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Uplink Capacity Comparison of OFDM / FBMC
based Cognitive Radio Networks
Haijian ZHANG
∗†
, Didier LE RUYET
∗
, Daniel ROVIRAS
∗
, and Hong SUN
†
∗
Electronics and Communications Laboratory, CNAM, Paris, France
†
Signal Processing Laboratory, Wuhan University, China
haijian.zhang@cnam.fr, leruyet@cnam.fr, daniel.roviras@cnam.fr, hongsun@whu.edu.cn
Abstract—Cognitive radio (CR) is proposed to automatically
detect and exploit unused spectrum while avoiding harmful
interference to the incumbent system. In this paper, we emphasize
the channel capacity comparison of a CR network using two
types of multicarrier communications: conventional Orthogonal
Frequency Division Multiplexing (OFDM) with Cyclic Prefix
(CP) and Filter Bank based MultiCarrier (FBMC) modulations.
We use a resource allocation algorithm in which subcarrier
assignment and power allocation are carried out sequentially. By
taking the impact of Inter-Cell Interference (ICI) resulting from
timing offset into account, the maximization of total information
rates is formulated under an uplink scenario with pathloss and
Rayleigh fading, subject to maximum power constraint as well as
mutual interference constraint between primary user (PU) and
secondary user (SU). Final simulation results show that FBMC
can achieve higher channel capacity than OFDM because of the
low spectral leakage of its prototype filter.
1
I. INTRODUCTION
Cognitive radio (CR) is a fully reconfigurable wireless
system that automatically changes its communication variables
in response to network and user demands. In CR context,
multicarrier communication has been suggested as a candidate
for CR networks due to its flexibility to fill the spectrum holes
[1][2]. Much of attention in the present literature emphasizes
on the use of conventional OFDM. In [1], OFDM has been
suggested as a candidate for CR systems. However, OFDM is
very sensitive to fast time variations of the radio channel and
to timing offset due to imperfect synchronization. In addition,
OFDM systems sacrifice data transmission rate because of the
insertion of Cyclic Prefix (CP).
The Filter Bank based MultiCarrier (FBMC) modulation
[3][4], does not require CP extension and shows higher robust-
ness to residual frequency offsets than CP-OFDM by taking
advantage of the low spectral leakage of its modulation pro-
totype filters. Filter bank based multicarrier system is already
considered as a physical layer candidate for CR [2]. Moreover,
filter banks can be used as a tool for spectrum sensing. In
[5], application of filter banks to spectrum sensing is proved
to be more promising than FFT components and Thomson’s
multitaper (MT) method because of its high performance and
low cost.
1
This work was supported in part by the European Commission under
Project PHYDYAS (FP7-ICT-2007-1-211887)
In the literature, optimal resource allocation problem in
multicarrier CR context with both power and mutual interfer-
ence constraints is still an open topic. In [6], a power loading
scheme to maximize the downlink capacity of the CR system
under the interference and power constraints is proposed, and
then according to this proposed scheme, the CR systems
based on OFDM and FBMC are evaluated and compared in
terms of power allocation and the system throughput in [7],
in which an iterative Power Interference constraint algorithm
(PI-algorithm) to iteratively allocate the subcarrier power is
proposed. However, the interference induced from PU to
SU is assumed to be negligible and channel pathloss is not
considered.
In this paper, we focus on the comparison of OFDM and
FBMC based CR networks in terms of the averaged channel
capacity, which depends on the resource allocation strategy
adopted by the secondary system. We propose a resource
allocation scheme [8] under an uplink scenario with pathloss
and Rayleigh channel, and a maximization of sum-rate is
formulated with both power constraint and inter-cell interfer-
ence [9] constraint. To reduce the complexity, our resource
allocation procedure is split into two steps. First of all, SUs
are assigned to the detected spectrum holes (a hole is a set
of free adjacent subcarriers), which is implemented by using
a proposed Averaged Capacity metric (AC-metric). When the
SUs are assigned to the spectrum holes, the power allocation is
solved by the Gradient Projection Method (GPM) [10] instead
of using PI-algorithm and the Lagrangian multiplier method.
The rest of this paper is organized as follows: In Section
II, we give the system model and formulate our problem.
