KR20120036416A - Method and apparatus of optimal hard decision for cooperative spectrum sensing in cognitive radio - Google Patents

Abstract

PURPOSE: A hard deciding method for cooperative spectrum sensing in a CR system and a device thereof are provided to realize sensing efficiency close to a hard decision method. CONSTITUTION: A detection unit(210) detects an energy value from a signal of a first user received from CR(Cognitive Radio) devices. A weight determining unit(220) determines weight values by calculating detection probability and error detection probability of each CR device. A weight value coupling unit(230) couples an individual decision result value with the weight values. A fusion center unit(240) fuses result values of the weight value coupling unit.

Description

Hard decision method and apparatus for cooperative spectrum sensing in wireless cognitive system TECHNICAL TECHNICAL FIELD OF APPARATUS OF OPTIMAL HARD DECISION FOR COOPERATIVE SPECTRUM SENSING IN COGNITIVE RADIO

The present invention relates to a hard decision method and apparatus for cooperative spectrum sensing in a wireless cognitive system.

Cognitive Radio (CR device) differs in sensing performance due to shading effect, fading, and path loss according to distance. In the conventional hard decision method, all individual devices are excluded from the performance of each CR device. The sensing results were combined by simply taking an arithmetic mean. In addition, there is a disadvantage that performance degradation may occur by performing spectrum sensing without reflecting a state change caused by time and position movement of each CR device.

The technical problem to be solved by the disclosed technology is to provide a hard decision method and apparatus for cooperative spectrum sensing in a wireless cognitive system to improve the performance of the sensing but to take advantage of the simple process of the conventional hard decision method.

In accordance with another aspect of the present disclosure, there is provided a cooperative spectrum sensing hard decision device, comprising: a detector configured to detect an energy value from signals of a primary user received from a plurality of CR (Cognitive Radio) devices; A weight determination unit configured to determine weight values by calculating detection probability and false detection probability of each CR device; A weight combiner configured to combine the individual determination result value calculated based on the energy value and the threshold value and the weight value, respectively; And a fusion center unit for fusing the result values of the weight combining unit.

According to a second aspect of the disclosed technology to achieve the technical problem, in the cooperative spectrum sensing hard decision method, an energy detection step of detecting an energy value from signals of a primary user received from a plurality of CR (Cognitive Radio) devices ; A weight determination step of determining weight values by calculating detection probability and false detection probability of each CR device; A weight combining step of combining the individual determination result value calculated based on the energy value and the threshold value and the weight value, respectively; And a fusion step of fusing the result values of the weight combiner.

Embodiments of the disclosed technology can have the effect of including the following advantages. However, the embodiments of the disclosed technology are not meant to include all of them, and thus the scope of the disclosed technology should not be understood as being limited thereto.

According to one embodiment of the disclosed technology, it is possible to improve the detection probability compared to the conventional hard decision method, it is possible to achieve a sensing efficiency close to the soft decision method without burdening the system. In addition, there is an advantage that can be applied to the field of cooperative spectrum sensing applied low-power CR device.

1 is a schematic diagram showing a system model for cooperative spectrum sensing according to the present invention.
2 is a block diagram of a cooperative spectrum sensing apparatus according to an embodiment.
3 is a flowchart illustrating a cooperative spectrum sensing hard decision method S300 according to an embodiment of the disclosed technology.
FIG. 4 is a graph illustrating a spectrum sensing probability according to a distance between a primary user and a CR device and a signal to noise ratio (SNR).
5 is a graph showing the probability of detecting cooperative spectrum sensing according to a false alarm rate.
6 is a graph showing a detection probability comparison according to a fusion method of individual sensing results.

The description of the disclosed technique is merely an example for structural or functional explanation and the scope of the disclosed technology should not be construed as being limited by the embodiments described in the text. That is, the embodiments may be variously modified and may have various forms, and thus the scope of the disclosed technology should be understood to include equivalents capable of realizing the technical idea.

