Wavelet Packet Based Cognitive UWB System with
Adaptive Spectrum and Power Allocation
Haleh Hosseini, Norsheila Fisal, and Sharifah Kamilah Syed-Yusof
TRG LAB,Electrical Engineering Department, Universiti Teknologi Malaysia, Malaysia
Emails: [email protected], [email protected], [email protected]
Abstract—Ultra wideband (UWB) is a promising technology for
future short and medium range wireless communication
networks with a variety of throughput options. However, wide
bandwidth of UWB causes mutual interference with other
primary users. In this paper, cognitive features are considered
for UWB to mitigate the interference. Spectrum and power
allocation is developed to adapt the transmission according to the
sensed condition from wavelet spectrum sensing and channel
estimator. In addition, an efficient pilot pattern strategy is
employed in the channel estimation to compensate the
impairment from interfering signal. The optimum power
allocation is derived by Lagrange multiplier method to minimize
the BER at the constraint of UWB power limit. As a benchmark,
the proposed system is compared to the conventional FFT based
system in UWB channel models using MATLAB simulation. The
numerical results verify that the proposed system outperforms
the traditional system with various metrics of performance
analysis.
Keywords- cognitive radio, power allocation, spectrum sensing,
ultra wideband (UWB), wavelet packet transform (WPT)
I.
INTRODUCTION
Ultra wideband (UWB) radio has an inherent potential to
fulfill the demand of high data rate wireless links with low
power and low cost [1]. According to IEEE 802.15.3a physical
layers standard [2], the entire bandwidth of UWB following the
FCC regulation, is allocated from 3.1 to 10.6
for
unlicensed usage, providing data rates from 110
at the
distance of 10 , to 480
at 2 . UWB system is
restricted to very low radiation power with a maximum mean
PSD of -41.3
/
. However, the mitigation of the NBI
impact is an important challenging issue of UWB system
implementation. Within UWB spectrum bandwidth, few
possible interfering primary systems are considered as
narrowband interference (NBI) including IEEE 802.11a
wireless local area network (WLAN) [3].Wavelet technology is
a significant candidate for UWB communication to suppress
the interference and offer the better utilization of spectrum
[4],[5]. Furthermore, cognitive UWB accommodates the
demand for higher capacity and data rates with new ways of
exploiting the available radio spectrum [6].
In this paper, an adaptive spectrum and power allocation is
proposed for cognitive UWB system to overcome the
drawbacks of narrowband interference that may occur in UWB
system. The transceiver is equipped with WPMCM, wavelet
spectrum sensing, and spectrum and power allocation
mechanism. Unoccupied spectrum bands are sensed and
determined for data transmission, and an efficient power
control is performed.
The rest of the paper is organized as follows. Section II,
provides the system model. The proposed wavelet based
cognitive UWB system is elaborated in Section III. Section IV
explains the development of wavelet spectrum sensing. Section
V highlights spectrum and power allocation. Simulation and
discussion are presented in Section VI. Then, the paper is
concluded in Section VII.
II.
SYSTEM MODEL
For the proposed cognitive system, we assume that the
primary and the secondary users are subjected to mutual
interference when they are geographically located in a certain
distance apart from each other, and simultaneously,
communicate to their receivers. Figure 1 shows the cognitive
, cognitive receiver,
, primary transmitter,
transmitter,
, and primary receiver,
. The transmission signals are
shown with solid lines, and the interference signals with dashed
lines. The radius of the cognitive system coverage area is
denoted as , and for primary system is denoted as .
Figure 1. Cognitive and primary transmission scenario
In this scenario, the cognitive UWB radio system needs to
keep its power under a limited value which is accepted by
licensed system, and at the same time mitigates the interference
from the primary system. The received signal at the cognitive
user, , is used by wavelet spectrum sensing for decision
making. During the sensing interval, the secondary transmitter
is off and sensing problem at the receiver is analyzed as a
binary hypothesis testing form, in which ℋ shows the absence
of primary user, and ℋ shows the presence of primary user.
Denote that is assumed to be the received SNR of primary
user under hypothesis ℋ , while !#" represents the variance of
primary user’s signal, and $#% stands for variance of noise. We
consider the test statistic as ( ), which is defined as:
( )=
)
#
∑)
/0 | [-]| ,
For the test threshold λ, the decision rule is defined as:
6
( )5ℋ
3ℋ4 7,
(1)
Receiver
Estimated
Message
WPMCM
(IWPT)
@ABC
8:; = 8 ( ( ) > 7|ℋ =) = > ? D ⁄ % G,
D
BC% √)
@A(HI )BC%
G,
89 = 8 ( ( ) > 7|ℋ =) = > ?
