Communications and Network, 2009, 46-50
doi:10.4236/cn.2009.11007 Published Online August 2009 (http://www.scirp.org/journal/cn)
Copyright © 2009 SciRes CN
A Novel Scheme with Adaptive Sampling for Better
Spectrum Utilization in Cognitive Radios
Qun PAN, Xin ZHANG, Ruiming ZHENG, Yongyu CHANG, Dacheng YANG
Beijing University of Posts and Telecommunications, Beijing, China
Abstract: In cognitive radio (CR) networks, cognitive users need continuously monitor spectrum to decrease
or avoid interference to primary users, yet attain a reasonable throughput. In this paper, we exploit a scheme
to dynamically change the detection time duration to gain maximum throughput. Meanwhile the average de-
tection time is kept as small as possible. The advantages of our approaches are approved by the deductions.
Also the simulation results enable us to recognize the improved performance of our scheme over those ones
with fixed detection time assignment.
Keywords: cognitive radio, cooperative sensing, average detection time, channel utilization ratio
With the rapid development of wireless communication
technology, the demand of spectrum resource is growing.
The spectrum resource becomes rare due to the popular-
ity of various new wireless applications. However, the
recent survey shows a low spectral efficiency in the li-
censed band. The spectral holes in licensed band can be
sensed and used to transmit information by employing
cognitive radios technology . The extra system
throughput can be provided by cognitive radios without
additional spectrum resource allocation. So the Federal
Communications Commission (FCC) has recommended
cognitive radio (CR) as the candidate for enhancing
overall spectral efficiency. Cognitive radio  enables
CR users (unlicensed users) to obtain the frequency
bands with minimal interference to active primary users
(licensed users) when they observe the bands allocated to
the primary user are vacant and meanwhile detect the
continuous spectrum sensing in CR networks. Therefore,
as the heart of the cognitive radio techniques, the spec-
trum sensing is so critical that it determines the through-
put and the agility of the CR networks.
One of the challenges of CR system’s application is
how to detect the primary user’s signal quickly and cor-
rectly in order to avoid interference to the licensed net-
work. The other is how to fully occupy the spectrum re-
sources acquired by CR network. In this paper, a novel
scheme is proposed to use the spectrum holes more effi-
ciently. Meanwhile the interference to licensed network
is controlled within an acceptable range. Our proposed
scheme could increase spectrum efficiency in full extent
in an applicable CR network. We consider the detection
performance in the more practical scenario based on
IEEE 802.22 WRAN system which uses cognitive radio
technology as its key feature. The fast sensing and fine
sensing process which utilize energy detection technol-
ogy and feature detection technology respectively are
both considered in our study. The cooperative sensing
technology which can increase the sensing performance
significantly is also involved in. The approaches of co-
operative sensing technology [2-5] all use diversity tech-
nologies to improve effective SNR for essential.
The rest of this paper is written as follows. In Section
2, we model the conventional detection scheme in ma-
thematic way. In Section 3, we analyze the influence to
channel utilization ratio made by time scheduling, and
we propose a novel scheme of maximizing the channel
utilization ratio. At last, we will conclude our study in
2. System Model
A practical sensing scheme is shown in Figure 1 .
There are two types of detection in the figure: the fast
sensing (energy detection) and the fine sensing (feature
detection). During these two types of detection time, all
the CR users should cease their transmission in order to
achieve high definition detection. Therefore, these detec-
tion periods are also called quiet periods.
The fast sensing processes are triggered periodically,
and all the detection information from all CR users is
summarized to give a final judgment. If the detection
indicates that no primary user exists, the CR users will
wait for next fast sensing process. On the other hand, the
fine sensing process will be triggered. Usually, the time
duration of fine sensing is much longer than that of fast
sensing. Also the veracity of fine sensing is much better.
Without loss of generality we assume the veracity of fine
sensing is 100% all through the rest of the paper.
A NOVEL SCHEME WITH ADAPTIVE SAMPLING FOR BETTER SPECTRUM UTILIZATION IN COGNITIVE RADIOS 47
2.1 Modeling Individual Detection
The binary hypothesis model is adopted in the study of
where is the receive signal by CR user at time t.
is the Additive White Gaussian Noise (AWGN),
and we assume nt follows a standard normal distribu-
tion, which means the expectation is 0 and the variance is
t is the pary user’s transmitting signal. H0 de-
notes the null hypothesis, which means there is no pri-
mary user signal in a certain spectrum band. H1 is the
alternative hypothesis, which indicates that there exists
primary user signal in that band. h is the channel coeffi-
First, we consider local spectrum sensing (energy de-
tection) at an individual CR user. We assume each en-
ergy detection interval contains M samples. j denotes the
sampling index. Then the statistic characteristic of the
i-th CR user is given by:
We can see is the sum of
ssian random variables’ square, whose deviation is unit.
So follows a central chi-square distribution with
degree freedom when H0 becomes true, or else fol-
lows a non-central chi-square distribution.
