CN111212423B - Credible cooperative interference node selection method based on hidden Markov model - Google Patents
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Abstract
The invention provides a credible cooperative interference node selection method based on a hidden Markov model. The method comprises the following steps: detecting the behavior of the cooperative interference node by using an energy detector, and judging whether the cooperative interference node sends an interference signal according to a detection result; updating the credit value of the cooperative interference node which sends the interference signal by using a hidden Markov trust model according to the judgment result of the energy detector; judging whether the cooperative interference node meets the system requirements or not according to the updated credit value of the cooperative interference node, and if so, continuing to use the cooperative interference node; otherwise, selecting other nodes as cooperative interference nodes. The embodiment of the invention designs a novel credible cooperative interference node selection mechanism based on a hidden Markov model, which can more dynamically analyze the behavior of the cooperative interference node, select a credible cooperative interference node and effectively ensure the safety performance of a wireless communication channel.
Description
Technical Field
The invention relates to the technical field of wireless communication networks, in particular to a credible cooperative interference node selection method based on a hidden Markov model.
Background
With the development of wireless communication networks, people's dependence on wireless networks is gradually deepened, and the security problem of wireless networks draws attention. Traditional high-level encryption techniques require strong support of computing power, which is laborious for energy-constrained wireless nodes. Therefore, researchers are beginning to research physical layer security techniques for improving the security of wireless communication. A mainstream physical layer security technology is a cooperative interference technology, a cooperative interferer is selected to assist in sending artificial noise to reduce the signal-to-noise ratio of an eavesdropper, and the purpose of secure communication is achieved. However, considering social attributes among users, the collaboration disturber refuses to send artificial noise with a certain probability for the purpose of saving energy for self transmission, which may result in that network security performance is not guaranteed. Therefore, it is necessary to design a confidence model for selecting a trusted cooperative interfering node.
Most of the traditional trust models are Bayesian trust models. The following assumptions exist in the bayesian belief model: the behavior of the node is described according to a probability distribution, and the confidence level is a function of the expected value of the probability distribution, which will be updated with each new rating received according to bayesian theorem. Hidden markov models are statistical markov models in which it is assumed that the system being modeled is a markov process with unobserved (hidden) states that control each selected component. Thus, each hidden markov model has a sequence of hidden states from a finite set of states and its corresponding sequence of observations. The hidden Markov model has more parameters than the Bayesian trust model, so that fine adjustment can be performed to adapt to a dynamic environment.
At present, an effective method for selecting the credible cooperative interference nodes based on the hidden Markov model does not exist in the prior art.
Disclosure of Invention
The embodiment of the invention provides a method for selecting a credible cooperative interference node based on a hidden Markov model, so as to select an effective credible cooperative interference node.
In order to achieve the purpose, the invention adopts the following technical scheme.
A credible cooperative interference node selection method based on a hidden Markov model comprises the following steps:
detecting the behavior of the cooperative interference node by using an energy detector, and judging whether the cooperative interference node sends an interference signal according to a detection result;
updating the credit value of the cooperative interference node which sends the interference signal by using a hidden Markov trust model according to the judgment result of the energy detector;
judging whether the cooperative interference node meets the system requirements or not according to the updated credit value of the cooperative interference node, and if so, continuing to use the cooperative interference node; otherwise, selecting other nodes as cooperative interference nodes;
the detecting the behavior of the cooperative interference node by using the energy detector, and judging whether the cooperative interference node sends an interference signal according to the detection result, includes:
the energy detector comprises a noise pre-filter, an energy operator and an integrator, and receives an output signal y of a cooperative interference node to be detectedc(t) applying a noise prefilter to yc(t) noise filtering and energy operation device for yc(t) amplifying the y-signal and integrating the y-signal with an integratorc(t) performing integration processing to obtain a detection signal Yp；
Setting e as detection threshold when detecting signal YpIf the detection threshold is larger than the maximum element, judging that the cooperative interference node sends an interference signal, otherwise, judging that the cooperative interference node does not send the interference signal;
cooperative interference node transmitting interference signal
Indicating that no interfering signal is being transmitted for cooperative interfering nodes