D ⁄
D
(HI )BC% √)
Figure 3 represents the flow diagram of the approach to the
design of the proposed system. The received signal, after being
converted to digital form, is processed by the wavelet spectrum
sensing, to detect primary users’ energy and determine the
potential unoccupied subcarriers to be used within the spectrum
band at a certain period of the time. In parallel, channel
estimator sends the UWB channel characteristics to the
spectrum and power allocation. The spectrum and power
allocation computes the power levels to be allocated to the
selected subcarriers according to their variant levels of fading.
Once the power level for the subcarriers has been determined,
this information is sent to the WPMCM for data processing and
transmission.
Channel
Estimation
Wavelet
Spectrum
Sensing
Refined information on
CSI & spectrum holes
Figure 2. Proposed Wavelet Based Cognitive UWB
Start
Signal
Digitization
(4)
The structure of proposed Wavelet Based Cognitive UWB
is illustrated in Figure 2. The wavelet spectrum sensing part
senses the UWB radio medium for the detection of unused
spectrum bands in the form of subcarriers, or possible
interference mitigation. Furthermore, appropriate allocated
power level of subcarriers is determined based on channel
estimation outcome. A limited feedback sends the status of the
spectrum band and power level to the spectrum and power
allocation part. Once the spectrum and power allocation
receives the information from the wavelet spectrum sensing
and channel estimation, it decides on allocating the appropriate
subcarriers and associated power levels in order to maximize
the spectrum usage, and minimize the interference in the UWB
radio medium.
WPMCM
(WPT)
Limited Feedback
(3)
PROPOSED WAVELET BASED COGNITIVE UWB
Forward
Link
Spectrum
& Power
Allocation
(2)
The metrics of probability of detection, 89 , and probability
of false alarm, 8:; , are derived to be employed for performance
evaluation. When both noise and signal are circularly
symmetric and complex Gaussian (CSCG) variables, the
corresponding probabilities of false alarm and detection in the
threshold test are given as [7]:
III.
Transmitter
Data
Wavelet Spectrum
Sensing
Channel
Estimation
Is the subcarrier
occupied?
yes
no
Spectrum and Power
Allocation
WPMCM
Data
Transmission
End
Figure 3.The flowchart of the proposed System
IV.
WAVELET SPECTRUM SENSING
The aim of wavelet spectrum sensing part is to develop an
adaptive cognitive system for better utilization of unoccupied
UWB spectrum band. The wavelet spectrum sensing uses
energy detection to determine the occupancy of the subcarriers.
The proposed wavelet spectrum sensing consists of a J level
WPT decomposition block, the energy detection that measures
the coefficient energy of each subcarrier, E-FCME algorithm,
and subcarriers deactivation vector generator
The embedded WPT block in wavelet spectrum sensing,
decomposes the received signal spectrum into different
subcarriers, and provides time-frequency resolution. Energy
detection is used to determine the energy levels of the
subcarriers at WPT coefficients. The magnitude squared of the
coefficients represents interference energy at a specified
frequency or subcarrier. Due to Parseval’s theorem, the power
of the received signal [-] is computed as:
8K% =
)
#
. ∑)
/0 | [-]| ,
(5)
Decision for the usage of subcarriers is made by a decision
rule which compares the energy level of subcarriers with the
threshold value. The adaptive thresholding, is made of FCME
algorithm which has been enhanced with double thresholding
algorithm, so called E-FCME [7]. Double thresholding detects
the number and location of concentrated interfering signals as
clusters. When the sample with maximum power is more than
the upper threshold; cluster is accepted as primary signal
interference. Then, a deactivation vector is produced to feed the
spectrum and power allocation. Normalized mean square error
(NMSE) for the estimated bandwidth, L , of detected signal is
defined as :
M NO = OP L − P ⁄
#
V.
#
,
(6)
considered for channel estimation. In block type pattern, a
WPMCM symbol of pilots is generated within a certain
number of data symbols. In addition, virtual pilots are derived
to compensate the effect of interference on the received pilot
values.
The virtual pilots are estimated by interpolation between
the neighbor pilots of the interfered positions. Linear
interpolation is applied to obtain the estimate of channel in
virtual pilot locations. The corresponding procedure is as
follows.
RS′,T′ = 1/2[ RS′ A
,T ′
+ RS′ I
,T ′ ],
(7)
where ′ is the virtual pilot position in the frequency
domain, and k ′ denotes its position in the time domain.
RSV A ,T V and RSV I ,T V define the estimated channel impulse
responses for previous and further subcarrier positions,
respectively. Power determination procedure is highlighted in
the next subsection.
