Two-Dimensional Signal Localization Algorithm for ... - ee.oulu.fi

IEICE TRANS. COMMUN., VOL.E93–B, NO.11 NOVEMBER 2010

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PAPER

Two-Dimensional Signal Localization Algorithm for Spectrum Sensing ¨ † , Harri SAARNISAARI† , Markku JUNTTI† , Nonmembers, Johanna VARTIAINEN†a) , Janne LEHTOMAKI and Kenta UMEBAYASHI†† , Member

SUMMARY The localization algorithm based on the doublethresholding (LAD) method was originally proposed for detecting and localizing narrowband (NB) signals with respect to the search bandwidth. Its weakness is that the localized signal is often split into several parts, especially when the signal-to-noise ratio (SNR) is low. This may lead to the illusion of unoccupied frequencies in the middle of the signals. In this paper, an extension of the LAD method, namely the two-dimensional LAD (2-D LAD), is proposed to solve that problem. In addition to offering low computational complexity, the proposed method is able to operate at lower SNR values than the original 1-D LAD method. key words: narrowband signals, signal sensing

1.

Introduction

In signal detection, the challenge is to find out whether there is a signal present or not. This can be formulated as a hypothesis testing problem. In binary signal detection, there are two possible hypotheses: H0 (signal is not present) and H1 (signal is present). Detection is usually based on some threshold testing: if a received sample exceeds the threshold, hypothesis H1 is true. In this paper, narrowband (NB) signal detection is of interest. It can be used in several applications, in which spectrum sensing in future cognitive radios [1], [2] is one of the most interesting. In cognitive radios, unoccupied frequencies can be used for transmission if that does not cause interference to other users. For that reason, unoccupied frequencies must be known, - or reliably estimated. In cognitive radios, the search bandwidth can be much larger than the bandwidth of the signals, i.e., signals are NB with respect to the search bandwidth. There is a large variety of NB signal detection methods and many of these are based on thresholding, see, for example, [3] and references therein. Usually, only frequency domain processing is used. However, also methods that combine signal features both in time and frequency domains have been considered, as in [4]. Therein, a cyclostationary feature detection combining time and frequency domain information was proposed. The main problems are that many of the detection methods are either too complex to implement or they need some a priori information about Manuscript received August 28, 2009. Manuscript revised June 4, 2010. † The authors are with the Centre for Wireless Communications (CWC), University of Oulu, Finland. †† The author is with Tokyo University of Agriculture and Technology (TUAT), Koganei-shi, 184-8588 Japan. a) E-mail: [email protected] DOI: 10.1587/transcom.E93.B.3129

the signals to be detected. The localization algorithm based on the double-thresholding (LAD) method, called the onedimensional LAD (1-D LAD) in the sequel, has been proposed in [5], [6]. Iterative 1-D LAD method is a computationally attractive method for NB signal detection in frequency (or in some other) domain, thus corresponding to an energy detector. Here, the term ‘localization’ means the ability to estimate in which frequencies the NB signal exists, meaning that the 1-D LAD method is able to estimate the number of NB signal(s) and their parameters including bandwidth, center frequencies and even their signal-to-noise ratio (SNR) values [7]. The 1-D LAD method is blind in the sense that knowledge of the noise level or NB signal parameters is not required. The performance of the 1-D LAD method has been widely studied, for example, for spectrum sensing purposes. It was found that the 1-D LAD method is able to detect several types of NB signals, for example, sinusoidal signals [5], [8], both computer and signal generator generated binary phase shift keying (BPSK) signals [9], [10], real life wireless local area network (WLAN) signals [7], [11], frequency modulated (FM) signals [10], and real life wireless microphone signals [10]. Note that in the case of the real-life WLAN signals, the signals were frequencyshifted. In addition to the single-signal case, the 1-D LAD method also performs very well in multi-signal situations [9]. This feature is very useful in spectrum sensing, where several NB signals can exist in the studied frequency band. However, the 1-D LAD method has problems at low SNR values. The lower the SNR value is, the more signal components are below the threshold and, thus, cannot be detected. Signal samples below the threshold lead to the illusion of unoccupied frequencies. Large numbers of adjacent signal samples below the threshold lead to the illusion of large “white space” and may cause a collision. In some cognitive radio applications, sufficient guard bands between the channels are assumed, as, for example, in IEEE 802.22 WRAN standard [12]. However, if the falsely assumed “white space” is larger than the guard bands, the collision may still happen. For that reason, it is desirable that the sensing method is able to estimate the existing signals correctly, even at low SNR values. One possible solution for that is the LAD with adjacent cluster combining (ACC) proposed in [9]. Therein, after signal detection, an extra condition is applied: if two signals are separated by n or fewer samples below the lower threshold, it is decided that there is only one signal. However, the problem is that n

