CN105141385B - Multiband cooperative cognitive frequency spectrum sensing method - Google Patents

Abstract

The present invention relates to multiband cooperative cognitive frequency spectrum sensing method, itself signal to noise ratio, confidence level, business demand and frequency spectrum detecting result to multiple primary users are sent to frequency spectrum perception fusion center by each user respectively, calculated by frequency spectrum perception fusion center, screen primary election cooperation time user, then distributed number of frequency bands and give primary election cooperation secondary user；The quotient between the signal to noise ratio of all primary election time user's signal to noise ratio root-mean-square value and each primary election time user is calculated, according to the relation between each quotient and signal to noise ratio predetermined threshold value, the final election time user for participating in cooperation is selected, and adjust the detection probability of final election time user；With reference to the average detected probability after the signal to noise ratio of each final election time user and adjustment, the step of returning to selection final election time user, obtain the final whole choosing time user for participating in cooperation, and the global detection probability cooperated using the OR criterions of weighting is the final detection result of frequency spectrum perception fusion center, it is to avoid baneful influence of the secondary user of low signal-to-noise ratio or poor performance to whole detection performance.

Description

Multi-band cooperative cognitive spectrum sensing method

Technical Field

The invention relates to the field of spectrum detection, in particular to a multi-band cooperative cognitive spectrum sensing method.

Background

With the continuous development of wireless communication technology, emerging technologies marked by LTE, Wi-Fi, satellite communication, cooperative communication and the like emerge in succession and emerge endlessly. These communication technologies put higher demands on wireless spectrum resources, so that the spectrum resources tend to become tight, and Cognitive Radio (CR) has come to work in this context.

The basic approach of cognitive radio is that, firstly, secondary users (or sensing users and cognitive users) adopt spectrum sensing to continuously detect authorized spectrum resources in the surrounding environment; and then, under the condition that the primary user (also called authorized user) can preferentially occupy the frequency spectrum and the transmission performance is hardly influenced, the secondary user adaptively adjusts the transceiver device, and adjusts the transceiver device to the idle frequency spectrum for communication. When the secondary user senses (or detects) that a primary user signal appears, the secondary user needs to fast vacate a channel for the primary user to use, and then normal communication of the primary user is prevented from being interfered, and therefore the spectrum resource utilization rate is improved.

In order to reduce adverse effects of multiple factors such as multipath fading, shadowing effect, noise uncertainty and the like on detection performance in an actual environment, a cooperative spectrum sensing method based on multiple secondary users is continuously proposed. The detection result of each secondary user is sent to the spectrum sensing fusion center for fusion, so that the purpose of sensing the spectrum is achieved.

However, most of the existing cooperative spectrum sensing methods only sense a single frequency band, and in order to improve the spectrum utilization rate, a cooperative sensing method for multiple frequency bands becomes a new research hotspot. However, in the multi-band cooperative sensing, since the detection performance and the signal-to-noise ratio of each secondary user are not completely excellent, when all secondary users participate in the multi-band cooperative sensing, the secondary users with low signal-to-noise ratio or poor detection performance may adversely affect the overall detection performance.

Disclosure of Invention

The technical problem to be solved by the present invention is to provide a multi-band cooperative cognitive spectrum sensing method capable of avoiding adverse effects on the overall detection performance caused by secondary users with low signal-to-noise ratio or poor detection performance, aiming at the above prior art.

The technical scheme adopted by the invention for solving the technical problems is as follows: the multi-band cooperative cognitive spectrum sensing method is characterized by sequentially comprising the following steps:

(1) setting the number of the main users in the cognitive network as N₁The number of sub-users is N₂The number of the spectrum sensing fusion centers is 1, and the master users respectively and independently occupy respective frequency bands in the spectrum; n is a radical of₂Respectively and independently acquiring signal-to-noise ratio SNR of each secondary user_jAnd to N₁Spectrum detection results of frequency bands occupied by the main users and respectively obtained SNR (signal to noise ratio)_jSpectrum detection result and detection confidence P_jAnd business requirement R_jAnd sending the information to a spectrum sensing fusion center, wherein,

the master user is marked as PU_iThe secondary user is marked as CR_jThe spectrum sensing fusion center is marked as FC, and the service requirement R_j∈[0,N₁]，P_j∈[0,1]Detection confidenceP_d,jiTo the secondary user CR_jFor master user PU_iThe detection probability of (2); i is more than or equal to 1 and less than or equal to N₁，1≤j≤N₂，N₁≥2，N₂≥2；

(2) Receiving each user CR by spectrum sensing fusion center FC_jSNR of transmitted signal to noise ratio_jSpectrum detection result and detection confidence P_jAnd business requirement R_jAnd judging the SNR of the secondary user_jSNR larger than preset SNR screening value_choseSelecting the secondary user corresponding to the signal-to-noise ratio as a primary selection secondary user participating in cooperative detection, and recording that the primary selection secondary user is CR'_tAnd (4) executing the step (3), otherwise, selecting the frequency spectrum detection result corresponding to the secondary user with the highest signal-to-noise ratio as the final detection result of the frequency spectrum sensing fusion center FC; wherein,

the number of primary users is N'₂Primary selection Secondary user CR'_tCorresponding Signal-to-noise ratio is SNR'_tAnd the detection confidence coefficient is P'_tAnd traffic demand is R'_t，1≤t≤N'₂≤N₂(ii) a The number of spectrum detection results received by the spectrum sensing fusion center FC is N₁×N₂A plurality of;

