2014 UKSim-AMSS 8th European Modelling Symposium
Energy Efficiency in Heterogeneous Wireless Networks using Cognitive Monitoring
Strategy
Afef Bohli
INNOV’COM Laboratory
High School of Communications (SUP’COM)
University of Carthage, Tunisia
[email protected]
Ridha Bouallegue
INNOV’COM Laboratory
High School of Communications (SUP’COM)
University of Carthage, Tunisia
[email protected]
wireless networks. In fact, we will interested to the concept
of a new technical strategy allowing a Vertical Handover
(VH) from a primary network (licensed band) to a secondary
network (unlicensed band) in heterogeneous networks based
on a new sensing model of free licensed band . This strategy
is based on the adding of a new element in each primary
wireless network called the Cognitive Monitor (CM), it
represent an intelligent transceiver with multi Radio Access
Technology (RAT) embedded in the Base Station (BS). Its
principal function is to sense its primary environment and
to cooperate with the other CM (relative to the neighbors
BS) in order to exchange information between its different
environments. The rest of the paper is organized as follows:
Section II discussed related work and described our contribution. Section III presented the modeling system and
the network scenario. Section IV Analyzed the simulation
result to study and evaluate the performance of the proposed
scheme. Finally, Section V concluded the paper.
Abstract—Given the rapid progress in wireless devices and
the serious demands of higher communication rate, future
solutions for wireless networks have to deal with these needs.
Proposed solutions must take into account the limited spectrum
resource while keeping the energy efficiency. So, a typical processing will realize a balanced tradeoff: max-throughput/minenergy consumption. In this paper, our contribution is to
conceive a new infrastructure of wireless networks converging
towards a fully heterogeneous systems based on the concept of
opportunistic spectrum sharing. The basic idea is to allow active users to be seamlessly linked to different networks (licensed
or unlicensed band). This architecture is based on Cognitive
Monitoring Strategy (CMS) characterized by a new sensing
model of unoccupied (free) licensed frequency belonging to
heterogeneous wireless networks.
Keywords–Heterogeneous networks; radio cognitive; monitoring strategy; cooperation; throughput; energy efficiency
metrics.
I. I NTRODUCTION
View the increasing demands of high speed and high
capacity adapting the existing systems, development of new
solutions for wireless networks becomes indispensable to
meet the requirements of emerging applications. Although,
those solutions has emerged primarily to improve the high
data throughput with an optimum use of spectrum and a
very low latency, the importance of energy consumption
create a new challenges. Therefore, the energy problem
become more and more attractive because of the limited
energy of users device which cannot provided a continuous
power all the time. So that, the crucial challenge in future
wireless networks is to develop a new resource allocation
schemes able to strike the optimal balance between reliably
transmitting with high rates and saving as much energy as
possible [1]. A lot of search hinged on this axe was made in
order to face this challenge. Such as in [2] who address the
problem of energy consumption by proposing a cooperation
method in heterogeneous wireless networks to track the
position of a moving users mobile and signaling overhead
via the node selection. [3] was interested on the optimization
of the spectrum handoff mechanism in cognitive radio.
The aim of this paper is to provide a solution to model and
handle energy-efficient problems in future heterogeneous
978-1-4799-7412-2/14 $31.00 © 2014 IEEE
DOI 10.1109/EMS.2014.11
II. R ELATED
WORKS
Given that the total energy consumption by user mobile
in the heterogeneous cognitive wireless networks can be
formulated as follows:
Etc = Es + Esw + Et
(1)
Where Es is the energy consumed during the sensing
phase, Esw is the energy consumed during the switching
phase (decision +VH) and Et is the energy consumed during
the transmission phase.
Most of searches was focused on the minimization of the
total energy consumption Etc by the optimization of the Es .
One of the characteristics key of a sensing phase is the ability
to discern the nature of the surrounding radio environment.
This is performed by the spectrum sensing. There are multi
technics to sense radio environment such as [4] who used the
energy detection schemes as a sensing model and discusses
the problem of energy efficiency from different aspects:
reliability of sensing, throughput and delay, thus it optimize
the two parameters: sensing duration and the probability that
the SU wait on current channel and does not transmit data.
[5] use the same technique, mentioned above, and studied the
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choose and select the same free channel) and, subsequently,
through the attempt of retransmission the desired lost packet.
Let assume Nc the number of collision, using (1) We will
get:
problem of designing the sensing slot duration to maximize
the achievable throughput for the secondary users under the
constraint that the primary users are sufficiently protected.