c 2010 The Institute of Electronics, Information and Communication Engineers Copyright


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must be rather large, meaning that two signals located close to each other are classified as one signal. In this paper, a novel improvement of the 1-D LAD method is proposed, called the time-frequency LAD or twodimensional LAD (2-D LAD). In the proposed extension, time domain processing (2-D processing) is performed after the 1-D LAD detection. If a sample is reported to exceed the threshold p times out of r examination periods, the sample is decided to belong to a signal. This approach coincides with “m-out-of n” detection (or m/n, binary detector) in radar systems [13]–[16]. The proposed 2-D LAD method offers better detection performance especially at low SNR values in terms of detecting the correct number of NB signals when compared to the 1-D LAD and 1-D LAD ACC methods. In addition, the computational complexity of the proposed extension remains at a reasonable level. This paper is organized as follows. In Sect. 2, the 1-D LAD method is presented. In Sect. 3, the 2-D LAD method is proposed. The numerical results are presented in Sect. 4 and conclusions are drawn in Sect. 5. 2.

Localization Algorithm Thresholding

Based

on

Double-

The 1-D LAD method is an iterative method for separating the signal samples into two or more sets: one or more NB signal sets and one noise set. The 1-D LAD method uses two thresholds. There are many advantages for using two thresholds instead of just one threshold. As the usage of the lower threshold helps to avoid separating a signal, the usage of the upper threshold helps to avoid false signal detection. It was found that the 1-D LAD method offers better detection performance than a method using just one threshold [5], [7]. The 1-D LAD method is illustrated in Fig. 1. The 1-D LAD method is composed of two parts, namely the threshold setting and signal detection, which are considered next.

2.1 Threshold Setting Procedure The thresholds can be calculated, for example, by using the forward consecutive mean excision (FCME) algorithm [17]. The FCME algorithm is an iterative threshold computation method that uses the threshold parameter T CME which is calculated a priori using the desired probability of false alarm PFA and the statistical properties of the noise. However, the noise variance does not have to be known. Assume that the Welch spectrum estimate is used. Furthermore, assume that M is the number of channels, K is the number of frequency domain samples in each channel so that the fast Fourier transformation (FFT) size is MK, L ≥ 2 is the number of overlapping blocks, N = 1, σ2 is the variance of the noise, windows are normalized so that the statistical mean of the frequency domain samples is LNσ2 , and 0 < γ ≤ 0.5 is the overlapping factor. Let w denote the coefficients of the used window matrix. Then, the PFA is defined to be [10], [18] PFA =

LN

λlLN−1 − T LN e LNσ2 λl , (λl − λm )

(1)

l=1 1≤m≤LN,ml

where λl is the lth eigenvalue of matrix [18] ⎡ ⎤ B ⎢⎢⎢ A ⎥⎥⎥ ⎢⎢⎢ BH A B ⎥⎥⎥ ⎢⎢⎢ ⎥⎥⎥ H ⎢⎢⎢ ⎥⎥⎥ A B B ⎢ ⎥⎥⎥ , H = ⎢⎢⎢⎢ .. ⎥⎥⎥⎥ ⎢⎢⎢ . ⎥⎥ ⎢⎢⎢ H B A B ⎥⎥⎥⎥⎦ ⎢⎢⎣ BH A

