CN112533222B - Communication network key node identification and optimal interference signal pattern selection method - Google Patents
Communication network key node identification and optimal interference signal pattern selection method Download PDFInfo
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Abstract
The invention discloses a method for identifying key nodes of a communication network and selecting optimal interference signal patterns, which combines key node identification with optimal waveform search, thereby avoiding the problem that the accuracy of dividing key node identification and optimal waveform search into two sections of learning and optimal waveform search seriously depends on the accuracy of key node identification.
Description
Technical Field
The invention relates to the field of communication, in particular to a method for identifying key nodes of a communication network and selecting an optimal interference signal pattern.
Background
With the application of various information technologies in the military field, the status of information warfare is increasingly important. However, many conventional techniques fail to work due to the particularities of military applications and the high security of tactical communications networks. Therefore, finding new technical methods suitable for tactical communication networks is crucial to obtaining information rights and dominance rights in the battlefield.
The traditional network key node identification method mainly comprises two types: the social network analysis method is characterized in that the criticality of the node is equivalent to the significance, and the integrity of the network is not damaged; the second is a node deletion method, which is to equate the key of a node to the destructiveness of the network after the node is deleted. However, these methods are completely dependent on the known network topology, and cannot be applied in the scene of unknown topology or rapid change of network topology. Due to the characteristics of no center, temporary autonomy, high confidentiality and the like of the tactical communication network, the network topology of the tactical communication network is restored into a difficult problem, an accurate topological structure is not available, and the traditional method cannot be used for identifying key nodes naturally.
At present, interference waveform research aiming at a tactical communication network is mainly based on interference of key nodes, that is, point-to-point interference or multi-node simultaneous interference is actually carried out on the premise of known key nodes by optimal waveform searching, so that the interference effect of the whole network depends on the correctness of key node identification to a great extent, and the identification of the key nodes depends on the restoration of a network topological structure. Thus, the optimal waveform design is actually made under a series of assumptions, assuming that the network topology is known, assuming that the key nodes are known, however these assumptions are hardly possible to hold under a particular network, the tactical communications network.
Disclosure of Invention
Aiming at the defects in the prior art, the method for identifying the key nodes and selecting the optimal interference signal pattern in the communication network solves the problem that the key nodes and the optimal interference waveform are simultaneously identified and found in a tactical communication network under the condition that the network topology structure is unknown.
In order to achieve the purpose of the invention, the invention adopts the technical scheme that: a method for identifying key nodes of a communication network and selecting an optimal interference signal pattern comprises the following steps:
s1, constructing an ad hoc network, and setting various interference signal patterns of the ad hoc network;
s2, combining various interference signal patterns and communication node numbers to construct an interference action space, initializing a learning frequency, an interference effect table Q and an action selection frequency table, and setting a learning frequency threshold;
s3, constructing an action selection model according to the interference action space, the learning times, the interference effect table Q and the action selection times table, selecting interference actions based on the action selection model, implementing interference, and recording the packet loss rate;
s4, according to the packet loss rate, updating the interference effect table Q by selecting the order table through actions;
s5, judging whether the learning times reach the learning times threshold value, if yes, jumping to S6, if not, jumping to S3, and adding 1 to the learning times;
s6, judging whether the interference action selected for the last time is the action with the maximum value in the updated interference effect table Q, if so, obtaining the optimal interference action, wherein the node corresponding to the optimal interference action is the key node, the interference signal pattern corresponding to the optimal interference action is the optimal interference signal pattern, otherwise, skipping to the step S2, and resetting the learning time threshold.
Further, step S1 includes the following substeps:
s11, building an ad hoc network with N communication nodes based on a network simulation platform;
and S12, setting an interference signal pattern according to the modulation mode m and the signal duty ratio z, and setting a constant signal-to-noise ratio SNR, a dry-to-noise ratio JNR, a signal power Ps, a white noise power Pn and an interference signal power Pj of the interference signal pattern to obtain multiple interference signal patterns of the ad hoc network, wherein the SNR is 10log (Ps/Pn), and the JNR is 10log (Pj/Pn).
Further, the step S2 includes the following sub-steps:
s21, combining various interference signal patterns of the ad hoc network with communication node numbers to construct an interference action space;
s22, setting an initial value of the learning times, and setting a learning time threshold according to the size of the interference action space;
s23, constructing a two-dimensional interference effect table Q of N x M, recording the interference effect, and initializing the interference effect table Q to 0;
and S24, constructing an N M two-dimensional table, recording the number of times each interference action is selected, obtaining an action selection number table, and initializing the action selection number table to 0.