In Section III, the proposed resource allocation algorithm for
multi-user is presented. Simulation results are given in Section
IV. Finally, Section V concludes this paper.
II.
SYSTEM MODEL AND PROBLEM FORMULATION
In the cognitive radio context, a group of secondary users
gathering and communicating with a hot spot called Secondary
Base Station (SBS), make up a CR system. In the rest of this
paper, we call one CR system with some secondary users and
a SBS as ”secondary cell”.
As shown in Fig. 1, an uplink scenario of CR networks
consisting of one primary system with one PU and one
secondary cell with one SU is graphed, where ”D” is the
978-1-4244-6404-3/10/$26.00 ©2010 IEEE
This full text paper was peer reviewed at the direction of IEEE Communications Society subject matter experts for publication in the IEEE ICC 2010 proceedings
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6HFRQGDU\FHOO
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3%6
6%6
38
68
*
VV
*VS
*SV
*SS
5S
5V
Fig. 1. CR networks with one primary system and one secondary cell
3838
38
38
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Fig. 2. Distributions of the primary users and the spectrum holes with
”N
all
= 48” and ”L = 18”
distance between the Primary Base Station (PBS) and SBS,
and ”R
p
” and ”R
s
” are the radius of primary system and
secondary cell, respectively. A ”B”MHz frequency band
of ”N
all
” clusters with ”L” subcarriers in each cluster is
licensed to primary system. Fig. 2 shows the distributions of
the primary users (referred to as ”1”) and the spectrum holes
(referred to as ”0”) with ”N
all
= 48” and ”L = 18”.
2
Given above basic uplink scenario, we make the following
assumptions for our system model:
1) The goal of this paper is the capacity comparison, so
the simple scenario with one primary system and one
secondary cell is considered.
2) Primary system and secondary cell apply the same
multicarrier modulation scheme (OFDM or FBMC).
3) SUs in the secondary cell are synchronized, and SBS can
perfectly sense the free bands of the licensed system.
4) SBS has the channel gain knowledge of G
ss
(indicated
in Fig. 1).
5) Primary system and secondary cell are assumed to be
unsynchronized, so ICI exists between PU and SU.
6) We consider a flat fading Rayleigh channel, and we
assume that the channel changes slowly so that the
channel gains will be constant during transmission.
In [9], OFDM/FBMC interference tables have been obtained
when transmitting a single complex symbol with power that
equals to ”1” on the k
th
frequency slot and the n
th
time
2
Here we have chosen the practical values of WIMAX 802.16 for the
number and size of clusters.
68
38
6838
Fig. 3. (a). ICI between PU and SU in OFDM based CR networks (b). ICI
between PU and SU in FBMC based CR networks
slot. As shown in Fig. 2, the primary users and secondary
users share adjacent frequency bands, and one spectrum hole
might have one or multiple clusters, that is, one secondary user
is permitted to occupy at least ”L” subcarriers. Nevertheless,
according to the OFDM/FBMC interference tables in [9], only
”1” subcarrier (FBMC) or ”8” subcarriers (OFDM) really
induces ICI to primary user. ICIs between primary user and
secondary user in OFDM and FBMC based CR networks are
graphed in Fig. 3. We can see that for primary user, only
the eight subcarriers (OFDM) or the one subcarrier (FBMC)
adjacent to secondary user suffer from the ICI, and the same
situation for secondary user.
When we transmit a burst of independent complex symbols,
the interference incurred by one subcarrier equals to the sum
of the interference for all the time slots. For our following
theoretical analysis, the interference vectors of OFDM with
CP length of T/8 (T indicates one symbol period) and FBMC
[4] are defined as
V
ofdm
=[8.94 × 10
−2
, 2.23 × 10
−2
, 9.95 × 10
−3
,
5.60 × 10
−3
, 3.59 × 10
−3
, 2.50 × 10
−3
,
1.84 × 10
−3
, 1.12 × 10
−3
]
V
fbmc
=[8.81 × 10
−2
, 0, 0, 0, 0, 0, 0, 0]
(1)
The secondary cell wants to maximize its sum data rate by
allocating power into the detected spectrum holes for its own
users, this problem can be formulated as
This full text paper was peer reviewed at the direction of IEEE Communications Society subject matter experts for publication in the IEEE ICC 2010 proceedings
max
θ,p
: C(θ, p)=
M
m=1
K
k=1
F
k
f=1
θ
kf
m
· log
2
1+
p
kf
m
G
mkf
ss
σ
2
+ I
k
f
s.t.
