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Physical Communication 9 (2013) 73–87
Contents lists available at ScienceDirect
Physical Communication
journal homepage: www.elsevier.com/locate/phycom
Full length article
On spectrum sensing in cognitive radio CDMA networks
with beamforming
Sanjay Dhar Roy a,∗,Sumit Kundu a,Gianluigi Ferrari b,Riccardo Raheli b
aDepartment of Electronics & Communication Engineering, NIT Durgapur, Durgapur, Pin-713209, India
bDepartment of Information Engineering, University of Parma, Parma, Italy
article info
Article history:
Received 23 November 2012
Received in revised form 15 May 2013
Accepted 1 August 2013
Available online 4 September 2013
Keywords:
Cognitive radio (CR)
Code division multiple access (CDMA)
Beamforming
Spectrum sensing
Outage probability
Blocking probability
Soft hand-off (HO)
abstract
In this paper, the performance of cognitive radio (CR) code division multiple access (CDMA)
networks is analyzed in the presence of receive beamforming at the base stations (BSs).
More precisely, we analyze, through simulations, the performance achievable by a CR
user, with and without spectrum sensing, in a three-cell scenario. Uplink communications
are considered. Three different schemes for spectrum sensing with beamforming are pre-
sented, together with a scheme without spectrum sensing. CR users belong to a cognitive
radio network (CRN) which is coexisting with a primary radio network (PRN). Both the CRN
and the PRN are CDMA based. The CRN is assumed to utilize beamforming for its CR users.
Soft hand-off (HO) and power control are considered in both the CRN and the PRN. The
impact of beamforming on the system performance is analyzed, considering various met-
rics. In particular, we evaluate the performance of the proposed systems in terms of outage
probability, blocking probability, and average data rate of CR users. The results obtained
clearly indicate that significant performance improvements can be obtained by CR users
with the help of beamforming. The impact of several system parameters on the perfor-
mance of the three considered spectrum sensing schemes with beamforming is analyzed.
Our results, in terms of probability of outage, show that the relative improvement brought
by the use of beamforming is higher in the absence of spectrum sensing (reduction of 80%)
than in the presence of spectrum sensing (reduction of 42%).
©2013 Elsevier B.V. All rights reserved.
1. Introduction
The term cognitive radio (CR) was first coined by Mitola
in 1999 [1]. CR networks allow the presence of primary
users (PUs) and secondary users (SUs). An SU may change
its radio parameters on demand. For example, it can adapt
its data rate when the number of PUs becomes smaller or
the interference level is low [2]. SUs access the channel
in an opportunistic way. Spectrum sensing is related to the
identification, by SUs, of unused spectrum portions, i.e., the
portions which are not being used by PUs or are being
used by SUs with an interference level below a pre-fixed
∗Corresponding author. Tel.: +91 9332107814.
E-mail addresses: s_dharroy@yahoo.com,
sanjay.dharroy4@gmail.com (S. Dhar Roy).
interference limit. After finding spectrum ‘‘holes’’ [3], an
SU selects the best available channel: this is known as
spectrum decision. Other users, either cognitive (secondary)
or primary, may utilize the spectrum via spectrum sharing.
An SU can change its transmission channel or frequency
if it detects the presence of a PU in the same channel or
if it finds that the channel has worsened. SUs can coexist
with PUs in two ways, either through spectrum underlay or
spectrum overlay [4]. In practice, CR users, as well as the CR
manager (if one exists) [4], would measure the interference
level on the basis of broadcast information from the
primary base stations (BSs) and would change their main
networking parameters to reduce the interference level at
the PU.
Smart antenna techniques have been used for capac-
ity enhancement in cellular networks [5]. Various types of
1874-4907/$ – see front matter ©2013 Elsevier B.V. All rights reserved.
http://dx.doi.org/10.1016/j.phycom.2013.08.001
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74 S. Dhar Roy et al. / Physical Communication 9 (2013) 73–87
beamforming (e.g., fixed, adaptive, flat-top) have been in-
vestigated. In [6], the capacity improvement experienced
by code division multiple access (CDMA) networks with
antenna arrays at the BSs is evaluated for both uplink
and downlink communications. In particular, the outage
probability is evaluated as a function of cell loading, array
parameters, fading, shadowing effects, and voice activity.
Different beamforming schemes, based on adaptive algo-
rithms for assigning weights to antennas and on direction-
of-arrival estimation methods, have been studied in [7].
The outage probability is also investigated analytically
in [8], where a simplified beamforming model is consid-
ered. Since beamforming affects the generated interfer-
ence, it is expected to have a significant impact on the
users’ performance in the presence of spectrum sensing.
Beamforming can be applied at both transmitter and re-
ceiver sides. The use of multiple antennas at the mobile
stations (MSs) is typically avoided to reduce the system
complexity. Hence, receive beamforming (based on the use
of multiple antenna elements at the BS) is preferred for
uplink communications. Through the use of receive beam-
forming techniques, the BS can modify the irradiation pat-
tern of the antenna array, in order to create a beam for
a specific user, upon calculation of the direction of ar-
rival (DoA) of the electromagnetic wave of the selected
user. There are many techniques for DoA estimation, such
as spectral estimation methods, the minimum variance
distortionless response (MVDR) method, linear prediction
methods, the multiple signal classification (MUSIC) algo-
rithm, and the estimation of signal parameters via rota-
tional invariance techniques (ESPRIT) method [7]. DoA is
used for steering the beam by changing its orientation an-
gle. More precisely, the main lobe of the antenna array is
oriented towards the desired user, while other (interfer-
ing) users are associated with nulls of the antenna irradia-
tion pattern.
Beamforming is a standard technique for reducing
interference in cellular CDMA networks. In the uplink,
near-orthogonal (but not perfectly orthogonal) CDMA
spreading codes (such as Gold and Kasami) are typically
used. Therefore, even for multirate CDMA networks, the
interference in the uplink would be higher. We consider
beamforming in the uplink to reduce the interference on
the desired SU. It is thus expected that beamforming, in
addition to spectrum sensing, will improve the uplink
system performance. In the downlink, the use of Walsh
Hadamard codes guarantees orthogonality, so the use of
beamforming is not crucial.
In this paper, we consider a heterogeneous network
consisting of a primary radio network (PRN) and a
cognitive radio network (CRN) with underlay spectrum
sharing. The PRN and the CRN have separate BSs, and both
networks are assumed to be multirate CDMA networks.
The CRN employs beamforming at the secondary BS (SBS)
for the CR user of interest. We consider beamforming
to reduce the interference in the CRN. Even though
beamforming may also be applied at a primary BS (PBS), in
order to reduce the interference to PUs, we do not consider
this case in this paper.
There are mainly three types of spectrum sensing
method [9]: (a) methods requiring both source signal
and noise power information; (b) methods requiring only
noise power information (semi-blind detection); and (c)
methods requiring no information on source signal or
noise power (blind detection) [9]. The current paper
extends the CR-CDMA scenarios presented in [10,11],
where several spectrum sensing schemes, in the absence of
any beamforming, are considered, in order to encompass
the use of beamforming. As beamforming is expected
to improve the performance of such spectrum sensing
schemes, we incorporate receive beamforming at the SBS
and extend the analysis of [11] to estimate the joint impact
of spectrum sensing and beamforming on the overall
network performance. Apparently, the gain provided by
beamforming is not directly related to spectrum sensing,
i.e., the gain due to beamforming is independent of the
performance improvement associated with any spectrum
sensing scheme. However, when combined with spectrum
sensing, the overall performance gain is likely to improve
significantly. By spectrum sensing, the SBS becomes aware
of the presence of PUs and SUs, together with their
corresponding data rates. Since beamforming reduces the
uplink interference experienced by an SU, the cellular
capacity increases: a larger number of SUs is allowed
even in the presence of many PUs. This is the major
advantage of using beamforming along with spectrum
sensing. A capacity increase is feasible owing to the outage
probability reduction brought by the use of beamforming.
