Carrier Aggregation for Underlay Cognitive Radio
Wireless Mesh Networks
Ousmane Zeba, Shunsuke Saruwatari and Takashi Watanabe
Graduate School of Information Science and Technology, Osaka University, Japan

Abstract—Cognitive radio (CR) has emerged as a promisingtechnology to solve the tremendous spectrum scarcity problem.
Although CR has shown very interesting results, it is still dealing
with some drawbacks such as blocking or forced termination
events in opportunistic mode and capacity limitation in underlay
mode. In this paper carrier aggregation is proposed for wirelessmesh networks to enhance the network throughput in underlay
CR condition. In the proposed scenario, the primary user
uses traditional channels whereas the secondary user aggregates
carriers in PU’s channels for its transmission. An analytical
performance study is carried out which shows the effectivenessof our proposal. With relatively few aggregated carriers perchannel, carrier aggregation outperforms single channel andchannel aggregation due to its higher detection probability. These
results can be used to extend the already well known propertiesof wireless mesh networks and their applications.

Keywords— Cognitive radio network, wireless mesh network, carrier aggregation, detection probability, underlay,
overlay.
I. I NTRODUCTION
Cognitive Radio (CR) or Dynamic Spectrum Access (DSA)
is one key solution to spectrum scarcity in wireless communications [1], [2] and [3]. According to [4] and [5] CR is aradio that adapts its transmitter parameters using interactions
with its environment.
Two main users are named when talking about CR, theprimary user (PU) and the secondary user (SU). The PU is aspectrum license owner and usually does not need to perform
CR. The SU is the one who implements CR to make profit of
the spectrum without owning any spectrum license.
There are mainly two paradigms in CR technology [6].The
opportunistic usage of the spectrum holes, with or without
the PU’s cooperation, which is called overlay mode, and theunderlay mode which allows the SU to share the spectrumwith the PU without harming the PU. According to [6], theUnderlay paradigm is more efficient in spectrum utilization.
There exist some interesting prior work in this context.
Carrier Aggregation has been investigated in [7]–[11]. The
main goal of those studies is bandwidth or capacity improvement. In [7] CA is used to provide substantial improvement
to LTE-Advanced by allowing up to 100MHz of bandwidth
increase after carrier aggregation. In [8] carrier aggregation
is associated to ralaying to enhance the capacity of LTEA networks. [7] and [8] do not consider CR technology
neither wireless mesh networks networks (WMNs). [9], [10]and [11] consider CR technology and carrier aggregation indifferent manners. [9] considers cooperation between PU and

SU in underlay CR fashion while [10] investigates power
allocation for joint overlay CR and underlay CR scheme. [11]investigates relay selection and carrier aggregation to improvethroughput in underlay CR technology. CR WMNs have been
investigated in [12]–[14]. Also a fair scheduling scheme forsubcarriers and power allocation to mesh routers (MRs) andmesh clients is presented in [15]. An orthogonal frequency
division multiple access WMN is investigated but without
considering CR.
In all the above mentioned works on CR, CR is applied
either on channels or carriers for both PU and SU. For thosestudies underlay CR means PU and SU sharing directly thesame spectrum segment. We consider a different strategy
where PU and SU share the spectrum by using different
spectrum segment. The PU uses channels while the SU uses
carriers or component carriers (CCs) of those channels.Then
the SU aggregates the carriers to reach sufficient capacity fortransmission.
The main contribution of this work is an increase in SU’s
detection probability which in turn increases SU’s capacity
inside the spectrum segment dedicated to PU. In addition,a WMN is considered for Carrier aggregation in underlay
cognitive Radio. We conducted numerical analysis to show
the effectiveness of our proposal.The results show that carrier
aggregation outperforms traditional underlay CR and channel
aggregation in terms of throughput, due to its higher signal
detection probability.
Wireless Mesh Networks (WMNs) are known to have very
interesting properties [15], [16] and can be used as backhaul
for internet connectivity [17] in either rural or urban area.
One interesting property of WMNs is their cost-effectiveness,
which means they do not require high cost for deployment. By
applying CA (proposed model) to WMNs in low frequency,
we will be able to build more robust and more cost-effective
WMNs.
The remainder of this paper is organized as follows. In
Section II, we introduce CR technology and the problem
caused by its overlay and underlay paradigms. In Section III,
we describe our method for solving the capacity limitation
problem and increasing the network throughput in an underlay
CR WMN. After that, we assess the performance of our
proposed method by numerical analysis in Section IV. Finally,
the conclusion is drawn in Section V.