(2)

where A is N × N matrix with elements τ pq =

MK−1

w2l exp

l=0

2π jl (p − q) MK

(3)

and B is N × N matrix with elements γ pq = e−2π j(1−γ)(Ik +q−1) ×

γMK−1 l=0

wl wl+(1−γ)MK exp

2π jl (p − q) , MK

(4)

where Ik = kK + K−N 2 . Thus, the threshold parameter T CME can be solved from (1). The threshold is defined as [17]

Fig. 1

The 1-D LAD method.

(5) T = εT CME , 1 Q where ε = Q i=1 βi is the average sample energy of the current data set, Q is the set size and βi are the frequency domain samples in a data set. The FCME algorithm is obtained by rearranging the samples in ascending order according to their energies. The iteration starts from a small initial set, usually the size of the initial set is approximately 5−10% of the number of samples. The FCME algorithm iteratively calculates a new value of ε and, thus, a new threshold T until there are no

VARTIAINEN et al.: TWO-DIMENSIONAL SIGNAL LOCALIZATION ALGORITHM FOR SPECTRUM SENSING

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new samples below the threshold. The FCME algorithm is a forward-type method, so the threshold increases in each iteration. After the iterative processing, the samples below the threshold are from the noise and samples above the threshold are from NB signal(s) [17]. However, the FCME algorithm is not able to determine the number of detected signals or their parameters, so the 1-D LAD processing is required. In order to get the two thresholds, the FCME algorithm is run twice with two different threshold parameters obtained with two different probabilities of false alarms. Thus, the upper T u and lower T l LAD-thresholds are defined as [5] (6) T u = εu T CME1 and T l = εl T CME2 , Q βi , h ∈ {u, l} is the average sample mean where εh = Q1 i=1 of the current data set and T CME1 and T CME2 are the upper and the lower threshold parameters, respectively [17]. Note that the mean εh differs in the two cases because the threshold parameters used differ and, thus, the set size Q differ. Because the threshold parameters are chosen so that T CME1 > T CME2 , also T u > T l [9]. 2.2 Signal Detection Procedure After the threshold settings, the 1-D LAD method clusters the adjacent samples above the lower threshold into the same cluster, i.e., tk (n) = {δi |δi > T l , i ∈ {lk , · · · , lk + pk − 1}},

(7)

where δi is the ith frequency domain sample, lk is the lowest index of the samples in the kth cluster and pk is the number of adjacent samples in the kth cluster. The cluster is accepted to be caused by an NB signal if at least one of the samples (the sample with the largest energy) in the cluster is also above the upper threshold, i.e., NB signal clusters are ik (n) = {tk (n)| max{tk (n)} > T u }.

(8)

The signal detection capability of the FCME algorithm and, thus, the 1-D LAD method, depends on the size of the initial set. The initial set has to be free of signal samples to ensure the successful detection. It means that if the size of the initial set is, for example, 10% of the number of samples, the signal samples can cover at most 90% of the number of samples. However, the 1-D LAD method operates best when the signal is narrowband. The effect of the bandwidth of the signal on the detection capability of the 1-D LAD method was studied, for example, in [5]. 3.

