 Research
 Open Access
Time domain averaging and correlationbased improved spectrum sensing method for cognitive radio
 Shenghong Li^{1}Email author and
 Guoan Bi^{2}
https://doi.org/10.1186/16876180201446
© Li and Bi; licensee Springer. 2014
 Received: 20 November 2013
 Accepted: 16 January 2014
 Published: 4 April 2014
Abstract
Based on the combination of time domain averaging and correlation, we propose an effective time domain averaging and correlationbased spectrum sensing (TDACSS) method used in very low signaltonoise ratio (SNR) environments. With the assumption that the received signals from the primary users are deterministic, the proposed TDACSS method processes the received samples by a time averaging operation to improve the SNR. Correlation operation is then performed with a correlation matrix to determine the existence of the primary signal in the received samples. The TDACSS method does not need any prior information on the received samples and the associated noise power to achieve improved sensing performance. Simulation results are presented to show the effectiveness of the proposed TDACSS method.
Keywords
 Cognitive radio
 Spectrum sensing
 Time domain averaging
 Correlation
1 Introduction
Cognitive radio (CR) networks allow unlicensed (or secondary) users to opportunistically exploit the underutilized spectrum bandwidth of the licensed (or primary) users. Spectrum sensing is a key operation performed by the CR networks to determine the spectrum holes of the spectrum allocated to a primary user. In the literature, several kinds of typical spectrum sensing methods have been reported, including energy detection methods [1, 2], matched filter detection methods [3, 4], and cyclostationarity feature detection methods [5]. The energy detection methods need the prior knowledge of noise power and are vulnerable to the noise uncertainty. The matched filter detection methods need to know the waveform of the primary user in advance. The methods of cyclostationarity feature detection require the information on the cyclic frequencies of the primary user. The prior knowledge requirements of these methods often limit their realistic applicability. To avoid these undesirable requirements, some new statistical covariancebased methods for spectrum sensing in additive i.i.d white noise environments have been proposed [68]. These reported methods do not need the prior information about the signal and the noise power to achieve a sensing performance in a low signaltonoise ratio (SNR) environment of 22 dB. However, these methods assume that the primary user’s signal is a stationary random process and that all the received data samples contain the primary user’s signal if it exists. In fact, most existing spectrum sensing methods require the last assumption. In fact, it is very possible that only a part of the received samples in practice contain the signal from the primary user or a part of or all the received samples are from some signals that are not stationary. For example, the initial transient signal, known as fingerprint of a wireless device [6], from the primary user is not stationary. If such samples are used, the methods reported in [7–9] will lose the supporting prerequisite and become unusable. Even in the cases that these required assumptions are valid, it is difficult for the methods in [7–9] to obtain the theoretical sensing performance in lower SNR environments when the number of the received samples available is limited.
To overcome these problems and support more effective sensing in low SNR environments, we propose an effective time domain averaging and correlationbased spectrum sensing (TDACSS) method based on time domain averaging and correlation for spectrum sensing in additive i.i.d white noise environments. Similar to the methods reported in [7–9], the proposed TDACSS method does not need any aforementioned prior information and achieve desirable sensing performance in very low SNR environments. The TDACSS method assumes the signal from the primary user to be deterministic. By making use of time domain averaging, the SNR of the received samples is increased. Then, the task of spectrum sensing is obtained by performing the correlation operation. Simulation results from various environments are presented to show the effectiveness of the proposed TDACSS method.
The rest of this paper is organized as follows. Section 2 describes the system model and sampling operations to obtain the input sample sequence. Section 3 gives the proposed TDACSS spectrum sensing method. Simulation results and discussions are presented in Section 4. Finally, conclusion is drawn in Section 5.
2 System model and sampling description
and s_{pri−c}(t) is any received primary signal, and w_{ c }(t) is the i.i.d white noise with a zero mean and a variance represented by δ^{2}.
where, z(n)=z_{ c }(n/f_{ s }), s(n)=s_{ c }(n/f_{ s }) and w(n)=w_{ c }(n/f_{ s }).
It is worth noting that if f_{0}+W/2 is very high, it is difficult to implement the sampling process because the available high speed samplers can support up to several tens of GHz [10–12]. Therefore, we should suitably select the value of f_{0}+W/2 to allow an appropriate large value of L_{mul} possible. It is also possible to use down conversion process [12] to generate the received samples when the value of f_{0}+W/2 is very high.
3 The proposed spectrum sensing method
The proposed TDACSS method treats the samples of the primary users to be deterministic. It firstly averages the received data samples in the time domain to increase SNR (or reduce the noise) then obtains the spectrum sensing by means of correlation operations.
3.1 Sample time domain averaging
where M is a small positive integer, for example, M=5, and N=[N_{0}/L_{mul}].
where ω(n) can be considered as the samples of the i.i.d white noise ω_{ c }(t) with a zero mean and a variance of $\frac{{\delta}^{2}}{2M+1}$. Let us define $\text{SNR}=10\mathit{\text{lg}}\frac{\sum _{n=1}^{N}{s}^{2}(n\xb7{L}_{\text{mul}})}{N\xb7\text{Var[noise]}}$, where Var[.] denotes the variance of a random variable. It can be easily proved that the SNR of the sequence produced by the time averaging process is increased by 10l g(2M+1) dB. If M=5, for example, the SNR of the time averaged output is increased by about 10 dB. This gain in SNR is very valuable for effective spectrum sensing in the environment of strong noise.
3.2 Correlation operation and sensing decision
Similar to the method in [7], let us perform the correlation operation on the input samples and make a decision on the signal presence of the primary user based on a constructed correlation matrix. Being different from that in [7], however, the correlation used here is to be discussed from the view point of deterministic signal samples.
where E[ ·] is the expectation operation. Finally, the first term in (6), i.e. $\frac{1}{N}\sum _{n=1}^{N}s\left(n{L}_{\text{mul}}\right)s\left(\right(ni\left){L}_{\text{mul}}\right)$, i=1,2,3,… are usually not identically zero when s(n) is a deterministic sample sequence.
3.3 The TDACSS Method
Based on the discussion above, we propose the an improved spectrum sensing method as follows.