Meanwhile, the meaning of the terms described in the present application should be understood as follows. The terms "first" and "second" are intended to distinguish one component from other components, and the scope of rights should not be limited by these terms. For example, the first component may be named a second component, and similarly, the second component may also be named a first component.

When a component is referred to as being "connected" to another component, it should be understood that there may be other components in between, although it may be directly connected to the other component. On the other hand, when an element is referred to as being "directly connected" to another element, it should be understood that there are no other elements in between. On the other hand, other expressions describing the relationship between the components, such as "between" and "immediately between" or "neighboring to" and "directly neighboring to", should be interpreted as well.

Singular expressions should be understood to include plural expressions unless the context clearly indicates otherwise, and terms such as "include" or "have" refer to features, numbers, steps, operations, components, parts, or parts thereof described. It is to be understood that the combination is intended to be present, but not to exclude in advance the possibility of the presence or addition of one or more other features or numbers, steps, operations, components, parts or combinations thereof.

Each step may occur differently from the stated order unless the context clearly dictates the specific order. That is, each step may occur in the same order as specified, may be performed substantially simultaneously, or may be performed in the reverse order.

All terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the disclosed technology belongs, unless otherwise defined. Terms defined in commonly used dictionaries should be interpreted to be consistent with meaning in the context of the relevant art and can not be construed as having ideal or overly formal meaning unless expressly defined in the present application.

Wireless recognition technology is based on SDR (Software Define Radio), which allows the CR device to use the frequency without interfering with the primary user when the wireless device measures the radio wave environment around it and the primary user does not use the frequency. It's technology. The Federal Communications Commission (FCC) currently permits limited use of wireless microphones for the DTV band, and the IEEE has established the IEEE 802.22 Working Group to ensure that key agencies in each country are also involved in cognitive radio communications and frequency relocation. We are researching to establish technical standards according to this.

The core technology of the wireless recognition technology is a spectrum sensing technology that detects idle frequencies not used by the primary user, and continuously recognizes the surrounding environment to properly apply the system according to the changed situation. However, in spectrum sensing, performance degradation occurs due to shadow areas and latent terminals. A cooperative spectrum sensing technique in which a plurality of CR devices share sensing information has been proposed to solve this problem and improve reliability and accuracy of spectrum sensing.

Cooperative spectrum sensing is divided into a hard decision method and a soft decision method according to a method of fusing individual sensing results. The soft decision method provides high sensing performance, high complexity, long sensing time, and reduced frequency efficiency of the entire system compared to the hard decision method. On the other hand, in the hard decision method, although the sensing performance is lower than the soft decision method, the hard decision method, which is a relatively simple method with less burden on the system, is more suitable for a small output CR device.

In the present invention, in order to increase the performance of the hard decision method, the detection probability is calculated through individual sensing results of each CR device, and weights are set based on the detection probability of each CR device. The calculated weight is combined with the individual sensing results and transmitted to the convergence center through the control channel. In the conventional hard decision method, the final judgment was performed by simply taking an arithmetic mean of individual sensing results of all CR devices. However, in the cooperative spectrum sensing, the CR device has a difference in sensing performance due to shading effects, fading, and path loss due to distance.

Wireless recognition technology measures the surrounding radio wave environment, and represents the state of the frequency in two states represented by Equation 1 according to the presence or absence of the primary user.

In Equation 1, h represents attenuation constant of a wireless channel, and s (t) represents a signal of a primary user. Assume that v (t) is an independent additive white Gaussian noise (AWGN). H ₀ represents an idle state where the primary user does not use frequency, and H ₁ represents that the primary user is using frequency. The signals received from the CR device are received in independent channels, respectively, and the energy value Y _m of the signal received in the m (m = 1, 2, 3, ....., M) th CR device is expressed by Equation 2 Is expressed.

는 수학식 3과 같이 표현된다.In Equation 2, L represents the number of sensing samples for energy value detection. y _m (k) represents a signal received from the primary user to the m-th CR device and follows a Gaussian distribution. If the average power of noise is 1 in y _m (k), the signal-to-noise ratio (SNR) of the mth CR device

Is expressed as in Equation 3.

