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Fractional Base Station Cooperation
Cellular Network
Naoki Kusashima
Tokyo Institute of Technology,
Department of Electrical and Electronic Engineering,
Araki-Sakaguchi Laboratories.
Contents• Background
– Cell-edge problem
– Conventional solution for the cell-edge problem
– Base station cooperation
• Base station cooperation block diagonalization
multi-user MIMO
– Combination of multi-user
– Cooperative region
• Fractional base station cooperation cellular network
– Fractional Base Station Cooperation
– Dynamic clustering
• Numerical simulation
• Conclusion
2
Cell-edge problem
• If user at the cell-edge location
– Low SNR
– Co-channel interference from adjacent BSs
– High antenna correlation
Low user throughput
Signal
Interference1st path
Co-channel interference High antenna correlation
2nd path
Cell-edge
problem
3
Conventional solution for cell-edge problem
• Conventional solution
– Frequency reuse
• Each BSs uses different frequency channel from surrounding
cells.
It causes degrading of system throughput.
– Interference cancellation using terminal adaptive array
• cancels interference by terminals.
It requires many antennas to perform multiplexing and
cancellation at the same time.Interference cancellation
using terminal adaptive arrayFrequency reuse
4
Base station cooperation• Base Station Cooperation (BSC)
– BSC transmits multiple streams to cooperate with adjacent BSs.
– There are no problems of nor inter-cell interference nor high antenna
correlation.
BSC single-user (BSC-SU)
• For single user
BSC-SU wastes many resources to a user.
BSC multi-user (BSC-MU)
• For multiple users
BSC-MU uses resource more efficient than BSC-SU.
Base station cooperation
Single-User
Base station cooperation
Multi-User
5
Base station cooperation block
diagonalization multi-user MIMO
2nd BS
111 dg
112 dDg 221 dDg
222 dg
1d 2d2nd user1st BS 1st user
• Transmission model of (KBS,M)×(KMS,N) BSC-MU-MIMO
:pathloss between jth BS and ith user:distance between ith BS and ith user ijgid
D
• Receive signal of ith user
iiKiKiiiii nxHxHxHy BSBS2211
ijH : channel matrix
ijx : transmit signal vectorin : noise vector
iy : receive signal vector
iiiii nsVQH ~
iQ : precoding matrix of
block diagonalization
is : transmit data signal vector6
iV~
: precoding matrix of
SVD-MIMO
Base station cooperation block
diagonalization multi-user MIMO
• Block diagonalization
MSBSBSBSMSMS
BSBS
MS
11
11
11111
~~
~~
KKKKKK
KK
K n
n
s
s
VQHVQH
VQHVQH
y
y
MSBSBSMS
1111
~~
~~
KKKK n
n
s
s
VHO
OVH
Channel matrix is block diagonalized
H||
\\svd iii QVOUH i
TT
K
T
i
T
i
T
\111 MS,...,,,..., HHHHH
• Precoding matrix Q
ij
ijiij
O
HQH
~
7
Base station cooperation block
diagonalization multi-user MIMO
iid
ijH :fading matrix
• We decompose the channel matrix into fading matrix
and pathloss matrix
IG ijij g ii
i
i
iiiii GHGO
OGHHHHH
iid
2
1iid
2
iid
121
iid1
1
1ijij
Fij
iji HGG
HQ OHH
iidiid
ijij
• Block diagonalization precoding matrix of BSC
OQH iji
• Receive signal of ith user
iiiiiiiiii nsVHnsVQHy ~~~
iid
21
21122211iid1iid
1
~1~i
ijij
ijijii
Fij
iiigg
ggggHHGGH
GQHH
iid~iH :The equivalent fading matrix of ith user
8
Base station cooperation block
diagonalization multi-user MIMO
• Power normalization factor
– Ω regulates the transmit power
2
1
2 1logk
ikiC
• Capacity of ith user
21
2221
21
1211
11
21
2221
22
1211
12
1
1
dd
gg
g
gg
g
dd
gg
g
gg
g
• Average receive SNR of ith user
k :kth eigenvalue of equivalent channel