Indeed, assuming a frequency band with a carrier frequency
fc, a bandwidth B, a received signal sampled at the frequency
fs and a number of sampling N, the received signal at the
secondary users (SU) ’Y(n)’, can be deduced, under two
hypothesis:
Ho : Y (n)
=
u(n)
(2)
H1 : Y (n)
=
s(n) + u(n)
(3)
Etc = Nc (Es + Esw + Et )
This paper contributes on a new technical model leading
to minimise the Etc by the disable of the sensing phase at
the user mobile taking into account the collision problems
between SU’s, thus,
Etc = Nc (Esw + Et )
Where s(n) is a transmitted signal by the primary user
(PU) and u(n) is a Gaussian, independent and identically
distributed random process with mean zero and variance σu2 ,
The receiving energy will be given by :
2
E(y) = ΣN
n=1 Y (n)
This technique allowing a VH decision, in heterogeneous
network, from a licensed band to unlicensed band is called
a Cognitive Monitoring Strategy (CMS) and defined by the
use of a new architecture element, Cognitive Monitor (CM)
which is the responsible of sensing phase. This proposed
strategy will be evaluated by the study of different metrics
of energy efficiency [6].
(4)
Using a detection threshold λ, and (4) two probabilities
will be defined: the probability of detection Pd and the
probability of false alarm Pf formulated as follows:
∞
p(E)
(5)
Pd = P r(E(y) > λ|H1) =
III.
We consider an heterogeneous network with M number
of Wireless Zones (WZ) each one was covered by a base
station (BS), supervised by a Cognitive Monitor (CM) and
composed by Np number of PU and Ns number of SU,
Let consider a zone W Zi (i ∈ {1,M}) with a radius Ri
and a CMi (i ∈ {1,M}), We denote P U(n,i) and SU(m,i) the
primary user number n (n ∈ {1,Np }) and the secondary user
number m (m ∈{1,Ns }), belonging ZWi , respectively and
d the distance between the P U(n,i) and the BSi as shown
in Figure 1.
λ
Pf = P r(E(y) > λ|H0) =
∞
p(E)
(6)
λ
Where p(E) is the probability density function (PDF) of
E(y).
Supposing that the SNR γ is the same for all the channels
during the packet transmission, using (5) and (6) the two
probabilities will be calculated, in a τs sensing time, as
follows:
1
(Q−1 (pf ) − τs f sγ))
(7)
Pd = Q( √
2γ + 1
Pf = Q( √
1
(Q−1 (pd ) + τs f sγ))
2γ + 1
MODELING SYSTEM AND NETWORK SCENARIO
(8)
where Q(x) is the Gaussian tail probability with inverse
Q−1 (x).
Noticing that (7) and (8) depends on the sensing time τs
its clear that an optimization of τsmin lead to minimize the
Es
τsmin
1
= 2 (Q−1 (pf ) − Q−1 (pd ) 2γ + 1)
γ fs
Fig. 1. An exemplary of heterogeneous network
with two WZ
(9)
We will focus our work in the case that the PU move
from one licensed W Zi to another unlicensed W Zj (the
ordinary case of a vertical handover from a license WZ to
another license WZ, in the heterogeneous networks, will be
not processed).
Let denote Soverl(i,j) the overlapping region between
the two neighboring W Zi and W Zj where both wireless
networks are available, D(i,j) the distance between BSi
We can see, that those studies are interested on the problem of minimization Es by the optimization of a different
sensing parameters (sensing time, sensing slot, number of
sampling, etc.) taking into account the interference between
PU and SU without addressing the problem of collision
between secondary users leading to growth the total energy
consumption through the lost of packet transmitted by the
SU during a collision between another SU (who sense,
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→
−
−−−→
and BSj and α the angle between d and D(i,j) during
the movement of P U(n,i) in the W Zi .
According to the cooperation between CMi and CMj and
during the movement of P U(n,i) a Control Algorithm (CA)
will be applied aims to check if the P U(n,i) penetrates the
Soverl(i,j) , which is given as follows:
will be selected only by one SU is characterised by the
probability given as follows:
q = Nf
D(i,j) − Rj ≤ d cos(α) ≤ Ri
T hen
P U(n,i) ∈ Soverl(i,j)
pnc
Thus CMi will inform CMj that this P U(n,i) want to
access on its free spectrum, thereafter, CMj will consider
this user as a new arrived SU(m,j) and increment its Ns .
Assuming that the number of arrival SU wanted to access
on a unlicensed band was deployed in the W Zj following
a Poisson Point Process (PPP) we can calculated the probability distribution P of SU number by the following formula
:
(ρeNs e−ρ )
Ns !
N s i
p (1 − p)Ns −i q(i)
i=1
Ns i
Ns −i
(1 −
i=1 p (1 − p)
1 i−1
Nf )
C = Blog2 (1 + snr),
(13)
(14)
where B is a bandwidth of a transmission signal,
Then the maximum throughout of the SU, using (13) and
(14) will be calculated through the following formula:
(10)
Rmax = pnc C = pnc Blog2 (1 + snr)
(15)
The first goal,of this paper, is to compare this Rmax with
the Rmax
the maximum throughput measured when employing the energy detection schemes for spectrum sensing, so
is given by:
using (7) and (11) Rmax
= pPd Blog2 (1 + snr)
Rmax
(16)
The second goal is to check the desired balance between
maximize throughput and minimize energy consummation,
which can be achieved by using a different standard metrics
for the measuring of energy efficiency. The study in this
paper will be based on two energy metrics:
1) The Absolute Energy Efficiency (AEE) [8] called dBε
and measured according to the following equation:
power
)
(17)
dBε = 10log10 (
bitrate(KT ln2)
and when Ns tend to infinity (Ns → ∞ )[7],
(ρeNs e−ρ )
Ns !