We defined the as the instantaneous SNR of the
i-th CR user.
The non-centrality parameter .
If M is large enough, the will approximately fol-
low Gaussian distribution according to the Central Limit
Theorem as follows:
2.2 Modeling Cooperative Detection
Individual detection is not sufficient in a practical sce-
nario due to the lower receive SNR of CR users. Hence,
cooperative technology is involved in spectrum sensing
as in [2-5]. We adopt a linear combine scheme which
sums the different CR users’ samples with weights in
Channel Detection Time
Fast sensingFine sensing
Transmission Fine Sensing
Figure 1. Detection process in cognitive radio network
opyright © 2009 SciRes CN
48 A NOVEL SCHEME WITH ADAPTIVE SAMPLING FOR BETTER SPECTRUM UTILIZATION IN COGNITIVE RADIOS
our cooperative sensing process. The weight coefficient
corresponding to the i-th CR user is , then the
weighted summation is given by:
Let be the decision threshold of energy detection.
Therefore the probability of false alarm
P, and that of
So the decision threshold and the optimal weight coef-
ficient can be given by (10) and (11) respectively :
3. System Performance Analysis and
3.1 Detection Performance
The performance of the cooperative sensing is evaluated
by receiver operating characteristics (ROC) which is
composed of probability of false alarm and that of detec-
tion in plenty of bibliography . But these two pa-
rameters can only indicate the system performance under
some special conditions. Our analysis is shown as fol-
Excellent CR system should exit when the primary us-
er is working on the target channel band, besides suffi-
ciently use the spectrum when primary user is idle.
Therefore the two objectives in CR system design are
interference avoidance to primary user and full utilization
of spectrum holes respectively. In order to decrease or
avoid interference to the primary user, the detection sen-
sitivity must be high enough for CR system, i.e. the exis-
tence of primary user’s signal must be discovered as
quickly as possible. If the detection process finds holes in
spectrum, the time duration for detection must be as short
as possible to leave more resource for data transmission.
We define channel utilization ratio (CUR) as the pro-
portion of time used for data transmission accounting for
the whole time when the primary user is idle .
x indicate the probabilities of energy detec-
tion, transmission and feature detection. The Markov
state transition diagram is shown in Figure 2. So the cor-
responding Equation (12) is demonstrated as below.
So the CUR can be expressed by (13)
T are the time duration of the
three states. Let
TTT=+. We define the sampling
proportion (SP) as E
TTa=. So (13) can be trans-
formed to (14).
TT PTha=-+ (14)
From (14) we can see it is the CUR that not only de-
P, but also T and α.
The average detection time (ADT)  indicates
the average time interval from the primary user’s pres-
ence until it is found by CR users. We assume the prob-
ability of detection in each sensing period is equal to
when the primary user is present. The final H1 decision
must be after feature detection, so the ADT required by
CR user to detect the primary user can be given by (16):
Figure 2. State transition diagram of CR system when no
primary user is active
opyright © 2009 SciRes CN
A NOVEL SCHEME WITH ADAPTIVE SAMPLING FOR BETTER SPECTRUM UTILIZATION IN COGNITIVE RADIOS 49
Copyright © 2009 SciRes CN
3.2 Maximizing the Channel Utilization Ratio
The interference provided by CR network should be
firstly small enough, so that primary user can work nor-
mally. In order to limit the interference to primary user,
the ADT should have a maximum target. So we will dis-
cuss the scheme which can maximize the CUR, and at
the same time the target of ADT can be realized.
The sampling number E
WT WTa== according
to the Nyquist sampling Theorem, where W is the sensing
bandwidth. (17) is acquired in terms of (9) and (16):
The CUR can be given by (18) which is combine
4) and (17).
(18) shows that there are too many parameters w
uld influence the CUR. The bandwidth W is fixed
and it depends on the spectrum specificationf primary
network. The SNR i
G of each CR user depends on the
radio scenario and the receive equipment of CR user. The
time duration of feature detection
T depends on the
character of primary user’s signal. Conventionally the
frequency of the fast detection 1T is predefined.
Therefore, the only parameter which can be adjusted is
the , i.e. the time duration of each fast sensing can be
changed to optimize the CUR.
Intuitively if the sensing duration is too short, and si-
multaneously the ADT can be guaranteed, the probability
of false alarm will be raised. As a result, the unnecessary
feature detection will be frequently triggered. The unnec-
essary feature detection will waste a large amount of time
which can be used for information transmission. But if the
sensing duration is too long, the spectrum resources
would be wasted by fast sensing process. Hence, there
must be an optimal sensing duration which can maximize
the CUR. From all above, it is obvious that the optimal
scheme is to integrate information from all CR users, and
then calculate the optimal sensing duration to inform each
To prove the existence of optimal sensing duration, the
numerical simulation is brought about in our study. The
simulation parameters are shown in Table 1. .