Represents;
N02representing bilateral noise power spectral density
t represents time
p represents the exponential power of the signal in the test statistic
xp(t) a transmission signal of a source node
nc(t) represents channel white noise
xj(t) represents an interference signal
The step of updating the credit value of the cooperative interference node which sends the interference signal by using the hidden Markov trust model according to the judgment result of the energy detector comprises the following steps:
the hidden Markov model is composed of N hidden states S ═ S1，...，sNA finite set of where s1Being a trusted state, s2Is an untrusted state and has a probability distribution associated with each hidden state, the initial probability distribution of each hidden state being defined by pi-piiShowing the state of the system at the beginning of energy detection, wherein the state of the detected cooperative interference node is represented by a discrete time Markov chain xk＝{x1，x2，...xk,.., where xkThe epsilon is a hidden state of the cooperative interference node at a sampling time k;
observed value y of detection signal of detected cooperative interference nodek＝{y1，y2，...yn,., wherein yke.V is the observed value at sampling time k, xkAnd ykThe relationship between the two is defined by a probability distribution matrix Q ═ Qi(m) }, wherein q isi(m)P(yk＝vm|xk＝si) I is more than or equal to 1 and less than or equal to N, M is more than or equal to 1 and less than or equal to M, M is the number of detection results, and an observation symbol v is observed under the condition that the system is in the state simThe probability of (d); suppose that
Is a set of state transition probabilities of detected cooperative interfering nodes, wherein
The output level of the cooperative interference node is determined by detecting a symbol set V ═ V1，...，vMClassified, the dynamics of the system is determined by the slew rate matrix Λ ═ λijDescription, as follows:
wherein λ isijIs the slew rate, indicating how fast the node state switches, and P (x (t + dt) ═ j | x (t) ═ i) is the state transition probability, indicating the transition probability that time t is state i and time t + Δ t is state j;
respectively modeling the state occupation time, wherein the state occupation time is expressed as H ═ H (H) without self-conversion in a conversion matrix1，h2) Wherein h is1Mean time of trust of cooperative interfering node, h2Representing the mean time of the cooperative interference nodes without trust, and obtaining a dynamic transition probability matrix according to the Kolmogorov equation
The expression of (a) is as follows:
The observed value obtained by detection is represented as ykThe time at which the detection result is obtained is denoted as tkThe time between the detection result k-1 and the detection result k is expressed as σk＝tk-tk-1X (t) represents the hidden state of the system at time t, let xk＝x(tk) The state distribution of the cooperative interference nodes is expressed as
This value is updated for each new detection result,
representing the current state distribution, n represents the nth cooperative interference node, and y is equal to ykIs the current detection result, δkRepresenting the time between the current detection and the last detection;
the detected cooperative interference node is in a credible state S at the current moment1As the current reputation value of the cooperative interfering node
Judging whether the cooperative interference node meets the system requirements or not according to the updated credit value of the cooperative interference node, and if so, continuing to use the cooperative interference node; otherwise, selecting other nodes as cooperative interference nodes, including:
setting a credit value threshold value of the cooperative interference node, and when the current credit value of the cooperative interference node is greater than the credit value threshold value, judging that the cooperative interference node meets the system requirement, and continuing to use the cooperative interference node; otherwise, selecting other nodes as cooperative interference nodes.
According to the technical scheme provided by the embodiment of the invention, a novel credible cooperative interference node selection mechanism based on a hidden Markov model is designed, so that the behavior of the cooperative interference node can be more dynamically analyzed, a credible cooperative interference node is selected, and the safety performance of a wireless communication channel is effectively guaranteed.
Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a processing flow chart of a method for selecting a trusted cooperative interfering node based on a hidden markov model according to an embodiment of the present invention;
FIG. 2 is a flow chart of a process of an energy detector according to an embodiment of the present invention;
fig. 3 is a schematic diagram of a markov transformation process and an observed value according to an embodiment of the present invention;
fig. 4 is a schematic diagram of a simulation result of a function in which the detection probability is different between the cooperative interference node received power and the access node received power according to an embodiment of the present invention;
FIG. 5 is a graph illustrating Receiver Operating Characteristic (ROC) curves for a conventional energy detector and an improved energy detector for different detection durations, according to an embodiment of the present invention;
fig. 6 is a schematic diagram of a hidden markov confidence model with different detection probabilities according to an embodiment of the present invention.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the accompanying drawings are illustrative only for the purpose of explaining the present invention, and are not to be construed as limiting the present invention.