PEP j
H
jj xx P
:maximum BS transmit power
:power normalization factor
2
21
21122211
2
2~~
P
gg
gggg
M
E
ijij
iii
i
sVH
• Transmit power from jth BS
9
jKjj MS,...,1 xxx
:transmit signal of jth BS
d1 [m]
d2 [
m]
0 100 200 300 400 5000
100
200
300
400
500
0.7
0.75
0.8
0.85
0.9
0.95
1
Power normalization factor Ω
BS2MS2MS1BS1
1d 2d
• d1=d2
Ωmax=1
• d1>>d2, d1<<d2
Ωmin=2/3
Co-schedulingThe BSs select users with the same SINR
Ω
10
0 100 200 300 400 5000
5
10
15
20
25
30
35
40
Distance from BS to user [m]
Av
era
ge u
ser1
cap
acit
y [
bp
s/H
z]
2x2 SC-MIMO Ideal
2x2 SC-MIMO
(2,2)x(2,2) BSC-MUco-schedule
(2,2)x(2,2) BSC-MUuser2 at cell-edge
Capacity of (2,2)x(2,2) BSC-MU-MIMO Average capacity of 1st user at different distance
• Co-scheduling
BSC-MU is almost the
same capacity as 2x2
SC-MIMO ideal
• User2 at cell-edge
Capacity is decreased
about 3bps where
distance is from 100m
to 300m
d1 [m]
d2 [
m]
0 100 200 300 400 5000
100
200
300
400
500
0.7
0.75
0.8
0.85
0.9
0.95
1
Co-scheduling
User2 at cell-edge
Co-scheduling is the optimal
user selection
Ω
11
Cooperative region
• Cell-inner
– Non-cooperative region
• Single-cell MIMO is
efficient at cell-inner
• Cell-edge
– Cooperative region
• BSC MIMO is efficient
at cell-edge
MC
MU
MC CR
SC
SU
SC CR
• Spectral efficiency
: bandwidth inefficiencies
considering overheadComparison of spectral efficiency
• Analysis considering overhead is
necessary– Overhead : the cost of resource for transmission
(ex. Reference signal, guard band, cyclic prefix)
12
Fractional Base Station Cooperation
• Single-cell MIMO (conventional cellular system)
– Cell-inner : efficient
– Cell-edge : not efficient
• Base station cooperation MIMO
– Cell-inner : not efficient
– Cell-edge : efficient
Fractional Base Station Cooperation (FBSC)
– Cell-inner:uses single-cell MIMO
– Cell-edge:uses BSC MIMO
FBSC is performed to achieve gains both at the cell-inner and cell-edge.13
Cooperation clustering• Clustering
– Static clustering
• Cooperation set is fixed
By-product cell-edge is created.
– Dynamic clustering
• makes cooperation set adaptively.
Cooperation is efficient at all cell-edges.
Static DynamicBy-product
cell-edge
14
Dynamic clustering
15
: master cell
: slave cell
Fractional base
station cooperation
Dynamic clustering
Fractional base station
cooperation cellular
network
Physical
layer
Fractional base station cooperation
cellular network
Network
layer
Distributed
controller
Cooperative
region
Non-cooperative
region 16
Simulation scenario• Comparison transmission
– Single-cell SISO
– Single-cell MIMO
• 2x2 SVD-MIMO
– Multi-cell static clustering
• BSC is performed at all locations
• Static clustering
– Fractional Base Station
Cooperation Cellular Network
(FBSC-CN)
17
Cooperative region
Non-cooperative region
Cooperative region
Cooperative region at 19-cell scenario
Simulation scenario
Parameters Values
Number of cell 19 cells
Number of user 10 users / cell
Number of cooperation set 3 cells
Number of antenna
( BS, user )1, 1 (SISO) or 2, 2 (MIMO)
Channel model i.i.d. Rayleigh
Pathloss model34.5 + 35log10(d[m]) [dB]
(3GPP TR 25.996 : Urban Macro)
Transmit power 40[dBm]
Noise level -100[dBm]
Site to site distance 1000 m
Scheduler Round-robin & co-scheduling
Single-user MIMO scheme SVD-MIMO
Multi-user MIMO scheme Generalized BD [*]
Simulation parameter
18* V. Stankovic, M. Haardt, “Generalized design of multi-user MIMO precoding matrices,” IEEE Trans. Wireless
Commun., vol.7, no.3, pp.953-961, Mar. 2008.