SPECTRUM AND POWER ALLOCATION
In this section, the proposed spectrum and power
allocation is elaborated which dynamically adjusts the transmit
power and positions of subcarriers to be used (Figure 4). In
order to optimize the subcarriers power and minimize the BER,
both the limitation on total UWB power, and the fading nature
of UWB channel across the subcarriers have been considered.
A. Spectrum Identification
Spectrum identification block, (Figure 4), is composed of
two main parts as active tones counting, and null tones
identification. This block receives the information of occupied
subcarriers by wavelet spectrum sensing. The information is
applied to count the number and identify the position of
unoccupied subcarriers for data transmission. The active tones
represent idle subcarriers, and null tones show the location of
occupied subcarriers. The selection of subcarrier positions is
performed using the deactivation vector which is provided by
wavelet spectrum sensing. Following the subcarrier selection,
the number of active tones is counted, and the positions of null
tones are identified. This information, joint with determined
power levels, provides the possibility to adapt the bandwidth
and power of subcarriers by spectrum shaping.
B. ChannelEestimation and Virtual Pilots
Channel estimation is the essential part to provide the
required information for data detection and power allocation
procedure. The UWB channel is assumed to be static during a
packet time. Figure 5 illustrates the channel estimation block
diagram with its connections to wavelet spectrum sensing and
power determination. Once the primary signal is received by
the wavelet spectrum sensing, a deactivation vector for
occupied subcarriers is produced. This vector is used for pilot
arrangement of channel estimator, and also for spectrum and
power allocation mechanism.
The determination of virtual pilot positions and channel
impulse response estimation are the functions of the channel
estimation part. The estimated CSI is sent to the spectrum and
power allocation for power determination. Due to the slow
varying UWB channel, block type pilot arrangement is
Information from
wavelet spectrum
sensing
CSI from
channel
estimator
Spectrum and Power Allocation
Spectrum
Identification
Power
Determination
Spectrum Shaping
WPMCM
(IWPT)
Figure 4. Proposed spectrum and power allocation mechanism
Information from
wavelet spectrum
sensing
Channel Estimation
Introduction of
Virtual pilots by
interpolation
Channel
Response
Estimator
Impulse
Response
Power Determination
Figure 5. Channel estimation method
C. PowerDetermination
The aim of the proposed power allocation is to avoid the
NBI introduced by primary user, and to minimize the BER.
The cost function of the Lagrange multiplier is applied to
minimize the BER at the receiver [8]. The BER is defined
under a power constraint, 8WXW;Y , which is the maximum UWB
allowable transmitted power, denoted as:
o WXW;Y = ∑q q ,
n8
m WXW;Y = ∑q q 8q ,
vf |]f |D
)6
w,
=
(9)
where 8q is the assigned power for e -th subcarrier, q is the
channel impulse response for e -th subcarrier, q is the bit
number of e-th subcarrier, WXW;Y is total bit number, O q is
BER in the e-th subcarrier, BER is total BER performance in
WPMCM symbol. The single side noise power spectrum
density has been showed as M . The Lagrange multiplier cost
function for optimum solution is defined as:
J=
∑f [f .[ghf
[ijikl
+ {(∑q
q 8q
− 8WXW;Y )
where { is the Lagrange factor. From the constraint equations,
the solution of our power allocation is defined as:
8q =
vijikl
|]f |D ∑f
|f
D
P}f P
,
(10)
(11)
D. Spectrum Shaping
Spectrum shaping applies the obtained information from
the spectrum identification and power determination to adapt
the subcarriers for data transmission. Figure 6 addresses the
block diagram of the proposed spectrum shaping. The
information of the occupied and idle tones is sent to spectrum
shaping to adjust the size of parallel to serial converter
according to the number of active tones. Then, the data is
distributed among the active tones with the assigned power,
while leaving the null positions of the total subcarriers. The
output is derived for modulation by IWPT and transmitted
through the UWB channel.
VI.
SIMULATION RESULTS
In this section, the developed system is evaluated by
MATLAB simulation. We consider 128 subcarriers, generate
primary signal with varying bandwidths and assume that the
interference information provided by sensing part is available
at both cognitive transmitter and receiver. The detected
interfering signal will be analyzed by E-FCME algorithm to
identify thresholds and determine deactivation subcarrier
vector. When cognitive user utilizes the bands in the adjacent
or in between the available subcarriers of primary user bands,
the subcarriers are adaptively deactivated in occupied spectrum
locations, and power loading is applied. The deactivation
vector is estimated by sensing mechanism and applied for
power allocation. The number of bits is set according to the
QPSK modulation, and 8q is calculated by (11).