Step 1: sample the received signal at frequency f_{ s } to obtain the discrete samples z(n),n=1,2,…,N_{0}.

Step 2: for a given value of M, calculate e(n),n=1,2,…,N by (3).

Step 3: for a given value of L, calculate r_{ e }(i),i=0,1,…,L−1, and construct the matrix R_{ e } in (7).

Step 4: calculate T in (8), and properly choose the value of λ for the sensing decision.
Similar to the approach reported in [7, 9], let us use the computer simulation approach based on the given probability of false alarm, P_{ f }, to choose the threshold λ. That is, first, a P_{ f } value is given and white noise is generated as the input, and then with a number of simulation results of T in (8), the threshold value, λ, is selected to meet the requirement of P_{ f }.
3.4 Performance analysis of the proposed TDACSS
From the description above, it is seen that the proposed TDACSS method does not need any prior information about the waveform and the cyclic frequencies of the primary user’s signal and the noise power, which is similar to those methods in [7–9]. In contrast, our proposed method is also valid when only part of the received sample sequence contain the primary user’s signal, which is the main different from those in [7–9] that assume that the primary user’s signal is a stationary random process and that all the received samples must contain the primary user’s signal. Therefore, the proposed method is more general and flexible.
By the averaging operation in the time domain, the proposed TDACSS method is able to achieve an SNR improvement by 10l g(2M+1) dB compared with the CAV method in [7] for the same values of N and L. Therefore, the proposed one is expected to improve the sensing performance substantially particularly in low SNR environments.
4 Simulations
In this section, simulation results are reported for the following three signal settings in the AWGN environments to verify the effectiveness of the proposed method.

Case I: the signal from the primary user is stationary. During spectrum sensing, all the received samples contain the signal of the primary user.

Case II: the signal from the primary user is stationary. During spectrum sensing, only a part of the received samples contain the signal of the primary user.