As used herein, the singular forms "a", "an", "the" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises" and/or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. It will be understood that when an embodiment of the invention is referred to as being "connected" or "coupled" to another element, it can be directly connected or coupled to the other element or intervening elements may also be present. Further, "connected" or "coupled" as used herein may include wirelessly connected or coupled. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items.
It will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the prior art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
For the convenience of understanding the embodiments of the present invention, the following description will be further explained by taking several specific embodiments as examples in conjunction with the drawings, and the embodiments are not to be construed as limiting the embodiments of the present invention.
The embodiment of the invention provides a method for selecting a credible cooperative interference node based on a hidden Markov model, and the processing flow of the method is shown as figure 1, and the method comprises the following processing steps:
and 3, judging whether the cooperative interference node meets the system requirements or not according to the updated credit value of the cooperative interference node, if so, continuing to use the cooperative interference node, otherwise, selecting other nodes as cooperative interference nodes.
The embodiment of the present invention provides an energy detection scheme for detecting whether a cooperative interference node transmits an interference signal, fig. 2 is a detection processing flow chart of an energy detector according to the embodiment of the present invention, and as shown in fig. 2, the energy detector includes a noise prefilter, an energy operator, and an integrator, and finally performs decision detection on an output signal.
In the step 1, the energy detector comprises a noise prefilter, an energy operator and an integrator, and the energy detector receives the output signal y of the cooperative interference node to be detectedc(t) applying a noise prefilter to yc(t) noise filtering and energy operation device for yc(t) amplifying the y-signal and integrating the y-signal with an integratorc(t) performing integration processing to obtain a detection signal Yp。
The output of the detection process of the energy detector is as follows:
wherein Y ispAnd E is a detection signal, wherein epsilon is a detection threshold, when the detection signal is greater than the detection threshold, the detection signal is judged to have an interference signal, otherwise, the detection signal is judged to have no interference signal.
N02Representing the noise power spectral density, δcRepresenting white noise, p represents an index value of energy operation,
indicating the length of the detection time from
A length to t of
The length of time of (a) is integrated with the energy.
The embodiment of the invention assumes that the cooperative interference node has two behaviors: 1, sending an interference signal. 2, no interference signal is transmitted. Therefore, a hypothesis test was designed, the formula being:
wherein the content of the first and second substances,
no interfering signal is transmitted on behalf of the cooperating interfering nodes,
an interfering signal is transmitted on behalf of the cooperating interfering nodes.
T in parentheses in the above formula represents time t,
representing the channel from the primary user to the energy detector,
optimal beamforming vector, x, representing primary userpIndicating the primary user signal, ncRepresenting ambient white noise received by the energy detector,
representing the channel, x, from the cooperating interfering nodes to the energy detectorjRepresenting an interfering signal.
In the step 2, after the cooperative interference node that has sent the interference signal is found according to the judgment result of the energy detector, the reputation value of the cooperative interference node that has sent the interference signal is updated by using the hidden markov trust model. The input signal of the hidden Markov trust model is
Fig. 3 is a schematic diagram of a markov transformation process and an observation value according to an embodiment of the present invention, and as shown in fig. 3, a hidden markov model is formed by N hidden states S ═ S1，...，sNA finite set of and with a probability distribution associated with each hidden state. In an embodiment of the invention, the model has two hidden fieldsHidden state S ═ S1(trusted State), s2(untrusted state) }. The initial distribution is formed by pi ═ piiThere is shown, a state of the system at the beginning of energy detection. The state of the detected cooperative interference node is formed by a discrete time Markov chain xk＝{x1，x2，...xk,.., where xkE S is the state that the cooperative interfering node may hide at the sampling instant k.