On the other hand, spectrum sensing allows the SBS to
know the activity of PUs and other SUs, thus leading to the
estimation of the overall interference. After interference
estimation, the transmit data rate and power of SUs can
then be optimized. Whenever the interference reduces,
more SUs (keeping the data rate fixed) are allowed,
or the data rate of existing users is increased (keeping
the number of SUs fixed); as the interference increases,
the number of SUs and/or their data rates are reduced.
Therefore, an SU opportunistically increases its data rate
in the presence of beamforming. Consequently, the use
of beamforming allows the CRN to increase its cellular
capacity opportunistically, so an operator can get higher
revenue from the secondary network.
The spectrum sensing technique of reference is the
covariance-based semi-blind spectrum sensing approach
originally proposed in [12,13]. In particular, covariance-
based spectrum sensing allows the secondary BS (SBS)
to know the spectrum activity of the SUs. By properly
thresholding the autocorrelation function of the received
signal, the presence of PUs can be detected [14,15]. We
assume that the PRN and the CRN contain fixed numbers
of PUs and CRs, respectively, and that the PBS broadcasts
information on the usage capacity percentage (UCP) of the
cell, defined as the ratio between the number of active PUs
in the cell and the maximum number of sustainable users
(including both PUs and SUs) in the system, to all PUs, to
the SBS, and to all SUs. A PU interferes at the SBS of its cell.
The SUs and the SBS listen to the control channel to obtain
UCP and take any consequent decision(s) about spectrum
access. Therefore, we are implicitly assuming some kind of
cooperation between the PBS and the SBS. At any particular
time, all PUs are not likely to be simultaneously active, so
SUs can transmit without hampering the quality of service
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S. Dhar Roy et al. / Physical Communication 9 (2013) 73–87 75
(QoS) of the PUs. At the SBS, the presence of PUs and other
SUs is detected, and the total interference from currently
active PUs and SUs is also measured using fluctuations of
correlation estimators. This sensing approach is followed
by the SBS in order to estimate the available resources
for an SU. The levels of interference caused by PUs and
SUs are compared with respect to chosen thresholds. The
procedure for interference estimation will be characterized
at system level, and its implementation in the simulation
model will be detailed.
After incorporating receive beamforming at the SBS,
we analyze the joint impact of spectrum sensing and
beamforming on the overall system performance. The
impact of the number of antenna elements and of other
parameters on the outage probability is evaluated. In this
scenario, we also evaluate the blocking probability of a
new SU attempting to access the CRN. The performance
improvement due to beamforming, with respect to that
of equivalent schemes without beamforming [11], is
investigated both qualitatively and quantitatively through
our extensive simulations.
The major contributions of this paper can be summa-
rized as follows.
•We develop a CR-CDMA networking model with beam-
forming and spectrum sensing at the SBS.
•We evaluate the impact of beamforming for various
numbers of antenna elements.
•We carry out a quantitative analysis of the relative im-
provements, brought by the use of beamforming at the
SBS, with and without spectrum sensing.
•We develop a novel simulation framework for the con-
sidered CR-CDMA networking model with beamform-
ing.
•The three spectrum sensing schemes proposed in [11]
are extended with the incorporation of beamforming
(to reduce interference at the SBS) and their perfor-
mances are analyzed in a comparative way.
•We investigate the trade-off between the probability of
blocking and the probability of outage for an SU, consid-
ering the three spectrum sensing schemes with beam-
forming already mentioned.
•We analyze the trade-off between the cost of including
multiple antennas and the performance improvement
using beamforming.
•We evaluate performance of the SU of interest in terms
of outage probability in the presence of higher load
(large number of users) in the networking model.
The rest of this paper is organized as follows. In Sec-
tion 2, the reference system model is accurately described.
In Section 3, we describe our simulation model. In Sec-
tion 4, numerical results are presented. Finally, Section 5
concludes the paper.
2. System model
2.1. Network model
The basic networking model (in the absence of beam-
forming) is same of that considered in [11], which we
briefly recall here (the interested reader is referred to [11]
for more details). The three-cell network model under
study is shown in Fig. 1a. We assume coexistence of the
PRN and the CRN. We also assume that the information
regarding interference due to the activity of PUs and SUs
is available to the SBS via measurement of autocorrela-
tion fluctuations as described in [13]. Moreover, we con-
sider two other modified schemes, denoted Scheme 1 and
Scheme 2, respectively, for data transmission permission
for SUs. In the absence of spectrum sensing, the PRN is a
multirate CDMA network with spreading codes with vari-
able spreading length, allowing two fixed data rates (rdand
2rd) for the PUs. The SUs in the CRN use a fixed data rate
equal to 4rd. As in any CR set up with underlay spectrum
sharing, PUs and SUs can coexist as long as the interfer-
ence experienced by the PRN is kept below an interference
threshold. In the absence of spectrum sensing, assuming
that the interference limit is not crossed, the SUs can trans-
mit at a data rate higher than that of a PU. In practice, how-
ever, whenever the SBS finds that the total interference is
above the predefined interference threshold, some SUs are
asked to reduce their rates—the PUs are priority users and
may need higher rates than the SUs. These aspects may
be captured by slightly extending our general model. More
precisely, we assume here that PUs do not need high data
rates for some specific application. On the basis of the spec-
trum sensing carried out by the SBS, the SBS knows the in-
terference caused by PUs and the interference caused by
SUs; and on the basis of some beacon information, i.e., the
UCP information sent by the PBS, the SBS can estimate how
much SU interference is allowed at a specific time. The CRN
then carries out appropriate power and rate adjustments to
reduce the SU interference. Furthermore, we consider the
presence of soft HO in our three-cell cellular model. Un-
der soft HO, an MS may have simultaneous traffic channel
communications with more than one BS and may be power
controlled by a BS different from the BS of its current cell.
The link gains of all links between the MS and BSs involved
in soft HO are evaluated, and a power control decision is
taken in favor of the BS which has the highest link gain.
Moreover, while undergoing the HO, a user may need to
change its current pseudo noise (PN) spreading code (used
with the old BS) to a new PN spreading code issued by the
new BS. As anticipated in Section 1, in the present study,
we focus on uplink communication.
Each cell is divided into three sectors. Each sector
is divided into two groups of regions: soft HO regions
(denoted B,C,D) and non-HO regions (denoted A,E,F).
Each cell is divided into three sectors with the same
number of data users (Nd=NPU +NSU)per sector. The soft
HO region is defined on the basis of the distance from the
BS, as shown in Figs. 1a and 1b. BS0indicates the location
of both the primary and the secondary BSs, denoted PBS0
and SBS0, in cell #0. We assume receive beamforming at
SBS0 to improve the performance of an SU of interest. More
precisely, we assume that a sectorized antenna is used
to cover all the SUs (in a particular sector) for paging,
synchronization, and call set up. Adaptive beamforming is
then used for managing voice (or data) traffic, i.e., after the
call setup. During the call, the desired user is tracked with
a specific beam in order to optimize system performance.
In the CRN, the SBS knows the positions of all SUs, and
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76 S. Dhar Roy et al. / Physical Communication 9 (2013) 73–87
Fig. 1a. A three-cell CR-CDMA networking model.
carries out beamforming for the desired user whenever
needed. When the SBS becomes aware that an SU is
requesting resources (e.g., he/she is making a call), it steers
the beam towards this user and the interference from
other users gets reduced. In fact, both PU interference
and SU interference are reduced if they are outside the
beam. As we consider that the desired user is tracked
by an antenna beam from SBS, it is implicitly assumed
that the number of users simultaneously tracked by the
SBS is not very large. However, should the SBS want to
track a larger number of users, a larger number of sharp
beams should be formed, thus increasing the complexity
of the beamforming network due to increased number
of antennas in the array along with additional complex
hardware and processing. In order to keep the analysis
tractable, in the current work we assume a single desired
SU, which requires a single beam from the SBS: this
makes the beamforming network simple. In the case of
multiple desired users, the analysis needs to be modified.
