 Research
 Open Access
Improved local spectrum sensing for cognitive radio networks
 Waleed Ejaz^{1},
 Najam ul Hasan^{1},
 Muhammad Awais Azam^{2} and
 Hyung Seok Kim^{1}Email author
https://doi.org/10.1186/168761802012242
© Ejaz et al.; licensee Springer. 2012
 Received: 13 February 2012
 Accepted: 8 October 2012
 Published: 21 November 2012
Abstract
The successful deployment of dynamic spectrum access requires cognitive radio (CR) to more accurately find the unoccupied portion of the spectrum. An accurate spectrum sensing technique can reduce the probability of false alarms and misdetection. Cooperative spectrum sensing is usually employed to achieve accuracy and improve reliability, but at the cost of cooperation overhead among CR users. This overhead can be reduced by improving local spectrum sensing accuracy. Several signal processing techniques for transmitter detection have been proposed in the literature but more sophisticated approaches are needed to enhance sensing efficiency. This article proposes a twostage local spectrum sensing approach. In the first stage, each CR performs existing spectrum sensing techniques, i.e., energy detection, matched filter detection, and cyclostationary detection. In the second stage, the output from each technique is combined using fuzzy logic in order to deduce the presence or absence of a primary transmitter. Simulation results verify that our proposed technique outperforms existing local spectrum sensing techniques. The proposed approach shows significant improvement in sensing accuracy by exhibiting a higher probability of detection and low false alarms. The mean detection time of the proposed scheme is equivalent to that of cyclostationary detection.
Keywords
 Cognitive radio
 Spectrum sensing
 Primary user
 Secondary user
 Softwaredefined radio
1. Introduction