II. P ROBLEM IN CR AND WMNIn CR technology, many drawbacks affect the network
performance.
Overlay CR uses sensing technique to opportunisticallyaccess idle channels or frequency holes [1]. The use of sensing
has an impact on the network throughput. Before transmitting
data, the network node needs to perform sensing. If TDAT A is
the time for sending data, TS the sensing time and C the link
capacity, the throughput can be computed as C(TDAT A − TS )for only one link instead of CTDAT A as usual. In case ofmany channels, the throughput becomes C(TDAT A − LTS )[18], where L is the number of channels for which sensing
was performed.
Besides the sensing drawback in overlay CR are the blocking and forced termination events which occur at anytime on
SU [19]. The blockade of SU occurs when an SU wants toaccess a channel while no idle channel is available. That SU
will be denied access to the spectrum. The forced terminationof SU occurs when a PU wants to access the spectrum while
no idle channel is available. In that case, the PU forces the
SU to leave the channel. The blocking and forced termination
probabilities associated to the above-mentioned events impact
on data transmission in the network and reduce the overall
network throughput when overlay CR is implemented.
The situation is quite different in underlay scenario. PU
and SU use the same frequency band at the same time withsome restrictions to the SU. SU’s transmit power is set so thatthe interference to PU should never exceed a threshold called
Interference Temperature Limit [20]. This limitation in SU’s
transmit power reduces drastically SU’s link capacity, resulting
in a very limited throughput in SU network.
Channel or Carrier Assignment is among the most importanttasks to perform in order to achieve considerable network
service quality in WMNs [21]. Channel assignment ensures aneffective utilization of bandwidth to avoid retransmissions in
the network [21]. A high number of retransmissions introduce
delay in successful end-to-end transmission, which in turn
reduces network throughput. The network delay depends also
on other factors such as queuing delay, channel switching
delay [22] etc.
III. S YSTEM M ODELThis section describes the proposed CA to overcome thecapacity limitation in underlay CR. The SU uses carriers
instead of channels for data transmission whereas the PU uses
channels. Channel is a segment of spectrum resource withgiven bandwidth B. A carrier is a smaller segment of spectrum
resource with bandwidth ∆f so that a channel can contain
K carriers. Carriers have to be created inside each channel
and used by the SU. As carrier capacity is not sufficient
for reliable data transmission, the system needs to use many
carriers to gain enough capacity for data transmission. The
carriers aggregated by one SU node can be contiguous or noncontiguous, from one channel or different channels. At thisstage, we do not include the additional requirements of noncontiguous carriers in this study. We only focus on the effectof carriers on SU’s capacity. Thus, we do not make difference
between contiguous carriers and non-contiguous carriers.
In the proposed model, the first step is carrier (or channel)
estimation. We assume a perfect CSI between SU nodes.
The second step is carrier aggregation. The SU nodesearches for available carriers for a given link. After obtaining
a sufficient number of carriers for transmission, those carriers
are assigned to the designated link.
A. System overview
The system is composed of a PU network and a SU network.
The PU network can be a TV broadcast station and its TV
receivers or a mobile network base station and its mobile
users as our target is the licensed low frequency band. The
SU network is a Multi-radio WMN, which means each Mesh
Router is equipped with R wireless network interfaces, R > 1.
The whole SU network is in the transmission range of a PU
Access Point as shown in Fig. 1. In these conditions, theSU network has necessarily to perform CR in order to beable to transmit. We assume a situation where only underlay
CR is possible, no spectrum holes nor TV white space areavailable. As underlay CR is already known for its efficiency
in spectrum utilization [6], the main goal of this study is toimprove underlay CR technique.
In the described model, the existing PU uses channels suchas Wifi channels or VHF/UHF channels. The SU creates
carriers inside those channels in order to perform Underlay
CR as illustrated in Fig.2. The bandwidth of the carriers has
to be chosen so as to obtain enough carriers for the desired
improvement. The carriers can be contiguous to each other ornot, from the same channel or from different channels. We do
not include the additional requirements of non-contiguousnessof carriers at this stage. We only focus on the effect ofcomponent carriers (CCs) on SU’s capacity. The SU makes useof CCs inside PU’s channel in underlay fashion. As each CC
has a very limited capacity, each MR has to aggregate manyCCs in order to gain sufficient capacity for data transmission.
The aggregated carriers are assigned to the active link boundto one interface of the MR. There are K component carriersin the frequency band indexed by k = 1, 2, .., K. In orderto achieve higher rates, each MR aggregates α available CCsfrom the frequency band for its active links as shown in Fig.
2.
B. Carrier Estimation phase
For the proposed model, full CSI is required only among
SU nodes, similar to the channel estimation model in [9]. The
sending node i sends pilot symbols to the receiving node j.
Node j estimates the channel coefficients hij for the current
CC. After estimation node j transmits the CSI values to node iusing a feedback link. CSI is not required from the PU network
because we do not assume any cooperation between PU and
SU. We assume a constant path loss between SU network andPU network hsp or hps .

