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Threshold optimization of a finite samplebased cognitive radio network using energy detector
EURASIP Journal on Wireless Communications and Networking volume 2013, Article number: 165 (2013)
Abstract
In this paper, we consider a cognitive radio network containing two cognitive radios (CRs) and one primary user. The CRs utilize finite number of received data samples for estimating the energy of the primary signals and forward these energy estimates to a fusion center (FC). The FC combines the energy estimates and utilizes a global threshold based on the exact knowledge of local thresholds of the CRs for determining the presence or absence of the primary signal. We propose selective and semiselective soft combining schemes for this setup. For the proposed schemes, we derive the total probability of error of detecting a spectrum hole. By minimizing the total probability of error in sensing a spectrum hole, we find optimized local and global thresholds. Moreover, we also discuss the optimization of conventional nonselective soft and 1bit hard combining schemes with multiple (equal to or more than two) collaborative CRs under the total probability of error minimization criterion. It is shown by simulations that the proposed selective soft combinationbased scheme significantly outperforms the conventional nonselective schemes based on soft combination and 1bit hard combination. Further, it is shown by simulation that the proposed selective soft combining scheme along with the total probability of error minimization criterion is able to properly utilize a spectrum hole with interference level less than the standard specified value.
1 Introduction
Due to the utilization of the radio electromagnetic spectrum up to saturation, there is scarcity of free radio spectrum [1, 2]. However, it is seen in practice that even the dedicated radio spectrum is not used efficiently. For example, in television broadcasting, the spectrum allocated is idle for considerable time. This free spectrum can be utilized for some other applications using a cognitive radio network [1, 2]. Hence, effective utilization of the spectrum plays an important role in today’s crowded spectrum environment. Whenever required, a licensed user (primary user) can use its licensed band, otherwise the spectrum can be used for secondary operations. A spectrum hole is said to exist when a particular band of frequencies is not used by the primary user. Searching for spectrum holes is termed as spectrum sensing[1]. Sensing of the spectrum holes is done by the cognitive radios (CRs) which are unlicensed users or secondary users [1, 2]. A CR is an intelligent wireless communication system that periodically monitors the radio spectrum and detects the occupancy of the different parts of the spectrum. The CR opportunistically communicates over spectrum holes without interfering with the primary user’s signals [3].
There are many challenges in detecting a spectrum hole. Few of these are fading, noise uncertainty, and shadowing. These effects may lead to significant interference to a licensed user. It is shown in the literature [4–8] that cooperation among the CRs can be utilized in order to overcome the problems of fading, noise uncertainty, and shadowing. An optimal linear cooperation framework of spectrum sensing for detecting the primary signals is proposed in [4]. The cooperative spectrum sensing in [4] is based on linear combination of local statistics from individual CRs. In [4], each CR uses an energy detector, which estimates the energy of the primary signal from the received data samples. These energy estimates are forwarded over noiseless channels to a fusion center (FC). The performance of the cognitive system is optimized using NeymanPearson (NP) criterion by assuming that large number of received data samples are used by each CR for energy estimation in [4]. In [7], a binary decisionbased cooperative spectrum sensing scheme is discussed for cognitive networks. Each CR takes a binary decision using energy detector about the presence or absence of the primary signal. The binary decisions are forwarded to the FC which combines them for taking final decision. The sum of probability of missed opportunity and probability of false alarm is minimized for obtaining an optimized threshold for the CRs. Since the hard decision is taken at the CRs in [7], the performance of this scheme is poorer than a soft decisionbased scheme [9]. In a soft decisionbased scheme, the CRs forward the energy estimates to the FC and FC combines them in order to take the decision about the presence of the primary signal. In [8], the setup of [7] is generalized to the scenario when the CRs collect very large number of data samples to take binary decisions. The optimized value of the local threshold in the CRs is numerically calculated by minimizing the total probability of error.