(3) the spectrum sensing fusion center FC is used for sensing primary and secondary users CR'_tConfidence of P'_tTraffic demand R'_tTo primary selection secondary user CR'_tAllocating the number of frequency bands C to be detected_t：

(3-1) according to each primary user CR'_tConfidence of P'_tFor each primary user CR'_tConfidence of P'_tNormalization is carried out to obtain each primary selection secondary user CR'_tNormalized confidence value of

(3-2) Each of primary users CR 'obtained according to the step (3-1)'_tCorresponding normalized confidence valueCalculating primary selection secondary user CR distributed by FC (fiber channel) in spectrum perception fusion center'_tNumber of frequency bands C to be detected_t：

(4) The spectrum sensing fusion center FC detects primary secondary users CR according to participation in cooperation'_tSNR of'_tCalculating the RMS of all the first-selected usersAnd let signal-to-noise ratio SNR'_t＝γ_tWherein the signal-to-noise ratio is the root mean square valueIs calculated as follows:

(5) respectively and sequentially calculating the signal-to-noise ratio root mean square values of all primary users by the spectrum sensing fusion center FCAnd each primary selection secondary user CR'_tSNR of'_tQuotient η between_tWherein

(6) FC calculation and acquisition information of spectrum sensing fusion centerPreset threshold lambda of noise ratio and optimum threshold lambda of signal-to-noise ratio_optimalAnd respectively according to the signal-to-noise ratio quotient η_tWith respect to the magnitude of the SNR preset threshold lambda, the users CR of the sub-checks participating in the cooperation are selected "_kCheck user CR "_kHas a signal-to-noise ratio of SNR "_kWherein

(6-1) Spectrum-aware fusion center FC from received N'₂Signal-to-noise ratio set { SNR'_tAcquiring a primary selection slave user signal-to-noise ratio set { SNR'_tThe maximum value of the signal-to-noise ratio in the symbol is recorded as SNR' max;

(6-2) taking the obtained signal-to-noise ratio maximum value SNR 'max as a reference, and adding N'₂Primary selection is from user CR'_tSNR of'_tRespectively carrying out quotient processing with the maximum signal-to-noise ratio SNR ' max, and calculating to obtain the signal-to-noise ratio SNR ' of each primary selected secondary user '_tCorresponding initial threshold lambda_tWherein

λ_t＝|SNR'_t/SNR'_max|，t＝1,2,…,N'₂，N'₂≤N₂；

(6-3) slave users CR 'according to primary selection'_tNormalized confidence value ofAnd signal to noise quotient η_tCalculating each primary selection slave user CR'_tCombined screening parameter value ξ_tAnd according to the combined screening parameter value ξ_tSelecting check subordinate users CR participating in collaboration "_kWherein the check is from user CR'_kThe number of (a) is M,t＝1,2,…,N'₂，k＝1,2,…,M，M≤N'₂：

if combined screening parameter value ξ_tWithin a predetermined range of values [ ξ ]_a,ξ_b]Inner, i.e. ξ_a≤ξ_t≤ξ_bThen the combined screening parameter value ξ is selected_tThe corresponding primary selection slave user is a check slave user and participates in the cooperative detection; otherwise, the primary selection is not selected by the user;

(6-4) obtaining M check subordinate users CR according to the signal-to-noise ratio preset threshold lambda in the step (6-3) "_kCooperative detection performance curves under an OR criterion AND an AND criterion, respectively, wherein,

OR criterion:

AND criterion:k＝1,2,…,M，M≤N'₂≤N₂；

wherein, P_d,kFrom user CR for the kth check "_kAverage detection probability of P_fa,kFrom user CR for the kth check "_kAverage false alarm probability of; p_d,ksFor checking slave users CR'_kProbability of detection of its assigned s-th frequency band, P_fa,ksFor checking slave users CR'_kThe false alarm probability of the s-th frequency band is distributed to the S-th frequency band; q_dFor global detection probability after cooperative detection, Q_faThe global false alarm probability after the cooperative detection is obtained; omega_kRepresenting the signal-to-noise ratio CR "_kCoefficient of weight, SNR "_kIs the kth check slave user CR "_kSignal to noise ratio, SNR "_maxRepresents the maximum signal-to-noise ratio, SNR, of M checksums from the user "_minRepresenting the minimum signal-to-noise ratio of the M check slave users;

