Introduction
Scenario
Resource Allocation Algorithm
Simulations results
Conclusion
Capacity Analysis of OFDM / FBMC based Cognitive Radio
Networks with Estimated CSI
Haijian Zhang12
Didier Le Ruyet1
1
Daniel Roviras1
Hong Sun2
Electronics and Communications Lab, CNAM, France
2
Signal Processing Lab, Wuhan University, China
CROWNCOM 2010
1 / 19
Introduction
Scenario
Resource Allocation Algorithm
Simulations results
Conclusion
Outline
1
Introduction
2
Scenario
3
Resource Allocation Algorithm
Problem Formulation
Cluster Assignment
Power Allocation
4
Simulations results
5
Conclusion
2 / 19
Introduction
Scenario
Resource Allocation Algorithm
Simulations results
Conclusion
Much of attention in broadband CR literature emphasized on the use of
OFDM modulation
Filter bank based modulation (FBMC) has been proposed as an
alternative scheme Farhang-Boroujeny, com. mag 2008, PHYDYAS FP7
european project
When using OFDM in asynchronous communication systems Inter-Cell
Interference (ICI) exists between PUs and SUs Hamdi, trans. wireless 2009
Y. Medjahdi et al., SPAWC 2009
In this work, we will consider capacity analysis of a CR Network using
OFDM and FBMC
3 / 19
Introduction
Scenario
Resource Allocation Algorithm
Simulations results
Conclusion
The considered Cognitive radio network :
3ULPDU\V\VWHP
5S
5V
'
*SS
*VV
*SV
*VS
6HFRQGDU\FHOO
3%6
38
6%6
68
We have the following assumptions:
Primary system and secondary cell apply the same multicarrier
modulation scheme (OFDM or FBMC)
SUs in the secondary cell are synchronized, and secondary base station
can perfectly sense the free bands of the licensed system
Primary system and secondary cell are assumed to be unsynchronized,
so Inter-Cell Interference (ICI) exists between PUs and SUs
4 / 19
Introduction
Scenario
Resource Allocation Algorithm
Simulations results
Conclusion
The considered Cognitive radio network :
3ULPDU\V\VWHP
5S
5V
'
*SS
*VV
*SV
*VS
6HFRQGDU\FHOO
3%6
38
6%6
68
We have the following assumptions:
Primary system and secondary cell apply the same multicarrier
modulation scheme (OFDM or FBMC)
SUs in the secondary cell are synchronized, and secondary base station
can perfectly sense the free bands of the licensed system
Primary system and secondary cell are assumed to be unsynchronized,
so Inter-Cell Interference (ICI) exists between PUs and SUs
5 / 19
Introduction
Scenario
Resource Allocation Algorithm
Simulations results
Conclusion
The considered Cognitive radio network :
3ULPDU\V\VWHP
5S
5V
'
*SS
*VV
*SV
*VS
6HFRQGDU\FHOO
3%6
38
6%6
68
We have the following assumptions:
Primary system and secondary cell apply the same multicarrier
modulation scheme (OFDM or FBMC)
SUs in the secondary cell are synchronized, and secondary base station
can perfectly sense the free bands of the licensed system
Primary system and secondary cell are assumed to be unsynchronized,
so Inter-Cell Interference (ICI) exists between PUs and SUs
6 / 19
Introduction
Scenario
Resource Allocation Algorithm
Simulations results
Conclusion
Illustration of ICI between PU and SU
(a) OFDM based CR networks (b) FBMC based CR networks
38
38
68
68
7 / 19
Introduction
Scenario
Resource Allocation Algorithm
Simulations results
Conclusion
Problem Formulation
The secondary cell wants to maximize the sum data rate by allocating
power into the detected spectrum holes for its users
This problem can be formulated as follows :
max : C(p) =
p
s.t.
Fk
M X
K X
X
m=1 k =1 f =1
·
kf
θm
log2
pkf Gmkf
1 + m2 ss k
σ + If
¸
PK PF
kf kf
k

 k =1 f =1 θm pm 6 Pth , ∀m;
kf
0 6 pm 6 Psub ;

kl(r ) n kl(r ) n mkl(r ) PN−n+1
PM PN
pm Gsp
VN−i+1 6 Ith ,
n=1 θm
m=1
i=1
∀k ;
where M is the number of secondary users, K is the number of spectrum
holes and Fk is the number of SC in the k th spectrum hole
 PN
kl kl f

f = 1, 2, · · · N
 Pn=f Pp Gps Vn ,
k
N
kr kr f
If =
n=Fk −f +1 Pp Gps Vn , f = Fk − N + 1, · · · Fk


