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Water-Filling Algorithm approach for power Allocation in OFDM
Based Cognitive Radio System
Sakib.R.Mujawar1, A.N.Jadhav2 1Department of Electronics & telecommunication Engg, D.Y.Patil college of Engineering & Technology
Kolhapur,
Abstractβ In this paper, a simple water-filling algorithm for OFDM based cognitive radio is
approach to solve the increasing demand for wireless multimedia service to creates a lack of
spectrum. A potential solution to this issue is to allocate the spectrum dynamically by means of
cognitive radio. Water-filling can offer a solution to this spectrum allocation. The proposed water-
filling power allocation algorithm cognitive radio user can achieve significantly higher transmission
capacity for given statistical interference constraints and a given budget compared to the classical
power allocation algorithm i.e. Uniform
KeywordsβCognitive Radio, Orthogonal frequency division multiplexing, Primary user, Secondary
user
I. INTRODUCTION
Now a day we are facing many problems regarding spectrums due to the growing application in
wireless fields. Lot of wireless applications is sharing the same medium and due to this there is
overload which leads to lack of spectrum in given frequency bands. On the other hand, measurement
show that[1] wide ranges of spectrum are rarely used most of the time, whereas other bands are used
heavily and as we know the radio frequency is scare natural resource and its efficient use is of the
utmost importance. Moreover, most spectrum bands are allocated to certain services but worldwide
spectrum occupancy measurements show that only portions of the spectrum band are fully used.
The utilization of radio spectrum can be improved significantly by using the cognitive radio (CR)
technology. In some case, the spectrum bands are not utilized because licensed user does not always
occupy their spectrum and unlicensed users are not allowed to operate in such spectrum bands.
Spectral efficiency can be increased significantly by giving Opportunistic access of the frequency
bands to a group of Potential users (referred to as secondary or CR users) for whom the band has not
been licensed. Cognitive radio (CR) has been proposed as a way to improve spectrum efficiency by
exploiting unused spectrum in dynamically changing environments. The CR design is an innovative
radio design philosophy which involves smartly sensing the swaths of spectrum and then
determining the transmission characteristics (e.g., symbol rate, power, bandwidth, latency) of a group
of secondary users based on the behavior of the users to whom the spectrum has been licensed
(referred to as primary users). Although opportunistic spectrum access would allow CR user to
identify and access available spectrum resources, one of the main concerns is to utilize the available
spectrum resources in an efficient manner.
OFDM base CR cognitive Radio:-OFDM is a multi-carrier modulation technique that can overcome
many problems that arise with high bit-rate communications, the most serious of which is time
dispersion. The data-bearing symbol stream is split into several lower-rate streams, and these streams
are transmitted on different carriers. Because this splitting increases the symbol duration by the
number of orthogonally overlapping carriers (subcarriers), multipath echoes affect only a small
portion of the neighboring symbols. The remaining inter-symbol interference (ISI) is removed by
extending the OFDM symbol with a cyclic prefix (CP). Other advantages of OFDM include high
spectral efficiency, robustness against narrowband interference (NBI). Orthogonal frequency
International Journal of Recent Trends in Engineering & Research (IJRTER) Volume 02, Issue 06; June - 2016 [ISSN: 2455-1457]
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division multiplexing (OFDM), because of its flexibility in allocating the spectrum, has been
recognized as an air interface technology for CR systems. Because of the coexistence of CR and
primary users in side-by-side bands, mutual interference between these users is the limiting factor in
order to achieve a good performance for CR systems .Use of the classical power allocation
Algorithms e.g., well known water-filling algorithm for CR systems may result in higher interference
to the primary user (PU) receivers. In, we propose a power loading algorithm that maximized the
downlink transmission rate of a CR user while keeping the total interference introduced to different
PU receivers below a specified threshold .A distribute d algorithm for optimal resource allocation in
orthogonal frequency division multiplexing access (OFDMA)-based CR systems has been proposed.
When using orthogonal frequency Division Multiplexing in cognitive radio network, the power
allocation schemes for spectrum resources will be very flexible and convenient .However, it become
very challenging to allocate power to individual sub channels in the OFDM-based cognitive radio net
works. In traditional power allocation problems, water-filling algorithms are prevalent. Because
additional interference constraints must be considered in cognitive radio networks, the water-filling
algorithm is a real ways performed geometric to solve the power allocation problem [4].In this paper,
we study the properties of the water-filling algorithm and propose a linear algorithm to reduce
computational complexity.
