阅读说明：本技术 一种存在智能干扰的频谱感知时间优化方法 (Frequency spectrum sensing time optimization method with intelligent interference ) 是由 邹玉龙 翟亮森 朱佳 于 2019-11-20 设计创作，主要内容包括：本发明公开了一种存在智能干扰的频谱感知时间优化方法,该方法适用于认知自组织网络。为了保障主用户通信,认知自组织用户只利用空闲频谱进行数据传输,然而智能干扰只有在检测到频谱被占用时才发送干扰以提高干扰效率。其中,利用频谱感知时间对数据传输性能的影响,考虑以整体中断概率最小为优化目标,采用迭代优化算法得到认知自组织用户的最佳频谱感知时间。本发明通过对认知自组织用户的频谱感知时间进行优化,最大程度地减小整体中断概率,可以有效抑制智能干扰对认知自组织用户性能造成的不利影响。(The invention discloses a frequency spectrum sensing time optimization method with intelligent interference, which is suitable for a cognitive self-organizing network. In order to guarantee communication of a master user, the cognitive self-organization user only utilizes idle frequency spectrum for data transmission, however, intelligent interference only sends interference when the occupied frequency spectrum is detected so as to improve interference efficiency. The method comprises the steps of obtaining the optimal spectrum sensing time of a cognitive self-organizing user by utilizing the influence of the spectrum sensing time on the data transmission performance and considering the minimum integral interruption probability as an optimization target and adopting an iterative optimization algorithm. According to the invention, the spectrum sensing time of the cognitive self-organization user is optimized, the overall interruption probability is reduced to the greatest extent, and the adverse effect of intelligent interference on the performance of the cognitive self-organization user can be effectively inhibited.)

1. A spectrum sensing time optimization method in the presence of intelligent interference is characterized by comprising the following steps:

according to the magnitude relation between the spectrum sensing time alpha of the cognitive transmitter and the spectrum sensing time beta of the intelligent interference, the method is divided into a stage of alpha being less than beta and a stage of alpha being more than or equal to beta; in the stage of alpha being less than beta, the magnitude relation between the spectrum sensing time alpha of the cognitive transmitter and the spectrum sensing time beta of the intelligent interference is alpha being less than beta; in the stage that the alpha is larger than or equal to the beta, the magnitude relation between the spectrum sensing time alpha of the cognitive transmitter and the spectrum sensing time beta of the intelligent interference is that the alpha is larger than or equal to the beta;

calculating the overall probability of interruption P of the alpha < beta phase_out,1Integral outage probability P for the phase with alpha ≧ beta_out,2；

According to the calculated P_out,1And P_out,2Obtaining an overall outage probability P for cognitive self-organizing user transmissions_out；

Optimizing the spectrum sensing time alpha of the cognitive transmitter by using an iterative optimization algorithm to ensure that the overall interruption probability P of the cognitive self-organizing user transmission_outAnd minimum.

2. The method for spectrum sensing time optimization with intelligent interference according to claim 1, wherein the method comprises the following steps: the overall probability of interruption P for the alpha < beta phase of the calculation_out,1The method comprises the following steps:

performing spectrum sensing by using an energy detection method to obtain a detection outline of the cognitive transmitter at a stage of alpha less than betaRate Pd_s,1And false alarm probability Pf_s,1Detection probability Pd of intelligent interference in alpha < beta stage_j,1And false alarm probability Pf_j,1；

Calculating the channel capacity C of the cognitive receiver in the alpha < beta stage_d,1；

According to the detection probability Pd of the cognitive transmitter_s,1And false alarm probability Pf_s,1Detection probability Pd of smart disturbance_j,1And false alarm probability Pf_j,1Channel capacity C of cognitive receiver_d,1And calculating the integral interruption probability P of the alpha < beta stage_out,1。

