CN103297159A - Spectrum sensing method and device - Google Patents

Abstract

The invention discloses a spectrum sensing method and device. The spectrum sensing method includes: the spectrum sensing device receives signals on an authorized frequency range, samples and filters the received signals before calculating a covariance matrix of the signals, and judges whether the authorized frequency range comprises signals of authorized users or not according to the covariance matrix of the signals; whether the frequency spectrum is idled or not by setting judgment variables to compare with judgment thresholds. The spectrum sensing method and device have the advantages of low calculation complexity, no authorized signal characteristics, noise nondeterminacy and insensitivity and the like, and are excellent in performance. The spectrum sensing method is applicable to a cognitive radio frequency spectrum sensing system.

Description

A kind of frequency spectrum sensing method, frequency spectrum sensing device

Technical field

The present invention relates to wireless communication technology field, particularly relate to frequency spectrum sensing method and device thereof in a kind of cognitive radio.

Background technology

Along with increasing rapidly of wireless data service, frequency spectrum resource is also more and more nervous.Authorize frequency range in order to take full advantage of some, the interference when avoiding simultaneously the work of mandate frequency range again, people have proposed cognitive radio technology.Cognitive radio technology obtains the state of authorizing frequency spectrum by frequency spectrum perception, if be in idle condition, then cognitive user can re-use this frequency spectrum.Therefore, frequency spectrum perception is that the most basic while in the cognitive radio system also is most crucial module, it is bearing the task of identifying idle frequency range, it should identify frequency range in the very short time be the free time or occupied, and it also is the problem that cognitive radio system work at first will solve.

Below introduce the frequency spectrum perception principle:

Can judge on this frequency range it is signal or noise according to the auto-correlation function that receives signal, the theoretical foundation of the work of frequency spectrum perception is as follows, supposes that the vector representation of reception signal is x (n), and the transmission signal phasor is s (n), and noise vector is w (n).So, when signal existed, the covariance matrix that receives signal phasor can be expressed as,

R _x＝E[x(n)x ^H(n)]＝R _s+R _w＝E[s(n)s ^H(n)]+E[w(n)w ^H(n)]

Wherein, () ^HThe expression conjugate transpose, the covariance matrix that Gauss makes an uproar is σ ²I, σ ²The expression noise variance, I is unit matrix.When only having noise to exist,

R _x＝R _w＝σ ²I

Therefore, we know under the situation that the signal existence is arranged usually, R _xNot a diagonal matrix, and when only noise exists, R _xBe the equal matrix of diagonal entry.

In addition, energy measuring, matched filtering detection and cyclo-stationary detection are methods the most frequently used in the frequency spectrum detection.Though the energy measuring complexity is low, still be subjected to probabilistic influence of noise, its mis-behave is serious.Matched filtering excellent performance, but the feature of the known transmission signal of needs.The performance that cyclo-stationary detects is also very excellent, but complexity is higher, when practical application, is subjected to certain limitation.

Summary of the invention

Technical problem: in order to overcome prior art to the uncertain influence of noise, realize simultaneously reducing algorithm complex and ensureing function admirable, the present invention proposes a kind of frequency spectrum sensing method and frequency spectrum sensing device.

Technical scheme: a kind of frequency spectrum sensing method, comprise the steps,

(1) receives the wireless signal for the treatment of on the perception frequency range;

(2) carry out sampling filter to received signal, the sample signal of the N behind the sampling filter is expressed as x (0), x (1) ..., x (N-1) calculates its covariance matrix then, and choosing calculation window length is L, from l=0 ..., L-1, the coefficient correlation of calculating sample signal,

${\mathrm{\λ}}_l=\frac{1}{N}\underset{n=0}{\overset{N-1}{\mathrm{\Σ}}}x\left(n\right)x^{*}(n-l)$

Wherein, when n-l＜0, x (0)=0, oeprator * represents to ask conjugation, and corresponding covariance matrix is designated as R,

$R=\left[\begin{array}{cccc}{\mathrm{\λ}}_0& {\mathrm{\λ}}_1& \·\·\·& {\mathrm{\λ}}_{L-1}\\ {\mathrm{\λ}}_1^{*}& {\mathrm{\λ}}_0& \·\·\·& {\mathrm{\λ}}_{L-2}\\ \·& \·& & \·\\ \·& \·& \·\·\·& \·\\ \·& \·& & \·\\ {\mathrm{\λ}}_{L-1}^{*}& {\mathrm{\λ}}_{L-2}^{*}& \·\·\·& {\mathrm{\λ}}_0\end{array}\right]$