⎧
⎪
⎨
⎪
⎩
K
k=1
F
k
f=1
θ
kf
m
p
kf
m
≤ P
th
, ∀m
0 ≤ p
kf
m
≤ P
sub
M
m=1
N
n=1
θ
k
l(r)
n
m
p
k
l(r)
n
m
G
mk
l(r)
sp
V
n
≤ I
th
, ∀k
(2)
where M is the number of secondary users, K is the number
of spectrum holes, and F
k
is the number of subcarriers in the
k
th
spectrum hole. θ
kf
m
∈{0, 1} is the subcarrier assignment
indicator, i.e. θ
kf
m
=1if the f
th
subcarrier in the k
th
spectrum
hole is allocated to SU m, p
kf
m
is the power of SU m on the f
th
subcarrier in the k
th
spectrum hole, G
mkf
ss
is the propagation
channel gain from SU m to SBS on the f
th
subcarrier in the
k
th
spectrum hole, σ
2
is the noise power, and I
k
f
is the ICI
fromPUtoSUonthef
th
subcarrier in the k
th
spectrum
hole. P
th
and P
sub
are the maximum user power limit and
per subcarrier power limit, respectively. N is the length of the
interference vector V , p
k
l(r)
n
m
is the power of SU m on the
left (right) n
th
subcarrier in the k
th
spectrum hole, G
mk
l(r)
sp
is
the propagation channel gain from SU m to PBS on the left
(right) first primary subcarrier adjacent to the k
th
spectrum
hole, and I
th
denotes the interference threshold prescribed by
the PU on the first primary subcarrier adjacent to SU.
The ICI from PU to SU I
k
f
can be expressed in the
mathematical form as follows
I
k
f
=
⎧
⎪
⎨
⎪
⎩
N
n=f
P
k
l
p
G
k
l
f
ps
V
n
,f=1, 2, ···N
N
n=F
k
−f+1
P
k
r
p
G
k
r
f
ps
V
n
,f = F
k
− N +1, ···F
k
0, others
(3)
where P
k
l(r)
p
is the transmission power of PU located in the
left (right) of the k
th
spectrum hole, and G
k
l(r)
f
ps
is the channel
gain from PU located in the left (right) of the k
th
spectrum
hole to SBS on the f
th
subcarrier of the k
th
spectrum hole.
Practically, the secondary cell is not capable of obtaining the
transmission power of PU and the channel information from
PU to SU, but I
k
f
can be measured during the spectrum sensing
by SBS without need to know these information.
III.
MULTI-USER RESOURCE ALLOCATION
In multicarrier based networks with multi-user , assuming
each free subcarrier can be used for transmission to at most one
secondary user at any time, then our optimal problem in (2) is
an integer programming problem, which has a high computa-
tional complexity. Generally, instead of searching an optimal
solution with an unacceptable computational complexity, the
combinatorial suboptimal method of subcarrier assignment
and power allocation is proposed: firstly the subcarriers are
assigned to the SUs and then the power is allocated to these
subcarriers.
A. Subcarrier Assignment
In a traditional multicarrier system, the maximum SNR-
metric can be applied to assign each subcarrier to the user
with a high value of SNR ”
PG
ss
σ
2
” (where P is the averaged
power by dividing the total power limit on the number of the
subcarriers). However, the SNR-metric is not always suitable
in cognitive radio systems due to the mutual interference be-
tween PU and SU, especially with low interference constraint
prescribed by PU.
In this section, an Averaged Capacity metric (AC-metric)
aiming to maximize the averaged channel capacity is proposed.
The averaged channel capacity depends not only the channel
gain G
ss
, but also the interference threshold I
th
, maximum
user power limit P
th
, as well as the channel gain G
sp
.AC-
metric makes a balance between all these influence factors.