Energy detector is proposed for cognitive spectrum sensing because it needs no information about the primary signal and has lower complexity in realtime detection of spectrum hole. For the detection of unknown deterministic signals corrupted by the additive white Gaussian noise (AWGN), an energy detector is derived in [10]. Performance analysis of the energy detector for random signals is studied in [11]. The performance of the energy detector for unknown transmit signal in AWGN and fading environment is discussed in [12]. In [13], optimal soft combination scheme for received energy data is explained. The detail study regarding the performance of spectrum sensing in different scenario is done in [14–24].
In this paper, we consider a collaborative cognitive radio network which utilizes a finite number of samples for deciding the presence or absence of the primary signals. The cognitive network contains two secondary users and one FC. We consider a cognitive setup, where the CRs do not take decision about the spectrum hole based on their local thresholds. Nevertheless, they can update the local thresholds as per the channel between the primary user and the CRs. These local thresholds are conveyed to the FC and the FC updates its global threshold accordingly. The CRs forward their energy estimates to the FC over noiseless reporting channels and the FC takes decision about the spectrum hole based on the normalized global and local thresholds. In selective soft combining, the FC combines the energy estimates of both CRs for making a decision of the primary signal only if their energy estimates are above the local threshold.
Expressions of the probability of false alarm and the probability of missed opportunity for this collaborative system are derived for the proposed selective combing scheme. The global and local thresholds are optimized by minimizing the total probability of error in detection of a spectrum hole. The proposed scheme of selective soft combination significantly outperforms the conventional soft and 1bit hard combining schemes. It is also shown that the total probability of error minimization criterion is able to utilize a spectrum hole more efficiently as compared to the NP criterion while keeping the interference to the primary user (PU) within the desired limits for the signaltonoise ratio (SNR) of the PUCR link considered in simulations.
The rest of the paper is organized as follows. In Section 2, the system model of the cognitive radio network is explained. Performance analysis of the proposed scheme of selective soft combining and semiselective soft combining is discussed in Section 3. Optimization of the normalized global and local thresholds is also performed in this section.
Section 4 discusses optimization of nonselective soft combining and 1bit hard combining schemes for arbitrary number of CRs under the total probability of error minimization criterion. Numerical results are discussed in Section 5. In Section 6, some conclusions are drawn.
2 System model
We consider a cognitive network consisting of two secondary users S_{1} and S_{2}, one primary user and a fusion center FC, as shown in Figure 1. In this cognitive radio network, it is possible that the spectrum allocated for the PU is not utilized at some time; therefore, the secondary users monitor the spectrum continuously in order to make sure that detection of signals transmitted by the PU cannot be ignored at any instance of time. To enhance the reliability of spectrum usage, interference to the PU from secondary users should be kept very low [3]. In our analysis, we will focus on how to minimize the interference to the PU while minimizing the probability of error in detecting the spectrum hole such that the free spectrum can be efficiently utilized. It is assumed that only one PU is in operation with highest priority to use its allocated spectrum as compared to each secondary user. In the cognitive network considered in Figure 1, there exists two hypotheses at the kth time instant
where y_{ i } is the received signal in ith CR, i is the secondary user index (i = 1,2), s denotes the signal transmitted by the primary user with energy E_{ s }, ${v}_{i}\left(k\right)\sim \mathcal{C}\mathcal{N}(0,{\sigma}_{n}^{2})$ is circularly symmetrical complex additive white Gaussian noise (AWGN), and ${h}_{i}\left(k\right)\sim \mathcal{C}\mathcal{N}(0,{\sigma}_{h}^{2})$ is the complex normal circularly symmetrical channel gain between the PU and ith CR, implying Rayleigh fading. The variance of the signal received at each secondary user under hypothesis H_{0} is ${\sigma}_{n}^{2}$, while that under H_{1} is given by
We assume that the local threshold of detection for each secondary user is the same and is given as λ. This assumption is valid when both CRs exist at the same distance from the PU. CRs can update λ according to the SNR ratio of the links between the PU and the CRs. It is assumed that the value of λ is also known in the FC. We call λ as local threshold because of local spectrum sensing is performed at individual CR. As decision is taken globally in the FC, we refer λ_{0} by global threshold of detection in the FC. Energy estimates of each CR normalized with number of samples is termed as summary statistics in this paper. Each secondary user calculates a summary statistics u_{ i } over a finite detection interval of L samples as
The summary statistics are transmitted to the FC through a lossless control channel using orthogonal time slots or orthogonal frequency bands. The FC computes a global test statistics as
The characteristic function (c.f.) of U obtained from Eq. (3) in the case of hypothesis H_{0} is given as [25]
Similarly, from Eq. (3), the c.f. of U under hypothesis H_{1} will be [25]
By taking the inverse Fourier transform of Eqs. (5) and (6), the probability density functions (p.d.f.s) of U under hypotheses H_{0} and H_{1} can be obtained as
and
respectively, where Γ (a) is the Gamma function [26, Eq. (6.1.1)].