=
=
Now, Assuming that the channel capacity is approximated
by the Gaussian formula, the maximum channel capacity
will be defined as follows:
Proof:
A number of SU follow a PPP, this, imply that the Ns
SU’s are uniformly and independently distributed within a
surface S of W Zj with the area ANs (A is the approximate
area of the occupied node of each SU) transforming this
hypothesis mathematically we will get:
|R S|
2
∀R ∈ R , P (SU ∈ R) =
ANs
p = P (N = N s) =
(12)
At a given time ’t’, with Ns SU’s and Nf free channels,
and using (11) and (12), the defining event that: each
secondary user data is transferred successfully only if there
are no collision between the other secondary users, will be
occurred by the probability of no collision pnc given as
follows:
If
P (N = N s) =
1
1 Ns −1
1 Ns −1
(1 −
)
= (1 −
)
Nf
Nf
Nf
(11)
2
Where ρ = Π ARs present the mean of the poisson
distribution (Rs is the radius of the surface S).
In the other hand, the CMj was in listening permanent
on its environment in order to sense continuously the white
space (no occupied band), let denote Nf the number of free
sensed channel at a given time t (Nf ≤ Np ).
The proposed technique focused on the broadcast, each τ
period, of the list of Nf free channel to the NS SU.
SU(m,j) will select a free channel h (h∈ {1,Nf }) in order
to access to it and transmit its desired packet, the problem
is when another SUk,j (k∈ {1,Ns } and k= m) select the
same free channel at the same time.
Assuming that each SU select a free channel and transmit
into it with the probability p and don’t select a free channel
with the probability (1-p), the event that a free channel
where K is the Boltzman’s constant and T is the
absolute temperature. The smaller value of dBε is the
greater of the achieved efficiency. Given the formula:
T hroughput = (1 − BER)n bitrate
(18)
We can deduct, using (17) and (18) the value of the
absolute energy efficiency given by:
dBε = 10log10 (
389
power(1 − BER)n
)
T hroughput(KT ln2)
(19)
2) The Energy Throughput Ratio [9] called ETR and
measured according to the following equation:
ET R =
P ower
T hroughput2
(20)
A lower ETR providing either: a lower energy consumed at the same throughput or a higher throughput
at the same energy consumed.
In the next section, we will try to implement the proposed
strategy and analyze the result.
IV. S IMULATIONS AND A NALYSIS
We will proceed to a comparative study between the two
methods of sensing free spectrum (Monitoring Cognitive
Strategy: our proposed strategy and the energy detection)
by the analysis of the measurement results of throughput,
and (ET R , dBε ) energy efficiency metrics.
The simulation parameters are summarized in table I
Fig. 3. Absolute Energy Efficiency [dBε ]
TABLE I. SIMULATION PARAMETERS
parameter
SNR
Bandwidth B
ρ
Power per bit
n
Nf
PER=(1-BER)
T
K(Boltzman’s constant)
value
-10 dB
5 Mhz
5
4510−6 J/b
4
20
0.03
300 K
1.3810−23 J/K
Fig. 4. Energy Throughput Ratio [J/b3 ps2 ]
The following pictures shows the simulation results:
efficiency. This shows the performance of the proposed
solution,
The same in Figure 4, We can see that the application of
the CMS resulted on a lower ETR .
Given that the measurements results of the two energy
efficiency metrics showed a lower value on applying the
CMS, resulting, a lower energy consumption keeping the
same throughout or similarly a greater throughput keeping
the same energy consumption, we can confirm that the
desired balance between throughput and energy consumption
is achieved.
V. C ONCLUSION
In this paper we have proposed a new wireless system
model enabling the desired balance between throughput and
energy efficiency in the heterogeneous wireless networks
based on the Cognitive Monitoring Strategy. We have studied
the problem of collision between secondary users and measured the maximum throughput considering this probability.
Finally, we conclude this paper, by a comparative analysis
, between the proposed CMS and the energy detection
technique, based on two energy efficiency metrics (absolute
energy efficiency and energy ratio efficiency). Simulation
Fig. 2. Maximum throughput [b/s]
We notice from Figure 2 that the maximum throughput
is more higher when we apply the Cognitive Monitoring
Strategy.
It’s clear from Figure 3 that the value of Absolute Energy
Efficiency is more lower when we apply the CMS knowing
that the smaller the dBε value is, the greater the achieved
390
results demonstrated that the desired balance was set. In the
next work we will try to optimise the probability of collision
to more improve those results.
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