Self-adaptive anti-interference scheduling method and system for wireless ad hoc network

Abstract

The invention discloses a self-adaptive anti-interference scheduling method and system for a wireless ad hoc network, and relates to the technical field of wireless communication, and the method comprises the steps: collecting a service flow in the wireless ad hoc network in real time, and monitoring data environment parameters which comprise interference intensity and a network load rate; calculating a basic resource rate by using a Mie equation according to the interference intensity, dynamically adjusting parameters of the Mie equation according to a network load rate, and generating a dynamic total resource amount; constructing an orthogonal ARU matrix according to the total amount of the dynamic resources, constraining ARU correlation coefficients of adjacent nodes through a Hadamard matrix, and generating a conflict-free ARU allocation matrix; and splitting the service flow into atomic data blocks, mapping the atomic data blocks to the conflict-free ARU allocation matrix by adopting a Hungary algorithm, and generating a three-dimensional resource scheduling table. According to the method, the resource allocation precision and the system robustness are improved; the multi-dimensional resource conflict probability is reduced, and the spectrum multiplexing efficiency and the network stability are improved.

Description

Technical Field

[0001] The present invention relates to the field of wireless communication technologies, and particularly to a wireless ad-hoc network adaptive anti-interference scheduling method and system. Background Art

[0002] With the rapid development of wireless communication technologies, wireless ad-hoc networks have been widely applied in fields such as military communications, emergency rescue, and the Internet of Things due to advantages such as not relying on fixed infrastructure and being quickly deployable. Especially in complex electromagnetic environments, how to achieve efficient and reliable dynamic resource scheduling and anti-interference capabilities has become a key research direction. In recent years, the development of orthogonal multiple access technologies based on time-frequency resource allocation and intelligent scheduling algorithms has provided new ideas for improving network capacity and anti-interference performance.

[0003] In existing wireless ad-hoc network scheduling methods, there are generally deficiencies: First, the resource allocation model fails to fully consider the dynamic coupling relationship between real-time interference intensity and network load rate, resulting in low resource utilization or an increased risk of overload; Second, the resource conflict control mechanism between nodes is relatively simple, lacking effective modeling of the resource correlation constraints of adjacent nodes, thus affecting the stability and throughput performance of the overall network. Summary of the Invention

[0004] In view of the above existing problems, the present invention is proposed.

[0005] Therefore, the present invention provides a wireless ad-hoc network adaptive anti-interference scheduling method to solve the problems of low resource utilization and poor network stability.

[0006] To solve the above technical problems, the present invention provides the following technical solutions: In a first aspect, the present invention provides a wireless ad-hoc network adaptive anti-interference scheduling method, which includes, Collecting service flows in a wireless ad-hoc network in real time and monitoring data environment parameters, where the data environment parameters include interference intensity and network load rate; calculating a basic resource rate using the Michaelis-Menten equation according to the interference intensity, and dynamically adjusting the Michaelis-Menten equation parameters according to the network load rate to generate a dynamic total resource; constructing an orthogonal ARU matrix according to the dynamic total resource, and generating a conflict-free ARU allocation matrix by constraining the ARU correlation coefficients of adjacent nodes through a Hadamard matrix; splitting the service flow into atomic data blocks, mapping the atomic data blocks to the conflict-free ARU allocation matrix using the Hungarian algorithm to generate a three-dimensional resource scheduling table; performing anti-interference scheduling according to the three-dimensional resource scheduling table, collecting bit error rate, end-to-end delay, and throughput metrics, verifying the interference suppression effect and optimizing the scheduling to generate an optimized anti-interference scheduling scheme.

[0007] As a preferred embodiment of the wireless ad-hoc network adaptive anti-jamming scheduling method of the present invention, wherein: the service flows include tactical instruction flows, video flows, and sensor flows.

[0008] As a preferred embodiment of the wireless ad-hoc network adaptive anti-jamming scheduling method of the present invention, wherein: for the monitoring of data environment parameters, the specific steps are as follows. Collect broadband noise interference, pulse interference, and co-frequency interference, and obtain the interference intensity through weighted summation. Generate the network load rate by quantifying the service resource requirements and combining the physical layer capacity and processing delay. The service resource requirements include tactical instruction flow requirements, video flow requirements, and sensor flow requirements.

