CN112383926A - Multi-agent transmission method and terminal equipment for cognitive radio network signals - Google Patents
Multi-agent transmission method and terminal equipment for cognitive radio network signals Download PDFInfo
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Abstract
The invention discloses a multi-agent transmission method and terminal equipment for cognitive radio network signals, wherein the method comprises the following steps: dividing network nodes distributed in the same area into different logic levels; the method comprises the following steps of establishing a logic topological structure among all network nodes on each logic level by using a CBBA algorithm to realize the transmission of cognitive radio network signals, and specifically comprises the following steps: when sudden failure occurs in a complex environment, a logic topological structure among all network nodes on each logic level is established; and resolving conflicts among different burst tasks in the logic topology. According to the invention, through researching various conditions (emergent threats, appearance or disappearance of random targets, change of target priority, signal problems or faults and the like) of emergent events in complex environments in the interconnected and intercommunicated cognitive wireless network, different emergent event models are established and solved corresponding to different task allocation objective functions and constraint condition models, and the transmission performance of the cognitive radio network is improved.
Description
Technical Field
The application relates to a multi-agent transmission method and terminal equipment for cognitive radio network signals, and belongs to the technical field of wireless signal transmission.
Background
The mutual interconnection relates to intelligent manufacturing, intelligent medical treatment, smart cities and Internet of things big data, and a large amount of wireless connections are needed, and all wireless connections need frequency spectrums, the limitation of frequency spectrum resources and the unlimited demand on the frequency spectrum resources create huge market application space for the civil cognitive radio technology.
The cognitive radio network can cover a wide frequency band, and aims to completely use software to completely complete baseband processing and intermediate frequency modulation of signals and generate radio frequency signal waveforms. By loading different software, the cognitive radio system can support different waveforms, different protocol stacks and brand new system capability, has the characteristics of ad hoc network, spectrum sensing, interference resistance, self adaptation, low time delay, simplicity in operation and maintenance and the like, and can realize interconnection with the existing radio system.
In an interworking cognitive wireless network, various emergencies may occur due to the complex environment, such as: the system comprises a wireless signal transmission system, a wireless signal transmission system and a wireless signal processing system, wherein the wireless signal transmission system comprises a wireless signal transmission system, a wireless signal processing system and a wireless signal processing system, wherein the wireless signal transmission system comprises a wireless signal transmission system and a wireless signal processing system, and the wireless signal transmission system comprises a wireless signal transmission system and a wireless signal processing system.
Disclosure of Invention
The present application aims to provide a multi-agent transmission method and a terminal device for cognitive radio network signals, so as to solve various emergencies in wireless signal transmission and ensure stability and accuracy of transmission.
The first embodiment of the invention provides a multi-agent transmission method of cognitive radio network signals, which comprises the following steps:
dividing network nodes distributed in the same area into different logic levels;
and establishing a logic topological structure among all network nodes on each logic level by using a CBBA algorithm to realize the transmission of the cognitive radio network signals.
Preferably, the network nodes distributed in the same area are classified into different logical levels, specifically:
the network node periodically collects the information of the adjacent nodes to obtain an adjacent node table, and the network nodes are distributed in the same area;
exchanging the adjacent node list among the network nodes, and operating a K-hop key node detection algorithm to detect to obtain key nodes in the area;
and according to the key nodes, dividing all the network nodes into different logic levels.
Preferably, the dividing, according to the key node, all the network nodes into different logic levels specifically includes:
when the network node is switched from a wlan frequency band to an unauthorized frequency band, judging whether the network node is the key node, if so, rejecting a switching request, and if not, accepting the switching request and switching;
when the network node is switched from an unauthorized frequency band to a wlan frequency band, directly switching; therefore, all network nodes are classified into a logic level corresponding to the wlan frequency band and a logic level corresponding to the unlicensed frequency band.
Preferably, after the network nodes distributed in the same area are classified into different logical levels, the method further includes:
judging the density degree of the network nodes in each logic level;
if the density degree is greater than or equal to the density threshold, reducing the transmitting power of the network node with the node degree greater than the maximum degree threshold;
if the density is smaller than the density threshold, the transmitting power of the network nodes with the node degrees smaller than the minimum degree threshold is increased.