3 Performance analysis of the combining schemes
In binary hypothesis problem, we are interested in finding the hypothesis which is true. It may be H_{1} or H_{0} depending upon the decision region. Here, we will focus our attention to the Bayes criterion for decision based on the received data. The expression of the Bayesian risk R can be written using [27, Eq. (5), Chapter (2)] as follows:
where P_{0} and P_{1} denote a priori probabilities of hypotheses H_{0} and H_{1}, respectively, Z_{1} and Z_{0} are the decision regions corresponding to hypotheses H_{1} and H_{0}, respectively, while C_{00}, C_{10}, C_{11}, C_{01} denote cost assigned as follows [27]:

1.
C _{00} corresponds to the cost of deciding hypothesis H _{0} when hypothesis H _{0} is true,

2.
C _{10} corresponds to the cost of deciding hypothesis H _{1} when hypothesis H _{0} is true,

3.
C _{11} corresponds to the cost of deciding hypothesis H _{1} when hypothesis H _{1} is true,

4.
C _{01} corresponds to the cost of deciding hypothesis H _{0} when hypothesis H _{1} is true.
It is assumed that C_{00} = C_{11} = 0 which implies that no cost is assigned when correct hypothesis is chosen. This assumption is valid because no error is made if the decision of the presence or absence of the primary signal is correct. However, in order to reduce the probability of error in decision, we have to set C_{01} = C_{10} = 1 so that maximum cost is assigned for a wrong decision.
In CR spectrum sensing, when the primary signal is present and the CR decides that it is not present, then it causes interference to the PU and leads to overutilization of the spectrum. Similarly, when the CR decides a the PU signal is present and actually it is not, then spectrum hole is underutilized. Therefore, we consider that cost assigned in both types of error is set as unity.
For designing a minimum total probability of errorbased receiver, we also assume that two hypotheses H_{0} and H_{1} are equally likely [27] such that P_{0} = P_{1} = 0.5. With these assumptions, Eq. (9) can be written as
It can be deduced that Eq. (10) refers to the total probability of error or average probability of error in deciding the presence or absence of the primary signal. In our analysis, we have chosen this type of error criterion specifically in order to minimize the total probability error. It will be shown in Section 5 that the total probability of error criterion enables the cognitive system to utilize free spectrum more efficiently than the NP criterion, for certain SNR values considered in simulations.
The probability of false alarm P_{ f } and probability of missed detection P_{ m } are defined as [27]
Therefore, substituting Eqs. (11) and (12) in Eq. (10), we get
For the twoCRbased cognitive setup considered in Section 2, decision regions Z_{1} corresponding to the detection of the PU are given as

1.
u _{1} > λ, u _{2} > λ, and u _{1} + u _{2} > λ _{0},

2.
u _{1} > λ, u _{2} < λ, and u _{1} + u _{2} > λ _{0},

3.
u _{1} < λ, u _{2} > λ, and u _{1} + u _{2} > λ _{0},

4.
u _{1} < λ, u _{2} < λ, and u _{1} + u _{2} > λ _{0}.
The decision regions corresponding to nondetection of the PU, i.e., Z_{0} will be complimentary to the decision regions of Z_{1} in all above conditions. The detection region corresponding to u_{1} < λ, u_{2}<λ, and u_{1} + u_{2} > λ_{0} is nonzero for λ_{0} < 2λ only.
