dynamic fractional comp for advanced cellular networks · dynamic fractional comp for advanced...

Tokyo Institute of TechnologyMobile Communications Research Group

Dynamic Fractional CoMP

for Advanced Cellular Networks

Kei Sakaguchi

Tokyo Institute of Technology

Advanced Cellular Networks

Oct. 21, 2010 2

• Spatial spectrum sharing via Multi-User MIMO and Femto Cell

Multi-User MIMO

Femto cell

Relay

Base Station Cooperation

• Pathloss compensation via Relay

• Interference Management via Base Station Cooperation

Cell throughput improvement

User throughput improvement

Cell-edge Problem

Oct. 21, 2010 3

1H 2H

• Worst SINR due to high pathloss and strong interference from adjacent BS

• Reduced MIMO multiplexing gain due to low SINR

• Further degradation due to higher spatial correlation at BSs

Single Cell Single User MIMO

Spectral efficiency

BS1 BS2UE1 UE2

Base Station Cooperation

Oct. 21, 2010 4

Cooperation between adjacent BSs for interference management

Coordinated Scheduling / Beamforming Base Station Cooperation

BS2BS1 UE1

UE2

BS1 BS2UE1

UE2

• without data sharing

• with data sharing

→ Coordinated Scheduling / Beamforming

→ Base Station Cooperation MIMO (CoMP JT)

Coordinated Scheduling / Beamforming

Oct. 21, 2010 5

Frequency Reuse (FR) Fractional Frequency Reuse (FFR)

Coordinated Power Control (CPC) Coordinated Beamforming

BS2BS1 UE1

UE2

BS2BS1 UE1

UE2

BS2BS1 UE1

UE2

BS2BS1 UE1 UE2

Base Station Cooperation MIMO

Oct. 21, 2010 6

11H

21H 22H

12H

BSC MU-MIMO

1H 2H

BSC SU-MIMO

(CoMP JT)• Data streams are shared by both BSs to perform distributed MIMO

• BSC SU-MIMO improves user spectral efficiency

via macro-diversity and cooperative multiplexing gain

• BSC MU-MIMO improves both user and cell spectral efficiency

by additional cooperative user multiplexing gain

Open Problem in BSC

• ClusteringCooperative BS set selection to

perform effective BSC MIMO

(static clustering or dynamic clustering)

• Backhaul architectureSmart backhaul architecture to share data streams with low latency

by using X2 interface and/or Remote Radio Head (RRH)

• Cell planning schemeInnovation from non-overlapped to overlapped cell planning by

controlling Inter Site Distance (ISD) or BS antenna down tilting

• Feedback schemeCodebook based digital precoding is not enough for BSC MU-MIMO

and additional feedback is needed (digital or analog)

Oct. 21, 2010 7

ISD

BSC cluster

Cooperation Region

Oct. 21, 2010 8

• Cell-inner (non-cooperative region)

• BSC MIMO is not effective due to unbalanced pathloss (high SIR)

• Single-cell MIMO is efficient at cell-inner

• Cell-edge (cooperative region)

• BSC MIMO is effective at cell-edge due to balanced pathloss (low SIR)

100 200 300 400 500 600 7000

2

4

6

8

10

12

14

Distance from BS to user [m]

Use

r sp

ectr

al

eff

icie

ncy

[b

ps/

Hz]

SC-SU transmission

BSC-MU transmission

Fractional CoMP

Oct. 21, 2010 9

Turning point

Cooperation regionNon-cooperation

region

• Non-cooperation region

– Single Cell Single User (SC-SU) MIMO transmission from local BS

is efficient

: Local BS : Neighbor BS

: Strong signal

: Weak interference

100 200 300 400 500 600 7000

2

4

6

8

10

12

14


Use

r sp

ectr

al

eff

icie

ncy

[b

ps/

Hz]

SC-SU transmission

BSC-MU transmission

Fractional CoMP

Oct. 21, 2010 10

Turing point

Cooperation regionNon-cooperation

region

• Cooperation region

– BSC Multi-User (BSC-MU) MIMO by local and cooperative BSs

is effective

: Local BS : Cooperative BS

: Non-cooperative BS

: Strong signal : Weak

interference

Fractional CoMP

11

SC-SU MIMO Fractional CoMP

Cell-inner : Good Cell-edge : NG

Cell-inner : NG Cell-edge : Good

BSC-MU MIMO

Oct. 21, 2010

• Fractional CoMP dynamically selects

effective transmission schemes

according to UE locations

non-cooperative region : SC-SU MIMO

cooperative region : MC-MU MIMO

Dynamic Clustering/Scheduling

Oct. 21, 2010 12

Scheduling slot A

Scheduling slot B

• Sets of cooperative BSs (incl. SC) are dynamically selected for

each scheduling slot (resource block)