Preferably, the CBBA algorithm is used to establish a logical topology structure between all network nodes on each logical plane, so as to implement transmission of cognitive radio network signals, specifically:
when a burst fault occurs in a complex environment, establishing a logical topology structure among all the network nodes on each logical level specifically includes:
respectively on each logic levelAnd calculating Agent iMargin scores for all task areas on the logical level
；
Combining the marginal scores of all task areas
Determining the Agent iA task area obtained by auction;
determining task areas meeting preset conditions from the task areas
And the insertion position at which the maximum score can be obtained
；
According to the task area meeting the preset conditions
And the insertion position where the maximum score can be obtained
Determining the task area
Margin score of (2)
；
As described the margin score
If the current value is larger than zero, the Agent is updatediAnd updating the shared information vector until the execution of all task areas is finished, so as to obtain the logical topological structure among all the network nodes on each logical level.
Preferably, said combining the margin scores of all task areas
Determining the Agent iThe task area of the auction specifically includes:
determining the Agent according to formula (1)iThe task area obtained by the auction is that the formula (1) is as follows:
in the formula (I), the compound is shown in the specification,
representing AgentiFor task area
The marginal score of (a) is calculated,
representing AgentiFor task area
The maximum bid amount of (a) is,
is the total number of task areas.
Preferably, task areas meeting preset conditions are determined from the task areas
And the insertion position at which the maximum score can be obtained
Determining the task area
Margin score of (2)
The method specifically comprises the following steps:
determining the task area meeting the preset condition according to the formula (2)
The formula (2) is:
in the formula (I), the compound is shown in the specification,
representing Agent iA path of (a);
determining an insertion position capable of obtaining a maximum score according to equation (3)
The formula (3) is:
in the formula (I), the compound is shown in the specification,
a time stamp representing the Agent i,
is the serial number of the task area,
the longest sequence number of the task area;
determining the task area according to equation (4)
Margin score of (2)
The formula (4) is:
preferably, the updating the AgentiThe information and the update shared information vector of (1) are specifically:
updating the Agent according to formula (5)iThe formula (5) is:
in the formula (I), the compound is shown in the specification,
is Agent iThe serial number of the nth task area obtained by current auction,
representing AgentiTask area obtained by auction
，
Is a set of winners;
updating the shared information vector according to equation (6), where equation (6) is:
preferably, after the establishing a logical topology between all the network nodes on each logical plane when a sudden failure occurs in a complex environment, the method further includes:
sharing messages between adjacent agents, the messages including a set of winners
Bids corresponding to winners
And time stamp
；
The receiver adopts a corresponding mechanism to process each task area in the receiver according to the received message, and the mechanism comprises the following steps: update mechanism, reset mechanism and leave mechanism, specifically:
the updating mechanism is as follows:
wherein
Representing a recipient AgentiFor task area
The maximum bid of;
representing sender AgentkFor task area
The maximum bid of;
representing the task area obtained by Agent i auction
；
Representing AgentkTask area obtained by auction
；
The reset mechanism is as follows:
；
the leaving mechanism is:
；
the time stamp
Updating according to formula (7), wherein formula (7) is:
in the formula (I), the compound is shown in the specification,
representing AgentiWith AgentkWhether the communication is directly connected or not, if the communication is known according to the communication topology, the Agent iWith Agent kCan communicate directly therebetween, then
Otherwise
(ii) a m represents the communication with Agent in communication topologykAgents directly connected;
representing Agent iReceiving Agents from a communication networkkThe time of the message.
A second embodiment of the present invention provides a terminal device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the steps of the above method when executing the computer program.
Compared with the prior art, the multi-agent transmission method and the terminal equipment for cognitive radio network signals have the following beneficial effects:
according to the invention, different emergency models are established in the interconnected and intercommunicated cognitive radio network by researching various conditions (emergency threat, appearance or disappearance of random targets, change of target priority, signal problems or faults and the like) of emergency under a complex environment, and corresponding to different task allocation objective functions and constraint condition models, and an Extended-CBBA (ECBBA) strategy is adopted for solving, so that the transmission performance of the cognitive radio network is improved.