If, with a given set of used data rates and transmit powers,
the interference on the PRN is above the interference
threshold, power and rate adjustment on the SUs are then
carried out.
Beamforming at the SBS and spectrum sensing act to-
gether to improve the performance of the CRN. However,
spectrum sensing information is not required for imple-
menting beamforming. Beamforming can be carried out
with users’ location information, regardless of the spec-
trum occupancy. PUs and SUs are power controlled by their
corresponding BSs. With reference to the three-cell model
shown in Fig. 1a, this applies to the two BSs of the other
two cells, i.e., BS1and BS2. An MS (either an SU or a PU) lo-
cated outside the HO boundary Rhis considered to be under
soft HO with three neighboring BSs. A PU would interfere
at the SBS, since the PBS and the SBS are co-located. Both
the SBS and the SU know the UCP of the cell, transmitted by
PBS and defined as the ratio between the number of active
PUs in the cell and the maximum number of sustainable
users (including both PUs and SUs) in the cell. In the case
Fig. 1b. Cell of interest, cell #0, with SU of interest (rd,θd), and corre-
sponding positions of an interfering user, (ri,θi).
of the PRN, a PU is assumed to transmit at a rate given by
mrd, where mis dependent on the spreading length of the
PN code in the multirate CDMA system and rdis the ba-
sic data rate. In contrast, all SUs are assumed to transmit
at the same rate 4rdin the absence of spectrum sensing in
the CRN. Two classes of PU are considered: PU1denotes the
first group of PUs using the basic rate rd, while PU2denotes
the second group of PUs using a data rate equal to 2rd. A
justification of the use of these types of rate will be given
in Section 3.
Next, we consider receive beamforming at SBS0. The dis-
tance between the antenna elements of the linear equally
spaced (LES) array (shown in Fig. 1c) is assumed to be
0.5λ, where λis the carrier wavelength (in meters). In the
LES array system, a combining network, which combines
outputs of an array of low-gain antenna elements, gener-
ates an ideal antenna pattern. Beamforming is achieved by
power combining of individual low-gain antenna signals.
The combining network can generate an antenna pattern
with the following gain [5]:
G(ϕ, θ )=
sin (0.5Mrπ(sin θ−sin ϕ))
Mrsin (0.5π(sin θ−sin ϕ))
2
,(1)
where Mris the number of antenna elements and θis a
variable. The beam can be steered to a desired direction ϕ
by varying θ[5].
The antenna gain is shown in Figs. 1d and 1e. In this
paper, we will use the antenna pattern specified in [5] to
evaluate the impact of beamforming on the CDMA uplink
capacity. The desired SU is in region Aand is identified by
a specific pair (rd, θd). The interference power of another
user, identified by an angle θiwith respect to the BS in
cell #0, will be multiplied by the following antenna gain
G(θi, θd):
G(θi, θd)=
sin (0.5Mrπ(sin θi−sin θd))
Mrsin (0.5π(sin θi−sin θd))
2
.(2)
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S. Dhar Roy et al. / Physical Communication 9 (2013) 73–87 77
Fig. 1c. A standard linear equally spaced (LES) array.
Fig. 1d. Beamforming gain, G(ϕ, θ )vs. θ.
The beamforming gain in (2) is much lower than that given
by (1) for a small difference between two angles, and it
decreases further for increasing values of the difference
between θiand θd. This is shown in Figs. 1d and 1e. In
general, θiand θddiffer significantly as θdis the desired
angle, whereas θiis a direction towards the null of the
antenna radiation diagram. Moreover, G(θi, θd)could be
reduced further by increasing the number (Mr)of array
elements (Fig. 1e). Consequently, the interference can be
reduced further. The interference model in the presence of
beamforming gain will be described in Section 2.3, whereas
the performance will be investigated in Section 3.1.
The number of each type of user and the interference
caused by each of them is obtained subsequently by using
fluctuations of estimated autocorrelation as in [14,15]. At
the SBS of the cell of interest, the interference caused
by all SUs except the desired SU is computed following
the spectrum sensing method proposed in [11]. The
interference contributions of all PUs can also be estimated
as in the case of SUs, i.e., following the method based on
the fluctuations of the estimated autocorrelation. Since PUs
and SUs have different data rates, the fluctuations of their
corresponding autocorrelation functions will be different.
Fig. 1e. Effects of the number of array elements. Beamforming gain,
G(ϕ, θ )versus with with respect to Eq. (1); , .
Therefore, the thresholds used to detect autocorrelation
fluctuations are chosen separately depending on the
data rate of the user, i.e., depending on the spreading
code length used. A user is detected whenever the
fluctuations of the estimated autocorrelation exceed the
chosen threshold for that class of users [14]. The number
of active CDMA users (both SUs and PUs) present in the
network is indicated by the number of times the threshold
is exceeded by the autocorrelation fluctuations. Similarly,
the numbers of users (both SUs and PUs) present in the
network and their interference at the SBS are estimated
in the same manner as above in the present CR-CDMA
networks. The received signal is divided into Mtemporal
windows, each of duration T. The fluctuations of the
autocorrelation function are estimated at each window.
Using these Mwindows, the second-order moment of
the estimated autocorrelation function can be found as
follows [14,15]: ϕ(τ)=1
MM−1
n=1
Rn
yy (τ)
2, where Rn
yy (τ)
is the estimated correlation of the received signal at
nth window. This fluctuation will exceed a predefined
threshold level if the signal is present along with noise as
the threshold level has been selected on the basis of the
noise power.
2.2. Spectrum sensing schemes
The spectrum sensing schemes in the absence of
beamforming, denoted Scheme 0, Scheme 1, and Scheme 2,
are discussed in detail in [11]. In this subsection, we only
recall their main features (for more details, the reader is
referred to [11]).
Scheme 0 (‘‘Ghavami SS’’): This is the spectrum sensing
scheme as proposed by Ghavami et al. in [12]. SUs are
prevented from transmitting (upon positive spectrum
sensing) when the condition √β+1>1
ucp is satisfied,
where β=I2
SU
I2
PU
.ISU is the received interference power
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78 S. Dhar Roy et al. / Physical Communication 9 (2013) 73–87
from SUs, and IPU is the received interference power from
PUs. As introduced previously, UCP ucpcorresponds to
the percentage of the cell capacity used by the primary
network. The PBS broadcasts ucp in the control channel
while the SUs and the SBS listen to the control channel
to obtain ucp and take any consequential decision(s) about
spectrum access. Thus some kind of cooperation between
the PBS and the SBS is implicitly assumed. The total
number of sustainable users in a CDMA network may
be determined on the basis of quality of service (QoS)
requirements. The SUs can transmit without hampering
the QoS of PUs, as all PUs are not likely to be simultaneously
active at any particular time. Since the PBS knows the
number of active PUs, it computes and broadcasts ucp as the
ratio between active PUs and total number of sustainable
users in the network.
Scheme 1: In this scheme, in the absence of SUs, a fixed
number of PUs in each sector is assumed. In what follows,
we will consider 30 PUs, equally divided into the two
groups PU1and PU2, and a basic data rate rd=10 kbps. The
maximum interference generated by all PUs, denoted Imax,
will be evaluated with this constraint. At any time, the total
interference caused by SUs and PUs must be lower than this
interference limit.
Scheme 2: In this scheme, we assume that the PRN can
tolerate some interference from SUs, even when all PUs are
present in the system, up to the limit Imax
ucp . At any time, the
total interference caused by SUs and PUs must be lower
than this interference limit.