0,
otherwise
8 / 19
Introduction
Scenario
Resource Allocation Algorithm
Simulations results
Conclusion
Problem Formulation
Practically, the channel gain between SU and PBS ”Gsp ” cannot be
perfectly estimated
A channel gain margin Gm is added on the estimated pathloss gain
Gsp = (1 + Gm )Gpl
where Gm depends on the prescribed outage probability
Pout = P(Gsp > Gsp ) = P(|Hsp |2 > 1 + Gm )
Given an acceptable outage probability Pout , the added channel gain
margin Gm can be obtained
Gm = 2µ2 loge (
1
)−1
Pout
9 / 19
Introduction
Scenario
Resource Allocation Algorithm
Simulations results
Conclusion
Cluster Assignment
The optimization problem is an integer programming problem
Suboptimal solution : first assign the bandwidth, then the clusters and
finally allocate the power
The bandwidth assignment algorithm increments the number of clusters
Nu for user u which exhibits the smallest channel capacity Lengoumbi et
al., VTC 2006
38
38
38
Four types of clusters in available spectrum holes
The Hungarian algorithm is used to implement the cluster assignment
10 / 19
Introduction
Scenario
Resource Allocation Algorithm
Simulations results
Conclusion
Power Allocation
After the cluster assignment, the power allocation of multi-user system
can be virtually regarded as a single-user system
max : C(p) =
p
Fk
K X
X
k =1 f =1
·
¸
pkf Gkf
log2 1 + 2 ssk
σ + If
Rosen’s Gradient Projection Method (GPM) can be applied to obtain the
optimal power allocation for this simple CR uplink scenario in a low
computational complexity.
11 / 19
Introduction
Scenario
Resource Allocation Algorithm
Simulations results
Conclusion
Table: system simulation parameters
Parameter
Value
Unit
Total bandwidth B
10
MHz
Center frequency
2.5
GHz
Number of sub-carriers
1024
Number of sub-carriers per cluster L
18
Power limit per subcarrier Psub
5
mWatt
Noise power per subcarrier
-134.10
dBm
Channel delays
10−9 [0, 110, 190, 410]
s
Channel powers
[0, −9.7, −19.2, −22.8] dB
Channel realizations
200
-
12 / 19
Introduction
Scenario
Resource Allocation Algorithm
Simulations results
Conclusion
We assume that the received primary signal in PBS always has a desired
Pp Gpp
SNR = Lσ
≈ 10. The capacity on each cluster occupied by PU is
2
C =log2 (1 +
Pp Gpp
)
Lσ 2
The value of interference threshold Ith can be automatically generated by
defining a tolerable capacity loss coefficient λ according to
(1 − λ)C = log2 (1 +
Pp Gpp
)
Lσ 2 + Ith
13 / 19
Scenario
Resource Allocation Algorithm
Simulations results
Conclusion
36 PUs, 6 SUs, 12 free clusters, Pth= 36 mWatt, D= 0.2 km, Pout=0.06
4.6
4.4
Averaged capacity (bits/s/Hz)
Introduction
4.2
4
3.8
3.6
3.4
PS
FBMC−ideal
FBMC−estimated
OFDM−ideal
OFDM−estimated
3.2
3
0.02
0.04
0.06
0.08
0.1
0.12
0.14
Capacity loss coefficient λ
0.16
0.18
0.2
Averaged capacity vs. interference level.
14 / 19
Scenario
Resource Allocation Algorithm
Simulations results
Conclusion
36 PUs, 6 SUs, 12 free clusters, λ= 0.04, Pth= 36 mWatt, D= 0.2 km
4.4
Averaged capacity (bits/s/Hz)
Introduction
4.2
4
3.8
3.6
PS
FBMC−ideal
FBMC−estimated
OFDM−ideal
OFDM−estimated
3.4
3.2
0,01
0.06
0,11
0,16
0,21 0,26 0,31
Outage probability
0,36
0,41
0,46
0,51
Averaged capacity vs. outage probability.
15 / 19
Scenario
Resource Allocation Algorithm
Simulations results
Conclusion
36 PUs, 6 SUs, 12 free clusters, λ= 0.04, D= 0.2 km, Pout=0.06
5.5
5
Averaged capacity (bits/s/Hz)
Introduction
4.5
4
3.5
PS
FBMC−ideal
FBMC−estimated
OFDM−ideal
OFDM−estimated
3
2.5
0.01
0.02
0.03
0.04
0.05
Maximum user power (Watt)
0.06
0.07
Averaged capacity vs. maximum user power.
16 / 19
Scenario
Resource Allocation Algorithm
Simulations results
Conclusion
36 PUs, 6 SUs, 12 free clusters, λ= 0.04, Pth= 36 mWatt, Pout=0.06
4.4
Averaged capacity (bits/s/Hz)
Introduction
4.2
4
3.8
PS
FBMC−ideal
FBMC−estimated
OFDM−ideal
OFDM−estimated
3.6
3.4
0.2
0.4
0.6
0.8
1
1.2
D (km)
1.4
1.6
1.8
2
Averaged capacity vs. distance between SBS and PBS.
17 / 19
Introduction
Scenario
Resource Allocation Algorithm
Simulations results
Conclusion
In this work we have evaluated the spectrum efficiency of a simple CR
network considering both OFDM and FBMC
Compared to OFDM based CR network, FBMC based CR network can
offer higher channel capacity
FBMC scheme achieves better performance gain if only rough estimated
channel information is available
FBMC is a good candidate for physical layer data communication in CR
context
18 / 19
Introduction
Scenario
Resource Allocation Algorithm
Simulations results
Conclusion
THANK YOU FOR YOUR ATTENTION
19 / 19