Evaluation of White Space Detection Algorithms for

Cognitive Radio Applications

Marcelo Molina Silva, Luiz A. Reis da Silva Mello and Carlos V. Rodriguez Ron Centro de Estudos em Telecomunica<;5es da PUC-Rio

Rio de Janeiro, Brazil molina@cetuc.puc-rio.br

Abstract-This article presents results of evaluations and comparisons of two detection algorithms for cognitive radio, the energy detection (ED) and spectral covariance sensing (SCS), using experimental data obtained in field measurement campaigns. The algorithms were tested to evaluate their performance in terms of probability of false alarm, probability of detection and probability of misdetection. The experimental data consists of received signal measurements at geo-positioned points along different routes in an urban area.

Keywords - Cognitive radio; Spectrum sensing; Energy

detection; Spectral covariance sensing; Propagation in urban

areas.

I. INTRODUCTION

The term cognitive radio is often used as a synonym for dynamic spectrum access (DSA). A commonly accepted defmition is: "A radio or system that senses and is aware of its operational environment and can dynamically, autonomously, and intelligently adjust its radio operating parameters". The cognitive radio is one of the current affairs and interest in research, development hardware and software aspects of regulation and standardization, since it is a set of technologies that enables to leverage the best way to access the radio spectrum. The initial idea of cognitive radio appears with the contributions of J. Mitola [1-3], that proposed the architecture of an intelligent radio system capable of sensing the radio spectrum environment and self-configure accordingly.

Cognitive radio uses the concept of dynamic spectrum access, in which an unlicensed user termed as secondary user (SU) can use the spectrum of a licensed user termed as primary user (PU) with a constraint that it should not interfere to the power level of PU[4].

The Main functions of CR are spectrum sensing, spectrum management, spectrum sharing and spectrum mobility [5].

Indeed, if we were to scan portions of the radio spectrum including the revenue-rich urban areas, we would fmd that [6], [7]:

I) Some frequency bands in the spectrum are largely unoccupied most of the time;

2) Some other frequency bands are only partially occupied; 3) The remaining frequency bands are heavily used.

978-1-4799-1397-8/13/$31.00 ©2013 IEEE

The underutilization of the electromagnetic spectrum leads us to think in terms of spectrum holes, for which we offer the following defmition [6]:

A spectrum hole is a band of frequencies assigned to a

pr imary user , but, at a par ticular time and specific geographic location, the band is not being utilized by that user .

Spectrum utilization can be improved significantly by making it possible for a secondary user (who is not being serviced) to access a spectrum hole unoccupied by the primary user at the right location and the time in question. Cognitive radio [8], [9], inclusive of software-defmed radio, has been proposed as the means to promote the efficient use of the spectrum by exploiting the existence of spectrum holes [10].

Regarding the access to spectrum, among the ways of implementing cognitive radio there are two main trends: to make use of spectrum sensing techniques (detection white spaces) and making use of information about the spectrum used in geo-referenced databases.

Spectrum sensing is one of the most important tasks for the operation of cognitive radios, as it affects both the performance of the secondary users (cognitive users) and the performance degradation due interference of the primary users. The secondary users are required to sense and monitor the radio spectrum environment within their operating range to detect the frequency bands or time intervals that are not occupied by primary users [11],[13].

This study will address issues related to the non­cooperative spectrum sensing, by the implementation of algorithms found in the literature and their evaluation in terms of probabilities of detection and false alarm, the ability to detect very low level inputs, as well as an evaluation of their computational complexity[14].

There are presently two categories of white space detection algorithms for cognitive radios:

• Blind detection, a set of techniques in which there is no dependence on prior information of the received signal.

• Feature detection, algorithms that handle specific characteristics of a known signal.

The algorithms, energy detector (ED) and the spectral covariance sensing (SCS) that will be part of our study are

often used to determine the presence of signals without prior knowledge of signal (Blind detection) [14].