3. The method for spectrum sensing time optimization with intelligent interference according to claim 2, wherein the method comprises the following steps: probability of detection of the cognitive transmitter Pd_s,1And false alarm probability Pf_s,1Detection probability Pd of smart disturbance_j,1And false alarm probability Pf_j,1Calculated from equation (1):

wherein Q (·) is defined as

4. The method for spectrum sensing time optimization with intelligent interference according to claim 2, wherein the method comprises the following steps: the channel capacity C of the cognitive receiver in the alpha < beta phase_d,1Calculated from equation (2):

wherein the random variable mu_sIs defined as

5. The method for spectrum sensing time optimization with intelligent interference according to claim 1, wherein the method comprises the following steps: the integral interruption probability P of the alpha stage and the beta stage is calculated_out,2The method comprises the following steps:

performing spectrum sensing by adopting an energy detection method to obtain the detection probability Pd of the cognitive transmitter at the stage of alpha being more than or equal to beta_s,2And false alarm probability Pf_s,2Detection probability Pd of smart disturbance_j,2And false alarm probability Pf_j,2；

Calculating the channel capacity C of the cognitive receiver at the stage of alpha being more than or equal to beta_d,2；

According to the detection probability Pd of the cognitive transmitter_s,2And false alarm probability Pf_s,2Detection of intelligent interferenceRate Pd_j,2And false alarm probability Pf_j,2Channel capacity C of cognitive receiver_d,2And calculating the integral interruption probability P of the alpha stage and the beta stage according to the interruption probability definition formula_out,2。

6. The method of claim 5, wherein the method comprises: probability of detection of the cognitive transmitter Pd_s,2And false alarm probability Pf_s,2Detection probability Pd of smart disturbance_j,2And false alarm probability Pf_j,2Calculated from equation (3):

wherein the content of the first and second substances,

7. The method of claim 5, wherein the method comprises: the channel capacity C of the cognitive receiver at the alpha stage is more than or equal to the beta stage_d,2Calculated from equation (4):

wherein the random variable mu_sIs defined as

8. The method for spectrum sensing time optimization with intelligent interference according to claim 1, wherein the method comprises the following steps: overall outage probability P of the cognitive ad hoc network_outCalculated from equation (5):

Technical Field

The invention relates to a frequency spectrum sensing time optimization method with intelligent interference, and belongs to the technical field of wireless communication.

Background

Under the traditional static spectrum allocation system, the utilization rate of wireless spectrum resources is not high, so that the contradiction between the shortage of the spectrum resources and the low utilization rate is generated. The cognitive radio allows an unauthorized user to opportunistically utilize the idle frequency spectrum for communication on the premise of not influencing the communication of the master user, thereby effectively improving the utilization rate of the frequency spectrum. Meanwhile, the mobile interconnection technology and the intelligent terminal are increasingly popularized, so that more demands are made on the aspects of self-organization, flexibility and the like of a future communication network, and compared with the traditional wireless communication network depending on a central infrastructure, the wireless self-organization network forms a network architecture without center and distributed control through self-organization communication of terminal nodes, and has the advantages of rapid deployment, strong robustness and the like. Under the background, the cognitive ad hoc wireless network is produced and is widely and continuously concerned by academic circles at home and abroad.

However, due to an open spectrum access mechanism, the physical layer of the cognitive ad hoc network is more susceptible to malicious attacks such as interference, and thus, the communication transmission efficiency is low.

Disclosure of Invention

The invention provides a frequency spectrum sensing time optimization method with intelligent interference, which can inhibit adverse effects caused by the intelligent interference and improve the effectiveness of a communication system.

In order to achieve the purpose, the technical scheme adopted by the invention is as follows: a spectrum sensing time optimization method in the presence of intelligent interference comprises the following steps: according to the magnitude relation between the spectrum sensing time alpha of the cognitive transmitter and the spectrum sensing time beta of the intelligent interference, the method is divided into a stage of alpha being less than beta and a stage of alpha being more than or equal to beta; in the stage of alpha being less than beta, the relation between the spectrum sensing time alpha of the cognitive transmitter and the spectrum sensing time beta of the intelligent interference is alpha being less than beta; in the stage that the alpha is larger than or equal to the beta, the relation between the spectrum sensing time alpha of the cognitive transmitter and the spectrum sensing time beta of the intelligent interference is that the alpha is larger than or equal to the beta; calculating the overall probability of interruption P of the alpha < beta phase_out,1Integral outage probability P for the phase with alpha ≧ beta_out,2(ii) a According to the calculated P_out,1And P_out,2Obtaining an overall outage probability P for cognitive self-organizing user transmissions_out(ii) a Using iterative optimizationThe algorithm optimizes the spectrum sensing time alpha of the cognitive transmitter, so that the integral interruption probability P of the cognitive self-organizing user transmission_outAnd minimum.