3838
38
Fig. 4. Four types of clusters in available spectrum holes
It can be envisaged that different clusters in the spectrum
holes suffer from different interference strengths introduced
by PU. In Fig. 4, four possible types of clusters in available
spectrum holes are displayed, where the cluster with index ”1”
suffers from the interferences introduced by both left PU and
right PU, the cluster ”2” (”3”) suffers from the interference
introduced by only left (right) PU, and cluster ”4” doesn’t
suffer from any interference at all.
Considering this practical situation, the AC-metric is defined
as
⎧
⎪
⎪
⎪
⎪
⎪
⎪
⎪
⎪
⎪
⎪
⎪
⎪
⎨
⎪
⎪
⎪
⎪
⎪
⎪
⎪
⎪
⎪
⎪
⎪
⎪
⎩
C
1
=
N
n=1
log
2
(1 + SINR
l
n
)+
N
n=1
log
2
(1
+SINR
r
n
)+(L − 2N )log
2
(1 +
P
th
−P
l
−P
r
(L−2N)σ
2
)
/L
C
2(3)
=
N
n=1
log
2
(1 + SINR
l(r)
n
)
+(L − N )log
2
(1 +
P
th
−P
l(r)
(L−N)σ
2
)
/L
C
4
= log
2
(1 +
PG
ss
σ
2
)
(4)
where
SINR
l
n
=
p
l
n
G
ln
ss
σ
2
+ I
l
n
,SINR
r
n
=
p
r
n
G
rn
ss
σ
2
+ I
r
n
P
l
=
N
n=1
p
l
n
,P
r
=
N
n=1
p
r
n
p
l
n
= min{P,
I
th
NV
n
G
l
sp
},p
r
n
= min{P,
I
th
NV
n
G
r
sp
}
where C
1
∼ C
4
are the averaged channel capacities of the four
different clusters in Fig. 4, respectively, N is the length of the
This full text paper was peer reviewed at the direction of IEEE Communications Society subject matter experts for publication in the IEEE ICC 2010 proceedings
interference vector V , ”L>2N” is the length of one cluster,
and SINR
l(r)
n
is the SINR on the left (right) n
th
subcarrier of
one cluster. p
l(r)
n
is the power on the left (right) n
th
subcarrier
of one cluster (we assume that each of the ”N” subcarriers
adjacent to PU introduces the same quantity of interference
to PU), and which is not supposed to overpass the averaged
power per subcarrier. G
l(r)n
ss
is the channel gain of SU to SBS
on the left (right) n
th
subcarrier of one cluster, I
l(r)
n
is the
interference from PU to SU on the left (right) n
th
subcarrier
of one cluster, P
l(r)
is the aggregated power on the left (right)
N subcarriers of one cluster, and G
l(r)
sp
is the channel gain
from SU to PBS on the left (right) first primary subcarrier
adjacent to one cluster.
B. Power Allocation
At the premise of knowing the result of the subcarrier
assignment, the power allocation of multi-user system can be
virtually regarded as a single-user system. For the special case
of single-user, the problem formulation in (2) is simplified,
where the SU is permitted to access all the ”F =
K
k=1
F
k
”
subcarriers.
In mathematical optimization, the method of Lagrangian
multipliers can provide a strategy for finding the optimal
solution, but the solution of extensive Lagrangian multipliers
is computationally complex when ”F ” increases. Instead,
herein Rosen’s Gradient Projection Method (GPM) [10] can be
applied to obtain the optimal power allocation for this simple
CR uplink scenario in a low computational complexity.
IV.
SIMULATIONS
In this section, the proposed resource allocation algorithm
of OFDM and FBMC based CR networks is evaluated in terms
of the averaged channel capacity by computer simulations in
a comparable way.
The CR network as shown in Fig. 1 with one primary system
and one secondary cell is simulated. Without loss of generality
for the proposed resource allocation algorithm, the case with
multiple PUs and multiple SUs is considered. Assuming 36
clusters (seventy-five percent of the total N
all
=48clusters)
are allocated to PUs, and these 36 licensed clusters are
occupied by 36 uniformly distributed PUs. The rest 12 clusters
are permitted to access by 12 SUs, each of which can only
use one cluster. Primary and secondary users centering around
PBS and SBS, respectively, are uniformly distributed within
the cell range (0.1∼1 km). As the increase of transmission
distance, the attenuation also increases due to the propagation
pathloss. The pathloss of the received signal at a distance d
(km) is [11]
P (d) = 128.1+37.6 · log
10
(d) dB (5)
other system simulation parameters are displayed in Table I.