[0009] As a preferred embodiment of the wireless ad-hoc network adaptive anti-jamming scheduling method of the present invention, wherein: for calculating the basic resource rate using the Michaelis-Menten equation based on the interference intensity and dynamically adjusting the parameters of the Michaelis-Menten equation to generate the dynamic total resources, the specific steps are as follows. Read the total physical layer capacity of the wireless ad-hoc network and initialize the parameters of the Michaelis-Menten equation in combination with the historical average interference intensity. Based on the initialized parameters of the Michaelis-Menten equation, calculate the basic resource rate in combination with the interference intensity and the network load rate. Adopt a network load rate threshold trigger mechanism to adjust the parameters of the Michaelis-Menten equation, and obtain the dynamic total resources through the adjusted parameters of the Michaelis-Menten equation.

[0010] As a preferred embodiment of the wireless ad-hoc network adaptive anti-jamming scheduling method of the present invention, wherein: for constructing an orthogonal ARU matrix based on the dynamic total resources and generating a conflict-free ARU allocation matrix by constraining the ARU correlation coefficient of adjacent nodes through a Hadamard matrix, the specific steps are as follows. Process the dynamic total resources through a resource quantization converter and obtain the number of ARU units in combination with the ceiling function. Based on the number of ARU units, obtain the time slot offset list, sub-carrier group allocation table, and beam index codebook respectively through resource block allocation and spatial domain beamforming, and generate an initial ARU matrix through assembly. Based on the number of nodes in the initial ARU matrix, match the Hadamard basis matrix to generate an orthogonalized ARU matrix, and generate a three-dimensional conflict detection report through three-dimensional conflict joint detection. Process the three-dimensional conflict detection report through a conflict resolution engine to generate a conflict-free allocation matrix.

[0011] As a preferred embodiment of the wireless ad-hoc network adaptive anti-jamming scheduling method of the present invention, wherein: the three-dimensional conflict joint detection includes time domain conflict detection, frequency domain conflict detection, and spatial domain conflict detection.

[0012] As a preferred solution of the wireless ad-hoc network adaptive anti-interference scheduling method described in the present invention, wherein: splitting the service flow into atomic data blocks, using the Hungarian algorithm to map the atomic data blocks to a conflict-free ARU allocation matrix, and generating a three-dimensional resource scheduling table, the specific steps are as follows. Split the service flow into atomic data blocks through a fixed block algorithm to generate a set of atomic data blocks; Based on the set of atomic data blocks, construct a data block cost matrix, and obtain the optimal mapping relationship through the optimal matching of the Hungarian algorithm; Map the atomic data blocks to a conflict-free ARU allocation matrix through the optimal mapping relationship to generate a three-dimensional resource scheduling table.

[0013] In a second aspect, the present invention provides a wireless ad-hoc network adaptive anti-interference scheduling system, including a data acquisition module, a metabolic calculation module, a matrix construction module, a service mapping module, and an execution optimization module; The data acquisition module is used to collect the service flow in the wireless ad-hoc network in real time and monitor the data environment parameters, and the data environment parameters include interference intensity and network load rate; The metabolic calculation module is used to calculate the basic resource rate using the Michaelis-Menten equation according to the interference intensity, and dynamically adjust the parameters of the Michaelis-Menten equation according to the network load rate to generate the total dynamic resources; The matrix construction module is used to construct an orthogonal ARU matrix according to the total dynamic resources, and generate a conflict-free ARU allocation matrix by constraining the ARU correlation coefficient of adjacent nodes through the Hadamard matrix; The service mapping module is used to split the service flow into atomic data blocks, and map the atomic data blocks to a conflict-free ARU allocation matrix using the Hungarian algorithm to generate a three-dimensional resource scheduling table; The execution optimization module is used to perform anti-interference scheduling according to the three-dimensional resource scheduling table, collect the bit error rate, end-to-end delay, and throughput indicators, verify the interference suppression effect and optimize the scheduling, and generate an optimized anti-interference scheduling scheme.