의 'none-centrality'파라미터를 갖는 'non-central chi-square 분포'를 따른다. 이를 수식으로 표현하면 수학식 4와 같다.In Equation 3, s _m (k) represents the signal value of the primary user received by the m-th CR device. If there is a signal from the primary user, the energy value Y _m follows the 'central chi-square distribution' of degrees of freedom L. If there is no signal from the primary user, Y _m is the degree of freedom L and

Follow the 'non-central chi-square distribution' with the 'none-centrality' parameter. If this is expressed as an equation, it is expressed as Equation 4.

In this case, assuming that the L value is infinitely large, the probability distribution of Y _m has a characteristic of normal distribution by the central limit theorem. This may be expressed as an equation (5).

At this time, the individual determination result value of each CR device is represented by the equation (6).

In Equation 6, D _m [k] is an individual determination result value of each CR device. T _m is a threshold determined by a constant false alarm rate (CFAR) algorithm. H (?) Function is a 'Heaviside step function' a, Y _m is the threshold value T is greater than or equal to _{m D m [k] = 1} , Y m if this is less than the threshold _{T m D m [k] =} 0 Becomes The detection probability by the individual sensing of each CR device is represented by equation (7), and the false detection probability is expressed by equation (8).

The individual sensing results are combined at the fusion center by equation (9).

In Equation 9, w _m represents a weight, and the weight w _m is calculated by Equation 10.

1 is a schematic diagram showing a system model for cooperative spectrum sensing according to the present invention. According to FIG. 1, a system for cooperative spectrum sensing includes a plurality of CR devices for receiving a signal of a primary user and a convergence center for fusing individual sensing results of each CR device. According to an embodiment, the system for cooperative spectrum sensing improves sensing performance by performing cooperative spectrum sensing in which individual sensing results of a plurality of CR devices are shared.

2 is a block diagram of a cooperative spectrum sensing apparatus according to an embodiment.

Referring to FIG. 2, the cooperative spectrum sensing hard decision device includes a detector 210, a weight determiner 220, a weight combiner 230, and a fusion center unit 240.

는 수학식 3과 같이 표현된다. 수학식 3에서, s_m(k)는 m번째 CR기기가 수신한 1차 사용자의 신호값을 나타낸다. 1차 사용자의 신호가 있는 경우 Y_m은 자유도 L의 'central chi-square 분포'를 따른다. 1차 사용자의 신호가 없는 경우에는 자유도 L 및

의 'none-centrality'파라미터를 갖는 'non-central chi-square 분포'를 따른다. 이를 수식으로 표현하면 수학식 4와 같다. 이때, L값이 무한히 크다고 가정하면, Y_m의 확률 분포는 중심 극한 정리(Central limit theorem)에 의해 정규분포의 특징을 가진다. 이를 수식으로 표현하면 수학식 5와 같다. 수학식 5에 따른 계산에 의해 검출부(210)는 각 CR기기에서 수신된 1차 사용자들의 신호로부터 에너지값 Y_m을 검출한다. According to an embodiment, the detector 210 detects an energy value Y _m from signals of primary users received from a plurality of CR devices. In more detail, the detector 210 performs individual sensing by applying an energy detection technique to the signals of the received primary users. The signal received at the CR device is received as an independent channel, respectively, as shown, and the energy value Y _m of the signal received at the m (m = 1, 2, 3, ....., M) th CR device is It is expressed as Equation 2. L represents the number of sensing samples for energy value detection. y _m (k) represents a signal received from the primary user to the m-th CR device and follows a Gaussian distribution. If the average power of noise is 1 in y _m (k), the signal-to-noise ratio (SNR) of the mth CR device

Is expressed as in Equation 3. In Equation 3, s _m (k) represents the signal value of the primary user received by the m-th CR device. If there is a signal from the primary user, Y _m follows the 'central chi-square distribution' of degrees of freedom L. If there is no signal from the primary user, then degrees of freedom L and

Follow the 'non-central chi-square distribution' with the 'none-centrality' parameter. If this is expressed as an equation, it is expressed as Equation 4. In this case, assuming that the L value is infinitely large, the probability distribution of Y _m has a characteristic of normal distribution by the central limit theorem. This may be expressed as an equation (5). By the calculation according to Equation 5, the detector 210 detects the energy value Y _m from the signals of the primary users received at each CR device.