And use r _{I, j}The capable j column element of i of expression R, wherein, i=1 ..., L, j=1 ..., L, number of samples N are the positive integers greater than 1, length of window L is the positive integer more than or equal to 1;

(3) element to covariance matrix calculates the mould value;

(4) calculate the mould value sum of each row all elements of covariance matrix, and all row are obtained the maximum of this and value, be designated as T _MaxAnd,

$T_{\max }=\underset{i}{\max }\left(\right|r_{i,i}|+\underset{j\≠i}{\overset{L}{\mathrm{\Σ}}}|r_{i,j}\left|\right)$

Wherein, || mould is asked in expression, and maximum computing is asked in max () expression;

(5) mould of diagonal entry deducts off diagonal element mould value sum in each row of calculating covariance matrix,

To all capable minimum values of obtaining this difference, be designated as T _MinAnd,

Wherein, minimum computing is asked in min () expression.If T _MinFor negative or be zero, then with T _MinAssignment is very little positive number;

(6) when decision threshold T greater than predefined thresholding, then judging has authorization signal to exist on this frequency spectrum, when T less than predefined thresholding, then judging does not have authorization signal, i.e. this frequency spectrum free time, wherein, T is judgment variables, and T=T _Max/ T _Min

Wherein, the computational methods of described covariance matrix are: described length of window L determines according to computing capability or the frequency spectrum perception precision of sensing device.The cycle of described N sample evidence frequency spectrum perception or the precision of frequency spectrum perception are determined.Described decision threshold according to desired false alarm probability or detection probability by theory or non-online the calculating of emulation.Described covariance matrix R is if its diagonal entry equates described T _MaxAnd T _MinBe reduced to:

$T_{\max }={\mathrm{\λ}}_0+\underset{i}{\max }\left(A_i\right)$

$T_{\min }={\mathrm{\λ}}_0-\underset{i}{\max }\left(A_i\right)$

A wherein _iIt is the mould value sum of the capable off diagonal element of i.

Described A _iCalculate fast by following recurrence formula:

$A_1=\underset{l=1}{\overset{L-1}{\mathrm{\Σ}}}\left|{\mathrm{\λ}}_l\right|$

A _i＝A _i-1-|λ _L-i+1|+|λ _i-1|，（i=2,…,M）

Wherein, M=ceil (L/2), ceil represents to round up.

Above method also is applicable to the frequency spectrum perception of the system that K root antenna receives.Suppose that k root antenna is y at n signal indication constantly behind the sampling filter _k(n), they can be arranged the signal phasor that is constructed as follows, y ₁(0), y ₂(0) ..., y _K(0), y ₁(1), y ₂(1) ..., y _K(1) ..., y ₁(N-1), y ₂(N-1) ..., y _K(N-1), this vector length is N * K.Ask coefficient correlation according to claim 1 step 2 then, carry out frequency spectrum perception by step 3 to 6.

A kind of frequency spectrum sensing device comprises: wireless signal samples and filtration module, coefficient correlation computing module, judgment variables computing module and judging module; Wherein,

Described wireless signal samples and filtration module are used for obtaining the wireless signal of institute's perception frequency range;

Described coefficient correlation computing module is used for calculating the auto-correlation coefficient for the treatment of perceptual signal;

Described judgment variables computing module comprises the accumulator of asking modular arithmetic device, phase relation digital-to-analogue value of coefficient correlation and subtracter, comparator, is used for calculating the maximum of covariance matrix off diagonal element mould value sum; Described judgment variables computing module comprises following calculating: the covariance matrix diagonal entry adds that aforesaid maximum, covariance matrix diagonal entry deduct aforesaid maximum, and calculates both ratio, the output judgment variables;

Described judging module comprises comparator, is used for judgment variables and thresholding are compared.