3.1 Proposed selective combining scheme
It is beneficial to check the received signal at each CR and include those signals, which are above a local threshold λ, in making the final decision in the FC. Let us consider a soft combiningbased scheme, where the FC takes decision of the presence or absence of the spectrum hole when u_{1} > λ, u_{2} > λ, and u_{1} + u_{2} > 2λ. Hence, selective combining in the FC concerns with the fact that both CRs contribute in the decision about the presence or absence of the primary signal provided that energy estimates of both CRs are above the local threshold λ. Intuitively, the selective combining scheme guarantees the minimum total probability of error in the detection of spectrum hole. Motivated by this fact, we consider a proposed scheme for soft combination of received data in which decision region is u_{1} > λ, u_{2} > λ, and u_{1} + u_{2} > λ_{0}. We will evaluate the performance of the proposed selective combining scheme by deriving the expressions of the probability of false alarm and the probability of missed opportunity for this collaborative system.
3.2 Total probability of error of the selective combining scheme
For finding performance analysis of the proposed soft combining scheme, we split our analysis into three parts λ_{0} > 2λ, λ_{0} = 2λ, and λ_{0} < 2λ.
3.2.1 Total probability of error of the selective combining scheme for λ _{ 0 } > 2λ
From Figure 2, it is found that the detection region for primary signal is shaded region labeled as Z_{1}.The probability of false alarm ${P}_{{f}_{1}}$ can be obtained from Eq. (11) and Figure 2 as follows:
where
Further simplifying Eq. (14), we get
where Γ(a,x) is the upper incomplete Gamma function given as Γ(a,x) =$\underset{x}{\overset{\infty}{\int}}{t}^{a1}exp(t)\mathit{\text{dt}}$[26, Eq. (6.5.3)]. By using series expansion of the incomplete Gamma function $\mathrm{\Gamma}(a,x)=(a1)!{e}^{x}\sum _{k=0}^{a1}\frac{{x}^{k}}{k!}$ in Eq. (16) and after some algebraic simplification of the integral, we have
where ${\lambda}_{0n}={\lambda}_{0}/{\sigma}_{n}^{2}$ denotes the n ormalized global threshold, ${\lambda}_{n}=\lambda /{\sigma}_{n}^{2}$ denotes the n ormalized local threshold, and $\left(\genfrac{}{}{0ex}{}{l}{i}\right)=\frac{l!}{i!(li)!}$. The expression of probability of missed detection ${P}_{{m}_{1}}$ for the proposed selective combining scheme can be obtained using Eq. (12) and Figure 2 as
After simplification of Eq. (18), we get
where $\gamma ={E}_{s}{\sigma}_{h}^{2}/{\sigma}_{n}^{2}$ is the a verage signaltonoise ratio (SNR) of the PUCR link.
Total probability of error for λ_{0} > 2λ can be obtained using Eq. (13) as
3.2.2 Total probability of error of the selective combining scheme for λ_{ 0 } = 2λ
The expression of probability of false alarm ${P}_{{f}_{2}}$ for the proposed scheme can be obtained using Eq. (11) and Figure 3 as
From Figure 3 and Eq. (12), the probability of missed detection ${P}_{{m}_{2}}$ of the proposed scheme will be
that reduces into
We can obtain the expression of the total probability of error for λ_{0} = 2λ using Eq. (13) as
3.2.3 Total probability of error for proposed scheme when λ _{ 0 } < 2λ
It can be seen from Figures 3 and 4 that the detection region of the primary signal Z_{1} for λ_{0} < 2λ is the same as that of λ_{0} = 2λ; therefore, the probability of false alarm ${P}_{{f}_{3}}$ for λ_{0}<2λ is given by Eq. (21). Similarly, it can be observed from Figures 3 and 4 that the probability of missed detection for λ_{0} < 2λ will be given using Eq. (23). Hence, the total probability of error ${P}_{{e}_{3}}$ of λ_{0} < 2λ is equal to ${P}_{{e}_{2}}$.
3.3 Optimization of the proposed selective soft combining scheme
In this subsection, we will discuss the optimization of the local and global thresholds of the proposed soft combining scheme. The optimization is divided into three different parts, i.e., λ_{0} > 2λ, λ_{0} = 2λ, and λ_{0} < 2λ.