The sensing schemes introduced above are now ex-
tended in order to incorporate beamforming for the de-
sired user. Using beamforming, the interference is reduced
regardless of the specific spectrum sensing scheme un-
der use. In the present work, all schemes are based on
covariance-based spectrum sensing [12,13], in the pres-
ence of beamforming for a desired SU. We also consider
spectrum sharing condition/permission for SUs. Through
spectrum sensing, the overall interference created by cur-
rently active SUs in the system can be measured. After
measuring the spectrum activity, there can be the two fol-
lowing possibilities.
(1) A number of SUs are asked to stop transmitting
when the total interference is more than the allowed
interference limit. Actually, SUs are asked to lower the
data rate and the transmit power before stopping the
transmission completely.
(2) A new SU is allowed into the network if the total
interference is lower than the allowed interference limit.
In this paper, we consider spectrum sensing as a com-
bination of spectrum activity measurement and spectrum
sharing decision for new SUs in the presence of beamform-
ing for an SU. Spectrum activity measurement is needed to
find the presence of PUs and of existing interfering SUs.
2.3. Interference modeling
The interference model in the absence beamforming is
discussed in detail in [11] and is briefly recalled here. We
assume that the total interference at SBS, due to SUs and
PUs, can be written as follows:
IUL
SBS =ISU +IPU1+IPU2,(3)
where the first term on the right-hand side of the equation
relates to the interference due to all active SUs in uplink,
whereas the other two terms are associated with the in-
terferences due to PU1and PU2, respectively, and will be
evaluated through the simulator described in Section 3. As
beamforming for the desired SU is considered, the inter-
ference caused by PUs and other SUs needs to be appro-
priately modified considering the geometrical parameter
of the scenario, shown in Fig. 1b. The beamforming gain,
given by Eq. (2), is included in the received power expres-
sion at the SBS from all PUs and SUs. The received power
of each user (either a PU or an SU) is normalized to 1 at
their corresponding BSs. As previously mentioned, the in-
terference powers ISU,IPU1, and IPU2will be evaluated via
simulations as described in Section 3. The PRN and the CRN
are co-located, with the corresponding BSs PBS and SBS po-
sitioned at the center of each cell. We evaluate the inter-
ference at SBS considering the two classes of PUs and SUs
separately. The propagation radio channel is modeled as
in [16]. More precisely, the link gain for a user at location
(r, θ ), with respect to BSi,i∈{0,1,2}, is
Gi(r, θ ) =di(r, θ )−αp10ξS/10,(4)
where di(r, θ ) is the distance between the MS and BSi,αpis
the path loss exponent, and 10ξs/10 is the log-normal fading
coefficient, with ξsnormally distributed with zero mean
and variance σ2
s. More precisely, the exponential normal
fading coefficient at ith BS can be written as [16]
ξs,i=aζ+bζi,(5)
where a2+b2=1, and ζand ζiare independent Gaus-
sian random variables (rvs) with zero mean and variance
σ2
s. The out-cell interference consists of the interference
due to MSs from regions E,C,G,Hof cell #1 and from re-
gions D,F,I,Jof cell #2. The MSs in the farthest sectors
(G,H,I,J) are assumed to be power controlled by the re-
spective BSs. The reference user is located in the non-HO
region of reference sector, i.e., in region A. The total in-cell
interference in cell # 0 is [16].
Iin =I1+I2,(6)
where I1is due to all MSs in Aand those in Bconnected to
BS0, and I2is due to MSs in Bbut connected to BS1and BS2.
The out-cell interference is [17]
Iout =2(IE+IC1+IC2+ICO +IG+IH), (7)
where the Ii(i=E,C1,C2,CO,G,H) are the interference
terms due to MSs in different regions such as E,C,G,H.
Explicit expressions for these terms can be found in [11].
The effectively received power from the desired SU can
be expressed as
U=SReS,(8)
where Sis a Gaussian random variable with zero mean and
variance equal to σ2
e. Therefore, σecan be interpreted as the
power control error (PCE). The desired SU is assumed to be
in the non-HO region, i.e., in region A.
The interference model derived above, which extends
the one proposed in [11] taking into account the presence
of beamforming, is considered in the developed simulator,
outlined in the following section.
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S. Dhar Roy et al. / Physical Communication 9 (2013) 73–87 79
3. Simulation model
The simulator has been developed with MATLAB, and it
takes the following parameters at its input: the degree of
soft HO (PRh), the shadowing correlation (a2), the PCE (σe),
and the numbers of PUs and SUs. As far as beamforming
is concerned, we assume that the desired user is tracked
by an antenna beam. The beamforming is carried out with
the use of (Mr)antenna array elements. Some typical
values of Mr, such as 3, 5, and 7, are considered in our
analysis. The soft HO region boundary Rhis given as Rh=
R0√1−PRh, where R0is the radius, normalized to unity,
of the circular cell which approximates the hexagonal cell.
Users are assumed to be uniformly distributed over the
cells. We formulate the spectrum sharing problem in the
reference CDMA network of Fig. 1a by considering the
value of mdepending on the spreading length. In our
work, we consider fixed values of rates and integer values
for m. This simplifies the management, by our MATLAB
simulator, of rate adjustment in order to account for
arbitrary (even non-integer) values of m; our simulation
algorithm should be properly extended. However, we
remark that in principle any value of mcan be considered.
The extension of our analysis is in this direction is the
subject of our future research activity. The simulation
model for the network in the absence of beamforming is
the same as that presented in [11]. For the sake of clarity,
we recall the main characteristics of this simulator (for
more details, the reader is referred to [11]), in order to
make the extension to the presence of beamforming clear.
3.1. Uplink signal-to-interference ratio estimation with beam-
forming
I. A number of PUs (NPU)and a number of SUs (NSU )
are generated.
II. The locations (in the (r, θ ) coordinate system) of all
SUs and PUs (Nd)are generated, and users are divided
into non-HO(Nh)and soft HO (Ns)regions on the
basis of their locations. The desired SU is assumed to
be in the non-HO region; the number of remaining
interfering users, considering all PUs and other SUs in
the non-HO region, is Nh−1. The number of users in
the soft HO region is Ns=Nd−Nh.
III. For each of the Nsusers in the soft HO region, the link
gains corresponding to each of the three BSs (either
the SBSs for the SUs, or the PBSs for the PUs) involved
in the soft HO are generated as Gi(r, θ ) =r−αp
ieξi,
i=0,1,2, where αpis the path loss exponent and
10ξs/10 is the log-normal component, with ξsnormally
distributed with zero mean and variance σ2
s[11]. The
correlation of shadow fading has been considered fol-
lowing [16,11]. The PUs and SUs are power controlled
by their corresponding BSs, for which the link gain is
maximum, i.e., a PU or SU is power controlled by BSi
if Giis maximum.
IV. The ideal (i.e., perfect) beamforming gain for each
user is generated according to (2); i.e.
G(θi, θd)=
sin (0.5Mπ(sin θi−sin θd))
Msin (0.5π(sin θi−sin θd))
2
.
The interference received at the reference BS can be
expressed as follows [17]:
I=SRexp(rn)G0
Gi0mG (θi, θd).(9)
Due to the incorporation of soft HO, any SU can be
power controlled by any one of the three BSs (i.e. BSi,
i=1,2,3). If the interfering node is connected to
BSi0, here i0=0,1,2. Where rnis a normal random
variable with zero mean and standard deviation σe,
and SRis the required received power at the corre-
sponding BS (normalized to unity in the simulation,
since the signal-to-interference ratio, SIR, is unaf-
fected by assigning SR=1). The data rate of any user
is mrd, where mis the spreading length of a CDMA
user.