The present invention adopts technique scheme, have following beneficial effect: in judgment variables T calculation stages, this method only needs simple addition and comparison operation, compares in the background technology frequency spectrum perception based on characteristic value, complexity is very low, and uncertain insensitive to noise.The judgment variables T that the present invention proposes calculates only needs 2L sub-addition (the subtraction number of times is by addition), and M-1 time relatively and a division, compares the method (complexity is) of characteristic value decomposition, and the complexity of the inventive method is very low.In addition, the present invention also is applicable to the frequency spectrum perception of multiaerial system.

Description of drawings

Fig. 1 is the frequency spectrum sensing method flow chart of the embodiment of the invention;

Fig. 2 is the low complex degree implementation method flow chart of the frequency spectrum sensing method of the embodiment of the invention;

Fig. 3 is the frequency spectrum sensing device block diagram of the embodiment of the invention;

Fig. 4 is the judgment variables computing module block diagram of the embodiment of the invention;

Fig. 5 is at the wireless microphone signal, the performance comparison diagram of embodiment of the invention method and background technology;

Fig. 6 is at the signal under many antenna frequencies selective channel, the performance comparison diagram of embodiment of the invention method and background technology.

Embodiment

Below in conjunction with specific embodiment, further illustrate the present invention, should understand these embodiment only is used for explanation the present invention and is not used in and limits the scope of the invention, after having read the present invention, those skilled in the art all fall within the application's claims institute restricted portion to the modification of the various equivalent form of values of the present invention.

A kind of frequency spectrum sensing method as shown in Figure 1, may further comprise the steps:

1) receives the wireless signal for the treatment of on the perception frequency range;

2) carry out sampling filter to received signal after, calculate its covariance matrix;

N behind a sampling filter sample signal is expressed as, x (0), and x (1) ..., x (N-1).Wherein, the precision of the cycle of N sample evidence frequency spectrum perception or frequency spectrum perception is determined.

When reality realized, covariance matrix smoothly obtained usually by the following method.Choosing calculation window length is L, and the auto-correlation coefficient of signal can be expressed as,

${\mathrm{\λ}}_l=\frac{1}{N}\underset{n=0}{\overset{N-1}{\mathrm{\Σ}}}x\left(n\right)x^{*}(n-l)$ [formula 1]

Wherein, l=1,2 ..., L.The covariance matrix that receives signal so can be expressed as R,

$R=\left[\begin{array}{cccc}{\mathrm{\λ}}_0& {\mathrm{\λ}}_1& \·\·\·& {\mathrm{\λ}}_{L-1}\\ {\mathrm{\λ}}_1^{*}& {\mathrm{\λ}}_0& \·\·\·& {\mathrm{\λ}}_{L-2}\\ \·& \·& & \·\\ \·& \·& \·\·\·& \·\\ \·& \·& & \·\\ {\mathrm{\λ}}_{L-1}^{*}& {\mathrm{\λ}}_{L-2}^{*}& \·\·\·& {\mathrm{\λ}}_0\end{array}\right]$

The R matrix is the conjugation symmetrical matrix as can be seen.Below we use r _{I, j}The capable j column element of i of expression R.

Traditional technical scheme has proposed a kind of characteristic value of the covariance matrix that receives signal of utilizing and has carried out frequency spectrum perception, and judgment variables can be made of the characteristic value of R, and has provided a kind of judgment variables building method, the i.e. ratio of minimax characteristic value.As can be seen, ideally, when having only noise to exist, the ratio of this minimax characteristic value is 1, and when having signal to exist, this ratio is greater than 1.Finding the solution the minimax characteristic value needs the complex features value to decompose or singular value decomposition, and computation complexity is O (L ³), complexity is very high, and especially when L was big, Project Realization was very difficult.

Decompose for fear of characteristic value, we propose to adopt following approximation method.According to the Gersgorin theory, the maximum of conjugation symmetrical matrix R, minimal eigenvalue satisfy as lower inequality,

${\mathrm{\α}}_{\max }\left(R\right)\underset{i}{\≤\max }\left(\underset{j=1}{\overset{L}{\mathrm{\Σ}}}\right|r_{i,j}\left|\right)$

${\mathrm{\α}}_{\min }\left(R\right)\≥\underset{i}{\min }(r_{i,i}-\underset{j\≠i}{\overset{L}{\mathrm{\Σ}}}|r_{i,j}\left|\right)$