3.3.1 Optimization of thresholds for λ_{ 0 } > 2λ
By partially differentiating Eq. (20) with respect to (w.r.t.) the normalized global threshold λ_{0n}, we get
where
and
We can solve Eq. (25) numerically to find the optimum value of λ_{0n}. In order to obtain optimal value of local normalized threshold λ_{ n }, partially differentiate Eq. (20) w.r.t. λ_{ n }, to get
where
and
For finding an optimal value of λ_{ n }, we need to numerically solve Eq. (28). We can also numerically find the joint values of λ_{0n} and λ_{ n } such that total probability of error is minimized [28].
Let us find the optimum value of the global threshold λ_{0n} with a single received data sample and the local threshold in each CR set to zero, i.e., λ_{ n } = 0. On partially differentiating Eq. (20) w.r.t. λ_{0n} when λ_{ n } = 0 and setting the result to zero, we get
where ${\lambda}_{0n}^{\ast}$ denotes the optimal value of the normalized global threshold in the proposed scheme. It will be shown by simulations in Section 5 that the minimum probability of error in detection of a spectrum hole is achieved for λ_{ n } = 0.
3.3.2 Optimization of thresholds for λ _{ 0 } ≤ 2λ
Since the total probability of error for λ_{0} = 2λ and λ_{0} < 2λ is the same, we can consider them together as λ_{0} ≤ 2λ for optimization of the thresholds. In order to obtain the optimal value of λ_{ n }, we need to partially differentiate Eq. (24) w.r.t. λ_{ n }, to get
We can numerically solve Eq. (32) to find the optimized value of λ_{ n }.
3.4 Performance analysis of semiselective combining scheme
In the semiselective combining scheme, the FC combines the energy estimates of the CRs corresponding to detection region Z_{1} for the condition that u_{1} > λ, u_{2} < λ, and u_{1} + u_{2} > λ_{0}, u_{1} < λ, u_{2} > λ, and u_{1} + u_{2} > λ_{0}, and u_{1} < λ, u_{2} < λ, and u_{1} + u_{2} > λ_{0} also in addition to u_{1} > λ, u_{2} > λ, and u_{1} + u_{2} > λ_{0} considered in previous subsections. By following the procedure given in Section 3.1, we can split the analysis into three parts λ_{0} > 2λ, λ_{0} = 2λ, and λ_{0} < 2λ and obtain the probability of false alarm and missed detection for these three conditions as follows^{1}.
3.4.1 Probability of false alarm and missed detection for λ _{ 0 } > 2λ
When u_{1} > λ, u_{2} < λ, u_{1} + u_{2} > λ_{0}, and λ_{0} > 2λ, the probability of false alarm and missed detection will be
For u_{1} < λ, u_{2} > λ, u_{1} + u_{2} > λ_{0}, and λ_{0} > 2λ, the false and missed detection probabilities will be found as
In case of u_{1} < λ, u_{2} < λ, u_{1} + u_{2} > λ_{0}, and λ_{0} > 2λ, the detection region will be zero and probability of miss will be one.
3.4.2 Probability of false alarm and missed detection for λ _{ 0 } = 2λ
The probability of false alarm and missed detection for u_{1} > λ, u_{2} < λ, u_{1} + u_{2} > λ_{0}, and λ_{0} = 2λ will be
For u_{1} < λ, u_{2} > λ, u_{1} + u_{2} > λ_{0}, and λ_{0} > 2λ, the false and missed detection probabilities will be as follows:
When u_{1} < λ, u_{2} < λ, u_{1} + u_{2} > λ_{0}, and λ_{0} > 2λ, the detection region will be zero and probability of miss will be one.
3.4.3 Probability of false alarm and missed detection for λ _{ 0 } < 2λ
The probability of false alarm and missed detection for u_{1} > λ, u_{2} < λ, u_{1} + u_{2} > λ_{0}, and λ_{0} = 2λ are
The false and missed detection probabilities for u_{1} < λ, u_{2} > λ, u_{1} + u_{2} > λ_{0}, and λ_{0} > 2λ can be obtained as
In the case of u_{1} < λ, u_{2} < λ, u_{1} + u_{2} > λ_{0}, and λ_{0} > 2λ, the false and missed detection probabilities will be
The semiselective soft combining scheme can be optimized by the procedure given in Section 3.2, and optimized values of the local and global thresholds can be obtained by the total probability of error minimization criterion.