The weight determination unit 220 determines weight values by calculating detection probability and false detection probability of each CR device. More specifically, the detection probability P _{D , m} is calculated by Equation 7, and the false detection probability P _{F , m} is calculated by Equation 8. The weight value w _m is calculated by Equation 10 based on the detection probability P _{D , m} and the false detection probability P _{F , m} .

The weight combiner 230 is the energy value Y _m and The individual determination result D _m [k] calculated based on the threshold value T _m is combined with the weight value w _m . In this case, the individual determination result value D _m [k] may be calculated by the weight combiner 230 according to one embodiment, or in another embodiment, may be calculated by the detector 210. The threshold value T _m is determined by a constant false alarm rate (CFAR) algorithm, and the false alarm rate of the CFAR algorithm according to one embodiment is 5% to 10%. desirable. On the other hand, the individual decision result value D _m [k] is, if the energy value Y _m is the threshold value that is greater than the T _m or equal to '1', the energy value Y _m is less than the threshold T _m has a value of '0'. This is as described in equation (6).

)들을 융합한다. 융합센터부(240)는 융합된 결과값

를 최종 임계값 T_H와 비교하여 1차 사용자의 유,무를 최종 판단한다. 이때, 판단 규칙은 판단의 효율성 및 신뢰성을 고려하여 메이져리티(Majority) 방식을 기반으로 수행함이 바람직하다. 한편, 판단 규칙에 따른 최종 임계값은 표1과 같다.The fusion center unit 240 is a result of each combined in the weight combiner 230 (

Fusion). Fusion center unit 240 is the result of the fusion

The final determination of the presence or absence of the primary user is compared with the final threshold value T _H. In this case, the decision rule is preferably performed based on the majority method in consideration of the efficiency and reliability of the decision. Meanwhile, the final threshold value according to the determination rule is shown in Table 1.

Decision Rule Threshold (T _H ) AND T _H = 1 / M OR T _H = 1 Majority T _H = M / 2

According to the embodiment of Figure 2, it is possible to improve the detection probability compared to the conventional hard decision device, it is possible to achieve the sensing efficiency close to the soft decision device without burdening the system. In addition, there is an advantage that can be applied to the cooperative spectrum sensing device to which a small power CR device is applied.

3 is a flowchart illustrating a cooperative spectrum sensing hard decision method S300 according to an embodiment of the disclosed technology. Referring to the spectrum sensing hard decision method (S300) as follows. In addition, since the hard decision apparatus of FIG. 2 is implemented in time series, the present invention will be described with respect to the detector 210, the weight determiner 220, the weight combiner 230, and the fusion center unit 240. The part is applied as it is in this embodiment.

In the cooperative spectrum sensing hard decision method according to an exemplary embodiment, an energy detection step S310, a weight determination step S320, a weight combining step S330, and a result value fusion step S340 are performed.

는 수학식 3과 같이 표현된다. 수학식 3에서, s_m(k)는 m번째 CR기기가 수신한 1차 사용자의 신호값을 나타낸다. 1차 사용자의 신호가 있는 경우 Y_m은 자유도 L의 'central chi-square 분포'를 따른다. 1차 사용자의 신호가 없는 경우에는 자유도 L 및

