New Technologies of B3G/4G

Xiaohui LiState Key Laboratory of ISN

Xidian Univeristy, Xi’an, Shaanxi, Chinaxhli@mail.xidian.edu.cn

31 July, 2009

Introduction of Our GroupResearch WorkProjectsAchievementsFamily AlbumFuture work

Introduction of Our GroupResearch WorkProjectsAchievementsFamily AlbumFuture work

Introduction of Our GroupCommunity of associated professor, Ph.D. and M.S. students.Research Interests mainly

Theory: Some key technologies of MIMO systemsApplication: Development of 3G Long Term Evolution(LTE)

Emphasize the integration of theory with practice, focus on science exchanges home and abroad to broad our scope

Introduction of Our GroupResearch WorkProjectsAchievementsFamily AlbumFuture work

Research works

Key technologies of MIMO systemsMulti-user schedulingMIMO detection

Development of 3G Long Term Evolution (LTE) Link level simulation and system level simulation with/without CoMP (Coordinated Multiple Point) Inter-cell Interference Co-ordination (ICIC) methodInter-cell power control technologyCell selection strategy for users

PSO Multi-user schedulingParticle Swarm Optimization

Kennedy and Eberhart, in 1995 , Proc. Int'l Conf. Neural Networks, vol. 5, Motivated by the behaviors of bird flock or bees finding food

Advantage Simple, easy realizationlow computation complexity

Evolutionary mechanism:

( ) ( ) ( ) ( )( )( ) ( )( )

1 1

2 2

1 1 1

1 1 i i i i

i

v t wv t c pbest t x t

c gbest t x t

η

η

= − + − − −

+ − − −

( ) ( ) ( )1i i ix t x t v t= − +

PSO Multi-user schedulingOur contribution:

Particle definition: candidate of the scheduled user subset

Fitness Function definition: capacity value corresponding to such particle

Key PSO scheduling method steps① Initialization. Generate certain number of particles and velocities② Evaluation. Calculate the fitness value of each particle③ Evolution. Apply PSO mechanism to these particles④ Termination. Repeat the above steps until the maximal iteration number

ParticleΔ

1Δ KΔ1, MS selected

0,otherwisek

kth⎧Δ = ⎨

⎩

( ) 2 det( ( ( )))Hi NtF log diag iτ γ= +I H H Δ

PSO Multi-user scheduling Simulation Results

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0.1

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1

Capacity(bit/s/Hz)

Out

age

Pro

babi

lity

Capacity CDF curves of different algorithms

Optimal ESPSONBS[3]Random

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4

6

8

10

12

14

16

SNR[dB]

Cap

acity

[bit/

s/H

z]

The effect of iterations on capacity performance

12 13 14

10

10.5

11

11.5

12

iteration=1iteration=5iteration=10iteration=15optimal

.

Parameters: one BS, 16 MSs with single antenna, L=4MSs scheduled

PSO: Inertia weight factor w=0.9, acceleration constant c1=c2=2.0, particle number = 20 , maximal iteration number= 15

MIMO detecting

1 2[ , , ]s s 　

1STBC1s

2s

Ks

2STBC

KSTBC

1X 1N

2X 2N

KX KN Nr

1

= +Y HX Z2

　

System model

The received signal over T time slots:

OSTBC codeword matrix of user k :

real constant coefficient matrices of the given OSTBC.

* *11 1 11 1 1 1 1 1

* *1 1

=T T

K K K K KT K KT K

= +

⎛ ⎞+ +⎜ ⎟

+⎜ ⎟⎜ ⎟+ +⎝ ⎠

Y HX Z

A s B s A s B sH Z

A s B s A s B s

KM O M

L

[ ] * *

1 1 1, , , ,k k kT k k k k kT k kT k= = + +⎡ ⎤⎣ ⎦X x x A s B s A s B sL L

, , , ,t t t = 1 TA B L

MIMO detecting

Two Existing Algorithms, Lin Dai, (IEEE Trans. Comm, vol. 53,no.9Type1 detecting:

