 Research
 Open access
 Published:
Improved local spectrum sensing for cognitive radio networks
EURASIP Journal on Advances in Signal Processing volume 2012, Article number: 242 (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.
1. Introduction
Wireless networks are regulated today by using a static spectrum allocation policy. However, an outsized portion of the spectrum is used sporadically and the utilization of the assigned spectrum ranges from 15 to 85%, as illustrated in Figure 1[1]. The growing number of wireless technologies and new applications are considerably increasing the demand for more bandwidth. Such stringent requirements cannot be met with the conventional inflexible spectrum management approaches in which each operator is granted an exclusive license to operate. As most of the useful radio spectrum has already been assigned, vacant spaces are difficult to find for setting up new services or add to existing services [2].
Cognitive radio (CR) is renowned for significantly enhancing the efficient utilization of the radio electromagnetic spectrum, which is considered a precious natural resource. CR is an intelligent wireless communication system built on the top of softwaredefined radio (SDR) that learns from experience. By making use of both the intelligence and reconfigurability, the radio can switch across the spectrum adaptively. Reconfiguration is performed by SDR, while CR relies on signal processing techniques for intelligence [2]. CR focuses on

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.
Recently, researchers have shown a great concern towards spectrum sensing to induce the effective interactivity of CR with the environment. One of the major spectrum sensing schemes is transmitter detection, in which we determine the frequency at which the transmitter is operating. A hypothesis model for transmitter detection is defined in [5] and models the signal received by the SU as
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
One of the wellknown techniques in the field of signal processing for identifying a known pattern from a received signal is matched filter detection. In the presence of additive stochastic noise, the matched filter is an optimal linear filter for maximizing the SNR [21]. Figure 2 depicts the block diagram of a matched filter. The signal r(t) received by SU is fed to the matched filter and is expressed mathematically as
which is a generic form of (1) and s(t) is 0 if the PU is absent. The matched filter is equivalent to convolving the received signal r(t) with a timereversed version of the known signal or template as
where T is the symbol time duration and τ is the shift in the known signal.
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].
The probability of detection, P_{d,1}, and false alarm, P_{f,1}, of a matched filter are given [23] as
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
When it is difficult for the SU to bring adequate information about the PU waveform, matched filter detection is not a favorable choice. However, if the SU is given the power of random Gaussian noise, energy detection becomes a better alternative [22] for spectrum sensing. Figure 3 depicts the block diagram for energy detection. The elementary approach behind energy detection is the estimation of the power of the received signal r(t). To evaluate the power of the received signal, the output of a band pass filter of bandwidth W is squared and integrated over an interval T. Finally, the integrated value is compared with a threshold λ_{2} in order to decide whether the PU is present or not [24].
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].
The probability of detection, P_{d,2}, and probability of false alarm, P_{f,2}, of energy detection over the AWGN channel are approximated in [14] as
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.
Commonly the primary modulated waveforms are coupled with patterns also characterized as cyclostationary features like sine wave carriers, pulse trains, repeating spreading, hopping sequences, and cyclic prefixes inducing periodicity [26]. SU can detect a random signal with a specific modulation type in the presence of random stochastic noise by exploiting periodic statistics like the mean and the autocorrelation of the PU waveform. Features like autocorrelation and mean are estimated by analyzing spectral correlation functions (SCFs). Implementation of the SCF is depicted in Figure 4.
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].
The probability of detection, P_{d,3}, and probability of false alarm, P_{f,3}, of oneorder cyclostationary detection over an AWGN channel are approximated in [15] as
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
Figure 5 illustrates a system model for the proposed spectrum sensing method. It is assumed that the PU signal structure is unknown but it allocates fraction of its power to transmit a deterministic pilot tone. This model is suitable for many practical communication systems in which the pilot tone is used for the data frame synchronization. A digital television (ATSC) signal is considered as the PU in which there is a strong pilot tone signal which is a sinusoidal signal in time domain. The spectrum sensing problem can be stated as
where f_{0} is the carrier frequency and P_{ s } and θ_{0} are the initial power and phase of the carrier, respectively. The signal s(t) is modeled as a(t)cos(2πf_{ m }t) in which f_{ m } is the frequency of pilot tone in the TV signal and a(t) is the analogue waveform. While on air, the signal becomes corrupted with AWGN noise n(t). However, any interference from users operating in a nonoverlapping band can be mitigated through linear filtering. The SU will scan the whole spectrum and detect whether there is a spectrum hole available or not. All three transmitter detection techniques can be applied on this model and are compared in [23] using this model.