In Figure 3 the maximum CUR can be achieved when
the sampling number M is at an appropriate value. How-
ever, the optimal sampling number is not fixed among
Table 1. The simulation parameters
Average Detection Time0.5s
Sensing Interval 10ms
Feature Detection Time 100ms
Instantaneous SNR -24dB, -20dB,-16dB, -12dB, -8dB
Sensing Bandwidth 6MHz
CR User Number 5
Channel Utilization Ratio
S NR= -24dB
S NR= -20dB
S NR= -16dB
S NR= -12dB
S NR= -8dB
Figure 3. The channel utilization ratio vs. sampling proportion
50 A NOVEL SCHEME WITH ADAPTIVE SAMPLING FOR BETTER SPECTRUM UTILIZATION IN COGNITIVE RADIOS
-24 -22 -20-18 -16 -14 -12 -10-8 -6-4
Channel Utilization Ratio
Conventional Scheme SP =0.1%
Conventional Scheme SP =4%
Conventional Scheme SP =16%
Figure 4. The channel utilization ratio vs. average SNR
ifferent SNR scenarios. Although the optimal sampling
At CR BS side:
e SNRs of all CR users.
culate the sampling
gy detection u
ast the sampling number M to all the CR users.
ber M to determine
number M cannot be calculated easily, the One-dimen-
sional Search Method can deal with the problem. It is clear
that in order to utilize the spectrum resource more effi-
ciently, the sampling number must be dynamically adjusted
according to the SNRs of CR users. Therefore, we propose
a novel sensing time schedule for CR system, which is
called dynamic adaptive sensing duration scheme:
1. Update th
2. Considering the target of ADT, cal
ber M which can maximize the CUR.
3. Calculate the threshold l of enersing
R User Side:
ampling number M. 1. Update the s
2. Use the updated sampling num
ng duration and send the sampling results back to BS.
In Figure 4 we compare our proposed scheme wit
on how to maximize the CUR
produce interference small enough to primary network.
We deduce the relationship between conventional ROC
software radios more personal. Personal Communications, IEEE,
ree conventional ones whose sampling numbers are
different from each other but not dynamically changed. It
is obvious that the conventional scheme can gain a high
CUR only when the SNRs are at an appropriate level.
However the proposed scheme can always maximize the
CUR, in other words the proposed scheme reaches the
upper bound of the CUR for detection in CR system.
We have focused and
 TAHERPOUR A, KENARI N M, and JAMSHIDI A. Efficient
cooperative spectrum sensing in cognitive radio networks. In
which is composed by Prob. of detection and that of false
alarm and our proposed ROC which contains CUR and
ADT. Our proposed scheme shows that the CUR can be
maximized by optimizing time duration of energy detec-
tions according to different SNR conditions.
1] MITOLA J and MAGUIRE G Q. Cognitive [
1999, 6: 13-18.
 GANESAN G and YE L. Cooperative spectrum sensing in
cognitive radio, Part I: Two user networks. IEEE Transactions on
Wireless Communications, 2007, 6: 2204-2213.
 UNNIKRISHNAN J and VEERAVALLI V V. Cooperative
spectrum sensing and detection for cognitive radio. In IEEE
Global Telecommunications Conference, GLOBECOM’07, 2007,
] ZHI Q, SHUGUANG C, and SAYED A H. Optimal linear
cooperation for spectrum sensing in cognitive radio networks.
IEEE Journal of Selected Topics in Signal Processing, 2008, 2:
 WEI Z, MALLIK R K, and LETAIEF K. B. Cooperative
spectrum sensing optimization in cognitive radio networks. In
IEEE International Conference on Communications, ICC’08,
 IEEE 802.22-06/0003r3. A PHY/MAC proposal for IEEE
802.22WRAN systems, Part 2: The cognitive MAC. Mar. 2006.
 MA J and LI Y G. Soft combination and detection for cooperative
spectrum sensing in cognitive radio networks. In Global
Telecommunications Conference, GLOBECOM’07, IEEE, 2007,
opyright © 2009 SciRes CN
A NOVEL SCHEME WITH ADAPTIVE SAMPLING FOR BETTER SPECTRUM UTILIZATION IN COGNITIVE RADIOS 51
IEEE 18th International Symposium on Personal, Indoor and
Mobile Radio Communications, PIMRC’07, 2007, 1-6.
 SOOK J W, J. GEUN D, H J AE, K. GWANGZEEN, and SUN S
M. An efficient quiet period management scheme for co
gnitive  GANESAN G, YE L, BING B, and LI S Q. Spatiotemporal
sensing in cognitive radio networks. IEEE Journal on Selected
Areas in Communications, 2008, 26: 5-12.
radio systems. IEEE Transactions on Wireless Communications,
2008, 7: 505-509.
 GANESAN G, LI Y, and LI S. Spatiotemporal sensing
incognitive radio networks. In IEEE 18th International
Symposium on Personal, Indoor and Mobile Radio Communi-
cations, PIMRC’07, 2007, 1-5.
opyright © 2009 SciRes CN