Wherein, α _Max(R) and α _Min(R) represent maximum, the minimal eigenvalue of R respectively.We adopt the upper bound of above-mentioned eigenvalue of maximum and lower bound being similar to as the minimax characteristic value of minimal eigenvalue.Therefore, we can be constructed as follows judgment variables T, namely

$T=\frac{T_{\max }}{T_{\min }}$ [formula 2]

$T_{\max }=\underset{i}{\max }(r_{i,i}+\underset{j\≠i}{\overset{L}{\mathrm{\Σ}}}|r_{i,j}\left|\right)$ [formula 3]

$T_{\min }=\underset{i}{\min }(r_{i,i}+\underset{j\≠i}{\overset{L}{\mathrm{\Σ}}}|r_{i,j}\left|\right)$ [formula 4]

As can be seen, when only having noise in the system, T=1 ideally, otherwise T〉1.It should be noted that because covariance matrix is positive definite matrix, if T _MinFor negative or be zero, then with T _MinAssignment is very little positive number.

According to top know-why, we are 3) – 6 as follows) obtain judgment variables.

3) frequency spectrum sensing device is asked the mould value to the element of covariance matrix;

4) frequency spectrum sensing device calculates the mould value sum of each row all elements of covariance matrix, and to all row, obtains the maximum of this and value, is designated as T _MaxThis step can realize [formula 3].

5) mould of diagonal entry deducts off diagonal element mould value sum in each row of frequency spectrum sensing device calculating covariance matrix, to all row, obtains the minimum value of this difference, is designated as T _MinIf T _MinFor negative or be zero, then with T _MinAssignment is very little positive number; This step can realize [formula 4].

6) note T _MaxDivided by T _MinBeing T, is judgment variables with T, when T greater than predefined decision threshold, then judging has authorization signal to exist on this frequency spectrum, when T less than predefined decision threshold, then judging does not have authorization signal, i.e. this frequency spectrum free time.Wherein, decision threshold can be according to desired false alarm probability or detection probability by theory or non-online the calculating of emulation.

As can be seen, judgment variables of the present invention has only been utilized the mould value of covariance matrix element, can realize that by a spot of add operation complexity reduces greatly.Particularly, we can simplify computing according to the characteristic of eigenmatrix, further reduce operand.Covariance matrix has following characteristic,

(a) diagonal entry equates, and non-negative;

(b) matrix R is the conjugation symmetrical matrix;

(c) the off diagonal element mould value sum that the i of matrix R is capable equals the capable off diagonal element mould value sum of L+1-i.

Therefore, according to character (a) and (b), judgment variables can further be simplified judgment variables and be:

$T=\frac{{\mathrm{\λ}}_0+\underset{i}{\max }\left(\underset{j\≠i}{\overset{L}{\mathrm{\Σ}}}\right|r_{i,j}\left|\right)}{{\mathrm{\λ}}_0-\underset{i}{\max }\left(\underset{j\≠i}{\overset{L}{\mathrm{\Σ}}}\right|r_{i,j}\left|\right)}=\frac{{\mathrm{\λ}}_0+\underset{i}{\max }\left(A_i\right)}{{\mathrm{\λ}}_0-\underset{i}{\max }\left(A_i\right)}$ [formula 5]

Wherein,

$A_i=\underset{j\≠i}{\overset{L}{\mathrm{\Σ}}}\left|r_{i,j}\right|$

According to R matrix characteristic (c), we further have following relational expression,

$A_1=\underset{l=1}{\overset{L-1}{\mathrm{\Σ}}}\left|{\mathrm{\λ}}_l\right|$ [formula 6]

A _I+1=A _i-| λ _L-i|+| λ _i|, (i=2 ..., M) [formula 7]

Wherein, M=ceil (L/2), ceil represents to round up.

In sum, we can obtain the calculating of judgment variables, and then by comparing the value of judgment variables and decision threshold, obtain the result of frequency spectrum perception.

Said method can also be in conjunction with multiaerial system.Only the calculating of R need be generalized to multiaerial system gets final product.System has K root antenna to receive.Suppose that k root antenna is expressed as y n sampled signal constantly _k(n), they can be arranged the signal phasor that is constructed as follows, y ₁(0), y ₂(0) ..., y _K(0), y ₁(1), y ₂(1) ..., y _K(1) ..., y ₁(N-1), y ₂(N-1) ..., y _K(N-1), this vector length is N * K.With above-mentioned y _k(n) to become length be the vector x (0) of N * K in the vector representation of Gou Chenging, x (1) ..., x (NK-1) also can calculate coefficient correlation according to this vector, and then obtains corresponding judgment variables.