[0014] The data acquisition module, the metabolic calculation module, the matrix construction module, the service mapping module, and the execution optimization module; The data acquisition module is used to collect the service flow in the wireless ad-hoc network in real time and monitor the data environment parameters, and the data environment parameters include interference intensity and network load rate; The metabolic calculation module is used to calculate the basic resource rate using the Michaelis-Menten equation according to the interference intensity, and dynamically adjust the parameters of the Michaelis-Menten equation according to the network load rate to generate the total dynamic resources; A matrix construction module, configured to construct an orthogonal ARU matrix according to the total amount of dynamic resources, and generate a conflict-free ARU allocation matrix by constraining the ARU correlation coefficients of adjacent nodes through a Hadamard matrix; A service mapping module, configured to split a service flow into atomic data blocks, and map the atomic data blocks to the conflict-free ARU allocation matrix by using the Hungarian algorithm to generate a three-dimensional resource scheduling table; An execution optimization module, configured to perform anti-interference scheduling according to the three-dimensional resource scheduling table, collect bit error rate, end-to-end delay, and throughput metrics, verify the interference suppression effect and optimize the scheduling to generate an optimized anti-interference scheduling scheme.

[0015] In a third aspect, the present invention provides a computer device, including a memory and a processor, where the memory stores a computer program, and: when the computer program is executed by the processor, any step of the wireless ad hoc network adaptive anti-interference scheduling method described in the first aspect of the present invention is implemented.

[0016] In a fourth aspect, the present invention provides a computer-readable storage medium, on which a computer program is stored, and: when the computer program is executed by the processor, any step of the wireless ad hoc network adaptive anti-interference scheduling method described in the first aspect of the present invention is implemented.

[0017] The beneficial effects of the present invention are as follows: by dynamically modeling the resource rate through the Michaelis-Menten equation and combining with the network load rate to adjust the parameters in real time, the accuracy of resource allocation and the system robustness are improved; at the same time, the Hadamard matrix is used to orthogonally constrain the ARU correlation of adjacent nodes to construct a conflict-free resource allocation matrix, reducing the probability of multi-dimensional resource conflicts and improving the spectrum reuse efficiency and network stability. Description of the Drawings

[0018] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0019] Figure 1 It is a flowchart of the wireless ad hoc network adaptive anti-interference scheduling method.

[0020] Figure 2 It is a schematic diagram of the wireless ad hoc network adaptive anti-interference scheduling system.

[0021] Figure 3 It is a flowchart of the flowchart for generating the conflict-free ARU allocation matrix.

[0022] Figure 4 It is a flowchart of anti-interference scheduling execution and optimization. Detailed implementation manners

[0023] To make the above objects, features, and advantages of the present invention more obvious and understandable, the following detailed description of the specific implementation manners of the present invention will be given in conjunction with the accompanying drawings of the specification.

[0024] In the following description, many specific details are set forth in order to fully understand the present invention. However, the present invention can also be implemented in other ways different from those described herein. Those skilled in the art can make similar extensions without departing from the connotation of the present invention. Therefore, the present invention is not limited by the specific embodiments disclosed below.

[0025] Secondly, the so-called "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that can be included in at least one implementation manner of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment that is mutually exclusive with other embodiments.

[0026] Referring to Figures 1 to 4 , which is an embodiment of the present invention. This embodiment provides a wireless ad-hoc network adaptive anti-jamming scheduling method, including the following steps: S1: Real-time collect the traffic flows in the wireless ad-hoc network and monitor the data environment parameters, where the data environment parameters include interference intensity and network load rate; S1.1: Real-time collecting the traffic flows in the wireless ad-hoc network includes tactical instruction flows, video flows, and sensor flows; Parse the source IP (Internet Protocol) address, destination MAC (Media Access Control) address, and packet length in the packet header through deep packet inspection, and mark it as a tactical instruction flow; use the sliding time window analysis method to count the arrival rate of packets, and extract the maximum allowable delay and urgency from the QoS (Quality of Service) field in the packet header; Filter the video flow through the transport layer port and the payload length, obtain the bit rate based on the transmission interval and size of the packet, calculate the delay jitter through the variance of the packet arrival timestamps, and parse the resolution parameter in the video payload header of the video flow and divide it by the reference value to obtain the resolution factor; Identify the sensor flow by analyzing the packet and packet length through the application layer protocol, calculate the sampling frequency based on the reciprocal of the continuous packet interval, parse the standard deviation of the payloads of the last 10 packets, and extract the data validity period from the packet header.