In order to define an interference threshold I
th
which is
predetermined by a practical licensed system
3
, we assume
3
Considering the absence of a standard interference threshold for CR
system, we have derived it using a tolerable capacity loss for the primary
system.
TABLE I
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 P
sub
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 -
that the received primary signal in PBS always has a desired
”SNR =
P
p
G
pp
σ
2
≈ 10”. The capacity on the first primary
subcarrier adjacent to SU is
C =log
2
(1 +
P
p
G
pp
σ
2
) (6)
where P
p
is the primary transmission power, and G
pp
is
the channel gain from PU to PBS. The value of I
th
can be
automatically generated by defining a tolerable capacity loss
coefficient λ according to
(1 − λ)C =log
2
(1 +
P
p
G
pp
σ
2
+ I
th
) (7)
The experimental results of the perfectly synchronized (PS)
case are also given for the sake of comparison with the results
of OFDM and FBMC based CR networks. In addition, the
performance comparison of the SNR-metric and the AC-metric
for channel assignment is investigated.
The averaged capacities at different interference levels
(λ =0.2, 0.3, ··· , 0.9) and different maximum power levels
P
th
for a fixed D =0.2km are given in Fig. 5 and Fig. 6,
respectively. As expected, OFDM shows a fast attenuation of
the channel capacity when less capacity loss is prescribed by
PU, but FBMC is slightly affected by different interference
levels. The channel capacities increase with the augmentation
of the averaged power per subcarrier
P (P =P
th
/F). Besides,
it can be noted that the performance of FBMC approaches the
performance of the perfectly synchronized case. At the same
time, we can see that the achieved channel capacity of the
OFDM based CR system by applying the AC-metric always
outperforms the SNR-metric, but there is a slight difference
by applying these two metrics for the FBMC based system.
This implies that the traditional subcarrier assignment methods
in wireless communication system can be used in FBMC
based CR network, which reduces the CR system complex-
ity. Nevertheless, some modified methods with computational
complexity have to be investigated for OFDM based CR
network due to its seriously additional interference.
In view of the fact that the distance D between SBS and
PBS can be random because of the flexibility of cognitive
radio, so the impact of D on channel capacity is investigated
and shown in Fig. 7. We can observe that as the distance
This full text paper was peer reviewed at the direction of IEEE Communications Society subject matter experts for publication in the IEEE ICC 2010 proceedings
2 4 6 8 10 12
x 10
−16
3.5
4
4.5
5
5.5
6
Interference level (Watt)
Averaged capacity (bits/Hz/s)
36 PUs, 12 SUs, 12 free clusters, P
th
= 36 mWatt, D= 0.2 km
PS
FBMC−SNR
FBMC−AC
OFDM−SNR
OFDM−AC
λ= 0.5
Fig. 5. Averaged capacity vs. interference level
0.01 0.02 0.03 0.04 0.05 0.06 0.07
3
3.5
4
4.5
5
5.5
6
6.5
7
Maximum user power (Watt)
Averaged capacity (bits/Hz/s)
36 PUs, 12 SUs, 12 free clusters, λ= 0.5, D= 0.2 km
PS
FBMC−SNR
FBMC−AC
OFDM−SNR
OFDM−AC
P
th
= 36 mWatt
Fig. 6. Averaged capacity vs. maximum user power
increases, all the performance curves of FBMC and OFDM
tend to merge. The reason is that there exists little interference
between the primary system and the secondary cell when they
are far away from each other.
In our scenario, the practical system parameters and con-
straints are used for our simulation. Based on this realistic
system model, final simulation results indicate that FBMC
based CR network can achieve higher channel capacity than
the case of OFDM. Besides, the inserted cyclic prefix (not
considered in this paper) in OFDM based CR system lowers
the total system capacity.