Further, the overall interruption probability P of the alpha < beta phase is calculated_out,1The method comprises the following steps: performing spectrum sensing by adopting an energy detection method to obtain the detection probability Pd of the cognitive transmitter at the stage of alpha being less than beta_s,1And false alarm probability Pf_s,1Detection probability Pd of intelligent interference in alpha < beta stage_j,1And false alarm probability Pf_j,1(ii) a Calculating the channel capacity C of the cognitive receiver in the alpha < beta stage_d,1(ii) a According to the detection probability Pd of the cognitive transmitter_s,1And false alarm probability Pf_s,1Detection probability Pd of smart disturbance_j,1And false alarm probability Pf_j,1Channel capacity C of cognitive receiver_d,1And calculating the integral interruption probability P of the alpha < beta stage_out,1。

Further, the detection probability Pd of the cognitive transmitter_s,1And false alarm probability Pf_s,1Detection probability Pd of smart disturbance_j,1And false alarm probability Pf_j,1Calculated from equation (1):

wherein Q (·) is defined as

Given the cognitive transmitter target detection probability in the alpha < beta phase,

Q^-1(. h) is the inverse function of Q (-), h_psIs the channel fading coefficient, gamma, of the main base station to the cognitive transmitter_p＝P_p/N₀，P_pIndicating the transmission power of the main base station, N₀Variance of additive white Gaussian noise，M_sFor the number of samples of the received signal of the cognitive transmitter in spectrum sensing and M_s＝αTf_sT and f_sRespectively representing the slot length and the sampling frequency,

target detection probability of intelligent interference given for alpha < beta phase, M_jFor intelligently interfering with the number of samples of the received signal and M_j＝βTf_s，h_pjChannel fading coefficient, h, representing the main base station to smart interference_sjChannel fading coefficient, gamma, representing cognitive transmitter to smart interference_s＝P_s/N₀，P_sRepresenting the transmission power of the cognitive transmitter.

Further, the channel capacity C of the cognitive receiver in the alpha < beta phase_d,1Calculated from equation (2):

wherein the random variable mu_sIs defined as

Indicating a permitted spectrum state, H, detected by a cognitive transmitter using spectrum sensing₀Indicating grant spectrum idle, H₁A random variable μ representing that the licensed spectrum is occupied_pIs defined asWherein B is₀Indicating that the main base station is not transmitting a signal, B₁Indicating that the main base station has transmitted a signal, the random variable mu_jIs defined as

Representing a permitted spectral state, gamma, detected by intelligent interference using spectral sensing_s＝P_s/N₀，P_sFor cognitive transmitters, N₀Is the variance of additive white Gaussian noise, gamma_p＝P_p/N₀，P_pIs the transmission power, gamma, of the main base station and cognitive transmitter_j＝P_j/N₀，P_jTransmission power for intelligent interference, h_sdChannel fading coefficient, h, for cognitive transmitter to cognitive receiver_pdIs the channel fading coefficient, h, of the main base station to the cognitive receiver_jdIs the channel fading coefficient of the intelligent interference to the cognitive receiver.