Suppose that
Is a set of state transition probabilities, wherein
The output level of the cooperative interference node is determined by detecting a symbol set V ═ V1，...，vMAnd (5) classifying. In the embodiment of the invention, the output value of the energy detector has two conditions
Suppose that in the observed data, the embodiment of the present invention has an observed value yk＝{y1，y2，...yk,., wherein ykE.v is the observed value at sampling instant k. x is the number ofkAnd ykThe relationship between the two is defined by a probability distribution matrix Q ═ Qi(m) } (used in step 2 of the Algorithm) description, where q isi(m)＝P(yk＝vm|xk＝si) I is more than or equal to 1 and less than or equal to N, M is more than or equal to 1 and less than or equal to M, and the system is in a state siObserve the observation symbol vmThe probability of (c). The dynamics of the system are determined by the slew rate matrix Λ ═ λijDescription of (algorithm step 1) as follows:
wherein λ isijIs a slew rate matrix, and P (x (t + dt) ═ j | x (t) ═ i) is a state transition probability, which indicates that time t is state i and time t + Δ t is state iTransition probability of state j.
The embodiment of the invention models the state occupation time respectively, and the conversion matrix has no self-conversion. The state occupancy time is expressed as H ═ H (H)1，h2) Wherein h is1Mean time of trust of cooperative interfering node, h2Representing the average time that the cooperative interfering node is untrusted. According to the Kolmogorov equation, the dynamic transition probability matrix can be obtained according to the embodiment of the invention
The expression of (a) is as follows:
In the embodiment of the present invention, the detection result is represented as ykThe time at which the detection result is obtained is denoted as tk. The time between the detection result k-1 and the detection result k is denoted as σk＝tk-tk-1. x (t) represents the hidden state of the system at time t, and for simplicity, the embodiment of the present invention makes xk＝x(tk). In the embodiment of the invention, the state distribution of the cooperative interference nodes is expressed as
This value is updated for each new detection result.
Representing the current state distribution, n represents the nth cooperative interference node, and y is equal to ykIs the current detection result, δkRepresenting the time between the current detection and the last detection.
In the hidden markov-based trust model considered in the embodiments of the present invention, the embodiments of the present invention use two hidden states S ═ S1(trusted State), s2(untrusted state) } and two test results
Wherein
And
the presence and absence of a detection result indicating the presence of an artifact. And if the artificial noise is not detected in the previous detection, the cooperative interference node is in an untrusted state, and if the artificial noise is detected, the cooperative interference node is in a trusted state. Reputation values are defined as dynamic variables that change over time. Therefore, the embodiment of the invention can capture the behavior characteristics of the cooperative interference nodes which are good in behavior but abnormal in behavior suddenly in a certain time period. Since the behavior of the cooperative interfering node may change over time, the cooperative interfering node may be in a different state than when it was last detected. The embodiment of the invention can only evaluate the reliability of the cooperative interference node according to the previous detection result of the cooperative interference node as best as possible. This means that the network state is hidden, so embodiments of the present invention use a hidden markov trust model in this network.
The initial state distribution of the cooperative interference nodes is defined as xi0＝{ξ0(1)，ξ0(2) Is where ξ0(1)＝ξ0(trusted)0.5，ξ0(2)＝ξ0(unregusted) ═ 0.5. According to the inventionAccording to the model of the embodiment, the embodiment of the invention can obtain the state probability distribution of the cooperative interference node, and updates the probability distribution according to the algorithm, which is expressed as
In particular, embodiments of the invention use being in a trusted state
The probability of (c) is used as the reputation value of the cooperative interfering node.
The detected cooperative interference node is in a credible state S at the current moment1The probability of (c) is used as the current reputation value of the cooperative interfering node.
In step 3, based on the reputation value list of each cooperative interference node, setting a reputation value threshold of the cooperative interference node (1> threshold >0, generally 0.5-0.8), and when the current reputation value of the cooperative interference node is greater than the reputation value threshold, determining that the cooperative interference node meets the system requirements, and continuing to use the cooperative interference node; otherwise, selecting other nodes as cooperative interference nodes.
Example two
Example 1 credible cooperative interference node selection strategy in Internet of things
In embodiments of the present invention, secure transmissions in an internet of things system are considered. The Internet of things system consists of two access nodes and various types of Internet of things equipment. In this system, the access node 2 wishes to send information to an internet of things device (intelligent hot water kettle). At the same time, there is an eavesdropper (sweeping robot) that intends to intercept and decode the message. In order to protect the transmitted message from being decoded by an eavesdropper, another access node 1 selects an internet of things device (intelligent safe) as a cooperative interference node. The cooperative interfering nodes send interfering signals to the eavesdropper so that the message cannot be decoded correctly.