Type 2 detecting

GroupSeperation

With ZF

Projection

1G Y

2G Y

KG Y

= +Y HX Z

1 1 1 1= +y H X Z

2 2 2 2= +y H X Z

K K K K= +y H X Z

Recombination1 1 1 1

ˆ ˆˆ = +r H s Z

2 2 2 2ˆ ˆˆ = +r H s Z

ˆ ˆˆK K K K= +r H s Z

1 1 1 1λ= +r I s Z　　1 1HH r

2 2 2 2λ= +r I s Z　　

K K K Kλ= +r I s Z　　

2 2ˆHH r

ˆHK KH r

Linear decoding

1s

2s

ˆKs

Linear decoding

Linear decoding

Recombination

Recombination

= +Y HX Z

1W r

2W r

KW r

1 1 1 1= +r Ω s N

2 2 2 2= +r Ω s N

　

STBC ML decoding 1s

2s

Ks

1

K

k kk=

= +∑r Ω s N

K K K K= +r Ω s N

STBC ML decoding

STBC ML decoding

MIMO detectingProposed approach

Advantages :Unitary projection matrix without suffering from noise enhancement Low Linear decoding complexity compared with type 2 ML decoding

= +Y HX Z

1W r

2W r

KW r

1 1 1 1= +r Ω s N

2 2 2 2= +r Ω s N

1 1 1 1ξ= +r I s N　　1 1HΩ r

2 2 2 2ξ= +r I s N　　

K K K Kξ= +r I s N　　

1 1HΩ r

HK KΩ r

Linear decoding

Linear decoding

Linear decoding

1s

2s

Ks

1

K

k kk=

= +∑r Ω s N

K K K K= +r Ω s N

Recombination

MIMO detectingSystem design under the case of transmitter employing different OSTBC

Assume K different STBCk ,with code length TkOutput of STBCk:The common code length : minimum common multipleDefine

Transmit signal matrix design:

represents the output code matrix where imag coefficient matrices

represents the output code matrix where real coefficient matrices

* *,1 ,1 , ,[ , , ]k k k k k k Tk k k Tk k= + +X A s B s A s B sL

/ , 1kGk T T k K= = L

[ ]

1,1 1, 1 1,1 1, 11 1

2,1 2, 2 2,1 2, 22 2

,1 , ,1 ,

( ) ( ) ( ) ( )( ) ( )( ) ( ) ( ) ( )( ) ( )

( ) ( )

( ) ( ) ( ) ( )( ) ( )

G G

G G

K K GK K K GKK K

⎡ ⎤⎡ ⎤⎢ ⎥⎢ ⎥⎢ ⎥⎢ ⎥= = =⎢ ⎥⎢ ⎥⎢ ⎥⎢ ⎥⎢ ⎥⎣ ⎦ ⎣ ⎦

X A X A X B X BX A X BX A X A X B X BX A X B

X X A X B

X A X A X B X BX A X B

L LL L

M M M M M MM ML L

k =B 0, ( )k jX A

, ( )k jX B

k =A 0

1 2, , KT MCM T T T= L

MIMO detectingThe received signal over T time slots:

With the proposed detecting approachFor the kth group, receive vector:

For each subgroup:The desired signal of each subgroup can be decoded by:

Note that the equivalent channel of any group has the orthogonal property

1 1 2 2 K K= + = + + + +Y HX Z H X H X H X ZL

k k k k k= = + r W r Ω s n)) )

,1 , ,1 , ,1 ,[ ]k k Gk k k k Gk k k Gk⎡ ⎤ ⎡ ⎤= +⎣ ⎦ ⎣ ⎦ r r Ω s s n n)) ) ) )L L L

, 1ki k ki ki i Gk= + = r Ω s n)) ) L

Hki k ki k ki kiξ= = +r Ω r s n

) )% %g

Hk k kξ= Ω Ω I

) )g

MIMO detecting Simulation Results

Parameters(Fig.1):Two groups, Alamouti coding, QPSK, Parameters(Fig.2): Two groups, Alamouti, 3*8 OSTBC,QPSK

0 2 4 6 8 10 12 14 16 18 2010-5

10-4

10-3

10-2

10-1

100

SNR[dB]

BE

R

BER Plots

type1proposedtype2

0 2 4 6 8 10 12 14 16 18 2010-5

10-4

10-3

10-2

10-1

100

101

SNR[dB]

BE

R

BER Plots

type1 avetype1 user1type1 user2proposed aveproposed user1proposed user2

Platform of LTE/LTE-A

Link level simulationSystem level simulation

System level simulation with CoMP

For UL-CoMP, a specific UE is processed not only by its serving cell, but also by its neighbor cell ,as its coordinate cell.

