CR–RSS location algorithm for primary user in cognitive radio

The Journal of China Universities of Posts and Telecommunications February 2014, 21(1): 22–25 www.sciencedirect.com/science/journal/10058885

http://jcupt.xsw.bupt.cn

CR–RSS location algorithm for primary user in cognitive radio YU Yin-hui1 ( ), SUN Chen2, QIN Nan-nan1, GAO Ke1, CHEN Deng-zhao1 1. College of Communication Eng., Jilin University, Changchun 130021, China 2. Jilin branch of China telecom co., LTD, Changchun 130033, China

Abstract This article introduces the classic locating method based on the receiving signal strength in the cognitive radio and puts forward a cognitive radio-receiving signal strength (CR-RSS) localization algorithm which solves the problem of secondary users locating the primary user and succeeds in estimating the primary user’s location and transmission power. Through the establishment of cognitive radio network, evaluating the number of secondary users ,sampling and the environmental factors to the results in CR-RSS approach. The consequence shows that this approach can effectively locate the primary user and the technology of localization in cognitive radio can assist network optimization. Keywords cognitive radio, Location of the primary user, secondary user, RSS location algorithm

1

Introduction 

Cognitive radio is a technology to solve the shortage and unreasonable allocation of spectrum [1]. In cognitive radio, the primary users have the absolute right to use the authorized spectrum. Secondary users only can use it when it’s idle. We detect primary user’s status through spectrum sensing and call the idle status as ‘spectrum hole’. As soon as the primary users appear, the secondary users must withdraw from the ‘spectrum hole’ immediately to insure the primary users’ absolute priority [2]. Through the primary user’s location, we can calculate the idle spectrum better and improve the opportunities for secondary users to access the ‘spectrum hole’. Otherwise, it’s important for the designing of cognitive radio network protocol and the improvement of performance [3–5]. Through the establishment of cognitive radio network, this article uses CR-RSS approach to estimate the location of primary user. And then it’s can realize the optimization of network, environmental features and transceivers and provide cognitive radio services based location.

Received date: 13-08-2013 Corresponding author: YU Yin-hui, E-mail: [email protected] DOI: 10.1016/S1005-8885(14)60264-8

2

Proposed model

The common location estimation algorithms can be classified under three categories, these are range-based, range-free and pattern matching-based methods [6–7]. In this paper, receiving signal strength (RSS) algorithm which belongs to range-based method is adopted [8]. According to the thought of time difference of arrival (TDOA) localization algorithm which is referred in paper [8]. The algorithm uses three different base stations which can measure two TDOA, the mobile station is in the intersection of the hyperbola that is determined by two TDOA,which can determine the location information of the mobile station.This paper puts the receiving signal strength detected by primary user into it,and then puts forward a CR-RSS localization algorithm.One of the reasons is that RSS algorithm can estimate not only the primary user’s location, but also the transmitter’s effective isotropic radiated power [9]. The second reason is that the critical technology in cognitive radio spectrum detection is energy detection. RSS algorithm can reduce the extra costs and lower the cost of hardware and software. In cognitive radio network, the primary transmitter is considered as transmitting terminal and cognitive nodes are receiving ends. Cognitive nodes process the receiving signals and make the preliminary judgment. Then

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YU Yin-hui, et al. / CR–RSS location algorithm for primary user in cognitive radio

cognitive station summarizes the preliminary information and makes a final judgment [10]. Establish RSS algorithm model in cognitive radio which called CR-RSS. The process to estimate the location and transmit power of primary user is as below. The ideal receiving signal strength equation Prideal in ,i cognitive radio is as below [11]. P Prideal Ci Et ; i 1, 2,..., N ,i di

(1)

Pt is the primary user’s effective isotropic radiated

power. Ci

is a constant about CR-RSS. E

is a

coefficient about Path Loss. Assumed that N cognitive nodes are placed. The distance between primary user and cognitive nodes is di . The two-dimensional distance formula is as below. ( x  xi ) 2  ( y  yi ) 2

di

(2)