(6-5) obtaining the maximum detection probability Q under the OR criterion AND the AND criterion respectively according to the cooperative detection performance curves under the OR criterion AND the AND criterion_(OR，d)-max、Q_{(AND，d)-max}To obtain Q_(OR，d)-maxAnd Q_{(AND，d)-max}Maximum value of Q_d-maxAnd using the best detection performance value Q_d-maxThe corresponding signal-to-noise ratio preset threshold is the signal-to-noise ratio optimal threshold, and the signal-to-noise ratio optimal threshold is recorded as lambda_optimal(ii) a Wherein Q is_d-max＝max(Q_(OR，d)-max，Q_{(AND，d)-max})；

(7) According to the obtained signal-to-noise ratio optimum threshold lambda_optimalTo obtain the optimal threshold lambda of the signal-to-noise ratio_optimalThe corresponding check from user CR ' is obtained for the adjustment factor α of the check from user CR ' and the other M-1 check from user CR '_kAdjustment factor α_kAnd according to adjustment factors α, α respectively_kCorrespondence adjustment checkup slave users CR ', CR'_kThe average false alarm probability after checking and checking the adjustment from the user CR is marked as P_faCheck slave users CR "_kThe adjusted average false alarm probability is recorded as P_fa,k(ii) a Wherein,

P_fa,k＝α_k·P_fa,k＝1,2,…,M-1；

wherein, α_kFor checking slave users CR'_kIs used to check from user CR "_kSNR of its own "_kThe adjustment of the average false alarm probability is realized; SNR "_kFrom user CR for the kth check "_kThe signal-to-noise ratio of (c);

(8) adjusting factor α from user according to M checks obtained in step (7)_kAnd corresponding adjusted average false alarm probability P_fa,kCalculating check Slave Users CR "_kAdjusted decision threshold lambda'_kAnd the average detection probability P_d,kWherein

wherein,k＝1,2,…,M，M≤N'₂(ii) a n is the number of sampling points;

(9) SNR from user according to M checks in step (8) "_kAnd the resulting adjusted average probability of detection P_d,kReturning to the step (6), reselecting the M check slave users to obtain T final check slave users CR 'participating in the cooperation'_tAnd taking the global detection probability after the weighted OR criterion cooperation as the final detection result of the spectrum sensing fusion center FC, wherein T is more than OR equal to 1 and less than OR equal to M and less than OR equal to N'₂。

Further, the weighted OR criterion in step (9) is as follows:

wherein, P'_d,tsIs selected from user CR'_tThe detection probability, P ', of the s-th frequency band is allocated to the channel'_fa,tsIs selected from user CR'_tThe false alarm probability of the s-th frequency band is distributed to the S-th frequency band; p'_d,tIs the t-th reselected final selection slave user CR'_tAverage detection probability of P'_fa,tIs the t-th reselected final selection slave user CR'_tAverage false alarm probability of; q'_dIs global detection probability, Q 'after cooperative detection'_faThe global false alarm probability after the cooperative detection is obtained; m' is the number of reselected end-users; omega'_tIs reselected end selection from user CR'_tThe weighting coefficient of (2).

Compared with the prior art, the invention has the advantages that: each secondary user respectively sends the signal-to-noise ratio, the confidence coefficient, the service requirement and the spectrum detection results of a plurality of primary users to a spectrum sensing fusion center, the spectrum sensing fusion center calculates and screens primary selection cooperative secondary users, the secondary users with poor detection performance and low signal-to-noise ratio are deleted, and then the number of frequency bands is distributed to the primary selection cooperative secondary users; calculating quotient values between the root mean square values of the signal-to-noise ratios of all the primary users and the signal-to-noise ratios of all the primary users, selecting the secondary users participating in the cooperation according to the relation between each quotient value and a signal-to-noise ratio preset threshold value, and adjusting the detection probability of the secondary users; and returning to the step of selecting the multiple sub-users again by combining the signal-to-noise ratio of each multiple sub-user and the adjusted average detection probability to obtain the final selection sub-users which finally participate in the cooperation, and taking the global detection probability of the weighted OR criterion cooperation as a final detection result of the spectrum sensing fusion center, thereby avoiding the adverse effect of the sub-users with low signal-to-noise ratio OR poor performance on the overall detection performance.

Drawings

FIG. 1 is a schematic diagram of a cognitive network structure according to an embodiment of the present invention;

FIG. 2 is a schematic flow chart of a multi-band cooperative cognitive spectrum sensing method according to an embodiment of the present invention;

fig. 3 is a schematic diagram of a simulation result of the multiband cooperative cognitive spectrum sensing method AND the conventional AND criterion cooperative spectrum sensing according to the embodiment of the present invention.

Detailed Description

The invention is described in further detail below with reference to the accompanying examples.