Further, the integral interruption probability P of the alpha stage and the beta stage is calculated_out,2The method comprises the following steps: performing spectrum sensing by adopting an energy detection method to obtain the detection probability Pd of the cognitive transmitter at the stage of alpha being more than or equal to beta_s,2And false alarm probability Pf_s,2Detection probability Pd of smart disturbance_j,2And false alarm probability Pf_j,2(ii) a Calculating the channel capacity C of the cognitive receiver at the stage of alpha being more than or equal to beta_d,2(ii) a According to the detection probability Pd of the cognitive transmitter_s,2And false alarm probability Pf_s,2Detection probability Pd of smart disturbance_j,2And false alarm probability Pf_j,2Channel capacity C of cognitive receiver_d,2And calculating the integral interruption probability P of the alpha stage and the beta stage according to the interruption probability definition formula_out,2。

Further, the detection probability Pd of the cognitive transmitter_s,2And false alarm probability Pf_s,2Detection probability Pd of smart disturbance_j,2And false alarm probability Pf_j,2Calculated from equation (3):

wherein the content of the first and second substances,

target detection probability of intelligent interference given for alpha ≧ beta phase, M_jFor intelligently interfering with the number of samples of the received signal and M_j＝βTf_sT and f_sRespectively representing the slot length and the sampling frequency,

target detection probability, gamma, for a given cognitive transmitter at the alpha ≧ beta stage_p＝P_p/N₀，P_pIndicating the main base station transmission power, N₀Is the variance of additive white gaussian noise,

h_jschannel fading coefficient, h, for intelligent interference to cognitive transmitters_psIs the channel fading coefficient, h, of the master base station to the cognitive transmitter_pjChannel fading coefficient, M, for main base station to smart interference_sIs the number of received signal samples in the spectrum sensing of the cognitive transmitter and M_s＝αTf_s，γ_j＝P_j/N₀，P_jThe power is transmitted for intelligent interference.

Further, the channel capacity C of the cognitive receiver in the alpha stage is larger than or equal to the beta stage_d,2Calculated from equation (4):

wherein the random variable mu_sIs defined as

Indicating a permitted spectrum state, H, detected by a cognitive transmitter using spectrum sensing₀Indicating grant spectrum idle, H₁A random variable μ representing that the licensed spectrum is occupied_pIs defined as

Wherein B is₀Indicating that the main base station is not transmitting a signal, B₁Indicating that the main base station has transmitted a signal, the random variable mu_jIs defined as

Representing a permitted spectral state, gamma, detected by intelligent interference using spectral sensing_s＝P_s/N₀，P_sFor cognitive transmitters, N₀Is the variance of additive white Gaussian noise, gamma_p＝P_p/N₀，P_pIs the transmission power of the main base station, gamma_j＝P_j/N₀，P_jTransmission power for intelligent interference, h_sdChannel fading coefficient, h, for cognitive transmitter to cognitive receiver_pdIs the channel fading coefficient, h, of the main base station to the cognitive receiver_jdIs the channel fading coefficient of the intelligent interference to the cognitive receiver.

Further, the overall outage probability P of the cognitive self-organizing network_outCalculated from equation (5):

the invention respectively calculates the integral interruption probability of the transmission of the self-organizing user in two stages of alpha being less than beta and alpha being more than or equal to beta, and then searches the frequency spectrum sensing time alpha which enables the integral interruption probability to be minimum by utilizing an iterative optimization algorithm, thereby improving the effectiveness of a communication system. The invention adopts an intelligent interference model, and is more suitable for the attack type adopted by the interference machine in the actual scene.

Drawings

Fig. 1 is a schematic model diagram of a spectrum sensing time optimization method with intelligent interference according to an embodiment of the present invention;

fig. 2 is a schematic flowchart of a method for optimizing spectrum sensing time in the presence of intelligent interference according to an embodiment of the present invention;

fig. 3 is a simulation diagram illustrating a relationship between sensing time of the cognitive transmitter and overall outage probability under different intelligent interference sensing times in fig. 1.

Detailed Description

For a better understanding of the nature of the invention, its description is further set forth below in connection with the specific embodiments and the drawings.