1
log2 (1 + γij [k])Pd(4)
L
where L is the link population in the subnet and Pd =Pr {SIN R ≥ ξ} is the probability of SU’s signal detection and ξ is the SINR threshold. Furthermore, we considersusu
Pd = 1 − Pout
where Poutis the outage probability of SU due
to PU. SU’s packet is detected when there is no outage of SU.
We consider the interference range as the target subnet because
those nodes in the interference range need necessarily touse different CCs to avoid interference. Thus the interference
comes only from the PU network. SU’s SINR in this condition
is computed as
φ=Fig. 1. Underlay CR model.

C. Carrier Aggregation

γsu [k] =According to [9] and [11], the received signal yij from nodei at node j can be written asqqyij = Pij [k]hij [k]x[k] + Pps [k]hps [k]z[k] + nj [k] (1)Where Pij , hij , x are respectively the transmit power, thechannel state information (CSI) of an SU node’s signal on
CC k; Pps , hps and z are respectively the transmit power, the
CSI of PU node’s signal on CC k. nj is the noise received at
node j on carrier k. The CSI includes the path loss for eachcarrier. We assume perfect CSI between SU nodes. The SU
network does not receive CSI from PU network as there is nocooperation between them. The hps in PU’s signal indicatesthe path loss. For a given active link l, the MR looks foravailable carriers in the designated channel with bandwidth
B, by computing the signal-to-noise-plus-interference (SINR)
value of each CC as shown in (2). ∆f is the CC bandwidthBsuch as ∆f = K.
γij =Pp [k]|hps [k]|2 +Pij[k]|hij [k]|2P22m6=i Pmj [k]|hmj | + σj [k](2)σj2 is the noise variance. Carrier capacity is computed asfollows, using Shannon’s law
Cij [k] = ∆f log2 (1 + γij [k])(5)

If R0 is the desired data rate for SU, the corresponding
value of SINR threshold isγ̂su [k] = 22R0 − 1(6)Then the outage probability is given assu
Pout[k] = Pr {SIN R ≤ γ̂su [k]}(7)

Similarly to [9] and [24] the outage probability of a CC
from SU side is given assu
Pout[k] = 1 −

[k]
2
σss
[k] exp(− γ̂σsu
2 [k] )ss

(8)

2 [k]γ̂ [k] + σ 2 [k]
σps
su
ss

2
2
are respectively the variance of hss (hss =and σss
Where σpshij ) and hps . The details of the above equation are provided
in [24]. The throughput of a single CC is given as follows

[k]
2
σij
[k] exp(− γ̂σsu)
2
1
ij [k]
φsu [k] = log2 (1 + γij [k]) 2
2 [k]L
σps [k]γ̂su [k] + σij(9)Then as independent events, the outage probability of the
SU node aggregating α CCs becomes(3)

We consider the outage probability of SU due to PU’s traffic.
According to [23] the throughput can be written as

Pss [k]|hss [k]|22 [k]Pps [k]|hps [k]|2 + σss

su
Pout
=

α
Y

su
Pout
[k]

k=1

=

α
Y

1−

[k]
2
σss
[k] exp(− γ̂σsu
2 [k] )!

k=1

2 [k]γ̂ [k] + σ 2 [k]
σps
su
ss

α
Y

[k]
2
σss
[k] exp(− γ̂σsu
2 [k] )

Pd = 1 −

k=1

(10)

ss

1−

!

ss

2 [k]γ̂ [k] + σ 2 [k]
σps
su
ss

(11)

The link capacity after carrier aggregation becomes
C=

α
X
k=1

Fig. 2. Underlay CR Carrier Components.