Efficient spectrum utilization

Seamless communication of both CR users and licensed users
Only an unallocated portion of the spectrum or white space can be utilized by a secondary user (SU, i.e., unlicensed users using CR). Therefore, a SU searches through the available spectrum for white space [3, 4], a process called spectrum sensing. The prime concerns of spectrum sensing are that primary users (PU, i.e., licensed users) should not be disturbed by SU communication and that spectrum holes should be detected efficiently for maintaining the required throughput and quality of service.
The most important local sensing techniques considered for CR are matched filter detection, energy detection, and cyclostationary detection [5]. Energy detection needs much less sensing time but performs poorly under low signaltonoise ratio (SNR) conditions. One of the wellknown coherent detection techniques in the field for spectrum sensing is matched filter detection. Cyclostationary detection provides reliable detection but is computationally complex.
Metrics for detection performance are the probability of detection and false alarms. The probability that a SU declares that a PU is present when the spectrum is idle is called the probability of a false alarm. Conversely, the probability that the SU declares that the PU is present when the spectrum is occupied by the PU is called the probability of detection. The probability of misdetection indicates the probability that the SU declares that the PU is absent when the spectrum is occupied. CR should exhibit a low probability of false alarm and a high probability of detection. Misdetection leads to interference with the PUs, while false alarms decrease the efficiency of spectrum utilization [6].
Fuzzy logic has been proposed to solve many telecommunication problems since the 1990s. Applications of fuzzy logic to CR systems are discussed in [7]. Fuzzy logicbased cooperative spectrum sensing is proposed in [8] in which estimated results of SUs are combined to get a final result at the fusion center. In this article, we propose a twostage fuzzy logicbased detection (FLD) system for local spectrum sensing. In the first stage, each CR performs existing spectrum sensing techniques, i.e., energy detection, matched filter detection, and cyclostationary detection. In the second stage, the outputs of those detection approaches are combined using fuzzy logic in order to deduce the presence or absence of primary transmitters.
The remainder of the article is organized as follows. Section 2 highlights the related work on transmitter detection techniques for spectrum sensing. The system model is presented in Section 3. The proposed fuzzy logicbased spectrum sensing approach is discussed in Section 4. Section 5 presents the numerical results confirming the accuracy of the simulation results and comparisons of the proposed approach with other detection techniques. Finally, conclusions are in Section 6.
2. Related study
Spectrum sensing plays a critical role for the efficient utilization of the radio spectrum. Researchers currently focus on two major aspects in spectrum sensing: (1) how to improve local sensing results and (2) cooperative spectrum sensing for better data fusion results.
Cooperative spectrum sensing is a twostage process composed of (1) local sensing and (2) fusion of local sensing results. In the first stage, each SU sniffs the spectrum and deduces the presence or absence of PU. In the second stage, local decisions of multiple users are fused together for making the final decision on whether a PU is absent or present. For improving cooperative sensing, researchers focus on how to optimally fuse local sensing results. Several optimal fusion schemes for cooperative spectrum sensing have been summarized in [6]. Although fusion rules may improve the final decision, the decision is highly dependent on the result of the first stage. Therefore, improving the first stage can improve cooperation results.
Researchers have recently focused on how to achieve reliable results with less mean sensing time. The most promising reforms applied to local spectrum sensing are: using multiple antennas, using twostage sensing schemes, and improving existing techniques. In [9, 10], the improvement of the sensing performance of energy detection is achieved using multiple antennas at the sensing node. In [11–14], twostage spectrum sensing techniques are explored, in which the first stage involves coarse sensing and the second one involves fine sensing. In the majority of twostage sensing techniques, coarse sensing performs energy detection while fine sensing is later performed to verify the presence or absence of PUs.
To improve the existing techniques, oneorder cyclostationary detection in the time domain is proposed in [15], where the mean characteristic of the PU signal is exploited in order to improve the efficiency of channel sensing. Both realtime operation and low computational complexity can be achieved using this detection scheme. In [16], the energy detection technique is improved by replacing the squaring operation with arbitrary positive power operation. Power operation depends on the probability of false alarms, the probability of detection, the average SNR, and the sample size. By choosing the value of the power operation, detection performance of a conventional energy detector can be improved. Advanced sensing techniques for energy detection, including multiple antenna sensing and cooperative sensing, are discussed in [17].
L. A. Zadeh first introduced fuzzy logic in order to cover more general linguistic notation for extending binary logic. Fuzzy logic can be applied in CR networks. In [18], fuzzy logic is used for the representation of crosslayer information and for the implementation of optimization strategies in CR networks. The fuzzy reasoning model that is appropriate for SU devices operating in heterogeneous networks is proposed in [19]. Fuzzy comprehensive evaluation is used for collaborative spectrum sensing in CR networks [8, 20]. Fuzzy collaborative spectrum sensing improves the performance in terms of the probability of detection and false alarms. However, introducing fuzzy logic at nodelevel sensing can further improve the performance by improving local sensing results. In our proposed approach, fuzzy logic is used to make the local spectrum sensing decision.
3. Spectrum sensing techniques
The most commonly employed spectrum sensing techniques for transmitter detection are: matched filtering, energy detection, and cyclostationary detection. These spectrum sensing techniques are used for detection in parallel and then the fuzzy logic approach is used to determine spectrum holes. First, we will discuss each of the transmitter detection techniques including their pros and cons.
where r(t) is the signal received by SU, s(t) is the signal transmitted by the PU, n(t) is additive white Gaussian noise (AWGN), and h is the amplitude gain of the channel. In general, the performance of spectrum sensing techniques is measured on the basis of two metrics: the probability of detection and the probability of false alarms. The probability of detection is the probability of SU’s correctly declaring the presence of a PU and the probability of false alarms is defined as the probability of falsely declaring the presence of a PU. For the best performance, the probability of detection should be high and the probability of a false alarm should be low.
3.1. Matched filter detection
Finally, the output of the matched filter is compared with a threshold factor λ_{1} in order to decide whether the PU is present on the sensed spectrum.
The intuition behind the matched filter relies on the prior knowledge of the PU waveform such as modulation type, order, the pulse shape, and the packet format. In order to meet such a stringent condition, CRs need to have a cache for pattern information in their memory and satisfy synchronization. Achieving synchronization is the most cumbersome part of demodulation. However, synchronization is still realizable because most PUs have pilots, preambles, synchronization words, or spreading codes that can be used for coherent detection [22].
where Q is the Gaussian complexity distribution function, E is the energy of the deterministic signal of interest, and σ_{ w }^{2} is the noise variance.
3.2. Energy detection
One of the major shortcomings of energy detection is that the performance is vulnerable to uncertainty in noise power. Energy detection determines the presence or absence of the PU based on the received signal energy. Since this detection scheme cannot discriminate between signal and noise power, it frequently causes false alarms at low SNR values [1].
where Γ(.) and Γ(.,.) are complete and incomplete gamma functions, respectively. Q_{ m }(.,.) is the generalized Marcum Qfunction, γ is the instantaneous SNR, M_{ E } is the time bandwidth product, and λ_{2} is the decision threshold of the energy detector.
3.3. Cyclostationary feature detection
Researchers suggest that cyclostationary feature detection is more suitable than matched filter and energy detector techniques. As discussed earlier, the matched filter as a coherent detector requires prior knowledge about the PU’s wave. Although the energy detector, as a noncoherent detection method, does not require any sort of prior knowledge about a PU’s waveform and so is easy to implement, it is highly susceptible to inband interference and changing noise levels [25] and cannot differentiate between signal and noise power.
SCF, also called a cyclic spectrum, is a twodimensional function with a cyclic frequency α. Power spectral density is a special case of a SCF with α = 0. The features detected are the number of signals, their modulation types, symbol rates, and presence of interferers. Using the computed SCF and a hypothesis model for spectrum sensing, we can determine whether a signal of a specific cyclic frequency of interest is present or not [27].
where σ_{ w }^{2} is the variance, δ_{ A }^{2} = σ_{ m }^{2}/(2M_{ C } + 1) in which M_{ c } is the number of samples for detection, L is the number of diversity branches, γ is instantaneous SNR, Q_{ m }(.,.) is the generalized Marcum Q –function, and λ_{ 3 } is a predetermined threshold.
4. System model and framework
4.1. FLD
Traditional set theory has crisp concept of membership, i.e., an element either belongs to a set or it does not. In contrast, fuzzy set theory allows for partial membership. Fuzzy logic was initially proposed to cover the problem of reasoning under uncertainty. Decisions based on fuzzy logic are made using vague information, humanunderstandable fuzzy sets, and inference rules (e.g., IF, THEN, ELSE, AND, OR, and NOT) instead of complicated mathematics [8].