As shown in fig. 1, the system model of this embodiment is composed of a main network and a cognitive ad hoc network, the main network is composed of a main base station (PBS) and a main user (PU), and the cognitive ad hoc network is composed of 1 cognitive transmitter (ST), 1 cognitive receiver (SR), and 1 smart jamming (J). The useful signal is represented by a solid line and the interfering signal by a dashed line. The method comprises the steps that a node in a cognitive self-organizing network needs to sense a frequency spectrum before transmitting a signal, in order to guarantee communication of a master user and reduce influence of interference signals, a cognitive self-organizing network user pair must send a useful signal on the premise of detecting the idle frequency spectrum, and otherwise, data transmission is stopped; however, smart jamming must transmit jamming signals when it is detected that the spectrum is occupied to improve jamming efficiency, otherwise the jamming transmission is stopped.

The invention discloses a method for optimizing spectrum sensing time, which specifically comprises the following steps as shown in figure 2:

in one time slot, the sensing time of the cognitive transmitter is represented as alpha, and the sensing time of the intelligent interference is represented as beta, so the transmission time of the cognitive transmitter and the intelligent interference can be represented as 1-alpha and 1-beta respectively.

Step 1, dividing the method into an alpha & ltbeta stage and an alpha & gtbeta stage according to the magnitude relation between the spectrum sensing time alpha of the cognitive transmitter and the spectrum sensing time beta of the intelligent interference. In the stage of alpha being less than beta, the relation between the spectrum sensing time alpha of the cognitive transmitter and the spectrum sensing time beta of the intelligent interference is alpha being less than beta. In the stage that the alpha is larger than or equal to the beta, the relation between the spectrum sensing time alpha of the cognitive transmitter and the spectrum sensing time beta of the intelligent interference is that the alpha is larger than or equal to the beta.

And 2, when alpha is less than beta, calculating the alpha stage less than the beta stage.

1) Performing spectrum sensing by adopting an energy detection method to obtain the detection probability Pd of the cognitive transmitter at the stage of alpha being less than beta_s,1And false alarm probability Pf_s,1Detection probability Pd of intelligent interference in alpha < beta stage_j,1And false alarm probability Pf_j,1。

By x_pRandom signal, x, representing transmission of the main base station_sExpressing the random signal transmitted by the cognitive transmitter, without loss of generality, assuming E [ | x [ ]_p|²]1 and E [ | x_s|²]1, wherein E [ ·]Indicating expected value operator, by P_pIndicating the transmission power, P, of the main base station_sRepresents the transmission power of the cognitive transmitter, so that the cognitive transmitter receives the signal y in the sensing phase when alpha < beta_s,1Intelligently disturbing the received signal y in the sensing phase_j,1Can be expressed as formula (1) and formula (2), respectively:

wherein n is_sMeans that the average value received by the cognitive transmitter is 0 and the variance is N₀Of additive white Gaussian noise, n_jMean 0 and variance N representing reception of intelligent interference₀Additive white Gaussian noise of h_psRepresenting the channel fading coefficient, h, from the master base station to the cognitive transmitter_pjChannel fading coefficient, h, representing the main base station to smart interference_sjChannel fading coefficient, random variable | h, representing cognitive transmitter to smart interference_ps|²、|h_pj|²And | h_sj|²Respectively obey parameters of

And

the distribution of the indices of (a) to (b),and

are respectively independent and identically distributed random variables | h_ps|²、|h_pj|²And | h_sj|²Is measured. Random variable mu_pIs defined as

B₀Indicating that the main base station is not transmitting a signal, B₁Indicating that the master base station has transmitted a signal, defining the probability that the master base station has transmitted the signal as P₀＝Pr(B₀). Similarly, the random variable μ_sIs defined as

Indicating a permitted spectrum state, H, detected by a cognitive transmitter using spectrum sensing₀Indicating grant spectrum idle, H₁Indicating that the licensed spectrum is occupied, i.e., the cognitive transmitter communicates only when it detects that the spectrum is free, and stops data transmission when it detects that the spectrum is occupied.