Primary user’s coordinate is ( x, y ). Cognitive node’s coordinate is ( xi , yi ). In order to estimate ( x, y ), putting Eq. (2) into Eq. (1). The equation changes as below. Ci [( x  xi ) 2  ( y  yi ) 2 ]E / 2 ; i 1, 2,..., N Pt Prideal ,i

(3)

Multiply 2 / E power to the Eq. (3) of both sides, it changes as below. 2

°­ Ci °½ E ® ideal ¾ ¯° Pr ,i ¿°

( x  xi ) 2  ( y  yi ) 2 2

; i 1, 2,..., N

(4)

Pt E

Transform the Eq. (4) as below. 2 2 i

2 i

x y

2 ­° Ci ½° E 2 2 2 xxi  2 yyi  ® ideal ¾  Pt E   x  y P ¯° r ,i ¿°

(5)

Can be seen from the above equation, every cognitive nodes’ location information are exist in Eq. (5). So we can make it expand to all nodes and the expanded matrix expression is as below. 2 ª º E ­ ½ C ° ° « 2x 1 1 »» 2 y1 ® ideal ¾ « 1 P ° ° ,1 r ¯ ¿ « » 2 2 2 « » ª x º ª x1  y1 º « » E « » °­ C2 °½ y » « x2  y 2 » 1 » « 2 y2 2 » « 2 ® ideal ¾ « 2 x2 2 °¯ Pr ,2 °¿ « » «  P E » « # » « t » « » « 2 2 # # # # »« 2 2» ¬« xN  y N ¼»  x y « »¬ ¼ 2 « » °­ CN °½ E « » 1» 2 yN ® ideal ¾ « 2 xN ¯° Pr , N ¿° ¬ ¼ (6)

23

Thus, we can see the location and transmission power of the primary user which need to be estimated by CR-RSS localization algorithm exist on the left side of the second row 1, column 4 matrix in the equation. It’s also the reason why the cognitive nodes must be at least 4. The elements in the first row N, column 4 matrix on the left side and the row N, column 1 matrix on the right side of the equation are known, so the result can be estimated by Eq. (6). But it is important to note that due to the known location information of cognitive nodes are four or more (at least four), the first matrix on the left side of the equation is not necessarily a phalanx that may not use a direct inversion method. Therefore, under the numerical domain, it can be solved by the least square method. The process is as below. Expand the 3 matrices of the Eq. (6). 2 ª º ½ ­° C1 ½° E « 2x » ° 2 1 y  ® ideal ¾ 1 « 1 » ° P ¯° r ,1 ¿° « » ° 2 « » ° ­° C2 ½° E « » ° 2 y2 1 » ° ® ideal ¾  « 2 x2 P ¯° r ,2 ¿° « » ° « # # # # » °° « » ¾ 2 « » ° ­° CN ½° E « » ° 2 2 1 x y  ® ideal ¾ N « N » ° P ¯° r , N ¿° ¬ ¼ ° 2 ° ª º x2  y 2 »  « x y  Pt E ° ¬ ¼ ° 2 2 2 2 2 2 ° ª º x2  y2 ... xN  yN ¼ °¿  ¬ x1  y1 Then we get  u   . Because  is not a square matrix, we calculate the pseudo-inverse matrix of  which is the minimum variance of the implementation of the scheme. That is   u  . Moore Penrose pseudo inverse matrix  can provide the minimum average only solution. Therefore, according to the cognitive nodes’ location information (coordinates), we can successfully estimate the primary transmitter’s location as well as the transmission power. Mentioned earlier, the elements in matrix Z and the locations of cognitive nodes ( xi , yi ) are known, and the N is known, the coordinates of the cognitive users not only direct the given value, because it is a cognitive user detection device that receives the user transmission power value, which can be read by directly testing. But Prideal is ,i not given, it can be detected from the cognitive nodes’

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The Journal of China Universities of Posts and Telecommunications

receivers. Under the condition of simulation experiment, we adopt the following solution instead. Because of the influence of terrain, obstacles and so on, there will be signal attenuation, namely shadow effect in a real environment. Conclusion from experiments, logarithmic normal path loss model is adopted to simulate the shadow effect. The model is as Eq. (7). P (7) Pr ,i Ci E t ; i 1, 2,..., N di si Si