II. SYSTEM MODEL
We consider a wireless system consisting of L sub-channels licensed to different primary users. Each
of these primary users behaves differently or has uncorrelated activity in their band. All the sub-
channels are divided into multiple subcarriers as shown in figure2.1 and they are opportunistically
available to some secondary or cognitive user which uses the band in OFDM fashion. the total
number of subcarriers are N with M sub-channels licensed to different primary users.
In this, first, we discuss the subcarrier grouping strategy that is used to maximize the transmission
rate and minimize interference. Second, we outline in details the system model used in this thesis.
This includes a description of the transmitter and receiver, the adaptive subcarrier allocation scheme
used in our work, as well as the channel model used in the analysis.
2.1 Underlay Model
We consider a downlink transmission scenario. It is assumed that the frequency bands of bandwidth
B1, B2... BL have been occupied by PU1, PU2... PUL. As in Figure 2.1, SUs can occupy either the
spectrum of PUs or the adjacent spectrum of PUs. The available bandwidth for CR transmission is
divided into N subcarriers based OFDM system, and the bandwidth for each subcarrier is βf H z.
In the downlink transmission scenario, there are three instantaneous fading gains: between the SUs
transmitter and SUs receiver for the ith subcarrier denoted as βππ π ; between the SUs transmitter and
lth PU receiver denoted as βππ π
; between lth Pus transmitter and SUs receiver denoted as βπππ
.We
assume that these instantaneous fading gains are perfectly known at the SUs transmitter.
International Journal of Recent Trends in Engineering & Research (IJRTER) Volume 02, Issue 06; June - 2016 [ISSN: 2455-1457]
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1 2 3 . . . . . . N
Figure 2.1 Illustration for underlay Model
2.1.1 Interference introduced to PU by SU
We assume that the signal transmitted on the subcarrier is an ideal Nyquist pulse. According to [6],
the power spectrum density of the ith subcarrier can be written as:
ππ(π)
= ππππ (π ππππππ
ππππ )2 (1)
Where Pi is the total transmits power in the ith subcarrier and Ts is the symbol duration.
Then the interference introduced to the lth PU band by the ith subcarrier is:
πΌπ(π)
= ππ|βππ π
|2
ππ β« (π ππππππ
ππππ )
2ππ
πππ+π΅π2
πππβπ΅π2
(2)
Where dil is the distance in frequency between the ith subcarrier and the lth PU band, and Bl
represents occupied bandwidth by the lth PU.
2.1.2 Interference introduced to SU by PU
According to [*], the power spectrum density of the PU signal after M-fast Fourier transform (FFT)
processing can be expressed as:
πΈ[πΌπ(π) =1
2ππβ« πππ(πππ) (
sin(πβπ¦)π
2sin(πβπ¦)
2
)
2
ππ¦ (3) π
βπ
Where XPU (ejΟ) is the power spectrum density of the PU signal. The PU signal has been taken to be
an elliptically filtered white noise process with amplitude PPU. According to the interference
introduced to the ith subcarrier by the lth PU band can be written as:
π½π(π)
(πππ) = |βππ π
|2
β« πΈ[πΌπ(π)]πππππ+
βπ
2
πππββπ
2
(4)
PU1
PU1
PU2 PUL
B1 βf B2 BL
PU1
PU1
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III. PROBLEM FORMULATION
The design goal is to find power value for each subcarrier, Pi (i=1, 2β¦ N) For given instantaneous
fading gain βππ π , given fading statistics of βπ
π π and the total transmit power budget PT. As such the
total transmission rate of the CR user, C is maximized while the probability that the interference
introduced to lth (l=1, 2β¦ L) PU band is kept below the threshold πΌπ‘βπ (l=1, 2β¦ L), respectively, with
the probability value Ξ± or above. Mathematically, the problem can be formulated as a constrained
optimization problem as follows.
πΆ = β πππ2 (1 +|βπ
π π |2ππ
π2 + β π½π(π)πΏ
π=1
)
π
π=1
πππππ₯ (5)
Subject to:
ππ (β πΌπ(π)
π
π=1
(πππ , ππ) β€ πΌπ‘β(π)
) β₯ π, βπ, (6)
ππ β₯ 0, βπ, (7)
β ππ
π
π=1
β€ ππ (8)
Where Pr. denotes the probability. Now the probabilistic interference constraint in Eq. (6) can be
written as.