Further, the action selection model in step S3 is:
wherein, pi (a) is an action selection model, a is an interference action in a selected interference action space, epsilon is probability, | A | is the size of the interference action space, and Q (a) is an interference effect table Q of the selected interference action.
Further, the probability ε is:
ε＝at/b，0<a<1
where t is the number of learning times and b is a parameter greater than 0.
Further, the step S3 includes the following sub-steps:
s31, constructing an action selection model according to the interference action space, the learning times, the interference effect table Q and the action selection times table;
s32, selecting a model according to the action, and randomly selecting interference action from an interference waveform library according to the probability of epsilon;
s33, selecting the action with the optimal interference effect from the interference effect table Q according to the probability of 1-epsilon;
s34, according to the randomly selected interference action and the action with the optimal interference effect, marking an interfered communication node in the ad hoc network, applying an interference signal of a corresponding pattern on the interfered communication node, implementing interference, starting simulation of the ad hoc network, and capturing the packet sending number Tx and the packet receiving number Rx of the ad hoc network in the simulation process;
s35, calculating the packet loss rate R of the ad hoc network according to the packet sending number Tx and the packet receiving number Rx of the ad hoc network in the simulation process:
further, the step S4 includes the following sub-steps:
s41, adding 1 to the action selected number table:
F1←Times(node，mode)+1
Times(node，mode)←F1
s42, updating the interference effect table Q according to the action selection time table and the packet loss rate:
F2←Q(node，mode)+(R-Q(node，mode))/Times(node，mode)Q(node，mode)←F2
the Times (node, mode) is the number of Times that the node number node and the mode number interference pattern signal are selected in the action selection number table, the node is the node number node, the mode is the mode number signal pattern, R is the packet loss rate, F1 and F2 are intermediate variables, ← is the value assignment, and Q (. multidot.) is the average interference effect obtained by applying the mode number interference pattern to the node number node in the interference effect table Q.
In conclusion, the beneficial effects of the invention are as follows:
firstly, the number of nodes in the network is only needed to be known without excessive prior knowledge, information such as a topological structure of the network is not needed to be known, the key nodes can be identified by interacting with the environment to acquire interference feedback, and the information of the number of the network nodes is easier to obtain than the information of the network topological structure.
And secondly, key node identification and optimal waveform searching are combined, so that the problem that the accuracy of dividing key node identification and optimal waveform searching into two sections of learning and optimal waveform searching seriously depends on the accuracy of key node identification is avoided, the key nodes and the optimal interference waveforms can be obtained simultaneously through the method, the learning efficiency is greatly improved, and the complexity of the problem is reduced. The importance of key node identification and optimal waveform searching on the network without knowing related information of the network in advance in the military field is self-evident, and the importance plays a vital role in obtaining information rights and dominant rights in the battlefield.
Drawings
FIG. 1 is a flow chart of a method for communication network key node identification and optimal interference signal pattern selection;
FIG. 2 is an Ad hoc network diagram of an AdHoc;
FIG. 3 is a diagram of the interference action selected by the action selection strategy each time in the 500 learning processes;
fig. 4 is a graph of the instantaneous feedback packet loss rate obtained in the process of 500 learning.
Detailed Description
The following description of the embodiments of the present invention is provided to facilitate the understanding of the present invention by those skilled in the art, but it should be understood that the present invention is not limited to the scope of the embodiments, and it will be apparent to those skilled in the art that various changes may be made without departing from the spirit and scope of the invention as defined and defined in the appended claims, and all matters produced by the invention using the inventive concept are protected.
As shown in fig. 1, a method for identifying a key node of a communication network and selecting an optimal interference signal pattern includes the following steps:
s1, constructing an ad hoc network, as shown in FIG. 2, and setting various interference signal patterns;
step S1 includes the following substeps:
s11, building an ad hoc network with N communication nodes based on a network simulation platform;
and S12, setting an interference signal pattern according to the modulation mode m and the signal duty ratio z, and setting a constant signal-to-noise ratio SNR, a dry-to-noise ratio JNR, a signal power Ps, a white noise power Pn and an interference signal power Pj of the interference signal pattern to obtain multiple interference signal patterns of the ad hoc network, wherein the SNR is 10log (Ps/Pn), and the JNR is 10log (Pj/Pn).