Two-Dimensional LAD

After the 1-D LAD method has clustered the samples, the following information is available: (1) Samples (clusters) caused by an unknown NB signal(s). Because the 1-D LAD method is able to separate several signals, there are m groups (sets) of samples, i.e.,

unknown NB signals. (2) The rest of the samples are by the receiver thermal noise (and possibly wideband signal(s) s(n)). Due to the needless separation of signals, the number of detected signals is usually larger than the actual number of signals m, i.e., m > m. The proposed method called the time-frequency LAD (2-D LAD) can enhance the probability of detecting the correct number of signals using time domain processing after the 1-D LAD (or 1-D LAD ACC) clustering. In other words, not one but r consecutive time intervals are considered to decide whether the samples are caused by an NB signal or not. Let Nl be the length of the considered frequency domain signal. If a frequency domain sample βi , i = 1, · · · , Nl is reported to belong to some NB signal set in at least p, 1 < p ≤ r times out of r examination periods, the sample βi is decided to belong to that NB signal set. For example, if p = 3 and r = 7, it means that if the frequency domain sample is reported to belong to some NB signal set at least 3 times out of 7, i.e., 3−7 times out of 7, the sample is decided to belong to that NB signal set. A sliding window is used, i.e., the first examination period consists of time intervals 1, · · · , p, the second examination period consists of time intervals 2, · · · , p + 1, and so on. Mathematically, in the “p out of r” case, the probability of detection is given by [16] r r! Pk (1 − P)r−k , (9) P[p/r] = k!(r − k) k=p where P is the probability of detection in the 1-D case. Note, that this is valid when the samples are i.i.d. For example, if p = 2 and r = 3, the probability of detection is 3P2 − 2P3 . In that case, the detection probability is higher when compared to the 1-D case if P > 0.5 (with P = 0.5, P[p/r] = P) [16]. If, for example, p = 2 and r = 4, the probability of detection is 6P2 − 8P3 + 3P4 . The probability of false alarms can also be obtained from (9). For example, when p = 2, r = 3 and PFA = 10−5 , P[p/r]FA = 3 · 10−10 . 3.1 Computational Complexity In the 2-D LAD method, the most complex parts are the Fourier transform which is required in frequency domain processing, and rearranging the samples (sorting) [17]. The computational complexity of FFT is N log2 N [19]. The sorting can be done, for example, using Heapsort (average computational complexity N log2 N) or Quicksort (average computational complexity 2 log2 N) [19]. The clustering in the 1-D LAD method and the time domain processing in the 2-D LAD method do not affect the overall complexity. Thus, the computational complexity of the 2-D LAD method is of the order of N log2 N. 4.

Numerical Results

In the next section, both the numerical examples and numer-


3132 Table 1 Probability of detecting one signal PD and the miss probability P M vs. p. A BPSK signal with 19% bandwidth and SNR = −7 dB, r = 10. 1-D LAD p p=1 p=2 p=3 p=4 p=5 p=6 Fig. 2 Illustration of the (a) 1-D LAD and (b) 2-D LAD methods. Frequency samples (N = 27) in the horizontal axis and time in the vertical axis. Black blocks present the signal samples as white blocks present the noise samples.

ical results are presented. 4.1 Numerical Examples The results of the 1-D and 2-D LAD methods are presented in Fig. 2. Frequency indices are in the horizontal axis and the time is in the vertical axis. There are N = 27 frequency samples (= columns) and 7 time intervals (= rows). In the 1-D LAD method in Fig. 2(a), only one time interval (i.e., one line) is considered at a time. In other words, the detection is performed independently at each time interval. The white blocks present the samples classified as being caused by the noise, and the black blocks are the samples classified as being caused by a signal. In this case, the signal (= black blocks) seems to be in frequency samples (= columns) 9 − 17. However, there are also white blocks inside the assumed signal samples. For example, when considering the bottom line, the signal is split into three parts because there are some samples that were below the threshold. In that case, the 1-D LAD method separates the signal into three parts. In the 2-D LAD method, r = 7 and p = 3 are used, so the r latest time intervals are used to combine the detection results. For example, when considering the ninth column (i = 9, the first one to include both white and black blocks), it can be seen that the specific frequency domain sample was five times above the threshold and only twice below the threshold. So, it is classified as a signal sample. In the tenth column, the frequency sample was six times above the threshold and only once below the threshold. As in the previous case, that frequency domain sample is classified as a signal sample. The combined result can be seen in Fig. 2(b): only one signal is decided to be present. 4.2 Measured Results As mentioned, the 1-D LAD method can detect different kinds of NB signals. Herein, a signal generator generated BPSK signal was selected instead of a computer-generated signal so as to obtain realistic results. The measured signal contains a BPSK signal and a measured thermal noise. The measurements were carried out using an Agilent E4446A spectrum analyzer. The software used was 89600 Series