의 'none-centrality'파라미터를 갖는 'non-central chi-square 분포'를 따른다. 이를 수식으로 표현하면 수학식 4와 같다. 이때, L값이 무한히 크다고 가정하면, Y_m의 확률 분포는 중심 극한 정리(Central limit theorem)에 의해 정규분포의 특징을 가진다. 이를 수식으로 표현하면 수학식 5와 같다. 수학식 5에 따른 계산에 의해 검출부(210)는 각 CR기기에서 수신된 1차 사용자들의 신호로부터 에너지값 Y_m을 검출한다. In operation S310, the detector 210 detects an energy value Y _m from signals of primary users received from at least one CR device. In more detail, the detector 210 performs individual sensing by applying an energy detection technique to the signals of the received primary users. The signal received at the CR device is received as an independent channel, respectively, as shown, and the energy value Y _m of the signal received at the m (m = 1, 2, 3, ....., M) th CR device is It is expressed as Equation 2. L represents the number of sensing samples for energy value detection. y _m (k) represents a signal received from the primary user to the m-th CR device and follows a Gaussian distribution. If the average power of noise is 1 in y _m (k), the signal-to-noise ratio (SNR) of the mth CR device

Is expressed as in Equation 3. In Equation 3, s _m (k) represents the signal value of the primary user received by the m-th CR device. If there is a signal from the primary user, Y _m follows the 'central chi-square distribution' of degrees of freedom L. If there is no signal from the primary user, then degrees of freedom L and

Follow the 'non-central chi-square distribution' with the 'none-centrality' parameter. If this is expressed as an equation, it is expressed as Equation 4. In this case, assuming that the L value is infinitely large, the probability distribution of Y _m has a characteristic of normal distribution by the central limit theorem. This may be expressed as an equation (5). By the calculation according to Equation 5, the detector 210 detects the energy value Y _m from the signals of the primary users received at each CR device.

In step S320, the weight determination unit 220 calculates the detection probability and the false detection probability of each CR device to determine the weight values. More specifically, the detection probability P _{D, m} is calculated by Equation 7, and the false detection probability P _{F, m} is calculated by Equation 8. The weight value w _m is calculated by Equation 10 based on the detection probability P _{D , m} and the false detection probability P _{F , m} .

In step S330, the weight combiner 230 and the energy value Y _m The individual determination result D _m [k] calculated based on the threshold value T _m is combined with the weight value w _m . In this case, the individual determination result value D _m [k] may be calculated by the weight combiner 230 according to one embodiment, or in another embodiment, may be calculated by the detector 210. The threshold value T _m is determined by a constant false alarm rate (CFAR) algorithm, and the false alarm rate of the CFAR algorithm according to one embodiment is 5% to 10%. desirable. On the other hand, the individual decision result value D _m [k] is, if the energy value Y _m is the threshold value that is greater than the T _m or equal to '1', the energy value Y _m is less than the threshold T _m has a value of '0'. This is described in equation (6).

들을 융합한다. S340 단계는, 융합된 결과값

를 최종 임계값 T_H와 비교하여 1차 사용자의 유,무를 최종 판단한다. 이때, 판단 규칙은 판단의 효율성 및 신뢰성을 고려하여 메이져리티(Majority) 방식을 기반으로 수행함이 바람직하다. 한편, 판단 규칙에 따른 최종임계값은 표1과 같다.In step S340, the result value combined in step S330

Fused them. In step S340, the fused result

The final determination of the presence or absence of the primary user is compared with the final threshold value T _H. In this case, the decision rule is preferably performed based on the majority method in consideration of the efficiency and reliability of the decision. Meanwhile, the final threshold value according to the determination rule is shown in Table 1.

According to one embodiment, it is possible to improve the detection probability compared to the conventional hard decision method, it is possible to achieve a sensing efficiency close to the soft decision method without burdening the system. In addition, there is an advantage that can be applied to the cooperative spectrum sensing method applied a small power CR device.

According to one embodiment of the present invention, the sensing performance was compared through a simulation. The system model for the simulation is the orthogonal frequency division multiplexing (OFDM) signal of the primary user, and consists of a number of CR devices and a single primary user. The spectral sensing method applied the energy detection method. Thresholds for individual sensing were performed using a constant false alarm rate (CFAR) algorithm, and were set by setting the false alarm rate of the CFAR algorithm to 5% to 10%.