V. The interference due to the MSs, in the non-HO region
(A) of the reference cell, power controlled by BS0, can
be expressed as
I2=SRmG (θi, θd)
Nh−1
i=1
ern,i.(10)
Next, we consider the interference caused by users in
regions E,C,D, and Fof cell #1 and cell #2. The inter-
ference by these users may be found in similar man-
ner following Eqs. (9) and (10). The number of MSs
in each of the regions Eand Fis (Nd−Ns). Denote
I3=IE+ICand I4=ID+IF.
VI. The interference from MSs in regions G,H,I, and J
is then generated using our simulator. We estimate
the interference as in the case of Eq. (9). Denote I5=
IG+IHand I6=II+IJ.
VII. The total interference, caused by interfering users at
different regions, can be written as
I=
6
k=1
Ik.(11)
VIII. The signal-to-interference ratio at the reference BS for
the desired user can be expressed as
SIR =U
I,(12)
where Uis the received (useful) power from the de-
sired user at the reference BS, given by (8), and Iis
the total interference power at the reference BS for
the desired user.
3.2. Outage probability in the absence of spectrum sensing
The outage probability is computed through the follow-
ing steps.
I. All users are considered to be continuously active.
II. The uplink SIR for a desired SU at the reference BS
is generated as shown in the previous subsection and
compared with a threshold value given by γth =γth/
pg, where pg indicates the processing gain and γth
is the SIR threshold. The beamforming gains are
considered while estimating the total interference at
the reference BS. In [11], the beamforming factor is not
considered.
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80 S. Dhar Roy et al. / Physical Communication 9 (2013) 73–87
III. If the SIR falls below γth , an outage counter (outagecount)
is incremented.
IV. Steps (II) and (III) are repeated a large (Nt≫1)number
of times to yield an accurate estimate of the probability
of outage as Pout =outagecount/Nt.
3.3. Probability of outage with spectrum sensing and beam-
forming
The following steps are followed.
I. The interference power from SUs and the interference
power from PUs are estimated following the steps in
Section 3.1.
II. The condition √β+1>1
ucp (or the conditions imposed
by Schemes 1 and 2 described in Section 2) needs
(need) to be satisfied at the reference SBS.
III. If the condition at the previous point is not met, then
SUs are removed one by one, initially from the non-HO
region of BS0(region ‘A’) and then from other zones,
i.e., regions ‘B’, ‘C’, ‘D’, ‘E’, ‘F’, ‘G’, or ‘H’. After each
removal, the condition is again checked. As we consider
beamforming at the SBS, the overall interference at
the SBS would be small. Therefore, the SU of interest
would not be in outage in many iterations of the
simulation run. Once the condition is satisfied or all
SUs are removed, the probability of outage is evaluated
as shown in Section 3.2, i.e., as Pout =outagecount/Nt.
The beamforming factor helps to reduce the outage
probability by reducing the overall interference for the
desired user.
3.4. Blocking probability with spectrum sensing
One SU is assumed as the desired user, and all other
SUs and PUs are considered as interfering users. The
PBS broadcasts the UCP information. On the basis of the
UCP information and the spectrum sensing information,
the admissibility of a new SU is considered at the SBS.
The overall interference at the SBS in the presence of
beamforming would be much lower than that in the case
without beamforming. This leads to rare blocking of the
SU of interest. Therefore, depending on beamforming, we
anticipate very low probability of blocking for the SU of
interest, and this will be confirmed in Section 4by our
simulation results.
The following cases may occur when a new SU wants to
make an active connection with the SBS.
(a) The new SU may be allowed with its current data rate
and transmit power.
(b) The SU may be allowed with reduced power. The
transmit power is reduced in steps according to the
rule Pnext =Pcurrent −αPcurrent, where α∈(0,1).
The transmit power is reduced up to Pmin (fixed to
50% of the original transmit power). The data rate of
the new SU may need to be reduced from 4rdto rd
in steps of rdif the power reduction does not make
interference reduction to the sustainable limit. The
new SU is blocked if the interference condition is not
met even after power and data rate reduction.
(c) The new SU is blocked if the present overall interfer-
ence is above the threshold limit.
3.5. Mean data rate with spectrum sensing and beamforming
In the absence of spectrum sensing, the average data
rate of an SU corresponds to the chosen data rate of
the desired SU. However, the data rate of the new SU
varies from 4rdto 0 when we consider call blocking with
spectrum sensing and beamforming. The data rate of the
SU is obtained as the arithmetic average of data rates in
different simulation runs. We expect the support of a high
data rate for the SU of interest, as with beamforming the
SU of interest will be blocked a smaller number of times.
This will be confirmed by simulation results in Section 4,
where a considerable increase of the average data rate will
be observed in the presence of beamforming.
4. Results and discussions
The main parameters of the analytical framework are
set as follows: the standard deviation of the shadow fading
is σs=6 dB; the distance between BSs of adjacent cells is
D=2000 m; the spread bandwidth is W=5.0 MHz;
the chip rate is Rch =5.0 Mcps; the PCE is σe=2 dB;
the SIR threshold is γth =6 dB; the path loss exponent is
4; the shadowing correlation is characterized by a2=0.3
and PRh=0.3; three values of Mr(namely 3, 5, and 7)
are considered; the basic data rate rdis set to 7 kbps, if not
otherwise explicitly stated; finally, the processing gain is
defined as pg =Rch/rd. In Schemes 1 and 2, rd=10 kbps,
the numbers of PU1and PU2are each set to 15, only at
the beginning, to evaluate Imax. In order to highlight the
impact of beamforming, the performance of the proposed
spectrum sensing schemes with beamforming will be
analyzed with direct comparisons to the corresponding
schemes without spectrum sensing.
In Fig. 2, the probability of outage for an SU is shown as
a function of the number of SUs. It can be observed that
the probability of outage increases for increasing values
of the number of SUs [11]. This is due to a corresponding
increase of the multiple access interference (MAI) caused
by SUs. Obviously, the probability of outage reduces when
spectrum sensing is considered. It can be observed that
the probability of outage is lowest when beamforming
is considered in the case of Ghavami spectrum sensing
(SS). In this case, in fact, the allowed interference for SUs
is determined on the basis of the number of currently
active PUs. The reason for the superior performance of
Ghavami SS is explained in detail in [11]. The percentages
of decrease of the outage probability with five antenna
elements (beamforming) and spectrum sensing are found
to be 46% and 55% for values of the number of SUs fixed at
7 and 9, respectively.
In Fig. 3, the blocking probability of an SU is shown as
a function of the number of SUs, considering Scheme 1
with beamforming. As in the absence of beamforming, the
blocking probability is an increasing function of the num-
ber of SUs in the system. However, the relative increasing
rate of the probability of outage reduces significantly when
the number Mrof antenna elements increases from 3 to 5.
On the other hand, a minor performance improvement is
observed when Mris increased beyond 5.
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S. Dhar Roy et al. / Physical Communication 9 (2013) 73–87 81
Fig. 2. Probability of outage for SUs as a function of the number of
SUs, with fixed numbers of PUs and fixed value of the SUs’ data rate.
Beamforming is considered only with Ghavami SS.
Fig. 3. Blocking probability for SUs as a function of the number of SUs,
with fixed number of PUs and fixed value of SUs’ data rate. Beamforming
is considered.
In Fig. 4, the average data rate of an SU is shown as
a function of the number of SUs, considering two possi-
ble values of the basic data rate. It can be observed that
the average data rate of an SU reduces for increasing val-
ues of the number of SUs. This is expected, as the inter-
ference is an increasing function of the number of users.
Furthermore, the number of users to be blocked would
be larger when the interference increases. Assuming that
the data rate of a blocked user is zero, then the data rate
would vary from 4rdto zero. For both considered values
of the data rate, the average data rate of SUs is a decreas-
ing function of the number of cognitive users. Note, how-
ever, that the decrease is relatively faster for higher values
of the data rate. The data rate of an SU increases signifi-
cantly if beamforming is applied at the secondary BS for
the desired CR user. Moreover, the achievable data rate re-
mains almost constant (at a high value) if the number of
Fig. 4. Average data rate of SUs as a function of the number of SUs,
with fixed numbers of PUs and various values of SUs’ data rate. Spectrum
sensing is considered.