100 200 300 400 5000
0.2
0.4
0.6
0.8
1
Distance from BS to user [m]
Use
r sp
ectr
al
eff
icie
ncy
[b
ps/
Hz/u
ser]
Single-cell SISO
Single-cell MIMO
Frequency reuse
Interference cancellation
Multi-cell static clustering
FBSC-CN
100 200 300 400 5000
0.2
0.4
0.6
0.8
1
Distance from BS to user [m]
Use
r sp
ectr
al
eff
icie
ncy
[b
ps/
Hz/u
ser]
Single-cell SISO
Single-cell MIMO
Frequency reuse
Interference cancellation
Multi-cell static clustering
FBSC-CN
100 200 300 400 5000
0.2
0.4
0.6
0.8
1
Distance from BS to user [m]
Use
r sp
ectr
al
eff
icie
ncy
[b
ps/
Hz/u
ser]
Single-cell SISO
Single-cell MIMO
Frequency reuse
Interference cancellation
Multi-cell static clustering
FBSC-CN
Simulation result
19
Fractional BSC
• User capacity at
the cell-inner is as
high as Single-
cell MIMO
• User capacity at
the cell-edge is
almost the same
with BS
cooperation
User capacity at the distance locations
0 0.2 0.4 0.6 0.8 1 0
0.05
0.2
0.4
0.6
0.8
1
User spectral efficiency [bps/Hz]
CD
F
Single-cell SISO
Single-cell MIMO
Multi-cell static clustering
FBSC-CN
0 0.2 0.4 0.6 0.8 1 0
0.05
0.2
0.4
0.6
0.8
1
User spectral efficiency [bps/Hz]
CD
F
Single-cell SISO
Single-cell MIMO
Multi-cell static clustering
FBSC-CN
Simulation result
20
• FBSC is higher
capacity than
other scheme at
all probability
CDF of instantaneous capacity at cell-edge user
Simulation result
21
0
1
2
3
4
Av
era
ge c
ell
spectr
al
eff
icie
ncy
[bp
s/H
z]
0
0.05
0.1
0.15
0.2
Cell
-ed
ge u
ser
spectr
al
eff
icie
ncy
[bp
s/H
z/u
ser]
w/o shadow fading
w/ shadow fading
w/o shadow fading
w/ shadow fading
Single-cell
SISO
Single-cell
MIMO
Single-cell
SISO
Single-cell
MIMO
Multi-cell
Static clustering
FBSC-CN
Multi-cell
Static clustering
FBSC-CN
• The average cell spectral efficiency of FBSC is slightly improved than
single-cell MIMO
• The cell-edge user spectral efficiency of FBSC is 2.4 times as high as
that of single-cell MIMO
Average cell spectral efficiency and cell-edge spectral efficiency
(shadow fading standard deviation = 8[dB])
Conclusion
• BSC solves the cell-edge problem.
• Fractional BSC is proposed.
– FBSC performs to achieve gains both at the cell-inner
and cell-edge.
• FBSC cellular network is proposed.
– In FBSC-CN, cooperation sets are constructed
dynamically.
• Numerical simulation shows that FBSC
performs efficiently both at cell-inner and cell-
edge.
22
Thank you for your attention.
23
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