As shown in FIG. 1, the number of primary users in the cognitive network is set to N₁The number of sub-users is N₂The number of the spectrum sensing fusion centers is 1, wherein the primary users are marked as PU_iThe secondary user is marked as CR_jThe spectrum sensing fusion center is marked as FC; i is more than or equal to 1 and less than or equal to N₁，1≤j≤N₂，N₁≥2，N₂≥2。

The following describes a multi-band cooperative cognitive spectrum sensing method in this embodiment with reference to fig. 1 and fig. 2. The multi-band cooperative cognitive spectrum sensing method sequentially comprises the following steps:

(1) setting the number of the main users in the cognitive network as N₁The number of sub-users is N₂The number of the spectrum sensing fusion centers is 1, and the master users respectively and independently occupy respective frequency bands in the spectrum; n is a radical of₂Respectively and independently acquiring signal-to-noise ratio SNR of each secondary user_jAnd to N₁Spectrum detection results of frequency bands occupied by the main users and respectively obtained SNR (signal to noise ratio)_jSpectrum detection result and detection confidence P_jAnd business requirement R_jAnd sending the information to a spectrum sensing fusion center, wherein,

the master user is marked as PU_iThe secondary user is marked as CR_jThe spectrum sensing fusion center is marked as FC, and the service requirement R_j∈[0,N₁]，P_j∈[0,1]Detection confidenceP_d,jiTo the secondary user CR_jFor master user PU_iThe detection probability of (2); i is more than or equal to 1 and less than or equal to N₁，1≤j≤N₂，N₁≥2，N₂≥2；

(2) Receiving each user CR by spectrum sensing fusion center FC_jSNR of transmitted signal to noise ratio_jSpectrum detection result and detection confidence P_jAnd business requirement R_jAnd judging the SNR of the secondary user_jSNR larger than preset SNR screening value_choseSelecting the secondary user corresponding to the signal-to-noise ratio as a primary selection secondary user participating in cooperative detection, and recording that the primary selection secondary user is CR'_tAnd (4) executing the step (3), otherwise, selecting the frequency spectrum detection result corresponding to the secondary user with the highest signal-to-noise ratio as the final detection result of the frequency spectrum sensing fusion center FC; wherein,

the number of primary users is N'₂Primary selection Secondary user CR'_tCorresponding Signal-to-noise ratio is SNR'_tAnd the detection confidence coefficient is P'_tAnd traffic demand is R'_t，1≤t≤N'₂≤N₂(ii) a The number of spectrum detection results received by the spectrum sensing fusion center FC is N₁×N₂A plurality of;

(3) the spectrum sensing fusion center FC is used for sensing primary and secondary users CR'_tConfidence of P'_tTraffic demand R'_tTo primary selection secondary user CR'_tAllocating the number of frequency bands C to be detected_t：

(3-1) according to each primary user CR'_tConfidence of P'_tFor each primary user CR'_tConfidence of P'_tNormalization is carried out to obtain each primary selection secondary user CR'_tNormalized confidence value of

(3-2) Each of primary users CR 'obtained according to the step (3-1)'_tCorresponding normalized confidence valueCalculating primary selection secondary user CR distributed by FC (fiber channel) in spectrum perception fusion center'_tNumber of frequency bands C to be detected_t：

(4) The spectrum sensing fusion center FC detects primary secondary users CR according to participation in cooperation'_tSNR of'_tCalculating the RMS of all the first-selected usersAnd let signal-to-noise ratio SNR'_t＝γ_tWherein the signal-to-noise ratio is the root mean square valueIs calculated as follows:

(5) respectively and sequentially calculating the signal-to-noise ratio root mean square values of all primary users by the spectrum sensing fusion center FCAnd each primary selection secondary user CR'_tSNR of'_tQuotient η between_tWherein

(6) FC calculation and signal-to-noise ratio preset threshold lambda acquisition of spectrum sensing fusion center and signal-to-noise ratio optimizationThreshold lambda_optimalAnd respectively according to the signal-to-noise ratio quotient η_tWith respect to the magnitude of the SNR preset threshold lambda, the users CR of the sub-checks participating in the cooperation are selected "_kCheck user CR "_kHas a signal-to-noise ratio of SNR "_kWherein

(6-1) Spectrum-aware fusion center FC from received N'₂Signal-to-noise ratio set { SNR'_tAcquiring a primary selection slave user signal-to-noise ratio set { SNR'_tThe maximum value of the signal-to-noise ratio in the symbol is recorded as SNR' max;

(6-2) taking the obtained signal-to-noise ratio maximum value SNR 'max as a reference, and adding N'₂Primary selection is from user CR'_tSNR of'_tRespectively carrying out quotient processing with the maximum signal-to-noise ratio SNR ' max, and calculating to obtain the signal-to-noise ratio SNR ' of each primary selected secondary user '_tCorresponding initial threshold lambda_tWherein

λ_t＝|SNR'_t/SNR'_max|，t＝1,2,…,N'₂，N'₂≤N₂；

(6-3) slave users CR 'according to primary selection'_tNormalized confidence value ofAnd signal to noise quotient η_tCalculating each primary selection slave user CR'_tCombined screening parameter value ξ_tAnd according to the combined screening parameter value ξ_tSelecting check subordinate users CR participating in collaboration "_kWherein the check is from user CR'_kThe number of (a) is M,t＝1,2,…,N'₂，k＝1,2,…,M，M≤N'₂：

if combined screening parameter value ξ_tWithin a predetermined range of values [ ξ ]_a,ξ_b]Inner, i.e. ξ_a≤ξ_t≤ξ_bThen selecting the combined screeningParameter value ξ_tThe corresponding primary selection slave user is a check slave user and participates in the cooperative detection; otherwise, the primary selection is not selected by the user;