C[k] = ∆fαX

log2 (1 + γij [k])k=1

The throughput of an SU link can be written as(12)

0.300

φsu =

α
1X

L


C[k] ×

k=1
α
X

∆f
=
L
1−

su
1 − Pout

0.275



0.250

0.225

0.200

log2 (1 + γij [k])×

(13)

0.150

k=1

α 
Y
k=1

0.175

1−

[k] !
2σss[k] exp(− γ̂σsu
2 [k] )
ss
2 [k]γ̂ [k] + σ 2 [k]
σps
su
ss

0.125

0.100

0.075

0.050

IV. N UMERICAL E VALUATION

0.025

0.000
1.0

In this section, numerical results are presented. We compare
the performances of carrier aggregation, single channel (Noaggregation) and channel aggregation to each other according
to our mathematical analysis in section III-C. Without loss ofgenerality, let R0 = 1bit/s/Hz for any link between two
SU nodes and L = 6 the number of links in the WMNinterference range. We assume that the channel parameters22are σss= 2dB, σps= 4dB, B = 20M Hz, ∆f = 2M Hz.
Thus, the channel between two nodes is assume to be Rayleighfading. In addition we consider all the carriers and channels
to have the same detection probability.
To numerically assess our proposed model, we first evaluate
its performance inside one channel. It is extremely important
to know the gain in capacity of CA compared to traditional
underlay CR in the same channel Fig.3, Fig.4 and Fig.5. A
logical way to increase the capacity of underlay CR is to allow
one SU to share many channels with different PUs. We called
it Channel Aggregation. We also compared the performance ofCarrier Aggregation to Channel Aggregation, Fig.6 and Fig.7,
to show the effectiveness of our method, to clearly make thedifference between the two solutions and also to show that theSU can use carriers from different channels.
As shown in Fig.3, the detection probability of carriers
inside a channel is higher than that of the channel itself
in underlay CR. Meaning that the signal has more chance
to be detected when using many carriers instead of one
channel, even though the channel has larger bandwidth than
the aggregated carriers. According to Fig.4 carrier aggregationshows interesting results,in terms of throughput, with only
four carriers (α = 4). Figure 5 confirms the effectivenessof carrier aggregation according to the SINR value of thesignal. CA clearly outperforms traditional underlay CR withfour aggregated carriers as shown in Fig.5.
A similar behavior is observed in Fig.6 and Fig.7 whenmany channels are considered. According to the SINR value,
CA shows better performance than Channel Aggregation when
only four (α = 4) carriers are used, Fig.6. and Fig.7. Twochannels are aggregated while four carriers are aggregated perchannel. As a result, we obtain for one link, 2 × 20M Hz =40M Hz with channels and 4 × 2M Hz × 2 = 16M Hzwith carriers. We saw that 16M Hz outperformed 40M Hz.
Knowing that the transmission power is proportional to the
bandwidth, the only remaining factor explaining this result isthe high probability of carrier aggregation.

1.5

2.0

2.5

3.0

3.5

4.0

4.5

5.0

Fig. 3. Detection probability versus number of Carriers in one channel for
CA and Channel sharing

0.80
0.75
0.70
0.65
0.60
0.55
0.50
0.45
0.40
0.35
0.30
0.25
0.20
0.15
0.10
0.05
0.00
1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

5.0

Fig. 4. Throughput versus number of carriers in one channel for CA andChannel sharing.

V. C ONCLUSION
In this study, Carrier Aggregation for underlay CR wirelessmesh networks is presented. Mathematical formulation andnumerical analysis have been conducted. The results showthat Carrier Aggregation outperforms traditional underlay CR(single channel sharing) and channel aggregation in underlay
CR scenario. This work is based on the fact that PU and
SU use different spectrum segment (channel Vs Carriers) toperform Underlay CR for data transmission.
ACKNOWLEDGMENT

This work was supported by JSPS Science Research Grants
JP16H01718
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