However, there is a need to address one challenge in cooperative interference techniques. Traditionally, cooperative interference techniques are considered to be based on a basic assumption: the cooperative interfering nodes are considered to be trustworthy in transmitting the artificial noise. However, for some selfish reasons (e.g., energy saving), the cooperative interfering node may choose not to generate the interfering signal, that is, the cooperative interfering node may not be trusted. If the cooperative interfering node is not trusted, the transmitted message cannot be protected. Therefore, it is necessary to design a trust model to evaluate the trustworthiness of each internet of things device and select a trustworthy internet of things device as an interference source.
Therefore, in the present invention, the access node 1 is used to detect the cooperative interfering node and determine whether the node transmits an interfering signal. The detection method uses the improved energy detection method proposed in the present invention. And according to the detection result, utilizing hidden Markov model to make credit value, namely in trust state
And (4) updating. And finally, the access node 1 can obtain a credit value list of all the internet of things devices, and according to the credit value list, the device with the highest credit value is selected as a cooperative interference node.
Analysis of this example is shown in fig. 4, and fig. 4 shows a schematic diagram of a simulation result of a detection probability which is a function that the reception power of the cooperative interference node is different from the reception power of the access node. In this figure, the false alarm probability threshold is fixed
The detection duration is set to
It can be concluded that the detection probability of the improved energy detector is better than that of the conventional energy detector under the condition that the received powers of the cooperative interference nodes are different. In addition, a tag with P is detectedjWith increasing detection probability, the detection probability is gradually increased to 1, and higher PaResulting in a lower detection probability. It is apparent that when the access node 1 receives a higher power interfering signalAnd is easier to detect. However, when the received power of the access node 1 is higher, the interfering signal will be masked and difficult to detect.
Fig. 5 shows a Receiver Operating Characteristic (ROC) curve diagram for a conventional energy detector and an improved energy detector for different detection durations. In this figure, the average received power of the access node and the interferer is set to Pa＝10W，Pj1W. Shows improved energy detector performance over conventional energy detection at different detection durations
The following properties. In addition, it can be derived that
The detection probability gradually increases to 1. This is because of the following
Is increased, samples are sampled
The number of the detection signals increases, and thus an accurate detection probability can be obtained.
FIG. 6 is a schematic diagram of hidden Markov model with different detection probabilities according to an embodiment of the present invention, and as shown in FIG. 6, the embodiment of the present invention provides reputation values of the hidden Markov model, where the input is 10
An observed value is input, and then 5 observed values are input
The observations are for different detection probabilities. The detection probability models the uncertainty of the detection. For example, q1(1) 0.8 indicates that even if the cooperative interfering node is in an untrusted state (no interfering signal is present), a probability of 0.2 is obtained
That is, there is a 80% probability of obtaining a correct detection result. As can be seen in fig. 6, the slope of the reputation value is steeper as the probability of detection is higher. This can be explained by the detection becoming more reliable as the probability of detection increases, resulting in a fast response to the reputation value.
In summary, in the embodiment of the present invention, the energy detection and the reputation value update are seamlessly connected, without time delay, and are more stable.
The credit value can be dynamically updated, so that the dynamic property of the system is ensured, and the system is more flexible.
The scheme of the invention has higher success rate and better practicability.
Those of ordinary skill in the art will understand that: the figures are merely schematic representations of one embodiment, and the blocks or flow diagrams in the figures are not necessarily required to practice the present invention.