Inter-cell Interference Co-ordination (ICIC)

The whole band is equally divided into eight sub-bands B=f1+f2+...f8

f1 f2 f8f6f5f3 f4 f7

Sector A: edge user :f1,f4,f7 centre user:f2,f3,f5,f6,f8

A

B

C

3 sectors per cell-site

Sector B: edge user :f2, f8 centre user:f1,f3,f4,f5,f6,f7

Sector C: edge user :f3, f5,f6 centre user:f1,f2,f4,f7,f8

Inter-cell Interference Co-ordination (ICIC)

0 0.2 0.4 0.6 0.8 1 1.2 1.40

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bps/Hz

CD

F

ICICNo ICIC

Inter-cell Power control

Power control in CoMP: to achieve maximum throughput The proposed power reallocation scheme

(1) Power requisition: UEs get power value according to LTE Rel.8 uplink power control formula

max 10 0_min ,10 log ( )MPUSCH MCS iP P P PL fα= + + + Δ + Δg g

Inter-cell Power control(2) Power reallocation: each UE transmit power is calculated by

This scheme does not increase the total power of system

11 22 33' ' '11 22 33 11 22 33

p p p pp : p : p (g ) : (g ) : (g )

+ + =⎧⎨ =⎩

' 1111

11 22 33

' 2222

11 22 33

' 3333

11 22 33

gp pg g g

gp pg g g

gp pg g g

⎧=⎪ + +⎪

⎪=⎨ + +⎪

⎪=⎪

+ +⎩

Inter-cell Power control

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x 105

0

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1

Throughout [bit/s]

CD

F

Power control schemes compaire with different threshld

Threshold = 12 fractional power controlThreshold = 12 power reallocateThreshold = 15 fractional power controlThreshold = 15 power reallocateThreshold = 18 fractional power controlThreshold = 18 power reallocate

Cell Selection algorithmCell-selection with CoMP in Uplink

Exploit inter-cell diversity,Increase cellular capacity, Balance the traffic densities.

ARSRPCRSRP

BRSRP

Cell selection

Cell Selection algorithm

The proposed schemeDetermine the serving eNB

Determine the specified UE type: cell-edge UE or center UEDecide the serving eNB for the specified UE according to the RSRP of each eNB

Determine the CoMP setWhen serving eNB is the originally accessed eNB, determine the number of coordinated cell in active CoMP set.When serving eNB is not the originally accessed eNB, decide whether the CoMP is needed or not and the number of coordinated cells in active CoMP set.

Cell Selection algorithm

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bps /Hz

CD

F

Non-Cell-Selection&&Non-CoMPCell-Selection&&Non-CoMPCell Selection&&CoMP

Introduction of Our GroupResearch WorkProjectsAchievementsFamily AlbumFuture work

Projects within 3 yearsKey technologies of adaptive multi-user MIMO systems，National Science Foundation of China，2008-2010Solutions on improving the throughput of edge cells for IMT-Advanced systems，Specialized project for state key laboratory，2008-2009Key technologies on MIMO for the uplink LTE systems，Project of ZTE corporation，2007-2008Enhanced technologies on MIMO for uplink LTE systems，Project of ZTE corporation，2008-2009

Introduction of Our GroupResearch WorkProjectsAchievementsFamily AlbumFuture work

Achievements of our group

Achievements:About 30 papers2 books1 translation4 patents4 proposal of B3G/4G

Introduction of Our GroupResearch WorkProjectsAchievementsFamily AlbumFuture work

Family Album

Introduction of Our GroupResearch WorkProjectsAchievementsFamily AlbumFuture work

Future workMulti-user MIMO systems

Multi-user PrecodingMulti-user scheduling combing with precoding

Cross-layer designCombing MIMO-OFDM With MAC protocol

Cognitive radioCognitive coexistences between WLAN and LTE systems