Fig. 5. Probability of blocking for SUs as a function of the number of
SUs, with fixed numbers of PUs and fixed value of SUs’ data rate, in the
presence of beamforming. Ghavami SS is considered.
SUs increases from 5 to 12. In the presence of beamform-
ing, the average data rate increases, with respect to the case
without beamforming [11], by 7.3%, 12%, and 53% for val-
ues of the number of SUs set to 5, 9, and 13, respectively.
It can be observed that, in the presence of beamform-
ing, the data rate increases faster (than in the case with-
out beamforming) for increasing values of the network
load.
In Fig. 5, the blocking probability for an SU is shown as
a function of the number of SUs, considering the Ghavami
SS scheme and beamforming. It can be observed that
the probability of blocking reduces significantly in the
presence of beamforming. In the presence of beamforming,
the blocking probability is reduced by 86%, 97%, and 99%,
with respect to that the corresponding cases without
beamforming [11], when the number of SUs is set to 5, 9,
and 13, respectively; the remaining system parameters are
set to the same values.
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82 S. Dhar Roy et al. / Physical Communication 9 (2013) 73–87
Fig. 6. Achievable data rate for SUs as a function of the number of SUs,
with fixed numbers of PUs, in the presence of beamforming.
Fig. 7. Probability of outage for SUs as a function of basic data rate, with
fixed numbers of PUs and SUs. Beamforming is considered in all cases.
In Fig. 6, the achievable data rate for an SU, in the
presence of beamforming, is shown as a function of the
number of SUs, considering Schemes 1 and 2. It can be
observed that the achievable data rate of an SU with
Scheme 2 is higher than that achievable with Scheme 1.
In fact, the interference level allowed by Scheme 2 is
almost twice that allowed by Scheme 1. Therefore, it is
expected that the blocking probability of an SU in the
case of Scheme 2 will be small with respect to that with
Scheme 1. Hence, the achievable data rate with Scheme 2
will be higher.
In Fig. 7, the probability of outage for an SU is shown
as a function of data rate of SUs, with fixed numbers of
PUs and SUs. Two different values of Mrare considered,
and both the presence and the absence of spectrum sensing
are investigated. The probability of outage increases when
number of antenna elements reduces from 7 to 5, both
in the absence and in the presence of spectrum sensing.
Fig. 8. Probability of outage for SUs as a function of data rate of SUs with
fixed numbers of PUs and fixed value of SUs’ data rate.
Fig. 9. Comparison of blocking probability for the three spectrum sensing
schemes in the presence of beamforming.
However, note that the increase is more limited in the case
of spectrum sensing.
In Fig. 8, the probability of outage for SUs is shown as
a function of the data rate of SUs, with fixed numbers of
PUs and SUs. The number of PU1and PU2is considered
as 5 and 5, respectively. The number of SUs is considered
as 10 for all the curves here. The effects of beamforming
on the outage probability, in the case of Scheme 1, are
investigated and compared with those of other schemes.
The outage probability is reduced in the presence of
beamforming by 94.3%, 89%, and 85%, with respect to that
of [11] when Scheme 1 is considered for Nsu fixed at 7, 9,
and 13, respectively. The probability of outage with Nsu =
13 is not shown in this figure.
In Fig. 9, the probability of blocking is shown as a
function of the number of SUs. The performance of all
three spectrum sensing schemes has been evaluated. The
number of both types of PU is fixed at 5 for Ghavami SS
(due to the predefined threshold the number of PUs is
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S. Dhar Roy et al. / Physical Communication 9 (2013) 73–87 83
Fig. 10. Comparison of the average data rate for the three spectrum
sensing schemes in the presence of beamforming.
fixed at 15 for Schemes 1 and 2). The blocking probability
for Schemes 1 and 2 is less as the allowable interference
threshold is high for Schemes 1 and 2 in the presence of
beamforming. The number of antenna elements is Mr=5.
In Fig. 10, the average data rate of an SU is shown as a
function of the number of SUs. A comparative performance
evaluation of all three spectrum sensing schemes has been
depicted in this figure. The number of both types of PU is
fixed at 5 for Ghavami SS and, as already mentioned, due
to the predefined threshold, the number of PUs is fixed
at 15 for Schemes 1 and 2. Still, the average data rate for
Schemes 1 and 2 is almost the same as for Ghavami SS in
the presence of beamforming (BF). Even in the presence
of a number of PUs larger than that considered in the
Ghavami SS scheme, Schemes 1 and 2 allow one to increase
the threshold level by using beamforming, and they can
thus support the same data rate as that of the Ghavami SS
scheme.
In Fig. 11, the blocking probability is shown as a
function of the basic data rate. The performances of
all three spectrum sensing schemes, in the presence of
beamforming, are compared. The number of PUs is set to
5 for the Ghavami SS and to 15 for Schemes 1 and 2 by
considering the fixed interference threshold. The blocking
probability for Schemes 1 and 2 is lower than that with the
Ghavami SS scheme, as the allowed interference threshold
is high for the latter scheme. The probability of blocking for
Scheme 2 is the lowest, being in the order of 10−5with the
number of antenna elements set to 5.
In Fig. 12, the average data rate is shown as a function
of the basic data rate, i.e., the data rate of an SU. The
performance of all three spectrum sensing schemes, in
the presence of beamforming, has been investigated. The
numbers of both types of PU and SU are the same as
considered for Fig. 12. The average data rate for Schemes 1
and 2 is almost the same as in case of Ghavami SS in the
presence of beamforming. In the presence of beamforming,
the performance of all three schemes is almost same.
In Fig. 13, the probability of outage is shown as a func-
tion of the number of SUs, considering various numbers
Fig. 11. Comparison of the blocking probability for the three spectrum
sensing schemes with respect to the basic data rate.
Fig. 12. Comparison of the average data rate for the three spectrum
sensing schemes in the presence of beamforming.
of antenna elements, in the absence of spectrum sensing.
Three different values of the number of antenna elements
are considered, namely 3, 5, and 7. In particular, we con-
sider the effects of only beamforming on the performance
of an SU in our three-cell CR CDMA networking model.
For comparison purposes, two more curves are added: one
curve, relative to a scenario with spectrum sensing and
beamforming, and another curve, which is representative
of the impact of soft hand-off on the CRN. As we consider
a higher degree of soft HO, a larger number of SUs would
be present in the soft HO region, thus reducing the uplink
interference for the SU of interest. A performance improve-
ment, with respect to the case with PRh=0.3, can be
observed—note that, unless otherwise stated, for all curves
PRhis set to 0.3. As can be observed from the figure, the
probability of outage of an SU reduces significantly if spec-
trum sensing, together with beamforming, is considered.
The effect is more pronounced when the cell is moderately
loaded with SUs, i.e., a larger number of SUs is present. We
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84 S. Dhar Roy et al. / Physical Communication 9 (2013) 73–87
Fig. 13. Probability of outage as a function of the number of SUs in the
presence of beamforming.
can observe the same behavior also for the case with Mr=
5. Another important observation can be made from the re-
sults in this figure. A similar performance can be achieved
either with Mr=7 in the absence of spectrum sensing
or with Mr=5 in the presence of spectrum sensing. In
other words, spectrum sensing may not be essential for an
SU to achieve a relevant performance gain in the presence
of beamforming, provided that the number of antenna el-
ements is sufficiently large. In the presence of spectrum
sensing with beamforming, the same level of performance
achievable with only beamforming can instead be achieved
with a smaller number of antenna elements. Therefore,
spectrum sensing, when used jointly with beamforming,
requires a smaller number of antenna elements to reach a
given performance level, i.e., a reduction of the complexity
of the beamforming system is allowed. On the other hand,
in the CR setup considered, spectrum sensing is essential,
as the interference on the PRN needs to be kept below the
interference threshold. The outage probability of an SU de-
creases by 67.7% as Mrincreases from 3 to 7 with the num-
ber of SUs fixed at 13—all other parameters being fixed as
in the previous cases considered. An increase of PRhfrom
0.3 to 0.7, in the CRN only, decreases the outage probability
by 16% when the number of SUs is fixed at 13 and all other
parameters are kept fixed. When the number of antenna
elements is set to 5 and the number of SUs is fixed at 11,
the outage probability decreases by 32%, in the presence
of spectrum sensing, with respect to the case with beam-
forming only. All other parameters are fixed at the same
values for both cases.