The aim of this study is to evaluate the main algorithms in these categories, the energy detector (ED) [16],[17] and the spectral covariance sensing (SCS) [18],[19] using experimental data obtained in measurement campaigns performed in an urban environment in in Rio de Janeiro, Brazil, in the frequency bands of 3.5 GHz (2008 campaign) and 2.5 GHz (2012 campaign) [20],[21].

II. SPECTRUM SENSING PERFORMANCE METRICS

The algorithms will be tested to evaluate their performance in terms of Probability of Detection (Pd), Probability of False Alarm ( Pf ), Probability of Misdetection ( P md ) and

Computational Complexity (energy);

The higher the probability detection, the better the protection of primary user. However, from the secondary user perspective, the lower the false alarm probability, the higher their achievable throughput. Thus there exists a fundamental tradeoff between sensing capability and achievable throughput for the secondary network.

Assume that there is either one or no primary transmitter to detect, and that the secondary node can be located inside or outside of the primary cell boundary. The detection problem can then be formulated as a binary decision under two hypotheses as in [15]:

Ho: x(n) = wen) HI: x(n) = sen) + wen)

(1)

where x(n) is the equivalent received signal at baseband, sen) is the signal component of received samples and wen) is the noise component.

The probability of detection is the probability that the algorithm correctly detects the presence primary signal, under hypothesis HI' The probability of false alarm is the probability that the algorithm falsely declares the presence of a primary signal, under hypothesis Ho. The probability of misdetection is the probability that the algorithm falsely declares the absence of primary signal, under hypothesis Hlobviously, for a good detection algorithm, the probability of detection should be as high as possible while the probability of false alarm should be as low as possible.

III. ENERGY DETECTOR (ED)

The fundamental problem of spectrum sensing in CR is to discriminate between samples that contain only noise and the samples contain signal information embedded with high noise power. Energy detection can be performed in both time and frequency domain [12].

The energy detector (ED) is the simplest and most popular spectrum-sensing scheme, since it does not need any information about the parameters (modulation, pulse format, data rate, etc.) of the primary signal sen). When the signal sen) can be modeled as a zero-mean stationary white Gaussian

process, independent of the white Gaussian noise wen), the energy detector is optimal in Newman-Pearson sense [15].

The statistic test variable for the energy detector, based in the binary hypothesis shown in (1), can be represented as:

(2)

where N is the number of samples and r is the threshold level to be determined, as shown with more detail in reference [17].

A. Performance Analysis for the Energy Detector

The performance of the energy detector is characterized by the false alarm probability (Pf) and the detection probability

(Pd). For a given threshold level r, the false alarm probability can be represented as:

Pf(r) = Prob{T(x) > r; Ha} = Q (C� - 1) �) (3)

where Q(.) is the complementary Gaussian probability distribution function i.e.

Q(x) = .Jzrr LX> exp ( - ZZ2) dz

Similarly, under hypothesis HI, the detection probability can be represented as:

Pd(r) = Q ((.2... - y -1) �) a� �2Y+1 (4)

It can be seen from (3) e (4) that the spectrum sensing performance depends on the threshold level. Therefore, it is necessary to determine this threshold to minimize the spectrum sensing error [11].

The misdetection probability can be represented as:

(5)

More details in reference [15], [17].

IV. SPECTRAL COVARIANCE SENSING (SCS)

The Spectral Covariance Sensing (SCS) exploits the different statistical correlations of the signal and noise in the frequency domain. Test statistics are computed from the covariance matrix of a partial spectrogram and compared with a decision threshold to determine whether a primary signal or arbitrary type is present or not. The spectral covariance sensing SCS detects spectral features for maximum sensitivity, but also is applicable for non-flat spectrum signals [18].

The SCS algorithm exploits correlation of spectral feature in the band of interest where the signal specific spectrum that distinguishes itself from other signal or noise is located. Once the spectrum of the received signal is obtained by periodogram

estimation, its correlation is computed using the sample covariance matrix. The primary signal typically has unique non-flat spectrum, so it is highly correlated, whereas the noise spectrum is flat and is almost entirely uncorrelated. To fmd the relative correlation, the autocovariance of the spectrum is compared with the total covariance. The signal is detected if the spectral correlation is higher than the predefmed threshold. The proposed spectral covariance sensing (SCS) algorithm is described in [18].