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].
In order to test the applicability of fuzzy logic for the mathematical hypothesis given in (1) and increase the performance of local sensing, we propose an FLD scheme for the final decision (presence or absence of a PU). FLD offers several unique features that make itself a particularly good choice for PU detection. It does not require precise inputs therefore it is inherently robust. Because the FLD system is governed by userdefined rules, it can be modified easily to improve system performance. Figure 6 shows the structure of FLD system. When the input is applied to the FLD, the output is computed by the fuzzy inference engine corresponding to each rule. The crisp output is then computed by defuzzification from output sets. The system has three inputs and one output using singleton fuzzification, MaxProduct, as the conclusion method and the center of area as the defuzzification method [7].
The FLD is designed to detect the PU accurately in order to increase reliability of the detection and to avoid interference with PU transmission. The detection of PU is based on three antecedents, i.e., descriptors

Antecedent 1: Normalized output of energy detector

Antecedent 2: Normalized output of matched filter

Antecedent 3: Normalized output of cyclostationary detector
The linguistic variance used to represent each antecedent is labeled high, medium, or low, indicating the possibility of the presence of the PU. Each antecedent uses two thresholds for the label choice. Let λ_{1A} and λ_{1B} are two thresholds for which linguistic variance is high, medium, and low when the output of detector is greater than or equal to λ_{1A}, between these two thresholds, and less than or equal to λ_{1B}, respectively. In this article, λ_{1A} and λ_{1B} are set to be 0.25 and 0.75, respectively. The consequence, i.e., the possibility of detection of PU, is divided into seven levels which are worst, very bad, bad, moderate, good, very good, and best. The triangular membership function is used to represent high, medium, and low for all three antecedents as shown in Figure 7a and to represent worst, very bad, bad, moderate, good, very good, and best for the consequence as shown in Figure 7b. Since there are 3 antecedents and 3 fuzzy subsets, there are totally 27 rules for the proposed FLD scheme. The fuzzy ifthen rules in this FLD scheme are of these types:
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.
Table 1 presents a rule base for combining the fuzzy information. The other rules can be interpreted in a similar way due to the symmetry of the rule base. The output y from the FLD system is
where N is the number of antecedents, in this case N = 3, and {\mu}_{{F}_{l}^{1}}, {\mu}_{{F}_{l}^{2}}, and {\mu}_{{F}_{l}^{3}} are the triangular membership functions for the all three antecedents. According to the draft IEEE 802.22 standard [27], the probability of false alarm and misdetection should be less than or equal to 0.1. The available literature on setting a threshold value of individual detectors suggests that a target P_{ f } should be fixed to calculate required threshold. Therefore, the threshold λ is set to 0.5 experimentally to achieve 100% detection even for the probability of false alarm less than 0.1. If the decision metric y is larger than the predetermined threshold, the SU will assert the presence of the PU. Otherwise, the SU will deny the presence of the PU.
4.2. Analysis of sensing performance
In this section, we analyze the sensing performance of proposed FLD scheme with respect to detection performance. The overall probability of detection and the probability of false alarm for the FLD scheme can be approximated as [28]
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.
The mean sensing time for each channel for the matched filter, T_{1}, can be calculated as
where M_{1} is the number of samples during the observation interval and T_{ s } is the symbol duration.
The mean sensing time for each channel for energy detection, T_{2}, can be calculated as
where M_{2} is the number of samples during the observation interval and W is the channel bandwidth.
The mean sensing time for each channel for cyclostationary detection, T_{3}, can be calculated as
where M_{3} is the number of samples during the observation interval and W is the channel bandwidth.
In the FLD scheme, all transmitter detection schemes run in parallel. The FLD will wait till all three detection algorithms finish their sensing. By doing so, the performance is increased and is better than individual performance of all the detectors. Main objective of this proposed FLD scheme is to increase reliability of detection at the cost of more hardware for each detector. Therefore, the total detection time of the proposed scheme can be expressed as
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
Figure 8 compares the probability of detection for transmission detection techniques with the proposed FLD scheme. The FLD scheme has a better performance over the entire SNR range compared to the other transmitter detection techniques. The FLD scheme detects the PU with 100% certainty even under a very low SNR value of −22 dB. In order to achieve the same degree of accuracy as a fuzzy logic scheme, the cyclostationary feature detector and the matched filter require relatively higher SNR values of −8 and 2 dB, respectively. The performance of energy detection seems to be better than all other mentioned techniques over the entire SNR range at the cost of high probability of false alarms. Due to inherent limitation of the energy detector, it is unable to discriminate between signal and noise energy. The probability of false alarm for the energy detection is the highest as compared to other detectors, which means that this comparison is not enough to determine the best detector for spectrum sensing.