Case III: the signal from the primary user is not stationary.
For comparison, we also present the simulation results of the CAV method [6] applicable for case I. For all the simulations, the values of threshold λ are chosen by the computer simulation approach described previously, and 1,000 Monte Carlo runs are carried out to estimate the value of λ.
4.1 Simulation for case I
We use a wireless microphone signal generated by the method in [14] with the following parameters: central frequency f_{0}=100 MHz, bandwidth W=36.8 KHz. Based on the discussion in Section 2, the sampling frequencies used for the proposed TDACSS method and the CAV method are f_{ s }=10.3 GHz≥L_{mul}(f_{0}+W/2), where, L_{mul}=100, and f_{ s }=103 MHz≥f_{0}+W/2, respectively.
4.2 Simulation for case II and case III
with ${L}_{f}=1{0}^{8},{C}_{f}=1010,R=0.08,\sigma =R/2{L}_{f}=4,000,000,{w}_{0}=\sqrt{1/{L}_{f}{C}_{f}}=1{0}^{9}$. The fingerprint signal has the central frequency f_{0}=160 MHz and the bandwidth W=8 MHz. The sampling frequency for the TDACSS method is f_{ s }=16.4 GHz≥L_{mul}(f_{0}+W/2), where, L_{mul}=100.
5 Conclusion
In this paper, an improved spectrum sensing method, TDACSS, based on time domain averaging and correlation has been proposed. The time domain averaging process has been typically used to decrease the noise effects, and correlation matrix has been constructed to decide the existence of the primary user’s signal. In comparing with other reported method, such as CAV method, the proposed one can sense a primary user’s signal in the white noise environment in very low SNR environments without requiring any prior knowledge about the signal and noise power. In particular, the proposed method is flexible to effectively sense the signals that are not stationary. Our simulation results have shown the desirable advantages of the proposed methods.
Declarations
Acknowledgements
This work is funded by the National Science Foundation of China (61271316, 61071152), 973 Program (2010CB731403, 2010CB731406, 2013CB329605) of China, Chinese National ‘Twelfth FiveYear’ Plan for Science & Technology Support (2012BAH38 B04), Key Laboratory for Shanghai Integrated Information Security Management Technology Research, and Chinese National Engineering Laboratory for Information Content Analysis Technology.
Authors’ Affiliations
References
 Cabric D, Mishra SM, Brodersen RW: Implementation issues in spectrum sensing for cognitive radios. In Proc. Asilomar Conf. Signals, Syst. Computers. Pacific Grove, CA, USA; 2004:772776.Google Scholar
 Sahai A, Cabric D: Spectrum sensing: fundamental limits and practical challenges. In IEEE Int. Symp. New Frontiers DySPAN. Baltmore, Maryland, USA; 2005:190.Google Scholar
 Cabric D, Tkachenko A, Brodersen RW: Spectrum sensing measurements of pilot, energy, and collaborative detection. In Proc. IEEE Military Commun. Conf. (MILCOM). Washington, D.C., USA; 2007.Google Scholar
 Chen HS, Gao W, Daut DG: Signature based spectrum sensing algorithms for IEEE 802.22 WRAN. In Proc. IEEE Int. Conf. Communications (ICC). Glasgow, Scotland, UK; 2007:64876492.Google Scholar
 Han N, Shon SH, Joo JO, Kim JM: Spectral correlation based signal detection method for spectrum sensing in IEEE 802.22 WRAN systems. In Proc. Intern. Conf. Advanced Commun. Technology. Phoenix Park, South Korea; 2006:17651770.Google Scholar
 Xu JY, Zhang HS: Detection to primary user based on radio frequency fingerprint in cognitive radio. In Proc. Intern. Cong. Image and Signal Processing (CISP). Tianjin, China; 2009.Google Scholar
 Zeng Y, Liang YC: Spectrumsensing algorithms for cognitive radio based on statistical covariance. IEEE Trans. Veh. Technol 2009, 58(4):18041815.View ArticleGoogle Scholar
 Zeng Y, Liang YC: Eigenvaluebased spectrum sensing algorithms for cognitive radio. IEEE Trans. Commun 2009, 57(6):17841793.View ArticleGoogle Scholar
 Zeng Y, Liang YC, Zhang R: Blindly combined energy detection for spectrum sensing in cognitive radio. IEEE Signal Process. Lett 2008, 15: 649652.View ArticleGoogle Scholar
 Lecklider T: Raising the B/W bar to 32 GHz. Eval. Eng 2010, 49(6):34.Google Scholar
 Zhao YJ, Hu YH, Wang HJ: Enhanced random equivalent sampling based on compressed sensing. IEEE Trans. on Instrum. Meas 2012, 61(3):579586.View ArticleGoogle Scholar
 LopezValcarce R, VazquezVilar G: Wideband spectrum sensing in cognitive radio: joint estimation of noise variance and multiple signal levels. In IEEE International Workshop on Signal Processing Advances for Wireless Communications (SPAWC 2009). Perugia, Italy; 2009:96100.Google Scholar
 Pini M, Akos DM: Exploiting GNSS signal structure to enhance observability. IEEE Trans. Aerospace Electron. Sys 2007, 43(4):15531565.View ArticleGoogle Scholar
 Zeng Y, Liang YC: Performance of eigenvalue based sensing algorithms for detection of DTV and wireless microphone signals. In IEEE 802.2206/186r0. Phoenix Park, South Korea; 2006.Google Scholar
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