Below in conjunction with accompanying drawing, the job step of the frequency spectrum sensing method of the embodiment of the invention is further described.

As shown in Figure 1, at first, frequency spectrum sensing device receives the wireless signal for the treatment of on the perception frequency range, after carrying out sampling filter to received signal, calculates its covariance matrix, and the element of covariance matrix is asked the mould value.Then, frequency spectrum sensing device calculates the mould value sum of each row all elements of covariance matrix, and to all row, obtain the maximum of this and value, the mould that frequency spectrum sensing device calculates diagonal entry in each row of covariance matrix deducts off diagonal element mould value sum, to all row, obtain the minimum value of this difference.Be judgment variables with aforesaid maximum divided by minimum value, when judgment variables greater than predefined thresholding, then judging has authorization signal to exist on this frequency spectrum, when judgment variables less than predefined thresholding, then judging does not have authorization signal, i.e. this frequency spectrum free time.

As preferably, can also be directly obtain judgment variables according to the coefficient correlation of signal, its method is as shown in Figure 2.At first, calculate the auto-correlation coefficient of signal and it is asked mould according to [formula 1], calculate A according to [formula 6] and [formula 7] then _i, and obtain its maximum, last according to [formula 5] calculating judgment variables.

As shown in Figure 3, frequency spectrum sensing device of the present invention comprises: wireless signal samples and filtration module, coefficient correlation computing module, judgment variables computing module and judging module.Key modules judgment variables computing module wherein comprises, the accumulator of asking modular arithmetic device, phase relation digital-to-analogue value of coefficient correlation and subtracter, comparator, it is used for obtaining the maximum of covariance matrix off diagonal element mould value sum, the judgment variables computing module also comprises divider, the covariance matrix diagonal entry is added that aforesaid maximum, covariance matrix diagonal entry deduct aforesaid maximum, import divider, obtain both ratio, the output judgment variables.

Below by the actual emulation checking, provided the performance comparison of present embodiment and background technology by Fig. 5 and Fig. 6.Energy measuring, the background technology that we have contrasted under the noiseless uncertainty is the detection of minimax characteristic value, the energy measuring under the noise uncertainty and the art of this patent.The art of this patent and minimax characteristic value detect all insensitive to the noise uncertainty, just, exist the noise uncertainty whether not influence their performance.What provide among the figure is to exist under the noise uncertainty, the performance that the art of this patent and minimax characteristic value detect.The noise coefficient of uncertainty is 0.5dB.

The simulation result of Fig. 5 is that the frequency spectrum detection with the wireless microphone signal is example.As can be seen from the figure, compare the energy measuring under the noiseless uncertainty, this method has the gain of 2dB, and in addition, the performance that this method and minimax characteristic value detect is suitable.It should be noted that energy measuring has serious " signal to noise ratio wall " phenomenon when existing noise uncertain, worsen more than the 11dB than this method.This explanation, this method not only computation complexity are lower than the detection of minimax characteristic value, and performance also is not worse than the minimax characteristic value and detects.

The simulation result of Fig. 6 is that the frequency spectrum detection with random signal is example.Adopt the QPSK signal, frequency selectivity Rayleigh fading channel, constant power 5 footpath channels, single transmit antenna, 4 reception antennas.For random signal, this method slightly is better than the minimax characteristic value and detects, and they all are inferior to energy measuring, the energy measuring when still still being much better than to exist noise uncertain.

Claims (7)

1. a frequency spectrum sensing method is characterized in that, comprises the steps:

(1) receives the wireless signal for the treatment of on the perception frequency range;

(2) carry out sampling filter to received signal, the sample signal of the N behind the sampling filter is expressed as x (0), x (1) ..., x (N-1) calculates its covariance matrix then, and choosing calculation window length is L, from l=0 ..., L-1, the coefficient correlation of calculating sample signal,

${\mathrm{\λ}}_l=\frac{1}{N}\underset{n=0}{\overset{N-1}{\mathrm{\Σ}}}x\left(n\right)x^{*}(n-l)$

Wherein, when n-l＜0, x (0)=0, oeprator * represents to ask conjugation, and corresponding covariance matrix is designated as R,