Unused outputs due to
tone nulling
IWPT
#
Mapping
= r cs tu
Spectrum Shaping
…
q
Interleaved
input bit
stream
Power
Determination
…
p O
n
[ijikl
Spectrum
Identification
Adaptive power distribution
9[S
S/P (Adaptive size)
8WXW;Y ( ) = −41.3 Z
_ + 10`ab (c] − cd ) (8)
\]^
where c] and cd are the higher and lower frequency of the
operating bandwidth in MHz. Deactivation vector is applied for
power nulling of the subcarriers which are occupied by the
primary user. Channel gains of the remained subcarriers are
employed to minimize the BER, and the optimization problem
∑ [ .[ghf
is formulated as e- O = f f
, constraints to:
Serial
output
symbol
s
Nulled
tones
Figure 6. Proposed Spectrum Shaping
Figure 7 and Figure 9 evaluate the performance of the
developed wavelet spectrum sensing part. Figure 7 illustrates
the NMSE of bandwidth estimation versus INR. NMSE is the
metric for measuring the normalized error of bandwidth
estimation using (6). Under the bandwidth of 4% for primary
user, we observe 4
improvement at M NO = 0.1 for the
proposed method comparing to the conventional method. At
the bandwidth of 24% , NMSE is near to zero for a wide range
of SNR, comparing to the conventional method which its
minimum is . 06. Also, the improvement of 18
has been
achieved at M NO = 0.1. Figure 8 investigates the probability
of false alarm and the probability of detection for the proposed
and conventional spectrum sensing procedures. These graphs
show that the new method successfully controls the false alarm
rate near to 0.02, and significantly enhances the detection rate
around 1 for varying INR from −10
to +10 .
The performance of the proposed spectrum and power
allocation in terms of BER for UWB channel models with
perfect channel estimation is depicted in Figure 9. We assume
that, cognitive users function in a packet by packet manner,
with a perfect channel estimation. The results prove significant
BER reduction for the proposed scheme comparing to the
system without spectrum and power allocation. In the case of
CM1, at NM = 32
, we achieve O = 10A• , while
without spectrum and power allocation this amount is higher
about O = 10A . Also in the case of CM2, with NM =
30 , the BER reduces to 5 ∗ 10A‚ , while without spectrum
and power allocation O = 2 ∗ 10Aƒ . This comparison for
CM3, at = 30
, shows O = 10A• , which is about 10A
without the proposed scheme. The last comparison for CM4, at
NM = 30 , presents
O = 10A# , which without the
proposed scheme increases to 5.6 ∗ 10A• .
VII. CONCLUSION
In this paper, adaptive spectrum and power allocation has
been proposed for wavelet packet based cognitive UWB
system to mitigate interference and enhance system
performance. Significant improvements have been achieved
based on the metrics such as spectrum leakage reduction,
accurate primary spectrum detection, and interference
mitigation compared to the FFT based system. The simulation
results showed that the new mechanism has reduced errors with
precise decision over various primary and cognitive channel
fading and noise conditions. As a future work, channel
modeling in wavelet domain can be applied for this wavelet
based systems. Wavelet domain channel modeling has accurate
characterization of time varying and frequency selective
multipath fading channels. The benefit of sparse UWB channel
in wavelet domain leads to the reduction of estimated
coefficients.
10
10
BER
10
10
10
10
10
0
-50
-100
CM1-Without spectrum and power allocation
CM1-Proposed spectrum and power allocation
CM2-Without spectrum and power allocation
CM2-Proposed spectrum and power allocation
CM3-Without spectrum and power allocation
CM3-Proposed spectrum and power allocation
CM4- Without spectrum and power allocation
CM4-Proposed spectrum and power allocation
-150
-200
-250
-300
0
5
10
15
20
SNR
25
30
35
40
Figure 9. BER comparison for proposed spectrum and power allocation
mechanism
ACKNOWLEDGMENT
1
This work was supported by research management unit of
University Technology Malaysia (UTM).
FFT based sensing for NBI BW=24%
0.9
Proposed sensing for NBI BW=24%
FFT based sensing for NBI BW=4%
0.8
Proposed sensing for NBI BW=4%
0.7
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NMSE
0.6
[1]
0.5
0.4
0.3
[2]
0.2
[3]
0.1
0
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-8
-6
-4
-2
0
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Figures 7. NMSE of NBI bandwidth estimation
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[5]
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0.8
Probability(Pd/Pf)
0.7
Pd-Theory
Pf- Theory
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