4 Soft and hard combination schemes for more than two CRs
It can be seen from the discussion above that analysis of the proposed soft combining scheme is very complex in the case of more than two users. Hence, it is very difficult to find the optimum local and global thresholds for a general collaborative soft combining scheme with more than two CRs. Therefore, in the case of more than two CRs, we can assume that the FC does not have information about the local threshold λ. Therefore, the FC cannot use selective combining discussed in Section 3.2.
4.1 Nonselective soft combination scheme for more than two CRs
In the nonselective soft combining scheme for more than two users, the FC always combines the energy estimates of all CRs for taking a decision. When the FC does not use the local threshold for decision making, the nonselective soft combining scheme is similar to the scheme discussed in [4, 13] for arbitrary number of CRs. However, in [4, 13], the NP criterion is used for finding the optimized value of the global threshold. Moreover, the existing schemes [4, 13] consider a slowly varying Rayleigh fading channel for analysis. It can be seen from Eq. (1) that we consider a fast fading Rayleigh channel between the PU and CRs in our analysis. In addition, it can be seen from Section 3.3 that we use the total probability of error for finding the optimized value of the threshold. Therefore, we need to derive the expression of the total probability of error for the conventional nonselective soft combining scheme of [4, 13], and based on that, we can find a closed form expression of the optimal value of the global threshold.
In the nonselective soft combining scheme, each CR forwards the energy estimate over noiseless channel to the FC, and the FC takes the decision about the spectrum hole based on the global threshold λ_{0}.
Let the number of secondary users or CRs be denoted by K. Each secondary user calculates a summary statistics u_{ i } over a finite detection interval of L samples as
where i = 1,2,…,K. The summary statistics are transmitted to the FC through a lossless control channel using orthogonal time slots or orthogonal frequency bands. The FC computes a global test statistics as
which is compared with a predefined threshold λ_{0} to make a decision about the presence of the PU. It can be noted from Eq. (48) that the process of combining summary statistics is similar to equalgain combining.
From Eq. (48), the c.f. of U in the case of hypotheses H_{0} and H_{1} will be
and
respectively. By taking the inverse Fourier transform of Eqs. (49) and (50), the p.d.f.s of U under hypotheses H_{0} and H_{1} can be obtained as
and
respectively. The decision rule in the FC is
From Eqs. (51) and (52), we can obtain the p robability of false alarm P_{ f } in FC as
and the p robability of missed detection P_{ m } in the FC as
The total error probability for equiprobable hypotheses is expressed from Eqs. (54) and (55) by
Next, the optimal value of λ_{0n} can be found by partially differentiating Eq. (56) w.r.t. λ_{0n} and setting the result to zero. The partial derivative of Eq. (56) w.r.t. λ_{0n} will be
By equating Eq. (57) to zero and after some manipulations, we get the closed form expression of the optimized normalized global threshold as
4.2 Onebit hard combination scheme
In [7], a nonselective 1bit hard combination scheme for arbitrary number of CRs is studied. Since the detection error analysis of a nonselective 1bit hard combination scheme over slowly fading Rayleigh channels is performed in [7], we will find the total probability of error and optimized value of the local threshold for fast fading Rayleigh channels for comparison with the proposed selective combining schemes. In hard combining scheme, each CR takes a binary decision about the presence or absence of the primary signal using energy detector. The FC combines these binary decisions to find the presence or absence of the PU as follows [7]:
where D is the sum of the all 1bit decisions from the CRs. Let n, n ≤ K corresponds to a number of cooperating CRs out of K CRs. The FC uses a majority rule for deciding the presence or absence of the PU. As per the majority decision rule if D is greater than n, then hypothesis ${\mathcal{\mathscr{H}}}_{1}$ holds otherwise the hypothesis ${\mathcal{\mathscr{H}}}_{0}$ is true. The hypotheses ${\mathcal{\mathscr{H}}}_{0}$ and ${\mathcal{\mathscr{H}}}_{1}$ can be written as [7]
The p.d.f.s of u_{ i } under hypotheses ${\mathcal{\mathscr{H}}}_{0}$ and ${\mathcal{\mathscr{H}}}_{1}$ can be obtained after some algebra as
and
respectively. The decision rule in the ith, i = 1,...,K, CR is
From Eqs. (62), (63), and (64), we get the probability of false alarm P_{ f } and probability of missed detection P_{ m } in each CR as
The probability of false alarm P_{ f } in the FC for cooperative sensing will be [7]
and the probability of missed detection P_{ m } in FC will be [7]
The total error rate of the hard combiningbased cooperative scheme will be the sum of P_{ f } and P_{ m }. The optimal number of CRs and optimized value of the local threshold can be calculated by minimizing the total error rate as shown in [7].