S2, combining various interference signal patterns and communication node numbers to construct an interference action space, initializing a learning frequency, an interference effect table Q and an action selection frequency table, and setting a learning frequency threshold;
the step S2 includes the following sub-steps:
s21, combining various interference signal patterns and communication node numbers of the ad hoc network to construct an interference action space, namely, each interference action consists of a node number and an interference signal pattern, and the action space has M × N interference actions;
s22, setting an initial value of the learning times, and setting a learning time threshold according to the size of the interference action space;
s23, constructing a two-dimensional interference effect table Q of N x M, recording the interference effect, initializing the interference effect table Q to 0, and expressing the average interference effect obtained by applying a mode number interference signal to a node number node by Q (node, mode) determined by an abscissa node and an ordinate mode;
s24, constructing an N-M two-dimensional table, recording the number of Times of each interference action, obtaining an action selection number table, initializing the action selection number table to 0, and representing the number of Times of the interference actions represented by the node number node and the mode number interference signal by Times (node, mode) determined by an abscissa node and an ordinate mode.
S3, constructing an action selection model according to the interference action space, the learning times, the interference effect table Q and the action selection times table, selecting interference actions based on the action selection model, implementing interference, and recording the packet loss rate;
the step S3 includes the following sub-steps:
s31, constructing an action selection model according to the interference action space, the learning times, the interference effect table Q and the action selection times table;
the action selection model in step S3 is:
wherein, pi (a) is an action selection model, a is an interference action in a selected interference action space, epsilon is probability, | A | is the size of the interference action space, and Q (a) is an interference effect table Q of the selected interference action.
The above formula shows that the interference action with the best interference effect in the current interference effect table should be selected with a probability of 1-epsilon, and the interference action should be randomly selected with a probability of epsilon. Meanwhile, to ensure convergence, ε should be a number that decreases as the number of learning increases, and the algorithm converges when ε decreases to 0. ε may be calculated as follows:
ε＝at/b，0<a<1
where t is the number of learning times and b is a parameter greater than 0.
S32, selecting a model according to the action, and randomly selecting interference action from an interference waveform library according to the probability of epsilon;
s33, selecting the action with the optimal interference effect from the interference effect table Q according to the probability of 1-epsilon;
s34, according to the randomly selected interference action and the action with the optimal interference effect, marking an interfered communication node in the ad hoc network, applying an interference signal of a corresponding pattern on the interfered communication node, implementing interference, starting simulation of the ad hoc network, and capturing the packet sending number Tx and the packet receiving number Rx of the ad hoc network in the simulation process;
s35, calculating the packet loss rate R of the ad hoc network according to the packet sending number Tx and the packet receiving number Rx of the ad hoc network in the simulation process:
s4, according to the packet loss rate, updating the interference effect table Q by selecting the order table through actions;
the step S4 includes the following sub-steps:
s41, adding 1 to the action selected number table:
F1←Times(node，mode)+1
Times(node，mode)←F1
s42, updating the interference effect table Q according to the action selection time table and the packet loss rate:
F2←Q(node，mode)+(R-Q(node，mode))/Times(node，mode)Q(node，mode)←F2
the Times (node, mode) is the number of Times that the node number node and the mode number interference pattern signal are selected in the action selection number table, the node is the node number node, the mode is the mode number signal pattern, R is the packet loss rate, F1 and F2 are intermediate variables, ← is the value assignment, and Q (. multidot.) is the average interference effect obtained by applying the mode number interference pattern to the node number node in the interference effect table Q.
S5, judging whether the learning times reach the learning times threshold value, if yes, jumping to S6, if not, jumping to S3, and adding 1 to the learning times;
s6, judging whether the interference action selected for the last time is the action with the maximum value in the updated interference effect table Q, if so, obtaining the optimal interference action, wherein the node corresponding to the optimal interference action is the key node, the interference signal pattern corresponding to the optimal interference action is the optimal interference signal pattern, otherwise, skipping to the step S2, and resetting the learning time threshold.
The experimental results are as follows:
fig. 3 shows the interference actions selected each time according to the action selection model in the 500 learning processes, and it can be seen that the selection of the interference actions in the early stage is relatively random, each action is likely to be selected, and as the learning times increase, the probability of selecting the No. 45 interference action becomes greater and greater until the No. 45 interference waveform starts to be stably selected about 300 times. It is described that the node corresponding to the interference action No. 45 is the key node, and the signal pattern corresponding to the interference action No. 45 is the optimal interference waveform.
Fig. 4 shows the instantaneous feedback packet loss rate obtained in the 500 learning processes, and it can be seen that, when the interference action is randomly selected in the early stage, the obtained packet loss rate is high, low, and the low packet loss rate is higher in occurrence frequency, and as the learning times increase, the low packet loss rate is reduced in occurrence frequency, and the high packet loss rate is increased in occurrence frequency. The packet loss rate still fluctuates after 300 times because of the randomness of network communication, but it can still be seen that the whole packet loss rate is maintained at a higher level.