ππ (|βππ π
|2
β πΎπ(π)
ππ β€ πΌπ‘β(π)
π
π=π
) β₯ π, βπ, (9)
Where πΎπ(π)
= ππ β« (π ππππππ ππππ
)2πππππ+π΅π/2
πππβπ΅π/2. Since |βπ
π π|is assumed to be Rayleight distributed with known
parameterππ, the distribution of |βππ π
|2 corresponds to an exponential distribution with the
parameterππ2. The Eq.(9) can be evaluated in closed form for the Rayleigh fading case as follows
1 β πβ
πΌπ‘β(π)
2ππ2 β πππΎ
π(π)π
π=1
,
β₯ π, βπ, (10)
After some mathematical manipulations, Eq.(10) can be written as
β ππ
π
π=1
πΎ(π) β€πΌπ‘β
(π)
2ππ2(β ln(1 β π))
, βπ (11)
The optimal power of the ith subcarrier is given by:
ππβ = [π€π β
π2 + β π½π(π)πΏ
π=1
|βππ π |
2 ] βπ, (12)
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Where π€π = 1
π½+β πΎππΎπ(π)πΏ
π=1
and π½ and πΎπ are deterministic Lagrange parameters.
IV. WATER-FILLING AND UNIFROM POWER LOADING SCHEMES
In this section, we proposed a water-filling algorithm and also describe classical algorithm namely
uniform power loading algorithm which are used for conventional OFDM system.
4.1 Water-Filling Loading Algorithm
In water-Filling algorithm, which is optimal power allocation algorithm in conventional OFDM
system, we use the total power allocation by uniform loading as the power constraint. The allocated
power in the ith subcarrier because of the πth subcarrier because of the πth interference constraint is
written as
ππ(π)
= π πΎπ(π)β , βπ, (13)
By using Eq.(11), after nulling (2L-1) subcarriers P can calculated by assuming strict equality in the
equality in the lth interference in Eq. (11). Using Eq. (13), this equality constraint can be written as
β πππΎπ(π)
π
π=1
=πΌπ‘β
(π)
2ππ2(β ln(1 β π))
(14)
π β (π
πΏ,π
πΏ+1,β¦.,π)
i=1
We can derive
π =πΌπ‘β
(π)
2(π β 2πΏ + 1)ππ2(β ln(1 β π))
(15)
Through Eq. (4.1) and (4.3), we can get ππ(π)
as following
ππ(π)
=πΌπ‘β
(π)
2πΎπ(π)(π β 2πΏ + 1)ππ
2(β ln(1 β π)) (16)
Now we need to calculate power valuesππ(πΏ+1)
due to the totalpower constraint. In order to meet the
total power constraint, we use the standard water-filling algorithm to distributetotal power ππamong
π CR subcarriers. According to the water-filling algorithm with a total power constraint ππ, the
power values can be written as
ππ(πΏ+1)
= πππ₯ {0,1
πΌβ
π2 + β π½π(π)πΏ
π=1
|βππ π |
2 } βπ, (18)
Where the Lagrange constant πΌ can be calculated from the following eqution
β πππ₯ {0,1
πΌβ
π2 + β π½π(π)πΏ
π=1
|βππ π |
2 } = ππ (19)
π
π=1
International Journal of Recent Trends in Engineering & Research (IJRTER) Volume 02, Issue 06; June - 2016 [ISSN: 2455-1457]
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i
The power value for πth subcarrier, denoted by ππ(ππΉ)
, are obtained using the standard water-filling
algorithmas mentioned in Eq (18) and (19) considering the total power constraint equal to the total
power allocated by uniform loading algorithm. The power values will satisfy the total power
constraint given in Eq. (8) however it is checked that if the power values satisfy the interference
constraints specified in Eq. (11). If a particular interference constraint is not satisfied, the power
value in each subcarrier ππ(ππΉ)
is reduced such that the all interference constraints are satisfied. Also,
if none of these interference constraints is met strictly, the power value ππ(ππΉ)
is increased until one
of these interference constraints is met strictly.
4.2 Uniform Loading Algorithm
In uniform power loading algorithm, which is used in the Conventional OFDM systems due to its
reduced complexity, equal amount of power is allocated in each subcarrier such that all πΏ
+1constraints in Eqs.(8) And (11) can be satisfied. By assuming equal power in each subcarrier and
solving Eq. (11) to satisfy the strict equality on πth interference constraint (πΌπ‘β(π)
), the corresponding
power for πth subcarrier can be written as
ππ(π)
=πΌπ‘β
(π)
2 β .ππ=1 πΎπ
(π)πππ
2lπ1
(1βπ)
, βπ. (20)
The power allocation for constraint in Eq. (8) can be written as
ππ(πΏ+1)
= ππ π.β (21)
The final power allocation to each subcarrier is done according
πππ = πππ{ππ
(1), ππ
(2), β¦ β¦ ππ
(πΏ+1)} βπ (22)
It should be noted that at least one of these πΏ+1constraints will be met strictly and hence, scaling of
power value is not required.