Distributed Clustering

User selection

Decision on

CoMP

Cooperation

user selection

Cooperation

request

Judgment of

CoMP

Response OK

Data sharing

CoMP starts

Master cell Cooperation cell

: Master cell : Cooperation cell

Oct. 21, 2010 13

• Dynamic clustering algorithm by using distributed cooperative controller

Distributed Clustering

User selection

Decision on

CoMP

Cooperation

user selection

Cooperation

request

Judgment of

CoMP

Response OK

Data sharing

CoMP starts

Master cell

: Master cell

Oct. 21, 2010 14

• Dynamic clustering algorithm by using distributed cooperative controller

Cooperation cell

: Cooperation cell

Backhaul for Distributed Clustering

Oct. 21, 2010 15

Base station unit A Base station unit B

base

band unit

base

band unit

base

band unit

base

band unit

Control unit Control unitX2 line

Optical fiberRemote Radio Head (RRH)

• High speed smart backhaul network composed of RRH and X2

(CoMP

control signal)

Backhaul for Distributed Clustering

Oct. 21, 2010 16

Base station unit A Base station unit B

base

band unit

base

band unit

base

band unit

base

band unit

Control unit Control unitX2 line

Optical fiberRemote Radio Head (RRH)

(CoMP

control signal)

• High speed smart backhaul network composed of RRH and X2

Oct. 21, 2010 17

Optical fiber

Base

station

unit #1

Base

station

unit #2

Base

station

unit #3

Base

station

unit #4

Base

station

unit #5

Base

station

unit #6

Base

station

unit #4

Base

station

unit #1

Base

station

unit #5

Base

station

unit #6

Base

station

unit #3

Base

station

unit #2

RRH based Backhaul Network

RRH

Numerical Examples

Oct. 21, 2010 18

Parameter Value

Number of BS 19

Number of users per cell 10

Number of antennas 2x2

CoMP cooperation set size 3

Transmit power 40dBm

Noise power -100dBm

Inter site distance 1000m

Pathloss model 34.5+35log10(d[m]) [dB]

Small-scale fading i.i.d. Rayleigh

Shadow fading standard deviation 8dB

Inter-cell shadow fading correlation 0.5

Scheduling Round-robin

SU-MIMO scheme SVD-MIMO

MU-MIMO scheme Block Diagonalization SVD-MIMO

Overhead of SC-SU MIMO 0.6387 (3GPP R1-093611)

Overhead of BSC-MU MIMO 0.5787 (3GPP R1-093611)

User Spectral Efficiency

Oct. 21, 2010 19

• Multi-cell static clustering BSC MIMO is still not effective at cell-edge

• Dynamic fractional cooperation is effective both at cell-inner and cell-edge

100 200 300 400 5000

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8


Use

r sp

ectr

al

eff

icie

ncy

[b

ps/

Hz/u

ser]

Single-cell MIMO

Frequency reuse

Multi-cell static cluster

Multi-cell semi-static cluster

Dynamic fractional cooperation

CDF of User Spectral Efficiency

Oct. 21, 2010 20

• Both cell-average and 5% cell-edge user spectral efficiency are improved

0 0.1 0.2 0.3 0.4 0.50

0.2

0.4

0.6

0.8

1

0.05

User spectral efficiency [bps/Hz/user]

CD

F

Single-cell MIMO

Frequency reuse




40

60

80

100

120

Gain

s fr

om

sin

gle

-cell

MIM

O [

%]

0

1

2

3

2.4

Av

era

ge c

ell

spectr

al

eff

icie

ncy

[bp

s/H

z]

0

0.05

0.1

0.07

Cell

-ed

ge u

ser

spectr

al

eff

icie

ncy

[bp

s/H

z/u

ser]

80

100

120

140

160

180

200

220

Gain

s fr

om

sin

gle

-cell

MIM

O [

%]

Cell-average & cell-edge spectral efficiency

Oct. 21, 2010 21

Single-

cell

MIMO

Multi-cell

static

cluster

Dynamic

fractional

cooperation

Cell-average spectral efficiency Cell-edge spectral efficiency

Frequency

reuse

Multi-cell

semi-static

cluster

Multi-cell

static

cluster

Dynamic

fractional

cooperation

Frequency

reuse

Multi-cell

semi-static

cluster

Single-

cell

MIMO

Multi-cell

static

cluster

Dynamic

fractional

cooperation

Frequency

reuse

Multi-cell

semi-static

cluster

Multi-cell

static

cluster

Dynamic

fractional

cooperation

Frequency

reuse

Multi-cell

semi-static

cluster

Cell Planning for BSC

Oct. 21, 2010 22

Form Cluster?