Claims (7)
1. A method for identifying key nodes of a communication network and selecting an optimal interference signal pattern is characterized by comprising the following steps:
s1, constructing an ad hoc network, and setting various interference signal patterns of the ad hoc network;
s2, combining various interference signal patterns and communication node numbers to construct an interference action space, initializing a learning frequency, an interference effect table Q and an action selection frequency table, and setting a learning frequency threshold;
s3, constructing an action selection model according to the interference action space, the learning times, the interference effect table Q and the action selection times table, selecting interference actions based on the action selection model, implementing interference, and recording the packet loss rate;
s4, according to the packet loss rate, updating the interference effect table Q by selecting the order table through actions;
s5, judging whether the learning times reach the learning times threshold value, if yes, jumping to S6, if not, jumping to S3, and adding 1 to the learning times;
s6, judging whether the interference action selected for the last time is the action with the maximum value in the updated interference effect table Q, if so, obtaining the optimal interference action, wherein the node corresponding to the optimal interference action is the key node, the interference signal pattern corresponding to the optimal interference action is the optimal interference signal pattern, otherwise, skipping to the step S2, and resetting the learning time threshold.
2. The method of claim 1, wherein the step S1 comprises the following sub-steps:
s11, building an ad hoc network with N communication nodes based on a network simulation platform;
and S12, setting an interference signal pattern according to the modulation mode m and the signal duty ratio z, and setting a constant signal-to-noise ratio SNR, a dry-to-noise ratio JNR, a signal power Ps, a white noise power Pn and an interference signal power Pj of the interference signal pattern to obtain multiple interference signal patterns of the ad hoc network, wherein the SNR is 10log (Ps/Pn), and the JNR is 10log (Pj/Pn).
3. The method of claim 1, wherein the step S2 comprises the following sub-steps:
s21, combining various interference signal patterns of the ad hoc network with communication node numbers to construct an interference action space;
s22, setting an initial value of the learning times, and setting a learning time threshold according to the size of the interference action space;
s23, constructing a two-dimensional interference effect table Q of N x M, recording the interference effect, and initializing the interference effect table Q to 0;
and S24, constructing an N M two-dimensional table, recording the number of times each interference action is selected, obtaining an action selection number table, and initializing the action selection number table to 0.
4. The method of claim 1, wherein the action selection model in step S3 is:
wherein, pi (a) is an action selection model, a is an interference action in a selected interference action space, epsilon is probability, | A | is the size of the interference action space, and Q (a) is an interference effect table Q of the selected interference action.
5. The method of claim 4, wherein the probability ε is:
ε＝at/b，0<a<1
where t is the number of learning times and b is a parameter greater than 0.
6. The method of claim 4, wherein the step S3 comprises the following sub-steps:
s31, constructing an action selection model according to the interference action space, the learning times, the interference effect table Q and the action selection times table;
s32, selecting a model according to the action, and randomly selecting interference action from an interference waveform library according to the probability of epsilon;
s33, selecting the action with the optimal interference effect from the interference effect table Q according to the probability of 1-epsilon;
s34, according to the randomly selected interference action and the action with the optimal interference effect, marking an interfered communication node in the ad hoc network, applying an interference signal of a corresponding pattern on the interfered communication node, implementing interference, starting simulation of the ad hoc network, and capturing the packet sending number Tx and the packet receiving number Rx of the ad hoc network in the simulation process;
s35, calculating the packet loss rate R of the ad hoc network according to the packet sending number Tx and the packet receiving number Rx of the ad hoc network in the simulation process:
7. the method of claim 1, wherein the step S4 comprises the following sub-steps:
s41, adding 1 to the action selected number table:
F1←Times(node，mode)+1
Times(node，mode)←F1
s42, updating the interference effect table Q according to the action selection time table and the packet loss rate:
F2←Q(node，mode)+(R-Q(node，mode))/Times(node，mode)
Q(node，mode)←F2
the Times (node, mode) is the number of Times that the node number node and the mode number interference pattern signal are selected in the action selection number table, the node is the node number node, the mode is the mode number signal pattern, R is the packet loss rate, F1 and F2 are intermediate variables, ← is the value assignment, and Q (. multidot.) is the average interference effect obtained by applying the mode number interference pattern to the node number node in the interference effect table Q.
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