Drawings
FIG. 1 is a flow chart of a method for multi-agent transmission of cognitive radio network signals in an embodiment of the present invention;
FIG. 2 is a basic diagram of an SRN-based radio network architecture in the multi-agent transmission method of cognitive radio network signals according to the embodiment of the present invention;
FIG. 3 is a flowchart of a topology control algorithm for node degree in a multi-agent transmission method of cognitive radio network signals according to an embodiment of the present invention;
fig. 4 is a design diagram of a sudden failure model and a processing strategy thereof according to the multi-agent transmission method of cognitive radio network signals in the embodiment of the invention.
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth, such as particular system structures, techniques, etc. in order to provide a thorough understanding of the embodiments of the invention. It will be apparent, however, to one skilled in the art that the present invention may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present invention with unnecessary detail.
The present invention will be described in detail with reference to examples, but the present invention is not limited to these examples.
Fig. 1 is a flow chart of a multi-agent transmission method of cognitive radio network signals according to the present invention.
Fig. 2 is a basic diagram of a radio network structure based on SRN in the multi-agent transmission method of cognitive radio network signals according to an embodiment of the present invention.
Software defined radio-SDR, as shown in fig. 2, changes its operating frequency, occupied bandwidth, and different wireless standards being adhered to by invoking supporting software algorithms.
The multi-agent transmission method of the cognitive radio network signal of the first embodiment of the invention comprises the following steps:
step 1, dividing network nodes distributed in the same area into different logic levels, and specifically comprising the following steps:
step 1.1, the network node periodically collects the information of the adjacent nodes to obtain an adjacent node list, wherein the network nodes are distributed in the same area;
specifically, the network nodes collect the neighbor node information by exchanging Hello packets and Ack packets, and count the number of neighbor nodes to obtain a neighbor node table.
Step 1.2, exchanging adjacent node tables among network nodes, and operating a K-hop key node detection algorithm to detect and obtain key nodes in the area;
specifically, adjacent node tables are exchanged among network nodes to obtain two-hop topology information, a K-hop key node detection algorithm is operated by using the information to detect key nodes in the network, and key node flag bits are set.
Step 1.3, according to the key nodes, dividing all network nodes into different logic levels;
specifically, the node switching behavior is analyzed, and the following two cases that may occur are processed separately. The method comprises the following steps:
case a: when a network node is switched from a wlan frequency band to an unauthorized frequency band (such as a wimax frequency band), when the network node finds that the network node has a channel switching requirement, firstly checking whether the network node is a key node, if so, rejecting a switching request, otherwise, accepting the switching request and switching;
case b: when the network node is switched from the unauthorized frequency band to the wlan frequency band, directly switching; that is, when the network node is switched back to the wlan frequency band from other unlicensed frequency bands due to a requirement (for example, an authorized user appears), the wlan frequency band is directly re-accessed. Therefore, all network nodes are classified into a logic level corresponding to the wlan frequency band and a logic level corresponding to the unlicensed frequency band.
The network nodes belonging to the same logic layer surface directly communicate by using the same working frequency to form a network logic topology structure on the layer.
Because the number of nodes in the network of each logic level can be more or less, for a logic level with dense network nodes or a logic level with sparse network nodes, it needs to be processed, specifically:
step 1 → 2, judging the density degree of the network nodes in each logic level;
if the density degree is greater than or equal to the density threshold, reducing the transmitting power of the network node of which the node degree is greater than the maximum degree threshold; therefore, the energy consumption of the network is reduced on the premise of ensuring the network capacity and the transmission performance;
and if the density degree is less than the density threshold, increasing the transmitting power of the network nodes with the node degrees less than the minimum degree threshold. It trades the performance of the network topology in terms of connectivity by sacrificing the energy consumption of the nodes to some extent.
By adopting the method, the network topology of the WLAN frequency band layer is changed by switching in and switching out the WLAN frequency band behaviors of the topology optimization node, so that the transmitting power of the node is adjusted by adopting a k-NEIGHLEV algorithm based on the node degree, namely, the transmitting power of the node with the overlarge node degree is reduced, and the node degree is reduced; the transmission power is increased for the nodes with the too small node degree, the node degree is increased, and the topology control algorithm structure based on the node degree is shown in fig. 3.