5 Numerical results
We consider a cognitive system with one PU, two CRs, and a FC for simulations. Moreover, we use local and global thresholds normalized with noise variance ${\sigma}_{n}^{2}$ in simulations. In Figure 5, the total probability of error versus normalized global threshold λ_{0n} plots of the proposed selective soft combining scheme with different number of received samples L = 1,2,3,4 and PUCR link SNR=0, 5 dB are shown. It can be seen from Figure 5 that the total probability of error is a convex function of the normalized global threshold in the proposed selective soft combining scheme. The total probability of error is calculated from Eq. (20). It can be observed from Figure 5 that by increasing the number of samples and SNR, very low value of the total probability of error can be achieved.
Figure 6 shows that the total probability of error obtained from Eq. (24) is a convex function of normalized local threshold in the proposed selective soft combining scheme when λ_{0} < 2λ. Moreover, it can be seen from Figure 6 that the total probability of error reduces by increasing number of data samples and SNR of the PUCR link. It can be observed from Figures 7, 8, and 9 that for a single received data sample, it is possible to minimize the total probability of error at a given SNR of the PUCR link by an optimized global threshold in the proposed selective soft combining scheme.
Optimized values of the global thresholds for the proposed selective soft combining scheme and nonselective combining scheme (Sections 3.3 and 4.1) are calculated for each SNR values from Eqs. (31) and (58), respectively. Whereas, the global thresholds for the hard combining scheme (Section 4.2) are calculated numerically at each SNR value using Eqs. (67) and (68). Table 1 shows the minimum value of total probability of error at optimal value of the normalized global threshold of the proposed selective soft combining scheme with two CRs, L = 1, and SNR = 10, 0, 10 dB for different numbers of normalized local threshold. It can be concluded from Table 1 that as the λ_{ n } is increased to 4.0, a given SNR total probability of error also increases.
In Figure 10, we have plotted the total probability of error versus SNR of the PUCR link plots for the proposed selective soft combining scheme, nonselective soft combining scheme, and 1bit hard combining scheme for two CRs, one received data sample, and λ_{ n }=0. It can observed from Figure 10 that the proposed selective soft combining scheme significantly outperforms the nonselective soft and hard combining schemes. In order to compare the proposed selective and nonselective soft combining schemes under NP and total error minimization criterion, plots of the probability of detection of spectrum hole and probability of false alarm versus SNR of PUCR link are shown in Figures 2 and 11, respectively, for a single received data sample.