Cooperative BS

Uncooperative BS

Desired Signal

Interfering Signal

Desired for JT CoMP or

Interfering for CS/CB

Non-BSC Cell Planning BSC Cell Planning

Typical Cell Planning Design

• Inter-site distance? BS Locations?

• Tx powers? Antenna parameters?

BSC Cell Planning Design

• Typical Cell Planning Design

+• Cell Partitioning? (Cooperation regions)

• BSC Cluster Partitioning?

Other Cells

are treated as

interferers so

coverage

overlap is

avoided

Cooperate

here?

*optimum ISD estimate results in

optimum network cost estimate

ISD?Intersite distance (ISD)


BSC Cluster TypesHigh-speed

backboneInter site CoMP

intracluster

cell-edge

(yellow)cell-inner

(grey)

intercluster

cell-edge

(blue)

site-edge

(light blue)

Intra site CoMP

Antennas of one transmission point of the same CoMP

cooperating set. Antenna directivity () is indicated by

the curves .

intracluster

cell-edge

(yellow)cell-inner

(grey) site-edge

(light blue)

High-speed

backboneHybrid site CoMP

Oct. 21, 2010 23

Cell Regions according to LUNR and LCR

Oct. 21, 2010 24

L 1C

2C

1U

2U

cooperation

Cooperative BS

Non-cooperative BS

power noise the is

BSs ecooperativ-non from powerRx total the is

BSs ecooperativ from powerRx total the is

BS local from powerRx the is

N

UUU

CCC

L

21

21

C

L

NU

L

LCR

LUNR

-10 0 10 20 30-10

-5

0

5

10

15

20

25

30

LCR (dB)

LN

R (

dB

)

site-inner

site-edge

-10 0 10 20 30-10

-5

0

5

10

15

20

25

30

LCR (dB)

LU

NR

(d

B)

Cell Regions

cell-inner

intracluster cell-edge

intercluster cell-edge

Cooperation Region according to LUNR and LCR

L 1C

1U

Non-cooperative SVD

(NC-SVD)

L 1C

1U

BSC Block Diagonalization

SVD (BD-SVD)

Cooperation

Region

Non-

cooperation

Region

Oct. 21, 2010 25

Cooperation Regions of BSC Clusters

Smallest

cooperation

region area

Oct. 21, 2010 26

Non-cooperation

region

Cooperation

region

Cooperative Region and Cluster Cells of


1

2

5

6

7

9

10

11

12

13

14

15

16

17

18

19

20

21

25

26

27

28

29

30

31

32

33

34

37

38

39

40

43

44

45

46

47

48

49

50

51

55

56

57

58

59

60

61

62

63

64

65

66

67

68

69

70

71

72

73

74

75

76

77

78

79

80

81

Grid Location (m)

Gri

d L

ocati

on

(m

)

48

3

-400 -200 0 200 400 600

-400

-200

0

200

400

600 Non-cooperation

Region (cyan)

Cooperation

Region (pink)

Boundary of Cluster Cell (3,4,8)

Boundary of

Cluster Cell (3,8,19)

Boundary of


Boundaries of Cluster Cell

(1,2,3)

Boundary of


cluster cell is the area at which its associated BSC Cluster performs CoMP to the UEs inside the area

Largest

cooperation

region area

Oct. 21, 2010 27

ISD Dependency

Oct. 21, 2010 28

Saturation due to

intercluster

interferenceFloor due to noise

• Inter site distance can be optimized via coverage of cooperation region

Dynamic fractional

Inter site dynamic

Inter site static

Hybrid static

Intra site static

Cell Planning for CoMP Conclusion

• Our Contribution: A framework

for cell planning of CoMP

networks based on receive

signal strength ratios

– Cluster Types

– LUNR and LCR

– Cell regions

– Cooperation regions

– Cluster cells

– Spectral Efficiency of CoMP

– ISD Dependency

– Cluster Selection Optimization

Form Cluster?

Cooperative BS

Uncooperative BS

BSC Cell Planning

BSC Cell Planning Design

• Typical Cell Planning Design

+• Cell Partitioning? (Cooperation regions)

• BS Cluster Partitioning?

Cooperate

here?

ISD?

I. Garcia, N. Kusashima, K. Sakaguchi, K. Araki, S.

Kaneko, Y. Kishi, “Impact of Base Station

Cooperation on Cell Planning,” EURASIP J.

Wireless Commun. and Networking,

Vol. 2010, Article ID 406749, Aug. 2010.