And 2, establishing a logic topological structure among all network nodes on each logic level by using a CBBA algorithm to realize the transmission of the cognitive radio network signals.
In the invention, nodes distributed in the same area respectively work in two different frequency spectrum intervals to logically form two different networks; the nodes above each logical topology form a logical topology at the cost level over the wireless links based on CBBA policies.
CBBA (Consensus-Based Bundle Algorithm) mainly studies which agents execute which task areas, is a distributed, polynomial-time Algorithm Based on a market auction task allocation protocol, and is a task allocation Algorithm capable of avoiding conflict and efficiently solving emergency events.
Before detailing this step, the parameters involved therein are explained.
Bundle (Bundle): define the bundle set as
In which the elements
Representing AgentiThe sequence number of the nth task area obtained by the current auction is, for example:
the 5 th element representing the Agent1 sequential auction gets is task area 3. AgentiThe length of the task set isl b Here, the maximum number of task areas that all agents can execute is specified asL t Will beL t As one of the task assignment end conditions. The bundle sets are sequentially arranged according to the sequence of the task areas, and if the current Agent does not auction to obtain any task area, the bundle sets are orderedb i Is an empty set.
Path (Path): defining a set of paths as
In which the elements
Indicates that the current Agent is includediThe current sequence number of the task area being auctioned, but different from the bundle set is the path setIs Agent represented byiThe order of the task zones is executed sequentially, so the length of the path order set is the same as the length of the bundle set.
Time (Time): the time set is defined as
Wherein
Representing Agent iFlying according to the current path sequence to reach the task area
Due to Agent's time ofiThe time to reach a certain task area is obtained according to the path sequence, so the time set is monotonously increased, and the length of the time set is the same as that of the path set.
Winner (Winning Agents): the set of winners may be represented as
And is and
the element stores AgentiWhich Agent is considered to execute the task areanThe information of (1) is an Agent set for successful auction, specifically, according to the AgentiThe current obtained message can know which Agent finally auctioned the task arean. If Agent iConsider that no Agent competes to the task area at presentnThen, then
。
Winner bid (Winning Bids): set of winner bids
Wherein
Representing Agent iKnowing the maximum bid of each Agent currently on task area n if the Agent isiNo Agent is considered to compete to the task area at presentnThen, then
。
Timestamp (Time Stamps): time stamp of
Wherein
Representing Agent iReceiving Agents from a communication networknThe time of the latest information, the time stamp is established to record the new and old of a series of information, and the new information is always updated based on the old information.
Step 2 will be described in detail below.
Fig. 4 is a design diagram of a sudden failure model and a processing strategy thereof according to the multi-agent transmission method of cognitive radio network signals in the embodiment of the invention.
When a burst fault occurs in a complex environment, establishing a logical topology structure among all the network nodes on each logical level specifically includes:
step a: respectively calculating Agent on each logic level iMargin scores for all task areas on the logical level
(ii) a When an emergency target in the target set is encountered, sorting is carried out according to the distance between the current position and the target, and the editing score of the first N faults meeting the conditions on the target is initialized to be infinite. The margin scores of other emergency situations are normally calculated at the stage;
step b: determining the Agent using equation (1)iWhich task areas to auction to:
step c: the task area which best meets the conditions is obtained by using the formula (2)
The insertion position where the maximum score can be obtained is obtained by the formula (3)
And determining the task area according to equation (4)
Margin score of (2)
：
Step d: if it is
Quitting, otherwise continuing to execute the next step;
step e: update Agent with equation (5) iThe information of (2):
step f: the shared information vector is updated using equation (6):
step g: if it is not
And exiting, otherwise, returning to the step a to continue the execution.