In Fig. 14, the outage probability of an SU is shown as a
function of the number of PUs per sector—note that in all
previous results the total number of users has been con-
sidered in the same manner. In particular, we evaluate the
effects of a higher load on the performance of an SU. The
number of PUs per sector is varied from 10 to 30, with the
latter value being considerably high. The number of SUs is
set to either 10 or 20. From the results presented in this fig-
ure, two observations can be made. As the number of PUs
is increased beyond a specific value, i.e., for large numbers
Fig. 14. Outage probability as a function of the number of PUs per sector.
of PUs, the outage probability degrades even in the pres-
ence of spectrum sensing and beamforming. For smaller
values of the number of PUs, the effects are similar to those
observed in the previous figures. The best performance is
obtained with the Ghavami SS scheme in the presence of
beamforming. It can also be noticed that, when the number
of PUs per sector increases beyond a critical value, the per-
formance mainly depends on the number of SUs. All curves
converge to a single one for a fixed number of SUs. If the
system is heavily loaded with 30 PUs, the outage proba-
bility in the case of Ghavami SS is only 3% lower than that
in the absence of spectrum sensing. However, in the same
scenario, the outage probability in the case of Ghavami SS
is only 1% lower than that in the case of Scheme 1. As the
number of PUs is increased from 22 to 30 with 10 SUs,
the outage probability for Ghavami SS increases from 0.17
to 0.319, whereas the outage probability for Scheme 1 in-
creases from 0.17 to 0.3225. As already observed in Fig. 13,
the improvement will be more significant in the case of a
heavily loaded system up to a moderate level of traffic load
(in correspondence to which the number of PUs is 10 and
the number of SUs is 10). In other words, the combined use
of spectrum sensing and beamforming outperforms the
use of beamforming only. However, as the load is increased
to a very high value, as in Fig. 14, the outage probability of
both schemes reaches a very high level (nearly a saturation
value) because of the heavy interference increase in both
cases. The use of beamforming for the SUs not only reduces
the interference from SUs but also the interference from
the active PUs. In fact, the SU of interest is under the princi-
pal beam of the antenna while all other users (both PUs and
SUs) come under the null of the antenna beam. Therefore,
the uplink interference from all PUs in the same geograph-
ical area is also mitigated. In particular, the probability of
outage reduces significantly, with joint beamforming and
cognition, in the presence of a moderate number of SUs.
However, in very high load conditions, we do not find sig-
nificant gains either from beamforming or from spectrum
sensing. In the case of a heavily loaded system, a satura-
tion effect is instead observed. It can thus be concluded that
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S. Dhar Roy et al. / Physical Communication 9 (2013) 73–87 85
Fig. 15. Probability of outage as a function of the probability of blocking.
cognition plays a crucial role in improving the performance
at moderate traffic loads.
In Fig. 15, we investigate the trade-off between prob-
ability of outage and probability of blocking, by evaluat-
ing the probability of outage as a function of the blocking
probability. For given traffic load and network conditions,
the probability of outage and the probability of blocking
are evaluated in the presence of beamforming and spec-
trum sensing: the sequence of pairs of probabilities values
lead to ‘‘trade-off curves’’. In particular, we consider two
different spectrum sensing schemes with beamforming. As
the number of antenna elements is fixed to 5 or 7 and
the number of SUs varies from 5 to 11, the probability of
blocking and the probability of outage are evaluated. With
higher values of Mr, the obtained trade-off indicates that
the performance with Scheme 1 with beamforming is bet-
ter. However, for Mr=5 it can be observed that the block-
ing probability of a SU is lower for the case of Scheme 1
with beamforming than for the case with Ghavami SS with
beamforming, and that the outage probability of an SU is
higher for the case of Scheme 1 with beamforming than
for the case of Ghavami SS with beamforming. In the same
figure, one curve with varying data rate is also shown. The
outage probability increases as the data rate is increased
from 5 to 11 kbps, but the blocking probability remains
the same for Scheme 1 in the presence of beamforming.
This is expected, as the change in the basic data rate does
not change the blocking probability. Let us now quantify
this trade-off. In the case with Mr=7 and Scheme 1, if
the number of SUs is changed from 5 to 7, then the block-
ing probability increases by 63%, while the outage prob-
ability increases by only 4%. In the case of Ghavami SS,
for the same setting, the blocking probability increases by
15%, whereas the outage probability increases by 11%. As
the number of SUs increases from 5 to 7 with Mr=5
and rd=10 kbps, the probability of blocking increases
from 2.3e−3 to 4.3e−3, while the outage probability in-
creases from 0.0142 to 0.0166 for Ghavami SS with beam-
forming. Again for the same setting, the blocking probabil-
ity increases from 1.5e−4 to 2.333e−4, while the outage
probability increases from 0.0178 to 0.0201 for Scheme 1
with beamforming.
Fig. 16. Probability of outage as a function of the number of antenna
elements.
In Fig. 16, the trade-off between the performance im-
provement brought by beamforming and the number of
antenna elements is investigated, by evaluating the prob-
ability of outage as a function of the number of antenna
elements. The performance improvement in the presence
of beamforming is significantly large when the number of
antenna elements is set to 5. More precisely, the decreas-
ing rate of the outage probability, when the number of
antenna elements is increased from 5 to 7, is lower than
that observed when the number of antenna elements in-
creases from 3 to 5 (i.e., a diminishing returns behavior is
observed). The outage probability decreases by 81% when
the number of antenna elements is increased from 1 to 3.
The outage probability decreases by 42% when the number
of antenna elements increases from 3 to 5, but it increases
further by only 30% when the number of antenna elements
varies from 5 to 7. All previous results are obtained setting
the data rate to 9 kbps and the number of SUs to 10, but
no significant change in performance increase is expected
if the number of antenna elements are to be set beyond 7.
5. Conclusions
In this paper, we have analyzed the performance of a
cognitive (secondary) user in a CR-CDMA cellular system
considering beamforming at secondary BSs. A simulation
model for a three-cell representative scenario, incorporat-
ing soft HO, has been developed to assess the performance
of an SU, considering three possible spectrum sensing
schemes. In particular, the proposed simulation model al-
lows fast performance evaluation of a CR-CDMA system
and allows one to jointly evaluate the effects of spectrum
sensing and beamforming. More precisely, the outage and
blocking probabilities of the above three schemes have
been compared in the presence of beamforming. With re-
gard to earlier work [11], significant performance improve-
ments, in terms of outage and blocking probabilities, are
brought by the use of beamforming, and this is especially
significant in the presence of moderate traffic load. In the
presence of spectrum sensing, the percentage decrease, in
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86 S. Dhar Roy et al. / Physical Communication 9 (2013) 73–87
terms of outage probability, with 5 antenna elements is
46% and 55%, in correspondence to numbers of SUs set to 7
and 9, respectively.
All the schemes with spectrum sensing perform better
than any scheme with no spectrum sensing. The SU per-
formance, in terms of outage and blocking probabilities,
improves if the data rate of the SUs decreases. Finally, a
larger number of SUs degrades the performance of an SU
of interest, in terms of outage and blocking probabilities,
for a fixed number of PUs. In all cases, receive beamform-
ing at secondary BSs improves the performance of an SU.