Oct. 21, 2010 29

Summary

Oct. 21, 2010 30

• Interference management scheme to improve cell-edge

throughput for advanced cellular networks

Dynamic Fractional

CoMPBSC MIMO

Inter site

CoMPIntra site

CoMP

Dynamic

clustering

Distributed

clustering

RRH

based network

Future Perspective

Oct. 21, 2010 31

• CoMP MIMO transmission scheme using non-linear algorithm

such as dirty paper coding or convex optimization

• CoMP between BSs with different cell size and backhaul

architecture (heterogeneous network)

• Standardization of Dynamic Fractional CoMP for

LTE-Advanced (Release 11) and amendment of 16m

Co-scheduling

• Average receive SNR

of 1st user

Oct. 21, 2010 32

d1 [m]

d2 [

m]

0 100 200 300 400 500 600 7000

100

200

300

400

500

600

700

-5

-4

-3

-2

-1

0

2

2221

211222111

P

gg

gggg

The n

orm

aliz

ed S

NR

of

1stuser

Co-schedulingThe BSs select users with the same SINR

• d1 = d2

• SNR is maximized

Co-scheduling

Oct. 21, 2010 33

10-4

10-2

100

10-3

10-2

10-1

100

Instantaneous cooperation user spectral efficiency [bps/Hz]

CD

F




MU Precoding based on V Feedback

Oct. 21, 2010 34

UE N

UE 1

V

N,

N

…

V

1,

1

• Channel State Information (CSI) feedback

The k-th UE feeds back to BS V

k,

kBS

BS

UE N

UE 11H

NH

…

• Channel estimation

H

k U

k

kV

k

H

Denote channel between BS and k-th user

H

k

HH

k V

k

kV

k

H

Correlation matrix of is given bykH

MU Precoding based on V Feedback

Oct. 21, 2010 35

• Precoding calculation

The k-th user’s precoding matrix is obtained by

\k k k

W V V

%Hk

H %Hk V

\k

H

Hk

HH

kV

\k

V\k

V

k

k

HU

k

HU

k

kV

k

HV

\k

V\k

H

Vk

kV

k

HV

\k

H

kkk

H

kk

H

kk

H

kkk

H

k

H

kkk

H

k

VΛV

VΣΣV

VΣUUΣVHH

~~~

~~~~

~~~~~~~~

Correlation matrix of block diagonalized channel matrix is represented askH

This equation can be expressed in another way using ED of as

Denote (Block diagonalized channel)

%H

k H

kV

\k

%Uk%

k%V

k

H

kH

V\k

H V1

L Vk1

Vk1

L VK

V\k

HV

\k

O

H

k kH HIt can be calculated by using reconstructed

Finally can be obtained

H

k kH H

H

kV

k,V

k

\k

V

is given by k-th user’s feedback

can be calculated by other user’s feedback

Finally can be reconstructedH

k kH H

Digital and Analog Feedback

Oct. 21, 2010 36

0 1

000

1000

0001

0001

1010

0

010

1

011

0

011

1

100

0 100

1101

0

101

1

Effect of

noise

0

010

1

001

1

010

1001

1

Example

0

It is difficult to design optimal

codebook

The quantization error exist in

high SNR

Amount of feedback

is increased with

exponentially

Analog signal is

Influenced by

noise

Hybrid method has both of

analog and digital properties

Feedback

Implicit feedback

Codebook

(Vector quantization)

Explicit feedback

Scalar quantization

Analog feedback

Hybrid feedback

Hybrid Feedback

37

optimal precoding

digital feedback

analog feedback

The area of analog

feedback estimation

error by noise

Codebook Analog feedback(Error vector between

optimal vector and vector

given by codebook)

Analog feedback

Normal analog feedback Hybrid feedback

Analog signal boosting

Vector of

In case of hybrid

feedback, the

influence of noise

is lower than

normal analog by

boosting

Oct. 21, 2010

Numerical Examples

Oct. 21, 2010 38

parameter Value

Channel model AWGN

Channel

estimation errorLTE

Codebook LTE Rel.8

DL/UL SNR Same

MIMO system 4X1 SU-MISO

Feedback

method

Digital

Feedback(fixed)

Additional

feedback

Codebook 4 bits Nothing

Analog 4 bits 4 symbols

Hybrid 4 bits 4 symbols

H H

1 2 1 1 2 2F

1,

2d V V V V V V

Chordal distance

Metric of distance between two matrices

The hybrid feedback method gives

accurate channel state information

Numerical Examples

Oct. 21, 2010 39

• Comparison transmission

– Single-cell SISO

– Single-cell MIMO

– Multi-cell static cluster

– Dynamic fractional cooperation

BS position Cooperation region

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