Antecedent 1: Normalized output of energy detector

Antecedent 2: Normalized output of matched filter

Antecedent 3: Normalized output of cyclostationary detector
R^{ l }: IF x_{1} is F_{ l }^{1}, and x_{2} is F_{ l }^{2}, and x_{3} is F_{ l }^{3}, THEN the possibility (y) that the PU is present is D^{ l } where l= 1,2, …, 27.
Example of rule base for fuzzy combining
Input 1  Input 2  Input 3  Output 

Low  Low  Low  Worst 
Low  Medium  Low  Very bad 
Low  Medium  Medium  Bad 
Low  high  medium  Moderate 
Medium  Medium  High  Good 
High  Medium  High  Very good 
High  High  High  Best 
4.2. Analysis of sensing performance
where d_{ i } {low, medium, high} for all i = {1, … N} and n ≥ N/2. P_{d,i} and P_{f,i} are the probability of detection and probability of false alarm, respectively, when using i th sensing technique. The summation based on ∑d_{ i } = k is used to include the effect of fuzzy logic and is conducted with all combinations of d_{ i } satisfying ∑d_{ i } = k. It is shown in the next section that approximated probabilities are close to the simulation results.
In order to evaluate the agility of the FLD scheme, mean detection time is compared with matched filter detection, energy detection, and cyclostationary detection. The number of samples during observation periods is known in each sensing technique. The symbol duration is known in the case of the matched filter and the channel bandwidth is known for energy detection and cyclostationary detection. Using this information, we can calculate the mean detection time represented as T_{1}, T_{2}, and T_{3} for matched filter, energy detection, and cyclostationary detection, respectively.
where M_{1} is the number of samples during the observation interval and T_{ s } is the symbol duration.
where M_{2} is the number of samples during the observation interval and W is the channel bandwidth.
where M_{3} is the number of samples during the observation interval and W is the channel bandwidth.
The probability of detection, P_{ d }, and probability of false alarm, P_{ f }, are calculated after combining the results of individual sensing techniques at each SU. Therefore, the overall P_{ d } is increased and P_{ f } is decreased when compared to individual techniques. Because the detection time of FLD scheme is equal to the maximum detection time of three sensing techniques, performance is improved with a similar sensing time at the cost of added parallel hardware at each SU.
5. Simulation
Comparison of the proposed scheme with existing improved local sensing techniques
Spectrum sensing scheme  Average SNR (dB)  P _{ f }  P _{ d } 

Proposed FLD scheme  −15  0.0001  0.50 
−10  0.0001  0.97  
PU detection in a multiple antenna CR [9]  0  0.01  0.45 
15  0.01  1  
A twostage sensing technique for dynamic spectrum access [12]  6  0.1  0.99 
Twostage spectrum sensing for CRs [13]  −10  0.1  0.99 
Combined energy detection and oneorder cyclostationary detection [14]  10  0.1  0.94 
Energy detector with bithresholds [15]  10  0.1  0.85 
Improved energy detector [16]  0  0.1  0.62 
10  0.1  0.99  
Advanced sensing techniques of energy detection [17]  −5  0.1  0.72 
−5  0.6  0.95 
6. Conclusion
In this article, a new FLD scheme for local spectrum sensing is proposed. In the first stage of FLD, each SU performs existing spectrum sensing techniques, i.e., energy detection, matched filter detection, and cyclostationary detection, in parallel. In the second stage, the outputs of those detection approaches are combined using fuzzy logic in order to deduce the presence or absence of PU.
Transmitter detection techniques are compared with the proposed fuzzy logicbased approach. By comparing these techniques, we conclude that the FLD scheme gives better results in terms of the probability of detection and false alarms. The FLD scheme has a mean detection time equal to the maximum time taken by any existing scheme, i.e., the mean detection time of cyclostationary detection. All the existing techniques perform at each SU in parallel, and therefore the hardware cost of the proposed FLD is slightly higher. However, since accurate detection is to be predicted, cost can be sacrificed for the accuracy of detection and fast detection time.
Every detection technique has an SNR threshold below which robust operation is not possible. We find that by simultaneously combining the results of different detection techniques using fuzzy logic, better results can be obtained.
Declarations
Acknowledgement
This work was supported by the CITRC (Convergence Information Technology Research Center) support program (NIPA2012H0401121003) supervised by the NIPA (National IT Industry Promotion Agency) of the MKE. It was partially supported by Seoul R&BD Program (SS110012C0214831) and Special Disaster Emergency R&D Program from National Emergency Management Agency (2012NEMA10002010100012012).
Authors’ Affiliations
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