FIG. 4 is a graph illustrating a spectrum sensing probability according to a distance between a primary user and a CR device and a signal to noise ratio (SNR). If the distance between the primary user and the CR device is different, it is the detection probability when single sensing is performed. The larger the number of Pd_Single #, the more likely the detection probability of CR device that is far from the primary user. As a result of experiment, sensing performance of each CR device varies according to the surrounding propagation environment such as distance, fading and shadow effect. Therefore, it can be seen that the CR device having a lower sensing performance requires cooperative spectrum sensing.

5 is a graph showing the probability of detecting cooperative spectrum sensing according to a false alarm rate. When the false alarm rate is set to 5% and 10%, the lower the false alarm rate, the lower the probability of detection. Accordingly, it can be seen that cooperative spectrum sensing shows superior performance compared to single sensing.

6 is a graph showing a detection probability comparison according to a fusion method of individual sensing results. The detection probability of the soft decision method (Pd_EGC), the conventional hard decision method (Pd_conventional), and the hard decision method (Pd_optimal) according to the embodiment of the present invention were compared and shown, respectively. Experimental results it can be seen that the detection probability of the hard decision method according to an embodiment of the present invention is superior to the conventional hard decision method. In addition, it can be seen that the hard decision method according to the embodiment of the present invention shows a performance close to the detection probability of the soft decision method.

The disclosed method and apparatus have been described with reference to the embodiments illustrated in the drawings for ease of understanding, but these are merely exemplary, and various modifications and equivalent other embodiments are possible to those skilled in the art. Will understand. Therefore, the true technical protection scope of the disclosed technology should be defined by the appended claims.

210: detector
220: weight binding unit
230: weight combining unit
240: convergence center

Claims (12)

In the cooperative spectrum sensing hard decision device,
A detector for detecting an energy value from signals of primary users received from a plurality of CR (Cognitive Radio) devices;
A weight determination unit configured to determine weight values by calculating detection probability and false detection probability of each CR device;
A weight combiner configured to combine the individual determination result value calculated based on the energy value and the threshold value and the weight value, respectively; And
Spectral sensing hard decision device comprising a fusion center unit for fusion result of the weight combining unit. The method of claim 1, wherein the threshold value,
Spectral-sensing hard decision device determined by the Constant False Alarm Rate (CFAR) algorithm. The method of claim 1, wherein the individual determination result value,
And a value of '1' if the energy value is greater than or equal to the threshold value and a value of '0' if the energy value is less than the threshold value. The method of claim 1, wherein the fusion center unit,
Spectral sensing hard decision device for determining the presence or absence of the primary user by comparing the final threshold value and the fused result. The method of claim 2,
Spectrum sensing hard decision device of the false alarm rate of the 'CFAR algorithm' is 5% to 10%. The method of claim 1,
And the detector is configured to calculate an individual determination result value based on the energy value and the threshold value. In the cooperative spectrum sensing hard decision method,
An energy detection step of detecting an energy value from a signal of a primary user received from a plurality of CR (Cognitive Radio) devices;
A weight determination step of determining weight values by calculating detection probability and false detection probability of each CR device;
A weight combining step of combining the individual determination result value calculated based on the energy value and the threshold value and the weight value, respectively; And
And a result fusion step of fusing the result values of the weight combiner. The method of claim 7, wherein the threshold value,
Spectral sensing hard decision method determined by the Constant False Alarm Rate (CFAR) algorithm. The method of claim 7, wherein the individual determination result value,
And a value of '1' if the energy value is greater than or equal to the threshold value and a value of '0' if the energy value is less than the threshold value. The method of claim 7, wherein the fusing step,
Spectral sensing hard decision method for determining the presence or absence of the primary user by comparing the final threshold value and the fused result. The method of claim 8,
The false alarm rate of the CFAR algorithm is 5% to 10% spectrum sensing hard decision method. The method of claim 7, wherein
And the detecting unit calculates an individual determination result value based on the energy value and the threshold value.

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