PD /P M 7%/93% 7%/93% 7%/93% 7%/93% 7%/93% 7%/93%

1-D LAD ACC PD /P M 51%/49% 51%/49% 51%/49% 51%/49% 51%/49% 51%/49%

2-D LAD PD /P M 100%/− 82%/18% 69%/31% 59%/41% 26%/74% 9%/91%

2-D LAD ACC PD /P M 100%/− 89%/11% 80%/20% 80%/20% 69%/31% 37%/63%

Vector Signal Analysis Software (SVA). The BPSK signal was generated using the Agilent E4438C signal generator. The total bandwidth was 8 MHz, 614306 samples were recorded corresponding to 60 ms integration time, and the sensitivity was set to the maximum value. The NB signal bandwidth was 25%, 19% or 12.5% of the total bandwidth. The LAD methods can operate in any frequency range (i.e., from kHz to GHz) [5]. Herein, the center frequency was 1 GHz. The SNR was defined as the ratio of the signal power to the noise power in the system’s bandwidth. Welch’s spectrum estimate method was used with ten overlapping blocks and 50% overlap [10]. The FFT size was 1024. The initial set size used in the threshold calculation was 64 samples. The upper and lower thresholds were calculated using PFA,DES = 10−5 and 10−1 , respectively [10]. When using the ACC processing, n = 8 samples. In Table 1, the probability of detecting one signal (PD ) as well as the miss probability (P M = 1 − PD ) are presented as a function of p for a BPSK signal with 19% bandwidth. The value of r = 10. The false alarm rate is negligible for all values of p, i.e., it cannot be obtained by the time limited measurements performed. Note, that this is a specific case and the results are valid only for the considered signal: for another signal with different bandwidth and SNR the results may differ. The value of p is used only in the 2-D LAD method, so when considering the 1-D LAD method, the value of p does not affect the probability. In the 2-D LAD method, the smaller the p, the higher the probability is. However, a very small value of p can not be used because of increased false alarm. The value of r defines how many time intervals are considered. The larger the r, the more time intervals are taken into account at the same time. In practical implementations, this increases the complexity of the system, for example, via a need for increased memory. Based on the values used in the literature and extensive simulations with signals with different bandwidths, p = 5 and r = 10 were selected. It means that at least 50% of the samples in the particular frequency have to be reported to belong to some NB signal, before that particular frequency is decided to be a part of a NB signal. However, it is also possible to use some other values of p and r. The probability of detecting the correct number of signals (i.e., one signal) as a function of SNR is presented in Fig. 3. The methods used are the 1-D LAD, 1-D LAD ACC and the proposed 2-D LAD method. The BPSK signal has


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Fig. 3 The probability of detecting one signal vs. SNR. A BPSK signal with bandwidth of 12.5% of the system bandwidth. The 1-D LAD method, 1-D LAD ACC method and 2-D LAD method.