Cognitive transmitter and intelligent interferenceReceiving signals y separately in the sensing phase_s,1And y_j,1The spectral state is then perceived using an energy detection method. Setting an energy detection threshold of a cognitive transmitter to lambda_sThe energy detection threshold of the intelligent interference is lambda_jThe detection probability Pd of the cognitive transmitter at the stage of alpha < beta can be obtained_s,1And false alarm probability Pf_s,1Detection probability Pd of intelligent interference in alpha < beta stage_j,1And false alarm probability Pf_j,1Respectively expressed as:

Pd_j,1＝Pd'_j,1(1-Pd_s,1)+Pd″_j,1Pd_s,1(5)

Pf_j,1＝Pf′_j,1(1-Pf_s,1)+Pf″_j,1Pf_s,1(6)

wherein Q (·) is defined as

γ_p＝P_p/N₀，M_sFor the number of samples of the received signal of the cognitive transmitter in spectrum sensing and M_s＝αTf_sT and f_sRespectively, the slot length and the sampling frequency.

Defining:

wherein M is_jFor intelligently interfering with the number of samples of the received signal and M_j＝βTf_s，γ_s＝P_s/N₀Given the target detection probability of the cognitive transmitter as

Target detection probability of intelligent interference is

While the target detection probability is usually set close to 1, so Pd_j,1≈Pd″_j,1。

Definition of

Wherein Q^-1(. cndot.) is an inverse function of Q (-),

equation (3), equation (4), and equations (7) through (10) can be converted to:

2) calculating the channel capacity C of the cognitive receiver in the alpha < beta stage_d,1。

According to the cognitive receiver receiving signal and combining with the Shannon formula, the channel capacity C of the cognitive receiver can be obtained_d,1Comprises the following steps:

wherein, γ_j＝P_j/N₀，P_jTransmission power for intelligent interference, h_sdRepresenting the channel fading coefficient, h, from the cognitive transmitter to the cognitive receiver_pdRepresenting the channel fading coefficient, h, from the primary base station to the cognitive receiver_jdRepresenting the channel fading coefficient of the intelligent interference to the cognitive receiver, random variable | h_sd|²、|h_pd|²And | h_jd|²Respectively obey parameters ofAnd

the distribution of the indices of (a) to (b),

and

are respectively independent and identically distributed random variables | h_sd|²、|h_pd|²And | h_jd|²Is measured. Random variable mu_jIs defined as

Indicating a permitted spectrum state detected by intelligent interference using spectrum sensing.

3) Probability of detection Pd from cognitive transmitter_s,1And false alarm probability Pf_s,1Detection probability Pd of smart disturbance_j,1And false alarm probability Pf_j,1Channel capacity C of cognitive receiver_dAnd calculating the integral interruption probability P of the alpha < beta stage_out,1。

Defining the formula P according to the formula (5), the formula (6), the formula (11) to the formula (17), and the interruption probability_out＝Pr(C_d< R) and considering the conditional probability, the overall interruption probability P of the alpha < beta stage can be obtained_out,1Comprises the following steps:

wherein, defineWhere R is the data rate.

And 3, when the alpha is larger than or equal to the beta, calculating the alpha is larger than or equal to the beta stage.

1) Performing spectrum sensing by adopting an energy detection method to obtain the detection probability Pd of the cognitive transmitter at the stage of alpha being more than or equal to beta_s,2And false alarm probability Pf_s,2Detection probability Pd of smart disturbance_j,2And false alarm probability Pf_j,2。

By x_jRandom signal representing intelligent interference transmission, without loss of generality, assuming E x_j ²]By P1_jThe transmission power of the intelligent interference is represented, therefore, when alpha is more than or equal to beta, the cognitive transmitter receives the signal y in the sensing stage_s,2Intelligently disturbing the received signal y in the sensing phase_j,2Can be expressed as equation (19) and equation (20), respectively:

wherein h is_jsRandom variable | h representing channel fading coefficient of intelligent interference to cognitive transmitter_js|²Compliance parameter of

The distribution of the indices of (a) to (b),

is independent and uniformly distributed random variable | h_js|²Is measured.