In Figure 9, the receiver operating curves (ROCs) of the matched filter, energy detector, cyclostationary detector, and FLD scheme are shown. In this scenario, it is assumed that the instantaneous SNR is −10 dB. Time bandwidth product M_{ E } for energy detection is 2. Noise variance σ_{ w }^{2} for both matched filter and cyclostationary detection is taken as 1 dB. The number of samples for detection M_{ c } and the number of diversity branches L for cyclostationary detection are assumed to be 2 and 3, respectively. It is seen that the simulated ROCs shown in Figure 9b agree with the theoretical results presented in Figure 9a. The result shows that the ROC performance of FLD scheme outclasses all the existing transmitter detection techniques. FLD scheme is an optimal choice even at lower SNR values.
Figure 10 shows the comparison of the detection time of the proposed FLD scheme with matched filter, energy detection, and cyclostationary detection. In proposed FLD scheme, all spectrum sensing techniques run in parallel and so detection time is the maximum time taken by any individual detection scheme, while detection performance comparatively increases relative to any individual detection performance. The key advantage of the proposed FLD scheme is that its detection performance is more reliable than existing spectrum sensing techniques with a mean sensing time equal to that of cyclostationary detection.
In Table 2, we compared the performance of proposed FLD scheme with existing, improved local sensing techniques as discussed in Section 2. The twostage spectrum sensing proposed in [13] gives the most reliable results in comparison with the remaining existing schemes mentioned in Table 2. The probability of detection of the proposed FLD scheme is 0.97 in comparison with 0.99 for the twostage spectrum sensing scheme, at an average SNR of −10 dB. However, FLD has a false alarm probability of 0.0001, while the corresponding value for the twostage spectrum sensing scheme is 0.1. False alarms reduce spectral efficiency and misdetection causes interference with the PU. In general, it is then vital for optimal detection performance that the maximum probability of detection is achieved with the minimum probability of false alarm [6]. Therefore, the advantage of the FLD scheme is that it achieves a high probability of detection with a minimum probability of false alarms in comparison with existing schemes at low SNR values.
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.
References
Akyildiz IF, Lee WY, Vuran MC, Shantidev M: Next generation /dynamic spectrum access /cognitive radio wireless networks: a survey. Comput. Netw. 2006, 50: 21272159. 10.1016/j.comnet.2006.05.001
Haykin S: Cognitive radio: brainempowered wireless communications. IEEE J. Sel. Areas Commun. 2005, 23(2):201220.
Ma J, Li G, Juang BH: Signal processing in cognitive radio. Proc. IEEE 2009, 97(5):805823.
Ghasemi A, Sousa ES: Spectrum sensing in cognitive radio networks: requirements. Challenges and Design Tradeoffs. IEEE Commun. Mag. 2008, 46(4):3239.
Yucek T, Arslan H: A survey of spectrum sensing algorithms for cognitive radio applications. IEEE Commun. Surv. Tutor. 2009, 11(1):116130.
Aklidz IF, Lo BF, Balakrishan R: Cooperative spectrum sensing in cognitive radio networks: a survey. Phys. Commun. 2011, 4(1):4062. 10.1016/j.phycom.2010.12.003
Matinmikko M, Rauma T, Mustonen M, Harjula I, Sarvanko H, Mammela A: Application of fuzzy logic to cognitive radio systems. IEICE Trans. Commun. 2009, E92B(12):35723580. 10.1587/transcom.E92.B.3572
Xuan TK, Koo I, Cooperative A: Spectrum sensing scheme using fuzzy logic for cognitive radio networks. KSII Trans. Internet Inf. Syst. 2010, 4(3):289304.