bandwidth 12.5% of the system’s bandwidth. It can be seen that when SNR is small, the performance of the 1-D LAD method is rather poor. Instead, when using the 2-D LAD method, the probability of detecting one signal is very good even when SNR is very small. For example, when SNR = −8 dB, the 1-D LAD method is able to estimate the correct number of signals correctly only in 10% of the cases. Instead, the 2-D LAD method is able to estimate the correct number of signals correctly even in 84% of the cases. The 1-D LAD method requires SNR = −3 dB to achieve at least 84% detection probability, as the 2-D LAD method requires SNR = −8 dB to achieve the same detection probability. In that case, the gain is about 5 dB. The performance of the 2-D LAD method is slightly better when compared to the performance of the 1-D LAD ACC method. A measured BPSK signal with bandwidth of 25% of the system’s bandwidth with SNR per sample equal to −4 dB is presented in Figs. 4 and 5. In Fig. 4, the detection was performed using the 1-D LAD method in the frequency domain. One line represents one time interval as in Fig. 2(a). The detected signal samples are marked in black. As can be seen, the signal has been split into several parts. The probability of detecting one signal is, on average, 4%. In Fig. 5, the detection was performed using the 2-D LAD method in the frequency domain. One time interval includes the combined results of r = 10 time intervals. When using the proposed 2-D LAD method, the signal is detected almost perfectly: the probability of detecting one signal is 87%. Moreover, if the ACC processing is used, the corresponding probabilities are 80% (1-D LAD ACC) and 100% (LAD ACC + time domain processing, i.e., 2-D LAD ACC). These figures are not shown here. In Figs. 6 and 7, the bandwidth of the BPSK signal is 19% of the system’s bandwidth. SNR equals −7 dB, i.e., the signal is weaker than in the previous case. The 1-D LAD method was used in Fig. 6. It can be noted that the signal was split into a larger number of parts than in the previ-

Fig. 4 A BPSK signal with bandwidth of 25% of the system bandwidth and with SNR = −4 dB after the 1-D LAD method.


ous case. In the time intervals 1−10, there are many white spaces inside the signal, these are due to measurement uncertainties. In that example, the probability of detecting one signal is 7%. Using the proposed 2-D LAD method, the signal was detected more accurately as can be seen from Fig. 7. The results show that the probability of detecting one signal is 26%. With ACC processing, the probabilities are 51% (1-D LAD ACC) and 69% (2-D LAD ACC). These latter figures are not shown here. Depending on the parameters, the proposed 2-D method can rapidly adapt to the changes in the environment, i.e., the signal disappears or changes its frequency, or a new signal emerges. In those cases, pure averaging may not necessarily operate correctly. This is illustrated in Fig. 8, where there is a BPSK signal with bandwidth of 12.5% of the system’s bandwidth and a point of discontinuity between 50


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Fig. 8 The 2-D LAD method. A signal with a point of discontinuity between time instants 50 and 70.

Fig. 9 Fig. 7 A BPSK signal with bandwidth of 19% of the system bandwidth and with SNR = −7 dB after the 2-D LAD method.

and 70 time instants. In Fig. 8, the 2-D LAD method is used and is able to find the signal correctly with a delay of p time intervals. Note that in the previous results, there was only one signal, so the ACC processing operates well. In the case when there are two (or more) closely spaced signals, i.e., the number of frequency domain samples between the signals is ≤ 8 = n samples, the ACC processing clusters these signals as one signal. This phenomenon is illustrated in Fig. 9: using 1-D LAD or 2-D LAD method, these signals are classified as independent signals. Instead, using ACC processing, these signals are joined together and deemed as only one signal. In that case, the performance of the ACC processing is poor. Instead, the proposed 2-D LAD method operates more efficiently because it operates in time domain instead of frequency domain.

The 1-D LAD method. Two closely spaced signals.

It can be concluded that the proposed 2-D LAD method gave a very good performance in detecting the correct number of signals. The computational complexity of the 2-D processing (time domain processing) is very low and the sensitivity of the 2-D processing is better than that of the ACC processing, because the 2-D processing does not widen the signal in the frequency domain thus avoiding the signal blending in with another neighboring signal. Thus, the 2-D processing can be used instead of or together with the ACC processing, depending on the situation. 5.