(6-4) obtaining M check subordinate users CR according to the signal-to-noise ratio preset threshold lambda in the step (6-3) "_kCooperative detection performance curves under an OR criterion AND an AND criterion, respectively, wherein,

OR criterion:

AND criterion:k＝1,2,…,M，M≤N'₂≤N₂；

wherein, P_d,kFrom user CR for the kth check "_kAverage detection probability of P_fa,kFrom user CR for the kth check "_kAverage false alarm probability of; p_d,ksFor checking slave users CR'_kProbability of detection of its assigned s-th frequency band, P_fa,ksFor checking slave users CR'_kThe false alarm probability of the s-th frequency band is distributed to the S-th frequency band; q_dFor global detection probability after cooperative detection, Q_faThe global false alarm probability after the cooperative detection is obtained; omega_kRepresenting the signal-to-noise ratio CR "_kCoefficient of weight, SNR "_kIs the kth check slave user CR "_kSignal to noise ratio, SNR "_maxRepresents the maximum signal-to-noise ratio, SNR, of M checksums from the user "_minRepresenting the minimum signal-to-noise ratio of the M check slave users;

(6-5) obtaining the maximum detection probability Q under the OR criterion AND the AND criterion respectively according to the cooperative detection performance curves under the OR criterion AND the AND criterion_(OR，d)-max、Q_{(AND，d)-max}To obtain Q_(OR，d)-maxAnd Q_{(AND，d)-max}Maximum value of Q_d-maxAnd using the best detection performance value Q_d-maxThe corresponding signal-to-noise ratio preset threshold is the signal-to-noise ratio optimal threshold, and the signal-to-noise ratio optimal threshold is recorded as lambda_optimal(ii) a Wherein Q is_d-max＝max(Q_(OR，d)-max，Q_{(AND，d)-max})；

(7) According to the obtained signal-to-noise ratio optimum threshold lambda_optimalTo obtain the optimal threshold lambda of the signal-to-noise ratio_optimalThe corresponding check from user CR ' is obtained for the adjustment factor α of the check from user CR ' and the other M-1 check from user CR '_kAdjustment factor α_kAnd according to adjustment factors α, α respectively_kCorrespondence adjustment checkup slave users CR ', CR'_kThe average false alarm probability after checking and checking the adjustment from the user CR is marked as P_faCheck slave users CR "_kThe adjusted average false alarm probability is recorded as P_fa,k(ii) a Wherein,

P_fa,k＝α_k·P_fa,k＝1,2,…,M-1；

wherein, α_kFor checking slave users CR'_kIs used to check from user CR "_kSNR of its own "_kThe adjustment of the average false alarm probability is realized; SNR "_kFrom user CR for the kth check "_kThe signal-to-noise ratio of (c);

(8) adjusting factor α from user according to M checks obtained in step (7)_kAnd corresponding adjusted average false alarm probability P_fa,kCalculating check Slave Users CR "_kAdjusted decision threshold lambda'_kAnd the average detection probability P_d,kWherein

wherein,k＝1,2,…,M，M≤N'₂(ii) a n is the number of sampling points;

(9) SNR from user according to M checks in step (8) "_kAnd the resulting adjusted average probability of detection P_d,kReturning to the step (6), reselecting the M check slave users to obtain T final check slave users CR 'participating in the cooperation'_tAnd taking the global detection probability after the weighted OR criterion cooperation as a final detection result of the spectrum sensing fusion center FC, wherein the weighted OR criterion is as follows:

wherein, P'_d,tsIs selected from user CR'_tThe detection probability, P ', of the s-th frequency band is allocated to the channel'_fa,tsIs selected from user CR'_tThe false alarm probability of the s-th frequency band is distributed to the S-th frequency band; p'_d,tIs the t-th reselected final selection slave user CR'_tAverage detection probability of P'_fa,tIs the t-th reselected final selection slave user CR'_tAverage false alarm probability of; q'_dIs global detection probability, Q 'after cooperative detection'_faThe global false alarm probability after the cooperative detection is obtained; m' is the number of reselected end-users; omega'_tIs reselected end selection from user CR'_tT is more than or equal to 1 and less than or equal to T and more than or equal to M and less than or equal to N'₂。

Fig. 3 shows a simulation result diagram of the multi-band cooperative cognitive spectrum sensing method in the present invention, AND meanwhile, a conventional AND cooperative detection method is simulated. In the conventional AND cooperative detection method, all secondary users participate in cooperative spectrum sensing detection. The simulation conditions were as follows:

setting the number N of primary users PU in the cognitive radio network₁2, 3, 5 and 7, respectively, from the number N of users CR₂Gradually increasing from 2 to 11; the signal-to-noise ratio of the slave users is respectively-5 dB, -8dB, -10dB, -13dB, -16dB, -17dB, -19dB, -23dB, -25dB and-27 dB, and the slave users adopt energy detection.