[0027] S1.2: Monitor the data environment parameters including interference intensity and network load rate; Use a spectrum analyzer to symmetrically select 8 sampling points on both sides of the center working frequency point, record the received power, perform 10 noise floor power measurements in the dedicated silent time slot and take the arithmetic mean as the noise floor, calculate the absolute difference between the received power of each sampling point and the noise floor, and obtain the arithmetic mean to generate broadband noise interference; perform a second-order differential operation on the instantaneous received power signal with a 10-microsecond time window, and record the maximum peak value of the differential result as the pulse interference; send known test signals at 3 own working frequency points, and the receiving end calculates the cross-correlation coefficient between the transmitted signal and the received signal, and takes the arithmetic mean of the cross-correlation coefficients of the 3 frequency points as the co-channel interference, and perform weighted summation of the noise interference, pulse interference and co-channel interference to generate the interference intensity.

[0028] Based on the traffic-based service resource requirements including tactical instruction stream requirements, video stream requirements and sensor stream requirements, combined with the physical layer capacity and processing delay, synthesize the network load rate.

[0029] Obtain the tactical instruction stream requirements through the arrival rate, maximum allowable delay and urgency of the tactical instruction stream; obtain the video stream requirements according to the bit rate and delay jitter of the video stream; extract the sampling frequency, data validity period and data fluctuation standard deviation from the sensor stream to generate the sensor stream requirements; Generate service resource requirements by weighted summation of the tactical instruction stream requirements, video stream requirements and sensor stream requirements, and combine the physical layer capacity and processing delay to synthesize the network load rate.

[0030] S2: Calculate the basic resource rate according to the interference intensity using the Michaelis-Menten equation, and dynamically adjust the parameters of the Michaelis-Menten equation according to the network load rate to generate the total dynamic resources; S2.1: Read the total physical layer capacity, and initialize the parameters of the Michaelis-Menten equation in combination with the historical average interference intensity; Furthermore, load the total physical layer capacity from the non-volatile memory, obtain the historical average interference intensity by statistically averaging the interference intensity in the most recent 100 sampling periods, and initialize the parameters of the Michaelis-Menten equation including the initial value of the maximum resource supply and the initial value of the half-saturation coefficient according to the total physical layer capacity and the historical average interference intensity.

[0031] S2.2: According to the initialized parameters of the Michaelis-Menten equation, combined with the interference intensity and the network load rate, calculate the basic resource rate, and the expression is: ; where, is the basic resource rate, is the initial value of the maximum resource supply, is the interference intensity, is the curvature factor (the value range is [0.0, 10.0]), is the network load rate, is the network load rate threshold, is the initial value of the half-saturation coefficient, is the interference mutation sensitivity coefficient (the value range is [0.0, 1.0]), is the interference intensity gradient; It should be noted that the network load rate of three hundred sampling periods is continuously collected by the resource monitoring unit of the wireless ad hoc network node; the fast selection algorithm is called to perform the calculation of the top five percentiles on the network load rate of the complete three hundred periods, and the peak load characterization value is output; the subset of the network load rate of the last two hundred periods is intercepted to perform the calculation of the eightieth percentile, and the steady-state load characterization value is output; based on the peak load characterization value and the steady-state load characterization value, the initial value of the network load threshold is obtained through weighted summation; the initial value of the network load threshold is input into the boundary protector to output the network load rate threshold.

[0032] S2.3: Adopt the network load rate threshold triggering mechanism to adjust the Michaelis-Menten equation parameters, and obtain the dynamic resource total through the adjusted Michaelis-Menten equation parameters; Input the network load rate value into the dual-threshold comparator to generate the maximum resource supply: When the network load rate is greater than the network load threshold, perform the expansion of the maximum resource supply; When the network load rate is less than the network load threshold, perform the reduction of the maximum resource supply.