Conclusions

In this paper, we proposed an extension of the 1-D LAD signal detection method, namely the time-frequency LAD (2-D LAD). The proposed extension prevents the separation of a detected signal into several parts, which is a problem especially at low SNR values. The numerical results show that the proposed extension offers greatly improved detec-


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tion performance in terms of detecting the correct number of narrowband signals as compared to the original 1-D LAD method. Together with the ACC processing, the results are even better. Acknowledgments This research was supported by the Finnish Funding Agency for Technology and Innovation (TEKES), Nokia, Nokia Siemens Networks, Elektrobit, Infotech Oulu Graduate School and the Academy of Finland. Ms Hilary Keller is acknowledged.

impulse detectors for communications based on the classical diagnostic methods,” IEEE Trans. Commun., vol.53, no.3, pp.395–398, March 2005. [18] S. Wang, F. Patenaude, and R. Inkol, “Computation of the normalized detection threshold for the FFT filter bank-based summation CFAR detector,” J. Computers, vol.2, no.6, pp.35–48, Aug. 2007. [19] W. Press, W. Vetterling, S. Teukolsky, and B. Flannery, Numerical Recipes in C, 2nd ed., Cambridge University Press, New York, USA, 1992.

References [1] J. Mitola and G.Q.M. Jr., “Cognitive radio: Making software radios more personal,” IEEE Pers. Commun., vol.6, no.4, pp.13–18, 1999. [2] S. Haykin, “Cognitive radio: Brain-empowered wireless communications,” IEEE J. Sel. Areas Commun., vol.23, no.2, pp.201–220, 2005. [3] S.M. Kay, Fundamentals of statistical signal processing: Detection theory, Prentice Hall, Upple Saddle River, NJ, USA, 1998. [4] Y. Qi, W. Wang, T. Peng, and R. Qian, “Spectrum sensing combining time and frequency domain in multipath fading channels,” Proc. Third Int. Conf. on Commun. and Networking in China (ChinaCom), Hangzhou, China, Finland, Aug. 2008. [5] J. Vartiainen, J.J. Lehtomäki, and H. Saarnisaari, “Double-threshold based narrowband signal extraction,” Proc. IEEE Veh. Technol. Conf., CD-ROM, Stockholm, Sweden, May/June 2005. [6] J.J. Lehtomäki, J. Vartiainen, M. Juntti, and H. Saarnisaari, “Analysis of the LAD methods,” IEEE Signal Process. Lett., vol.15, pp.237–240, Jan. 2008. [7] J. Vartiainen, H. Saarnisaari, J. Lehtomäki, and M. Juntti, “A blind signal localization and SNR estimation method,” Proc. IEEE Military Commun. Conf., Washington DC, USA, Oct. 2006. [8] J. Vartiainen, J. Lehtomäki, S. Aromaa, and H. Saarnisaari, “Localization of multiple narrowband signals based on the FCME algorithm,” Proc. Nordic Radio Symposium (NRS) 2004 including the Finnish Wireless Communications Workshop (FWCW) 2004, CDROM, Oulu, Finland, Aug. 2004. [9] J. Vartiainen, H. Sarvanko, J. Lehtomäki, M. Juntti, and M. Latva-Aho, “Spectrum sensing with LAD-based methods,” Proc. IEEE Int. Symp. Pers., Indoor, Mobile Radio Commun., Athens, Greece, Sept. 2007. [10] J. Lehtomäki, S. Salmenkaita, J. Vartiainen, J.P. Mäkelä, R. Vuohtoniemi, and M. Juntti, “Measurement studies of a spectrum sensing algorithm based on double thresholding,” Proc. Wireless Vitae, Aalborg, Denmark, May 2009. [11] J. Vartiainen, M. Alatossava, J.J. Lehtomäki, and H. Saarnisaari, “Interference suppression for measured radio channel data at 2.45 GHz,” Proc. IEEE Int. Symp. Pers., Indoor, Mobile Radio Commun., Helsinki, Finland, Sept. 2006. [12] H. Zhu and K.J.R. Liu, Resource Allocation for Wireless Networks: Basics, Techniques and Applications, Cambridge University Press, 2008. [13] J. Harrington, “An analysis of the detection of repeated signals in noise by binary integration,” IRE Trans. Inf. Theory, no.1, pp.1–9, 1955. [14] M. Schwartz, “A coincidence procedure for signal detection,” IRE Trans. Inf. Theory, no.2, pp.135–139, 1956. [15] J.V. DiFranco and W.L. Rubin, Radar Detection, Artech House, Dedham, MA, 1980. [16] M.I. Skolnik, Introduction to Radar Systems, McGraw-Hill, NY, USA, 2001. [17] H. Saarnisaari, P. Henttu, and M. Juntti, “Iterative multidimensional