$R=\left[\begin{array}{cccc}{\mathrm{\λ}}_0& {\mathrm{\λ}}_1& \·\·\·& {\mathrm{\λ}}_{L-1}\\ {\mathrm{\λ}}_1^{*}& {\mathrm{\λ}}_0& \·\·\·& {\mathrm{\λ}}_{L-2}\\ \·& \·& & \·\\ \·& \·& \·\·\·& \·\\ \·& \·& & \·\\ {\mathrm{\λ}}_{L-1}^{*}& {\mathrm{\λ}}_{L-2}^{*}& \·\·\·& {\mathrm{\λ}}_0\end{array}\right]$

And use r _{I, j}The capable j column element of i of expression R, wherein, i=1 ..., L, j=1 ..., L, number of samples N are the positive integers greater than 1, length of window L is the positive integer more than or equal to 1;

(3) element to covariance matrix calculates the mould value;

(4) calculate the mould value sum of each row all elements of covariance matrix, and all row are obtained the maximum of this and value, be designated as T _MaxAnd,

$T_{\max }=\underset{i}{\max }\left(\right|r_{i,i}|+\underset{j\≠i}{\overset{L}{\mathrm{\Σ}}}|r_{i,j}\left|\right)$

Wherein, || mould is asked in expression, and maximum computing is asked in max () expression;

(5) mould of diagonal entry deducts off diagonal element mould value sum in each row of calculating covariance matrix,

To all capable minimum values of obtaining this difference, be designated as T _MinAnd

Wherein, minimum computing is asked in min () expression.If T _MinFor negative or be zero, then with T _MinAssignment is very little positive number;

(6) when judgment variables T greater than decision threshold, then judging has authorization signal to exist on this frequency spectrum, when judgment variables T less than decision threshold, then judging does not have authorization signal, i.e. this frequency spectrum free time, wherein, T is judgment variables, and T=T _Max/ T _Min

2. a kind of frequency spectrum sensing method according to claim 1 is characterized in that, the cycle of described N sample evidence frequency spectrum perception or the precision of frequency spectrum perception are determined.

3. a kind of frequency spectrum sensing method according to claim 1 is characterized in that: described decision threshold according to desired false alarm probability or detection probability by theory or non-online the calculating of emulation.

4. a kind of frequency spectrum sensing method according to claim 1 is characterized in that: for described covariance matrix R, if its diagonal entry equates described T _MaxAnd T _MinBe reduced to:

$T_{\max }={\mathrm{\λ}}_0+\underset{i}{\max }\left(A_i\right)$

$T_{\min }={\mathrm{\λ}}_0-\underset{i}{\max }\left(A_i\right)$

A wherein _iIt is the mould value sum of the capable off diagonal element of i.

5. a kind of frequency spectrum sensing method according to claim 4 is characterized in that: described A _iCalculate fast by following recurrence formula:

$A_1=\underset{l=1}{\overset{L-1}{\mathrm{\Σ}}}\left|{\mathrm{\λ}}_l\right|$

A _i＝A _i-1-|λ _L-i+1|+|λ _i-1|，（i=2,…,M）

Wherein, M=ceil (L/2), ceil represents to round up.

6. according to each described a kind of frequency spectrum sensing method of claim 1-5, it is characterized in that: this method is applicable to the frequency spectrum perception of the system that K root antenna receives.

7. a frequency spectrum sensing device is characterized in that, comprising: wireless signal samples and filtration module, coefficient correlation computing module, judgment variables computing module and judging module; Wherein,

Described wireless signal samples and filtration module are used for obtaining the wireless signal of institute's perception frequency range;

Described coefficient correlation computing module is used for calculating the auto-correlation coefficient for the treatment of perceptual signal;

Described judgment variables computing module comprises the accumulator of asking modular arithmetic device, phase relation digital-to-analogue value of coefficient correlation and subtracter, comparator, is used for calculating the maximum of covariance matrix off diagonal element mould value sum; Described judgment variables computing module comprises following calculating: the covariance matrix diagonal entry adds that aforesaid maximum, covariance matrix diagonal entry deduct aforesaid maximum, and calculates both ratio, the output judgment variables;

Described judging module comprises comparator, is used for judgment variables and thresholding are compared.

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