The conflict resolution stage of the ECBBA algorithm facing different burst tasks in a complex environment specifically comprises the following steps:
step a: when all agents get from the adjacent Agent
And
and then, further judging whether the Agent wins the bid or not by combining the obtained information, and if the Agent wins the bid, judging that the Agent wins the bid iRacing task areajRepresents the Agent at that momentiFor task areajIf the bid (score) is the highest, the task area is required to be coveredjThe bundle set of other agents is modified, the task area and all task areas added into the bundle set later are released, and the Agent is selected again, because if the tasks are not released, the agents make more wrong decisions according to wrong scores, and the overall performance of the algorithm is reduced.
Step b: the following messages are assumed to be shared simultaneously between the adjacent agents: set of winners
And corresponding offer
Time stamp
The timestamp updating formula of the Agent is shown in formula (7):
m is the communication topology with AgentkAgents directly connected;
Step c, updating mechanism of the stage, specifically, when the information sender Agent is sentkPassing information to Agent iThen, the Agent as the receiver iNeed to be based on received information such as
、
、
Determining AgentkAnd Agent iWhich information is up-to-date and reacts and changes accordingly. Known Agent iFor task area jThe following three actions may be taken:
(1) an updating mechanism comprises the following steps:
；
(2) a reset mechanism:
；
(3) the leaving mechanism:
；
step d, when the receiver AgentiWith the sender AgentkAbout task areasjDuring communication, the receiver can know which mechanism should be executed according to the received information from the table 1, and the values of the first two columns in the table indicate the senderkAnd the receiveriWho each think is the task areajThe third column explains the receiveriMechanisms that should be implemented, where the default is the away mechanism. After the conflict resolution of the second stage is finished, the algorithm returns to the first stage again to continue the task selection, and a new task area is added, and the process is circulated until the information set is completed
And
when no more changes occur, the ECBBA algorithm ends.
TABLE 1
A second embodiment of the present invention provides a terminal device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the steps of the above method when executing the computer program.
According to the invention, different emergency models are established in the interconnected and intercommunicated cognitive wireless network by researching various conditions (emergency threat, appearance or disappearance of random targets, change of target priority, signal problems or faults and the like) of emergency under a complex environment, and are corresponding to different task allocation objective functions and constraint condition models, and an Extended-CBBA (ECBBA) strategy is adopted for solving, so that the tactical use flexibility of the multi-way aircraft is improved.
Although the present application has been described with reference to a few embodiments, it should be understood that various changes, substitutions and alterations can be made herein without departing from the spirit and scope of the application as defined by the appended claims.
Claims (10)
1. A multi-agent transmission method of cognitive radio network signals is characterized by comprising the following steps:
dividing network nodes distributed in the same area into different logic levels;
establishing a logic topological structure among all network nodes on each logic level by using a CBBA algorithm to realize the transmission of the cognitive radio network signals, which specifically comprises the following steps:
when sudden failure occurs in a complex environment, a logic topological structure among all the network nodes on each logic level is established;
and resolving conflicts among different burst tasks in the logic topological structure, and realizing the transmission of the cognitive radio network signals.
2. The multi-agent transmission method of cognitive radio network signals according to claim 1, wherein the network nodes distributed in the same area are classified into different logical levels, specifically:
the network node periodically collects the information of the adjacent nodes to obtain an adjacent node table, and the network nodes are distributed in the same area;
exchanging the adjacent node list among the network nodes, and operating a K-hop key node detection algorithm to detect to obtain key nodes in the area;
and according to the key nodes, dividing all the network nodes into different logic levels.
3. The multi-agent transmission method of cognitive radio network signals according to claim 2, wherein said assigning all network nodes to different logical levels according to said key node is specifically:
when the network node is switched from a wlan frequency band to an unauthorized frequency band, judging whether the network node is the key node, if so, rejecting a switching request, and if not, accepting the switching request and switching;
when the network node is switched from an unauthorized frequency band to a wlan frequency band, directly switching; therefore, all network nodes are classified into a logic level corresponding to the wlan frequency band and a logic level corresponding to the unlicensed frequency band.
4. The multi-agent transmission method of cognitive radio network signals according to claim 3, further comprising, after said dividing network nodes distributed in the same area into different logical levels:
judging the density degree of the network nodes in each logic level;
if the density degree is greater than or equal to the density threshold, reducing the transmitting power of the network node with the node degree greater than the maximum degree threshold;
if the density is smaller than the density threshold, the transmitting power of the network nodes with the node degrees smaller than the minimum degree threshold is increased.