Pandharipande A, Linnartz JPMG Proc. IEEE Conf. Commun. In Performance analysis of primary user detection in a multiple antenna cognitive radio. Glasgow; 2007:64826496. June 2007
Shiu DS, Foschini G, Gans M, Kahn J: Fading coorelation and its effect on the capacity of multielememt antenna systems. IEEE Trans. Commun. 2000, 48(3):502513. 10.1109/26.837052
Hur Y, Park J, Woo W, Lim K, Lee CH, Kim HS, Kaskar J IEEE International Symposium on Circuits and Systems. In A Widband Analog MultiResolution Spectrum Sensing (MRSS) Technique for Cognitive Radio (CR) Systems. Kos; 2006:40904093. May 2006
Luo L, Neihart NM, Roy S, Allstot DJ: A twostage sensing technique for dynamic spectrum access. IEEE Trans. Wirel. Commun. 2009, 8(6):30283037.
Maleki S, Pandharipande A, Leus G IEEE International Conference on Acoustic Speech and Signal Processing. In Twostage spectrum sensing for Cognitive Radios. Dallas; 2010:29462949. May 2010
Yue W, Zheng B, Meng Q, Yue W: Combined energy detection and oneorder cyclostationary feature detection techniques in cognitive radio systems. J. China Univ. Posts Telecommun. 2010, 17(4):1825. 10.1016/S10058885(09)604829
Yue W, Zheng B: Spectrum sensing algorithms for primary detection based on reliability in cognitive radio systems. J. Comput. Electr. Eng. 2010, 36(3):469479. 10.1016/j.compeleceng.2009.12.001
Chen Y: Improved Energy Detector for Random Signals in Gaussian Noise. IEEE Trans. Wirel. Commun. 2010, 9(2):558563.
Wang H, Noh G, Kim D, Kim S, Hong D: Advanced Sensing Techniques of Energy Detection in Cognitive Radios. J. Commun. Netw. 2010, 12(1):1929.
Baldo N, Zorzi M: Fuzzy Logic for crosslayer optimization in cognitive radio networks. IEEE Commun. Mag. 2008, 46(4):6471.
Merentitis A, Patouni E, Alonistioti N, Doubrava M IEEE 68th Vehicular Technology Conference. In To reconfigure or not to reconfigure: Cognitive mechanisms for mobile devices decision making. Calgary; 2008:15. Sep. 2008
Wendong Y, Yuhuang Y, Yueming C, Youyun X: A collaborative spectrum sensing scheme using fuzzy comprehensive evaluation in cognitive radio. J. Electron. (China) 2009, 26(3):326331. 10.1007/s1176700800120
Saklar B: Digital Communications: Fundamentals and Applications. 2nd edition. Prentice Hall, Upper Saddle River; 2001.
Cabric D, Tkachenko A, Brodersen RW IEEE Military Comm Conference. In Spectrum sensing measurements of pilot, energy, and collaborative detection. Washington; 2007:17. Oct. 2007
Bhargavi D, Murthy CR IEEE Eleventh International Workshop on Signal Processing Advances in Wireless Communications. In Performance Comparison of Energy, MatchedFilter and CyclostationarityBased Spectrum Sensing. Morocco; 2010:15. Dec. 2010
Tandra R, Sahai A: SNR walls for signal detection. IEEE J. Sel. Topics Signal Process. 2008, 2(1):417.
Fehske A, Gaeddert J, Reed JH First IEEE Symposium on New Frontiers in Dynamic Spectrum Access Networks. In A New Approach to Signal Classification Using Spectral Correlation and Neural Networks. Baltimore; 2005:144150. Nov. 2005
Cabric D, Mishra SM, Brodersen RW Proceedings of the 38th Asilomar Conference on Signals Systems and Computers. In Implementation Issues in Spectrum Sensing for Cognitive Radios. Pacific Grove; 2004:772776. Nov. 2004
Chang SY IEEE P802.2206/0032r0 Wireless RANs. Analysis of Proposed Sensing Schemes 2006.
Ling L, Yin L, Hongbo Z International Conference on Wireless Communications, Networking and Mobile Computing. In HalfVoting Based TwiceCooperative Spectrum Sensing in Cognitive Radio Networks. Beijing; 2009:13. Sep. 2009
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).
Author information
Authors and Affiliations
Corresponding author
Additional information
Competing interests
The authors declare that they have no competing interests.
Authors’ original submitted files for images
Below are the links to the authors’ original submitted files for images.
Rights and permissions
Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License ( https://creativecommons.org/licenses/by/2.0 ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
About this article
Cite this article
Ejaz, W., ul Hasan, N., Azam, M.A. et al. Improved local spectrum sensing for cognitive radio networks. EURASIP J. Adv. Signal Process. 2012, 242 (2012). https://doi.org/10.1186/168761802012242
Received:
Accepted:
Published:
DOI: https://doi.org/10.1186/168761802012242