V. C
ONCLUSION
The objective of this paper is to compare the channel ca-
pacity performance of OFDM and FBMC based on a realistic
uplink CR network. A resource allocation algorithm with the
considerations of power constraint and interference constraint
is proposed for evaluating the averaged channel capacity. In-
stead of using the interference due to the out-of-band radiation
of the power spectral density, inter-cell interferences resulting
from timing offset in OFDM and FBMC based networks
are considered in our proposed algorithm. Final simulation
0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2
4.7
4.8
4.9
5
5.1
5.2
5.3
5.4
5.5
5.6
5.7
D (km)
Averaged capacity (bits/Hz/s)
36 PUs, 12 SUs, 12 free clusters, λ= 0.5, P
th
= 36 mWatt
PS
FBMC−SNR
FBMC−AC
OFDM−SNR
OFDM−AC
Fig. 7. Averaged capacity vs. distance between SBS and PBS
results demonstrate that in a realistic CR network FBMC
offers higher channel capacity and is more applicable for
the CR network. Furthermore, the performance of FBMC is
close to that of the perfectly synchronized case because of its
frequency localization and therefore some traditional wireless
communication system methods are s uitable for FBMC based
CR network. As a result, FBMC has practical value and is a
potential candidate for physical layer data communication of
future CR networks.
R
EFERENCES
[1] T. Weiss, F. Jondral, ”Spectrum Pooling: An Innovative Strategy for the
Enhancement of Spectrum Efficiency”, IEEE Communications Maga-
zine, Vol. 42, no. 3, March 2004, pp. 8-14.
[2] P. Amini, P. Kempter, RR Chen, L Lin, ”FILTER BANK MULTITONE:
A PHYSICAL LAYER CANDIDATE FOR COGNITIVE RADIOS”,
Proceeding of the SDR Forum technical Conference, Nov. 2005, pp.
14-18.
[3] P. Siohan, C. Siclet and N. Lacaille, ”Analysis and design of
OFDM/OQAM systems based on filterbank theory”, IEEE Trans. Signal
Processing, Vol. 50, May 2002, pp. 1170-1183.
[4] M. Bellanger, ”Filter banks and OFDM/OQAM for high throughput
wireless LAN”, 3rd International Symposium on Communications, Con-
trol and Signal Processing. ISCCSP 2008, pp 758-761, Malta, Mar 2008.
[5] B. Farhang-Boroujeny, ”Filter Bank Spectrum Sensing for Cognitive
Radios”, Signal Processing, IEEE Transactions, Volume: 56, Issue: 5,
On page(s): 1801-1811, May 2008.
[6] M. Shaat, F. Bader, ”Power Allocation with Interference Constraint in
Multicarrier Based Cognitive Radio Systems,” Book Title: Multi-Carrier
Systems and Solutions. Chapter 4: Adaptive Transmission .Eds. Plass,
S; Dammann, A; Kaiser, S; Fazel, K. Springer 2009. ISBN: 978-90-481-
2529-6 (HB) [4]. Netherlands.
[7] M. Shaat, F. Bader, ”Power Allocation and Throughput Comparison in
OFDM and FBMC Based Cognitive Radio,” the 22nd Meeting of the
Wireless World Research Forum, Paris, France. May 2009.
[8] H. ZHANG, D. Le Ruyet, D. Roviras, Y. MEDJAHDI, H. Sun, ”Spectral
Efficiency Comparison of OFDM / FBMC for Uplink Cognitive Radio
Networks”, accepted to EURASIP Journal on Advances in Signal
Processing.
[9] Y. Medjahdi, M. Terr
´
e, D. Le ruyet, D. Roviras, ”Inter-cell Interference
Analysis for OFDM/FBMC Systems”, IEEE Signal Processing Work-
shop (SPAWC 2009), Page(s): 598-602, Perugia, Italy, june 2009.
[10] M. S. Barzaraa, H. D. Sherali, C. M. Shetty, ”Nonlinear programming:
theory and algorithms”, 2ed ed, John Wiley Sons,1993.
[11] Draft IEEE 802.16m Evaluation Methodology Document C80216m-07-
080r2.
This full text paper was peer reviewed at the direction of IEEE Communications Society subject matter experts for publication in the IEEE ICC 2010 proceedings