Johanna Vartiainen graduated with an M.Sc. in Mathematics from the University of Oulu, Finland, in 2001 and with a Licentiate in Information Engineering from the University of Oulu, Finland, in 2005. Currently she is a Research Scientist at the Centre for Wireless Communications (CWC). She is also preparing her Ph.D. in Telecommunications. Her main research areas are interference suppression and signal detection mainly for cognitive radios and passive radars. She is a Student Member of IEEE.

Janne Lehtomäki graduated with an M.Sc. (Tech.) and Ph.D. (Tech.) in Telecommunications from the University of Oulu in 1999 and 2005, respectively. Currently, he is a postdoctoral Research Fellow at the Centre for Wireless Communication (CWC), University of Oulu. In 2008, he worked as a visiting researcher at Tokyo University of Agriculture and Technology in three months. His research interests are in statistical signal processing and cognitive radios. He is a Member of IEEE.

Harri Saarnisaari graduated with an M.Sc. in Applied Electrical Physics from the University of Kuopio in 1993 and a Ph.D. (Tech.) in Digital Communications from the University of Oulu in 2000. Since 1994 he has been working as a lecturer, researcher and project manager at the Telecommunications laboratory and Centre for Wireless Communications (CWC) at the University of Oulu. His research interest lies in signal processing for communications. Especially he is interested in direct sequence code phase synchronization in hostile environment, adaptive antenna algorithms, channel estimation, positioning, network synchronization and system design. He is a Senior Member of IEEE.


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Markku Juntti graduated with his M.Sc. (Tech.) and Ph.D. (Tech.) in Electrical Engineering from the University of Oulu, Oulu, Finland in 1993 and 1997, respectively. Dr. Juntti was with the University of Oulu in 1992–1998. In the academic year 1994–1995 he was a Visiting Scholar at Rice University, Houston, Texas. In 1999–2000 he was a Senior Specialist with Nokia Networks. Dr. Juntti has been a Professor of Telecommunications at the University of Oulu, Department of Electrical and Information Engineering and Centre for Wireless Communications (CWC) since 2000. His research interests include signal processing for wireless networks as well as communication and information theory. He is an author or coauthor in some 200 papers published in international journals and conference records as well as in book WCDMA for UMTS published by Wiley. Dr. Juntti was an Associate Editor for IEEE Transactions on Vehicular Technology in 2002–2008. He was Secretary of IEEE Communication Society Finland Chapter in 1996–1997 and the Chairman for years 2000– 2001. He has been Secretary of the Technical Program Committee (TPC) of the 2001 IEEE International Conference on Communications (ICC’01), and the Co-Chair of the Technical Program Committee of 2004 Nordic Radio Symposium and 2006 IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC 2006). He is a Senior Member of IEEE.

Kenta Umebayashi received his B.E., M.E. and Ph.D. degrees from Yokohama National University in 1999, 2001 and 2004, respectively. From 2004 to 2006 he was Research Scientist at the Centre for Wireless Communications, University of Oulu. He is currently an Assistant Professor in Tokyo University of Agriculture and Technology. His research interests lie in the areas of statistical signal processing and detection and estimation theory in wireless communication such as cognitive radio. He is a Member of IEEE.