As can be seen from fig. 3, under the condition that the number of master users AND the number of slave users are certain, the global detection probability in the invention is greater than the detection probability of the conventional AND cooperative detection, which indicates that the cooperative spectrum sensing method in the invention has better detection performance; meanwhile, under the condition that the number of the cooperative detection method and the number of the master users are certain, the overall detection probability after cooperative detection is gradually increased along with the increase of the number of the slave users; under the condition that the number of the cooperative detection method and the number of the secondary users are certain, the overall detection probability after the cooperative detection is gradually reduced along with the increase of the number of the primary users. Compared with the traditional AND cooperation detection method, the multi-band cognitive cooperation spectrum sensing method in the embodiment of the invention avoids the adverse effect of secondary users with low signal-to-noise ratio or poor detection performance, greatly improves the overall cooperation detection performance, AND has better detection performance.

Claims (1)

1. The multi-band cooperative cognitive spectrum sensing method is characterized by sequentially comprising the following steps:

(1) setting the number of the main users in the cognitive network as N₁The number of sub-users is N₂The number of the spectrum sensing fusion centers is 1, and the master users respectively and independently occupy respective frequency bands in the spectrum; n is a radical of₂Respectively and independently acquiring signal-to-noise ratio SNR of each secondary user_jAnd to N₁Spectrum detection results of frequency bands occupied by the main users and respectively obtained SNR (signal to noise ratio)_jSpectrum detection result and detectionConfidence measure P_jAnd business requirement R_jAnd sending the information to a spectrum sensing fusion center, wherein,

the master user is marked as PU_iThe secondary user is marked as CR_jThe spectrum sensing fusion center is marked as FC, and the service requirement R_j∈[0,N₁]，P_j∈[0,1]Detection confidenceP_d,jiTo the secondary user CR_jFor master user PU_iThe detection probability of (2); i is more than or equal to 1 and less than or equal to N₁，1≤j≤N₂，N₁≥2，N₂≥2；

(2) Receiving each user CR by spectrum sensing fusion center FC_jSNR of transmitted signal to noise ratio_jSpectrum detection result and detection confidence P_jAnd business requirement R_jAnd judging the SNR of the secondary user_jSNR larger than preset SNR screening value_choseSelecting the secondary user corresponding to the signal-to-noise ratio as a primary selection secondary user participating in cooperative detection, and recording that the primary selection secondary user is CR'_tAnd (4) executing the step (3), otherwise, selecting the frequency spectrum detection result corresponding to the secondary user with the highest signal-to-noise ratio as the final detection result of the frequency spectrum sensing fusion center FC; wherein,

the number of primary users is N'₂Primary selection Secondary user CR'_tCorresponding Signal-to-noise ratio is SNR'_tAnd the detection confidence coefficient is P'_tAnd traffic demand is R'_t，1≤t≤N'₂≤N₂(ii) a The number of spectrum detection results received by the spectrum sensing fusion center FC is N₁×N₂A plurality of;

(3) the spectrum sensing fusion center FC is used for sensing primary and secondary users CR'_tConfidence of P'_tTraffic demand R'_tTo primary selection secondary user CR'_tAllocating the number of frequency bands C to be detected_t：

(3-1) according to each primary user CR'_tConfidence of P'_tFor each primary user CR'_tConfidence of P'_tNormalization is carried out to obtain each primary selection secondary user CR'_tNormalized confidence value of

$\begin{array}{cc}\stackrel{\‾}{{P^{\′}}_t}=\frac{{P^{\′}}_t}{\underset{t=1}{\overset{{N^{\′}}_2}{\Σ}}{P^{\′}}_t},& 1\≤t\≤{N^{\′}}_2;\end{array}$

(3-2) Each of primary users CR 'obtained according to the step (3-1)'_tCorresponding normalized confidence valueCalculating primary selection secondary user CR distributed by FC (fiber channel) in spectrum perception fusion center'_tNumber of frequency bands C to be detected_t：

$\begin{array}{cc}{C}_t=\stackrel{\‾}{{P^{\′}}_t}\·{N^{\′}}_2,& 1\≤t\≤{N^{\′}}_2;\end{array}$

(4) The spectrum sensing fusion center FC detects primary secondary users CR according to participation in cooperation'_tSNR of'_tCalculating the RMS of all the first-selected usersAnd let signal-to-noise ratio SNR'_t＝γ_tWherein the signal-to-noise ratio is the root mean square valueIs calculated as follows:

$\begin{array}{cc}\stackrel{\‾}{\mathrm{\γ}}=\sqrt{\frac{1}{{N^{\′}}_2}\underset{t=1}{\overset{{N^{\′}}_2}{\Σ}}{\left({{\mathrm{SNR}}^{\′}}_t\right)}^2},& {N^{\′}}_2\≤N_2;\end{array}$

(5) respectively and sequentially calculating the signal-to-noise ratio root mean square values of all primary users by the spectrum sensing fusion center FCAnd each primary selection secondary user CR'_tSNR of'_tQuotient η between_tWherein