Cognitive transmitter and intelligent interference respectively received signal y_s,2And y_j,2Then, sensing the spectrum state by using an energy detection method to obtain the detection probability Pd of the cognitive transmitter at the stage of alpha being more than or equal to beta_s,2And false alarm probability Pf_s,2Detection probability Pd of smart disturbance_j,2And false alarm probability Pf_j,2Respectively expressed as:

Pd_s,2＝Pd′_s,2(1-Pd_j,2)+Pd″_s,2Pd_j,2(23)

Pf_s,2＝Pf′_s,2(1-Pf_j,2)+Pf″_s,2Pf_j,2(24)

defining:

target detection probability given cognitive transmitters and intelligent interference

And

while the target detection probability is usually set close to 1, so Pd_s,2≈Pf″_s,2. Definition of

Then equation (21), equation (22), and equations (25) through (28) can be transformed into:

2) calculating the channel capacity C of the cognitive receiver at the stage of alpha being more than or equal to beta_d,2。

According to the cognitive receiver receiving signals and combining with the Shannon formula, the channel capacity C of the cognitive receiver at the stage of alpha is larger than or equal to beta can be obtained_d,2Comprises the following steps:

3) probability of detection Pd from cognitive transmitter_s,2And false alarm probability Pf_s,2Detection probability Pd of smart disturbance_j,2And false alarm probability Pf_j,2Channel capacity C of cognitive receiver_d,2Calculating to obtain the integral interruption probability P of the alpha stage and the beta stage_out,2。

Defining the formula P according to the formula (23), the formula (24), the formula (29) to the formula (35), and the interruption probability_out＝Pr(C_d< R) and considering the conditional probability, the integral interruption probability P of the alpha ≧ beta stage can be obtained_out,2Comprises the following steps:

step 4, according to the calculated integral interruption probability P of the stage of alpha < beta_out,1Integral outage probability P for the phase with alpha ≧ beta_out,2Obtaining the overall outage probability P of the cognitive self-organizing network_out：

Step 5, optimizing the spectrum sensing time alpha of the cognitive transmitter by using an iterative optimization algorithm so as to enable the integral interruption probability P of the cognitive self-organizing network_outThe optimization problem of the perception time α in the minimum, i.e. in the presence of intelligent disturbances, can be described as:

setting initial conditions:

and

the content of the organic acid is 0.5,

is 1, probability of spectrum being free P₀0.8, cognitive transmitter signal-to-noise ratio γ_sIntelligent interference signal-to-noise ratio gamma_jSignal-to-noise ratio gamma to primary user_p10dB, 5dB and 5dB, respectively, slot length T being 20ms, sampling frequency f_s25kHz, data transmission rate R0.5 b/s/Hz, target detection probability

And

are set to 0.99.

Fig. 3 is a simulation diagram of cognitive transmitter sensing time α and overall outage probability under different intelligent interference sensing times β. By a trade-off between the spectrum sensing phase and the data transmission phase, there is always an optimal spectrum sensing time α^*The overall outage probability of cognitive self-organization is minimized. As can be seen in the figure, α^*Only at the segmentation point and at the inflection point of the curve, alpha which minimizes the overall probability of interruption when beta is 0.2, 0.5, 0.8 and 1^*0.18, 0.41, 0.07 and 0.07 respectively, but as β increases, the overall outage probability minimum tends to increase first and then decrease. It can also be seen from the figure that when β is fixed, as α increases, the overall outage probability also tends to decrease and then increase, and a fragmentation condition may occur. This is because when α is small, spectrum sensing is inaccurate, so that the signal-to-noise ratio of the cognitive self-organizing user decreases, and the overall outage probability increases, and when α is large, the transmission time is too short, so that the overall outage probability increases. Segmentation occurs because of intelligent interference reception at the segmentation pointThe energy of the cognitive transmitter signal is too weak to stop sending interference, so that the overall outage probability of the cognitive receiver is improved.

It should be noted that while the invention has been described in terms of the above-mentioned embodiments, there are many other embodiments of the invention. It will be apparent to those skilled in the art that various changes and modifications may be made without departing from the spirit and scope of the invention, and it is intended that all such changes and modifications be covered by the appended claims and their equivalents.