Under the NP criterion, the nonselective soft combining scheme explained in Section 4.1 is optimized as discussed in [4, 13]. It can be seen from Figure 11 that the probability of detection under the total probability of error minimization criterion is better than that obtained using the NP criterion for the selective and nonselective soft combining schemes for the considered SNR values. Moreover, under the total probability of error minimization criterion, the proposed selective soft combining scheme significantly outperforms the nonselective soft combining scheme over the range of SNR values used in Figure 11. For example, an SNR gain of approximately 3 dB is achieved at the probability of detection = 0.7 by the proposed selective scheme as compared to the nonselective scheme under the total probability of error criterion. Figure 11 shows that the proposed selective soft combining scheme under the total error probability minimization criterion provides better probability detection than the nonselective soft combining scheme using the proposed total error probability minimization and NP criteria for the considered SNR values. The probability of false alarm versus SNR plots of the proposed selective and nonselective soft combining schemes are shown in Figure 12. It can be seen from Figure 12 that the probability of false alarm of the selective and nonselective schemes under the NP criterion is much less than 0.1, which corresponds to the tolerable value of interference at the PU as specified by IEEE 802.22 cognitive wireless regional area network (WRAN) standard [29]. Under the NP criterion, this very low (much lower than the tolerance level) value of the probability of false alarm is achieved at the cost of very poor probability of detection, as shown in Figure 11, which leads to significant underutilization of the available spectrum hole. Whereas, it can be seen from Figures 2 and 11 that the proposed selective combining scheme under the total error minimization criterion provides better probability of detection than the nonselective scheme using the NP criterion while keeping the probability of false alarm lower than the specified value, i.e., 0.1 for 3≤ SNR ≤20. Therefore, the proposed selective soft combining scheme along with the total probability of error minimization criterion is able to properly utilize a spectrum hole with interference level less than the specified value. It can be seen from Table 2 that with increasing value of L of the proposed selective combining scheme, the probabilities of false alarm and missed detection decreases at given SNR = 20,2 dB. Table 2 shows that requirements proposed in IEEE 802.22 WRAN is not achieved for the proposed selective combining scheme when L is varied from 1 to 8 at SNR = 20 dB. However, at SNR = 2 dB, the requirements can be achieved when L = 8 with t_{CDT} = 0.000128 s, where t_{CDT} denotes the channel detection time, while no other processing delays are assumed. It can be noted that with increasing number of samples, the time to detect the primary signal will also increase for a given sampling frequency. In order to detect a spectrum hole quickly while satisfying IEEE 802.22 WRAN standards, we have considered finite small number of samples in our numerical results.
It can be seen from Figure 13 and Table 3 that as the number of CRs is increased from K = 5 to K = 15, the probability of false alarm also increases under NP criterion at SNR = 10 dB. However, probability of missed detection reduces with increasing number of CRs from K = 5 to K = 15 under NP criterion. It can also observed from Figure 13 and Table 3 that at SNR=10 dB, the probability of missed detection decreases with increasing number of CRs under total error probability criterion and NP criterion. Table 3 also shows that the probability of false alarm under total error probability criterion is much less than that under the NP criterion; hence, it indicates that the total error probability criterion performs better than NP criterion. On the other hand, this improvement in the probability of false alarm is achieved at the cost of much higher probability of missed detection. It can also be observed from Figure 13 that at a very low SNR (say 20 dB), there is almost negligible difference in the probabilities of false alarm and missed detection under NP and the total error probability criterion. As the number of CRs are increased to 15 for single sample of received signal, the values of probability of false alarm and missed detection is almost same. Figure 13 shows that at SNR = 0 dB, the probability of missed detection ≤ 0.1 and probability of false alarm ≤ 0.1 under the total error probability criterion, and hence, the requirement proposed in IEEE 802.22 WRAN can be satisfied in this low SNR which is very difficult to achieve in the case of NP criterion.
6 Conclusions
In this paper, we have demonstrated that it is possible for a collaborative cognitive radio network to detect the spectrum hole in an optimal manner by minimizing the total probability of error in decision making with a finite number of the received data samples. It is shown by simulations that the proposed selective scheme for soft combination significantly outperforms the nonselective soft and hard combining schemes. Moreover, the total probability of error minimization criterion performs better than the NP criterion while keeping the probability of false alarm within the desired limits for specified values of SNR of the PUCR link.
Endnote
^{a} We are skipping analytical details here and writing the final expressions to avoid repetition.
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This work was partially supported by the IDRC Research Grant RP02253.
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Singh, A., Bhatnagar, M.R. & Mallik, R.K. Threshold optimization of a finite samplebased cognitive radio network using energy detector. J Wireless Com Network 2013, 165 (2013). https://doi.org/10.1186/168714992013165
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Keywords
 Cognitive radio; Collaborative networks; Energy detector; Probability of error; Spectrum hole detection