$\begin{array}{ccc}{\mathrm{\η}}_t=|\stackrel{\‾}{\mathrm{\γ}}/{\mathrm{\γ}}_t|,& t=1,2,...,{N^{\′}}_2,& {N^{\′}}_2\≤N_2;\end{array}$

(6) FC calculation and acquisition of signal-to-noise ratio preset threshold lambda and signal-to-noise ratio optimal threshold lambda of spectrum sensing fusion center_optimalAnd respectively according to the signal-to-noise ratio quotient η_tWith respect to the magnitude of the SNR preset threshold lambda, the users CR of the sub-checks participating in the cooperation are selected "_kCheck user CR "_kHas a signal-to-noise ratio of SNR "_kWherein

(6-1) Spectrum-aware fusion center FC from received N'₂Signal-to-noise ratio set { SNR'_tAcquiring a primary selection slave user signal-to-noise ratio set { SNR'_tThe maximum value of the signal-to-noise ratio in the symbol is recorded as SNR' max;

(6-2) taking the obtained signal-to-noise ratio maximum value SNR 'max as a reference, and adding N'₂Primary selection is from user CR'_tSNR of'_tRespectively carrying out quotient processing with the maximum signal-to-noise ratio SNR ' max, and calculating to obtain the signal-to-noise ratio SNR ' of each primary selected secondary user '_tCorresponding initial threshold lambda_tWherein

λ_t＝|SNR'_t/SNR'_max|，t＝1,2,…,N'₂，N'₂≤N₂；

(6-3) slave users CR 'according to primary selection'_tNormalized confidence value ofAnd signal to noise quotient η_tCalculating each primary selection slave user CR'_tCombined screening parameter value ξ_tAnd according to the combined screening parameter value ξ_tSelecting check subordinate users CR participating in collaboration "_kWherein the check is from user CR'_kThe number of (a) is M,t＝1，2，…，N′₂，k＝1，2，…，M，M≤N'₂：

if combined screening parameter value ξ_tWithin a predetermined range of values [ ξ ]_a,ξ_b]Inner, i.e. ξ_a≤ξ_t≤ξ_bThen the combined screening parameter value ξ is selected_tThe corresponding primary selection slave user is a check slave user and participates in the cooperative detection; otherwise, the primary selection is not selected by the user;

(6-4) obtaining M check subordinate users CR according to the signal-to-noise ratio preset threshold lambda in the step (6-3) "_kCooperative detection performance curves under an OR criterion AND an AND criterion, respectively, wherein,

OR criterion:

AND criterion:

$\begin{array}{cc}{P}_{d,k}=\frac{\underset{k}{\overset{{N^{\′}}_2}{\Σ}}\underset{s=1}{\overset{C_t}{\Σ}}{P}_{d,ks}}{{N^{\′}}_2\·C_t},& P_{fa,k}=\frac{\underset{k}{\overset{{N^{\′}}_2}{\Σ}}\underset{s=1}{\overset{C_t}{\Σ}}{P}_{fa,ks}}{{N^{\′}}_2\·C_t};\end{array}$

wherein, P_d,kFrom user CR for the kth check "_kAverage detection probability of P_fa,kFrom user CR for the kth check "_kAverage false alarm probability of; p_d,ksFor checking slave users CR'_kProbability of detection of its assigned s-th frequency band, P_fa,ksFor checking slave users CR'_kThe false alarm probability of the s-th frequency band is distributed to the S-th frequency band; q_dFor global detection probability after cooperative detection, Q_faThe global false alarm probability after the cooperative detection is obtained; omega_kRepresenting the signal-to-noise ratio CR "_kCoefficient of weight, SNR "_kIs the kth check slave user CR "_kSignal to noise ratio, SNR "_maxRepresents the maximum signal-to-noise ratio, SNR, of M checksums from the user "_minRepresenting the minimum signal-to-noise ratio of the M check slave users;

(6-5) obtaining the maximum detection probability Q under the OR criterion AND the AND criterion respectively according to the cooperative detection performance curves under the OR criterion AND the AND criterion_(OR，d)-max、Q_{(AND，d)-max}To obtain Q_(OR，d)-maxAnd Q_{(AND，d)-max}Maximum value of Q_d-maxAnd using the best detection performance value Q_d-maxThe corresponding signal-to-noise ratio preset threshold is the signal-to-noise ratio optimal threshold, and the signal-to-noise ratio optimal threshold is recorded as lambda_optimal(ii) a Wherein Q is_d-max＝max(Q_(OR，d)-max，Q_{(AND，d)-max})；

(7) According to the obtained signal-to-noise ratio optimum threshold lambda_optimalTo obtain the optimal threshold lambda of the signal-to-noise ratio_optimalThe corresponding check from user CR ' is obtained for the adjustment factor α of the check from user CR ' and the other M-1 check from user CR '_kAdjustment factor α_kAnd according to adjustment factors α, α respectively_kCorrespondence adjustment checkup slave users CR ', CR'_kThe average false alarm probability after checking and checking the adjustment from the user CR is marked as P_faCheck slave users CR "_kThe adjusted average false alarm probability is recorded as P_fa,k(ii) a Wherein,