V SIMULATION RESULTS
In this section we present simulation results where we assume that there are three PU bands (L=3),
and there are twelve OFDM subcarriers (N = 12) for the CR user. The values of Ts , βf, B1, B2, and
B3 have been assigned to be 4 seconds, 0.3125 MHz, 1MHz, 2 MHz, and 5 MHz, respectively.
AWGN variance, (Ο2) is assumed to be equal to10β8W and the channel fading gains are assumed to
follow Rayleigh distribution. The average channel power gains for |βππ π |2, |β1
π π|
2, |β2
π π|
2 and
|β3π π
|2are assumed to be -10 dB, -5 dB, -7 dB, and -10 dB, respectively. The values of J (l) are
generated randomly with an average value of 1Γ10β6W. The values of πΌπ‘β(1)
, and πΌπ‘β(3)
have been
assumed to be 1Γ10β6W, and 5Γ10β6W , respectively.
In Figure 5.1, we plot the achievable maximum transmission rate for the CR user versus the total
power budget for various algorithms. The value of πΌπ‘β(2)
has been fixed to 2Γ10β6W, and the value of
Ξ± has been considered to be 0.95.The second curve is made by water-filling method; the lowest curve
is made by using uniform power allocation method. From this figure, we observe that the proposed
water-filling algorithm is able to achieve higher transmission rate for a given power budget than. It
should be noted that as we increase the power budget for CR user, the interference constraint
becomes dominant and the transmission rate of CR user does not increase as the power budget
International Journal of Recent Trends in Engineering & Research (IJRTER) Volume 02, Issue 06; June - 2016 [ISSN: 2455-1457]
@IJRTER-2016, All Rights Reserved 435
increases. This is expected as in this region the CR system operates in an interference limited
scenario.
Figure 5.1: Maximum transmitted data rate vs. power budget (Total Power Constraint) for CR.
In Figure 5.2, we plot the achievable transmission data rate for the CR user versus interference
threshold for second PU band, (πΌπ‘β(2)
) for all the algorithms under consideration. The value of total
transmits power, PT has been assumed to be 5Γ10-4W. Again, we observe that the proposed
suboptimal algorithm achieves highest transmission rate over other algorithms. Further, water-filling
algorithm achieves higher transmission rate than the uniform algorithm. The transmission rate
versus interference threshold curve saturates after a certain value of (πΌπ‘β(2)
)). The reason is that
although (πΌπ‘β(2)
) is relaxed by increasing its value, other constraints ((πΌπ‘β(1)
), ( πΌπ‘β(3)
), and PT) becomes
dominant.
Figure 5. 2: Maximum transmitted data rate vs. Interference threshold for 2nd PU band, π°ππ
(π)
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In Figure 5.3, we plot achievable transmission rate for the CR user versus probability Ξ±, the values of
PT , and (πΌπ‘β(2)
), is assumed to be 5Γ10β4 W , and 2Γ10β6W , respectively.
Figure 5.3: Transmission rate of the CR user vs. probability, Ξ±
In Figure 5.4, we plot the achievable maximum transmission rate for the CR user versus individual
power constraints for various algorithms. The value of πΌπ‘β(2)
has been fixed to 2Γ10β6W, the total
power PT has been fixed to be 5 Γ 10β4W, and the value of Ξ± has been considered to be 0.95. From
this figure, we observe that the proposed water-Filling algorithm is able to achieve higher
transmission rate for a given power budget than the Uniform algorithm.
Figure 5.4: Maximum transmission rate for the CR user Vs individual power constraints.
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VI. CONCLUSION
In this letter, We have developed a Water-filling allocation algorithm for orthogonal frequency
division multiplexing (OFDM) based cognitive radio CR system As such the transmission rate of the
cognitive radio user is maximized for given power budget and different probabilistic interference
constraints imposed by different PU receiver. The result has shown that our proposed Water-filling
allocation can achieve significantly higher transmission rate for CR user compared to the classical
power allocation algorithm namely the Uniform power allocation algorithm that are used for
conventional OFDN-based system
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