From the above description of the embodiments, it is clear to those skilled in the art that the present invention can be implemented by software plus necessary general hardware platform. Based on such understanding, the technical solutions of the present invention may be embodied in the form of a software product, which may be stored in a storage medium, such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the method according to the embodiments or some parts of the embodiments.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for apparatus or system embodiments, since they are substantially similar to method embodiments, they are described in relative terms, as long as they are described in partial descriptions of method embodiments. The above-described embodiments of the apparatus and system are merely illustrative, and the units described as separate parts may or may not be physically separate, and the parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (1)
1. A credible cooperative interference node selection method based on a hidden Markov model is characterized by comprising the following steps:
detecting the behavior of the cooperative interference node by using an energy detector, and judging whether the cooperative interference node sends an interference signal according to a detection result;
updating the credit value of the cooperative interference node which sends the interference signal by using a hidden Markov trust model according to the judgment result of the energy detector;
judging whether the cooperative interference node meets the system requirements or not according to the updated credit value of the cooperative interference node, and if so, continuing to use the cooperative interference node; otherwise, selecting other nodes as cooperative interference nodes;
the detecting the behavior of the cooperative interference node by using the energy detector, and judging whether the cooperative interference node sends an interference signal according to the detection result, includes:
the energy detector comprises a noise pre-filter, an energy operator and an integrator, and receives an output signal y of a cooperative interference node to be detectedc(t) prefiltering by noiseDevice pair yc(t) noise filtering and energy operation device for yc(t) amplifying the y-signal and integrating the y-signal with an integratorc(t) performing integration processing to obtain a detection signal Yp；
Setting e as detection threshold when detecting signal YpIf the detection threshold is larger than the maximum element, judging that the cooperative interference node sends an interference signal, otherwise, judging that the cooperative interference node does not send the interference signal;
cooperative interference node transmitting interference signal
Indicating that no interfering signal is being transmitted for cooperative interfering nodes
Represents;
the step of updating the credit value of the cooperative interference node which sends the interference signal by using the hidden Markov trust model according to the judgment result of the energy detector comprises the following steps:
the hidden Markov model is composed of N hidden states S ═ S1，...，sNA finite set of N-2 in the model presented here, with two hidden states, where s is1Being a trusted state, s2Being untrusted and having a probability distribution associated with each hidden state, of the respective hidden stateThe initial probability distribution is formed by pi ═ piiShowing the state of the system at the beginning of energy detection, wherein the state of the detected cooperative interference node is formed by a discrete time Markov chain x ═ { x ═ x1，x2，...xk,.., where xkThe epsilon is a hidden state of the cooperative interference node at a sampling time k;
observed value y ═ y of detection signal of detected cooperative interfering node1，y2，...yk,., wherein yke.V is the observed value at sampling time k, xkAnd ykThe relationship between the two is defined by a probability distribution matrix Q ═ Qi(m) }, wherein q isi(m)＝P(yk＝vm|xk＝si) I is more than or equal to 1 and less than or equal to N, M is more than or equal to 1 and less than or equal to M, and the system is in a state siObserve the observation symbol vmThe probability of (d); suppose that
Is a set of state transition probabilities of detected cooperative interfering nodes, wherein
I is more than or equal to 1, j is less than or equal to N, and the output level of the cooperative interference node is determined by detecting a symbol set V ═ V1，...，vMClassified, the dynamics of the system is determined by the slew rate matrix Λ ═ λijDescription, as follows:
p (x (t + dt) ═ j | x (t) ═ i) is a state transition probability indicating that time t is state i and time t + Δ t is state j;
respectively modeling the state occupation time, wherein the state occupation time is expressed as H ═ H (H) without self-conversion in a conversion matrix1，h2) Wherein h is1Mean time of trust of cooperative interfering node, h2Indicating cooperative interfering nodes are untrustedThe dynamic transition probability matrix is obtained according to the Kolmogorov equation
The expression of (a) is as follows:
the observed value obtained by detection is represented as ykThe time at which the observed value is obtained is denoted as tkObserved value yk-1And the observed value ykThe time between is expressed as σk＝tk-tk-1X (t) represents the hidden state of the system at time t, let xk＝x(tk) The state distribution of the cooperative interference nodes is expressed as
This value is updated for each new detection result,
representing the current state distribution, n represents the nth cooperative interference node, and y is equal to ykIs the current detection result, δkRepresenting the time between the current detection and the last detection;
the detected cooperative interference node is in a credible state S at the current moment1The probability of the interference is used as the current reputation value of the cooperative interference node;
judging whether the cooperative interference node meets the system requirements or not according to the updated credit value of the cooperative interference node, and if so, continuing to use the cooperative interference node; otherwise, selecting other nodes as cooperative interference nodes, including:
setting a credit value threshold value of the cooperative interference node, and when the current credit value of the cooperative interference node is greater than the credit value threshold value, judging that the cooperative interference node meets the system requirement, and continuing to use the cooperative interference node; otherwise, selecting other nodes as cooperative interference nodes.
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