P_fa,k＝α_k·P_fa,k＝1,2,…,M-1；

$\begin{array}{cc}{\mathrm{\α}}_k=1+\frac{\stackrel{\‾}{{\mathrm{SNR}}^{\′}}-{{\mathrm{SNR}}^{\′\′}}_k}{\stackrel{\‾}{{\mathrm{SNR}}^{\′\′}}},& k=1,2,...,M-1;\end{array}$

$\begin{array}{cc}\stackrel{\‾}{{\mathrm{SNR}}^{\′\′}}=\sqrt{\underset{k=1}{\overset{M}{\Σ}}\frac{{\left({{\mathrm{SNR}}^{\′\′}}_k\right)}^2}{M}},& M\≤{N^{\′}}_2;\end{array}$

wherein, α_kFor checking slave users CR'_kIs used to check from user CR "_kSNR of its own "_kThe adjustment of the average false alarm probability is realized; SNR "_kFrom user CR for the kth check "_kThe signal-to-noise ratio of (c);

(8) adjusting factor α from user according to M checks obtained in step (7)_kAnd corresponding adjusted average false alarm probability P_fa,kCalculating check Slave Users CR "_kAdjusted decision threshold lambda'_kAnd the average detection probability P_d,kWherein

$\begin{array}{c}\mathrm{\λ}={\mathrm{\σ}}_w^2\[\sqrt{2n}{Q}^{-1}\left(P_{fa,k}\right)+n\]\\ ={\mathrm{\σ}}_w^2\[\sqrt{2n}{Q}^{-1}(\mathrm{\δ}\·P_{fa})+n\]\\ ={\mathrm{\σ}}_w^2\[\sqrt{2n}{Q}^{-1}\left(\right(1+\frac{\stackrel{\‾}{{\mathrm{SNR}}^{\′\′}}-{{\mathrm{SNR}}^{\′\′}}_k}{\stackrel{\‾}{{\mathrm{SNR}}^{\′\′}}})\·P_{fa})+n\]\end{array};$

$P_{d,k}=Q\[Q^{-1}\left(P_{fa,k}\right)-\sqrt{n\·{{\mathrm{SNR}}^{\′\′}}_k}\];$

$n=2{\[Q^{-1}\left(P_{fa,k}\right)-Q^{-1}\left(P_{fa}\right)\sqrt{1+2{{\mathrm{SNR}}^{\′\′}}_k}\]}^2\·{\left({{\mathrm{SNR}}^{\′\′}}_k\right)}^{-2};$

wherein,n is the number of sampling points;

(9) SNR from user according to M checks in step (8) "_kAnd the resulting adjusted average probability of detection P_d,kReturning to the step (6), reselecting the M check slave users to obtain T final check slave users CR 'participating in the cooperation'_tAnd taking the global detection probability after the weighted OR criterion cooperation as the final detection result of the spectrum sensing fusion center FC, wherein T is more than OR equal to 1 and less than OR equal to M and less than OR equal to N'₂(ii) a Wherein:

the weighted OR criterion is as follows:

$\begin{array}{cc}{Q^{\′}}_d=1-\underset{t=1}{\overset{M^{\′}}{\Π}}{{\mathrm{\ω}}^{\′}}_t(1-{P^{\′}}_{d,t}),& {Q^{\′}}_{fa}=1-\underset{t=1}{\overset{M^{\′}}{\Π}}{{\mathrm{\ω}}^{\′}}_t(1-{P^{\′}}_{f,t});\end{array}$

$\begin{array}{ccccc}{{\mathrm{\ω}}^{\′}}_t=\frac{{P^{\′}}_{d,t}}{\underset{t=1}{\overset{M^{\′}}{\Σ}}{P^{\′}}_{d,t}},& {P^{\′}}_{d,t}=\frac{\underset{t}{\overset{M^{\′}}{\Σ}}\underset{s=1}{\overset{C_t}{\Σ}}{P^{\′}}_{d,ts}}{M^{\′}\·C_t},& {P^{\′}}_{fa,t}=\frac{\underset{t}{\overset{M^{\′}}{\Σ}}\underset{s=1}{\overset{C_t}{\Σ}}{P^{\′}}_{fa,ts}}{M^{\′}\·C_t},& t=1,2,\mathrm{...},M^{\′},& M^{\′}\≤M;\end{array}$

wherein, P'_d,tsIs selected from user CR'_tThe detection probability, P ', of the s-th frequency band is allocated to the channel'_fa,tsIs selected from user CR'_tThe false alarm probability of the s-th frequency band is distributed to the S-th frequency band; p'_d,tIs the t-th reselected final selection slave user CR'_tAverage detection probability of P'_fa,tIs the t-th reselected final selection slave user CR'_tAverage false alarm probability of; q'_dIs global detection probability, Q 'after cooperative detection'_faThe global false alarm probability after the cooperative detection is obtained; m' is the number of reselected end-users; omega'_tFor the final selection of the reselected slaveHousehold CR'_tThe weighting coefficient of (2).