The test statistic is T = Td Tz of the Spectral Covariance Sensing (SCS) algorithm [20], where

and em is the element of covariance matrix C at the r-th row and u-th column, which is the covariance of mr and mu. In other words, T2 is the sample means of the diagonal terms of the C matrix, which are the autocovariances of the spectrograms, and Tl is the sum of all the elements of the covariance matrix.

Compare T with decision threshold y as

y = arg sup PFA (T) = PFA" (8)

Where PFA (T) is the probability of false alarm with

threshold T and PFA, is the required false alarm probability

given by the specification. If T exceeds y, detection is declared (Hi)' Otherwise, the decision is made that no signal is present (Ho).

A. Performance Analysisfor Sensitive White Space Detection

The performance of spectrum sensing can be measured by two probabilities: probability of detection PD and probability of false alarm PFA, which are defmed as

PD = Pr(T > y I Hi)' PFA = Pr(T > y I Ho),

(9) (10)

The primary goal of the proposed sensing algorithm is achieving high PD and low PFA at the lowest received signal power level, however these two probabilities trade off each other depending on the decision threshold y. Therefore the required detection capability is determined by the application as a mllllmum PD and maximally allowed PFA pair at the required SNR.

Rigorous comparisons with the energy detector (ED) [17] show that spectral covariance sensing (SCS) [18] achieves better results in terms of probability than the energy detector (ED) as shown in section VI.

V. EXPERIMENTAL DATA

The measurement setup in the experiments consists of a transmitter and a receiver programed to generate and receive OFDM signals 7 MHz of bandwidth with the central frequency of 3,44 GHz.

The transmitter was set-up on the top of a building at the Catholic University of Rio de Janeiro. The region has a relative flat terrain, with moderately high residential buildings surrounded by hills [22] and [23].

A 15 dBi sector antenna with 1200 horizontal beamwidth was mounted at 42 meters of height above ground level with no vertical tilt. A MG3700A Anritsu signal generator and a class A power amplifier were configured to provide 1 watt EIRP. The receiver set up includes a 5 dBi antenna, a low noise amplifier with 30 dB gain, a GPS receiver and a signal vector analyzer MS2781B Anritsu. The receiver system was mounted in a vehicle and data were collected while travelling in the urban neighborhood with an average speed of 40 kmlh.

The equipment was configured to obtain 50Msps in 262 /!s, repeated once every second. Each captured dataset includes at least two complete OFDM symbols. The transmission is intentionally stopped for a period of guard of a half OFDM symbol to test the energy detector and simulate a secondary user (cognitive) detecting the blank spaces with no OFDM symbols transmitted. For practical reasons, each measurement run was recorded over a 15 minutes period, producing data files of 200 ME.

m

p I I

u

d

Sampln

Figure 1. Example of a captured OFDM signal.

VI. RESULTS AND COMPARISONS

In this section, we present results of the energy detector (ED) algorithm and spectral covariance sensing (SCS) algorithm that show its practicality and superior detection performance versus existing solution in terms of their probabilities.

As shown in section II and section IV, the two algorithms are implemented by the summation of the test variable T over N samples and comparison with the threshold level T.

In this paper we make several simulations with different SNR levels in order to verify the performance difference between the ED and SCS,

In Figure 2 we present detection probability for each SNR, we made this comparison for both algorithms in order to better appreciate the performance of the two algorithms.

