of ant

Implementation

β€’ WARP V3 platformβ€’ Up to four antennas on each AP/client

β€’ Layered prototyping: FPGA/C/MATLAB

β€’ WARPLab framework with modificationsβ€’ Standard MIMO processing in FPGA

β€’ Two-step optimization in MATLAB

69

Experimental setup

β€’ Single interference domain

β€’ MU-MIMO network with two cells

β€’ Four cases with different node and antenna configurations

β€’ 20 repeated experiments for each case

β€’ CoaCa compared with 802.11ac

70

AP1 AP2

71

Case 1: two streams for CoaCa (no gain)

AP1

Client1

AP2

Client2

Client3

Client4

Cell 1 Cell 2

72

Case 1: CoaCa achieves similar capacity

0

5

10

15

20

25

0 5 10 15 20 25

Cap

acit

y b

y 1

1ac

(b

its/

s/H

z)

Capacity by CoaCa (bits/s/Hz)

Measured

Expected

73

Case 2: three streams for CoaCa (50% gain)

AP1

Client1

AP2

Client2

Client3

Client4

Cell 1 Cell 2

74

Case 2: CoaCa improves capacity by 40%

0

7

14

21

28

35

0 7 14 21 28 35

Cap

acit

y b

y 1

1ac

(bit

s/s/

Hz)

Capacity by CoaCa (bits/s/Hz)

Measured

No gain

Expected

75

76

Max # of streams β‰  max capacity

77

Trivial gain for larger-scale networks

Related works

β€’ Inter-cell interference in 802.11 networksβ€’ K. C. Lin, S. Gollakota, and D. Katabi. β€œRandom Access

Heterogeneous MIMO networks”. In Proc. ACM SIGCOMM, 2011.

β€’ Network-MIMOβ€’ H. Rahul, S. Kumar, and D. Katabi. β€œJMB: Scaling Wireless

Capacity with User Demands”. In Proc. ACM SIGCOMM, 2012.β€’ H. V. Balan, R. Rogalin, A. Michaloliakos, and K. Psounis.

β€œAchieving High Data Rates in a Distributed MIMO System”. In Proc. ACM MobiCom, 2012.

β€’ X. Zhang, K. Sundaresan, M. A. Khojastepour, S. Rangarajan, and K. G. Shin. β€œNEMOx: Scalable Network MIMO for Wireless Networks”. In Proc. ACM MobiCom, 2013.

78

CoaCa: practical solution to combat inter-cell interference in

802.11ac-based MU-MIMO networks

79

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