100.1 X i is a logarithmic normal random variable.

X i is a Gaussian random variable, whose average value is

zero and the variance is V 2 . In order to reduce the influence of unnecessary shadow effect and randomness of data caused by single measurement, we average the CR-RSS value Pr ,i as below. Prideal | ,i

1 M

M

¦P

r , ij

(8)

j 1

M is the total number of samples. Pr ,ij is CR-RSS value of the jth sample of the ith cognitive node.

3

Simulation model and result

2014

between the cognitive nodes and primary transmitter, averaging method over times is taken to lower the error. In this approach, it’s assumed that the cognitive nodes’ locations and CR-RSS measurements are known in Fig. 1. In the mode, the error by shadow effect is lower through the averaging method. The results are estimated by Eq. (1). Assumed that constant Ci 1 and path loss E 3 . And then configure the 12 cognitive nodes in the location system. Every receiving power of cognitive nodes Pr ,i is known by measurement. The primary user’s effective isotropic radiated power and location are estimated by Eq. (6). Because  is not a square matrix, the equation can only change to   u  by calculating pseudo reverse matrix  . Moore Penrose pseudo inverse  matrix can provide minimum average to get the results we required. In order to evaluate the performance based on the CR-RSS approach, the number of secondary users and sampling and the environmental factors are changed in each simulation. In the experiments, assuming that the primary user’s effective isotropic radiated power is 1 w, and location is (145 0, 100 0) m. Figs. 2–7 show the effect of the number of secondary user and sampling and the environmental factors to the result.

In order to estimate the primary user’s effective isotropic radiated power and location, Fig. 1 shows the scenario of deploying the cognitive radio nodes and the primary transmitter over interest area. There are over 4 cognitive nodes and their locations are known. One of them is considered as decision maker to calculate the primary user’s transmission power and location. In the actual situation, every cognitive node exist a detection device which can measure the CR-RSS values of primary transmitter. Fig. 2 The effect of different number of cognitive node on transmit power

Fig. 1 Network for CR-RSS approach

No matter where the primary user is, it objective exist and the distance between the cognitive nodes is constant at a certain moment. Because of the instable environment

 Fig. 3 The effect of different number of cognitive node on primary user’s location

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Fig. 4

YU Yin-hui, et al. / CR–RSS location algorithm for primary user in cognitive radio

The effect of different number of sample on transmit power

 Fig. 5 The effect of different number of sample on primary user’s location

cognitive radio evaluates the primary user’s transmission power and location more accurately. From Fig. 6, we can see that the larger environmental factors is, namely, the environment is bad,the smaller the estimated value of the transmission power of the main user is. When the environmental factor is less than 3, the change of environmental factors have a serious effect on the estimation of the primary user transmission power , When environmental factors reach 3, the estimated effects of the primary users transmission power estimation are serious. From Fig. 7 we can see that if the environmental factor is large, the main user’s location detection accuracy is low, with the decrease of the environmental factors, detection accuracy increases. When environmental factors close to 3, the slope of the curve increases, namely, it improves the detection accuracy of the main user’s location, when environmental factors close to 2, it has been able to achieve our desired results. Otherwise, the number of secondary user and sampling and the environmental factors will effect the result remarkably. The bigger the number of secondary users and sampling and smaller the environmental factors parameter, the more accurate result we will get.

4

Fig. 6 The effect of different environmental factor on transmit power

25

Conclusions

This paper which combines the characteristics of cognitive radio, flexibly uses the CR-RSS approach for primary users location in cognitive radio. Through the simulation analysis, evaluated the number of secondary user and sampling and the environmental factors to the results. It turns out that the proposed method more accurately evaluates the primary user’s location and transmission power. Acknowledgements This work was supported by the Hi-Tech Research and Development Program of China (2012AA011505).

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 Fig. 7 The effect of different environmental factor on primary user’s location

The above experiments show that CR-RSS approach in

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