Detection algorithms

0.9 -------f-------f-------f-------f-- ---i-------i-------i----- -i------I , I , , , , , I I I I I , I 1 I I I " I I I " " I ,

0.8 -------� -------� -------� ----- -� -------; -------; -------; --- ---; ------, , , , , , , , I I I I I , I I , , , , , , � 0.7 -------�-------�----- �-------�-------;-------;-------;-- - -- -;--- - --

u I , I , I , I , I I I I , I I

� I I I I , I I

-0 0.6 ----- : -----f-------f-------f-------i-------i-------i -----i------I , I , , , , , I , I , I , , , I I I I I , I I

>. I , I , I , I I

0.5 :g

I , I , " I - - -- - -- r- - ----- r- - ---- - r- - ---- - r- - ---- - T- - ---- - T--- - -- - --- - -- - T--- - --I I I I " I I , I , " , I , I , I , I --" I I I I " I

£ 0.4 I , I , " , - - - - - - - � - - - - - - _ .. - - - - - - _ .. - - - - - - _ .. - - - - - - - � - - - - - - _ .. - - - - - _ .. --- - -- - .. ------

I I I I I , I I I , I , I , I I I , I , I , I , I I I I I , I I I I I I I , I I 0.3 -------� -------!--------!--------!--------f -------f--- ---f -------f------I I I I I , I , , , , , , , ' ,------�-------,

0.1-l-��_44_+_ ...... =*='*='='t=:lt:::::C:.L._...L_._L _ ____.l _ _____l -50 -45 -40 -35 -30 -25 -20 -15 -10 -5 SNR(d8)

Figure 2. Probability of detection in function of the SNR.

As a response of detection probability we have the misdetection probability as we show in (5) (Pmd), with the data obtained in Figure 2 we obtained the probability of misdetection. In Figure 3 shows an average of the data obtained in Figure 2. The differences in percentages between the two detection algorithms are shown in Figure 3.

Probability of miss detection

0,9 +-----------------0,8 +-----------------0,7 +-----------------0,6 0,5 0,4 0,3 0,2 0,1

° DE scs

Figure 3. Probability of misdetection.

Since we have no information on the signal (we do not even know if there is a signal or not), it is difficult to set the thershold based on PD, the spectral covariance sensing (SCS) threshold is obtained with relation to the probability of false alarm required, as shown in as (8) and reference [18], it is also possible adjust the threshold to meet the PFA.

The probability of false alarm is the same in both algorithms because we actually define the required false alarm probability to be 0.1 to determine the noise threshold. The threshold depends on the required false alarm probability. However, limitation for energy detection is the decision threshold is subject to changing signal to noise ratio (SNR) [14]. An example of energy detection suffers severe degradation under uncertain noise is illustrated in these

examples: Figure 1 for this sample the threshold is (r) = 0.0011, in the other hand in Figure 4 the threshold is (r) = 0.0819. As shown in the Figure 4 and this has an influence, if threshold chosen is too high, signals will pass as noise and will not be detected. On the other hand, the robustness of SCS mainly comes from the fact that it exploits the statistical independence of the signal and noise components, especially that the noise is un correlated as illustrated in Figure 5. Thus, uncertainty in the noise only reduces the possible correlation and does not strongly affect signal detection.

m

d

m

• d

811Dplet

Figure 4. Example of a captured OFDM signal with high noise power: energy detector (ED) case.

_Uncotrmttd

SlIDplet

Figure 5. Example of a captured OFDM signal with high noise power: spectral covariance sensing (SCS) case.

VII. CONCLUSIONS

From the results obtained, we conclude that energy detector (ED) suffers from severe degradation due noise uncertainty. The threshold should be carefully chosen and there is a crucial balance between the probability of false alarm and probability of detection. The threshold depends on the probability of false alarm required and should be well defined. If threshold chosen is too high, signals will pass as noise and will not be detected. Conversely, if the threshold too low, noise may be mistaken as a sign.

On the other hand, the spectral covariance sensing (SCS) is highly resistant against noise uncertainty, this algorithm exploits the different statistical correlations of the received signal and noise in the frequency domain.

The spectral covariance sensing (SCS) algorithm achieves better detection performance than energy detector (ED) algorithm. The results suggest that SCS is an extremely effective sensing algorithm and should be preferred for white space detection algorithms for cognitive radios.

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