Beamforming, together with spectrum sensing, dramati-
cally improves the performance of an SU. The blocking
probability is reduced in the presence of beamforming by
86%, 97%, and 99% with respect to that of [11] for the num-
ber of SUs fixed at 5, 9, and 13, respectively, keeping all
other parameter values set as in [11].
Acknowledgment
The activities of G. Ferrari are carried out within the
framework of the COST Action IC0902: ‘‘Cognitive Radio
and Networking for Cooperative Coexistence of Heteroge-
neous Wireless Networks’’ (http://newyork.ing.uniroma1.
it/IC0902/).
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Sanjay Dhar Roy received his B.E. (Hons.) degree
in Electronics and Telecommunication Engineer-
ing in 1997 from Jadavpur University, Kolkata,
India, and his M.Tech. degree in Telecommuni-
cation Engineering in 2008 from NIT Durgapur.
He received his Ph. D. degree from NIT Durga-
pur in 2011. He worked for Koshika Telecom
Ltd. from 1997 to 2000. After that, he joined
the Department of Electronics and Communica-
tion Engineering, National Institute of Technol-
ogy Durgapur, as a Lecturer in 2000, and he is
currently an Assistant Professor there.
His research interests include radio resource management, hand-off,
and cognitive radio networks. As of today, he has published 60 research
papers in various journals and conference proceedings. Dr. Dhar Roy is a
member of IEEE (Communication Society) and is a reviewer of IET Com-
munications, Electronics Letters and Journal of PIER, IJCS, Wiley, Inter-
national Journal of Electronics, Taylor & Francis. Dr. Dhar Roy has also
reviewed for IEEE GlobeCom, IEEE PIMRC, IEEE VTC, etc.
Sumit Kundu received his B.E. (Hons.) degree
in Electronics and Communication Engineer-
ing in 1991 from NIT, Durgapur, India, and his
M.Tech. degree in Telecommunication Systems
Engineering and Ph.D. in Wireless Communi-
cation Engineering from IIT Kharagpur, India,
respectively.
He has been a faculty member in the De-
partment of ECE, National Institute of Technol-
ogy, Durgapur since 1995, and is currently a Pro-
fessor there. His research interests include radio
resource management in wireless networks, cognitive radio network,
wireless sensor network and co-operative communication and wireless
ad hoc and sensor networks.
He is a senior member of IEEE (Communication Society) and is a re-
viewer of several IEEE journals.
Gianluigi Ferrari was born in Parma, Italy, in
1974. He received his ‘‘Laurea’’ and Ph.D. degrees
from the University of Parma, Italy, in 1998 and
2002, respectively. Since 2002, he has been with
the University of Parma, where he currently is
an Associate Professor of Telecommunications.
He was a visiting researcher at USC (Los Angeles,
CA, USA, 2000–2001), CMU (Pittsburgh, PA, USA,
2002–2004), KMITL (Bangkok, Thailand, 2007),
and ULB (Brussels, Belgium, 2010). Since 2006,
he has been the Coordinator of the Wireless Ad-
hoc and Sensor Networks (WASN) Lab (http://wasnlab.tlc.unipr.it/) in the
Department of Information Engineering of the University of Parma.
As of today, he has published more than 160 papers in leading
international journals and conference proceedings, and more than 10
book chapters. He is coauthor of seven books, including ‘‘Detection
Algorithms for Wireless Communications, with Applications to Wired
and Storage Systems’’ (Wiley: 2004), ‘‘Ad Hoc Wireless Networks:
A Communication-Theoretic Perspective’’ (Wiley: 2006; technical best
seller), ‘‘LDPC Coded Modulations’’ (Springer: 2009), and ‘‘Sensor
Networks with IEEE 802.15.4 Systems: Distributed Processing, MAC, and
Connectivity’’ (Springer: 2011). He edited the book ‘‘Sensor Networks:
where Theory Meets Practice’’ (Springer: 2010). His research interests
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S. Dhar Roy et al. / Physical Communication 9 (2013) 73–87 87
include digital communication systems analysis and design, wireless ad
hoc and sensor networking, and adaptive digital signal processing. He
participates in several research projects funded by public and private
bodies.
Prof. Ferrari is a co-recipient of a best student paper award at
IWWAN’06; a best paper award at EMERGING’10; an award for the
outstanding technical contributions at ITST-2011; and the best paper
award at SENSORNETS 2012. The WASNLab team won the first Body
Sensor Network (BSN) contest, held in conjunction with BSN 2011. He acts
as a frequent reviewer for many international journals and conferences.
He acts also as a technical program member for many international
conferences. He currently serves on the Editorial Boards of several
international journals. He was a Guest Editor of the 2010 EURASIP JWCN
Special Issue on ‘‘Dynamic Spectrum Access: From the Concept to the
Implementation’’.
Riccardo Raheli received his Dr. Ing. degree
(Laurea) in Electrical Engineering ‘‘summa cum
laude’’ from the University of Pisa in 1983,
his Master of Science degree in Electrical and
Computer Engineering with full marks from the
University of Massachusetts at Amherst, USA,
in 1986, and his Doctoral degree (Perfeziona-
mento) in Electrical Engineering ‘‘summa cum
laude’’ from the Scuola Superiore S. Anna, Pisa,
in 1987. From 1986 to 1988, he was a Project En-
gineer with Siemens Telecomunicazioni, Milan.
From 1988 to 1991, he was a Research Professor at the Scuola Superiore S.
Anna, Pisa. In 1990, he was a Visiting Assistant Professor at the University
of Southern California, Los Angeles, USA. Since 1991, he has been with
the University of Parma, as a Research Professor, Associate Professor since
1998, and Full Professor since 2001. In this role, he was Chairman of the
Communication Engineering Program Committee from 2002 to 2010 and
Member of the Scientific Committee of CNIT (Consorzio Nazionale In-
teruniversitario per le Telecomunicazioni) from 2000 to 2005.
He has also been a Member of the Executive Committee of CNIT since
2008 and a Member of the Scientific Committee of the Doctoral School in
Engineering and Architecture since 2011.
His scientific interests are in the general area of information and com-
munication technology, with special attention towards systems for com-
munication, processing, and storage of information. His research has led
to numerous international publications in journals and conference pro-
ceedings, as well as a few industrial patents. He is coauthor of a few
scientific monographs such as ‘‘Detection Algorithms for Wireless Com-
munications, with Applications to Wired and Storage Systems’’ (John Wi-
ley & Sons, 2004) and ‘‘LDPC Coded Modulations’’ (Springer, 2009). He is
supervising coauthor of the paper which received the ‘‘2006 Best Student
Paper Award in Signal Processing & Coding for Data Storage’’ from the
Communications Society of the Institute of Electrical and Electronics En-
gineers (IEEE).
He served on the Editorial Board of the IEEE Transactions on Com-
munications from 1999 to 2003. He was Guest Editor of a special issue of
the IEEE Journal on Selected Areas in Communications (JSAC) published
in 2005. He served on the Editorial Board of the European Transactions
on Telecommunications (ETT) from 2003 to 2008. He was Guest Editor
of a special issue of the IEEE Journal of Selected Topics in Signal Pro-
cessing (JSTSP) published in 2011. He served as Co-Chair of the General
Symposium on Selected Areas in Communications at the International
Communications Conference (ICC 2010), Cape Town, South Africa, and
the Communication Theory Symposium at the Global Communications
Conference (GLOBECOM 2011), Houston, Texas, USA. He has also served
on the Technical Program Committee of many international conferences,
such as ICC, GLOBECOM, IEEE Intern. Symp. Power-Line Commun. and its
Appl. (ISPLC), and European Signal Processing Conf. (EUSIPCO).