5. The multi-agent transmission method of cognitive radio network signals according to claim 1, wherein when a sudden failure occurs in a complex environment, a logical topology is established among all the network nodes on each logical level, specifically:
when a burst fault occurs in a complex environment, establishing a logical topology structure among all the network nodes on each logical level specifically includes:
respectively calculating Agent on each logic level iMargin scores for all task areas on the logical level
；
Combining the marginal scores of all task areas
Determining the Agent iA task area obtained by auction;
determining task areas meeting preset conditions from the task areas
And the insertion position at which the maximum score can be obtained
；
According to the task area meeting the preset conditions
And the insertion position where the maximum score can be obtained
Determining the task area
Margin score of (2)
；
As described the margin score
If the current value is larger than zero, the Agent is updatediInformation of (2) and update shared information toAnd measuring until the execution of all task areas is finished, and obtaining the logical topological structures among all the network nodes on each logical layer.
6. The method of multi-agent transmission of cognitive radio network signals as claimed in claim 5, wherein said combining marginal scores of all said task zones
Determining the Agent iThe task area of the auction specifically includes:
determining the Agent according to formula (1)iThe task area obtained by the auction is that the formula (1) is as follows:
in the formula (I), the compound is shown in the specification,
representing AgentiFor task area
The marginal score of (a) is calculated,
representing AgentiFor task area
The maximum bid amount of (a) is,
is the total number of task areas.
7. The method of claim 6, wherein the task areas meeting the predetermined condition are determined from the task areas
And the insertion position at which the maximum score can be obtained
Determining the task area
Margin score of (2)
The method specifically comprises the following steps:
determining the task area meeting the preset condition according to the formula (2)
The formula (2) is:
in the formula (I), the compound is shown in the specification,
representing Agent iA path of (a);
determining an insertion position capable of obtaining a maximum score according to equation (3)
The formula (3) is:
in the formula (I), the compound is shown in the specification,
representing Agent iThe time stamp is a time stamp of the time,
is the serial number of the task area,
the longest sequence number of the task area;
determining the task area according to equation (4)
Margin score of (2)
The formula (4) is:
8. the method of claim 7, wherein the updating the Agent is performed by a cognitive radio network signal multi-Agent transmission methodiThe information and the update shared information vector of (1) are specifically:
updating the Agent according to formula (5)iThe formula (5) is:
in the formula (I), the compound is shown in the specification,
is Agent iThe serial number of the nth task area obtained by current auction,
representing AgentiTask area obtained by auction
，
Is a set of winners;
updating the shared information vector according to equation (6), where equation (6) is:
9. the multi-agent transmission method of cognitive radio network signals according to claim 8, wherein the resolving conflicts between different bursty tasks in the logical topology is specifically:
sharing messages between adjacent agents, the messages including a set of winners
Bids corresponding to winners
And time stamp
；
The receiver adopts a corresponding mechanism to process each task area in the receiver according to the received message, and the mechanism comprises the following steps: update mechanism, reset mechanism and leave mechanism, specifically:
the updating mechanism is as follows:
wherein
Representing a recipient AgentiFor task area
The maximum bid of;
representing sender AgentkFor task area
The maximum bid of;
representing the task area obtained by Agent i auction
；
Representing AgentkTask area obtained by auction
；
The reset mechanism is as follows:
；
the leaving mechanism is:
；
the time stamp
Updating according to formula (7), wherein formula (7) is:
in the formula (I), the compound is shown in the specification,
representing AgentiWith AgentkWhether the communication is directly connected or not, if the communication is known according to the communication topology, the Agent iWith Agent kCan communicate directly therebetween, then
Otherwise
(ii) a m represents the communication with Agent in communication topologykAgents directly connected;
representing Agent iReceiving Agents from a communication networkkThe time of the message.
10. A terminal device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor implements the steps of the method according to any of claims 1 to 9 when executing the computer program.
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Application publication date: 20210219 |