[0033] Collect the interference intensity arrays of the last ten sampling periods, obtain the mean ratio of the interference intensity variances, and generate the half-saturation coefficient through coefficient superposition operation.

[0034] Substitute the adjusted Michaelis-Menten equation parameters into the Michaelis-Menten equation to generate the resource rate, and obtain the dynamic resource total through the resource rate.

[0035] S3: Construct an orthogonal ARU matrix according to the dynamic resource total, and generate a conflict-free ARU allocation matrix by constraining the ARU correlation coefficients of adjacent nodes through the Hadamard matrix; S3.1: Process the dynamic resource total through the resource quantization converter, and obtain the number of ARU units in combination with the ceiling function; Furthermore, read the capacity of a single ARU (atomic resource unit), perform numerical conversion on the dynamic resource total and the single ARU capacity through floating-point processing, generate an integer result in combination with the rounding function, and input the integer result into the ARU allocation control unit, which is marked as the number of ARU units.

[0036] S3.2: Based on the number of ARU units, obtain the time slot offset list, sub-carrier group allocation table, and beam index codebook respectively through resource block allocation and spatial domain beamforming, and generate the initial ARU matrix through assembly; Time-domain resource block allocation: Obtain the neighbor node list from the routing protocols OSPF (Open Shortest Path First) / OLSR (Optimized Link State Routing) as network topology information. Process the network topology information through a distributed graph coloring algorithm, and generate a time slot conflict graph according to the neighbor node distance constraint. Invoke the greedy coloring strategy to allocate continuous time slot blocks for each node, ensuring that the time slot interval between adjacent nodes is greater than three times the single time slot length. Adjust the time slot block length in combination with the maximum allowable delay in the service flow requirements to generate a time slot offset list.

[0037] Frequency-domain resource block allocation: Apply the fast Fourier transform to process the spectrum scanning results to identify clean frequency bands with interference intensity lower than -100 dBm. Execute the best matching algorithm to calculate the required number of subcarriers according to the service flow code rate requirements. Allocate continuous non-overlapping subcarrier groups, and preferentially select low-frequency band resources to reduce path loss, generating a subcarrier group allocation table.

[0038] Spatial domain beamforming: Use the multiple signal classification algorithm to process the three-dimensional coordinate data of the nodes, and calculate the direction of arrival of the target receiving node. Invoke the beam control algorithm to generate a radiation pattern with the main lobe aligned to the target node and the null pointing to the strongest interference source. Select the optimal beam index according to the antenna array response characteristics to generate a beam index codebook.

[0039] Assemble the time slot offset list, the subcarrier group allocation table, and the beam index codebook to generate an initial ARU matrix.

[0040] S3.3: Based on the number of nodes in the initial ARU matrix, match the Hadamard basis matrix to generate an orthogonalized ARU matrix, and perform three-dimensional conflict joint detection and three-dimensional conflict detection reporting; Process the resource parameters of each node through the binary vector coding method, convert the time slot offset into a binary time slot vector, encode the frequency band range as a frequency domain mask vector, and map the beam index to a beam state vector. Invoke the Hadamard dot product calculation protocol to process all node vector pairs and perform correlation coefficient calculation. When the correlation coefficient exceeds 0.3, activate the random perturbation algorithm to adjust the time slot offset of the conflict nodes, and re-encode and calculate until all node pairs satisfy the orthogonal constraint (correlation coefficient < 0.3).

[0041] Three-dimensional conflict joint detection includes time-domain conflict detection, frequency-domain conflict detection, and spatial domain conflict detection; Furthermore, time-domain conflict detection: Use the time slot overlap analysis algorithm to process the time slot offset list, calculate the time slot overlap percentage of any two neighbor nodes, and mark the conflict node pairs with an overlap rate > 30%; Frequency-domain conflict detection: Apply the frequency band isolation verification method to process the subcarrier group allocation table, calculate the frequency band overlap ratio of adjacent nodes (number of overlapping subcarriers / total number of subcarriers), and mark the conflict node pairs with an overlap rate > 15%; Spatial conflict detection: Execute the beam space angle calculation method to process the beam index codebook, calculate the main lobe axis angle through the direction cosine formula, and mark the conflicting node pairs with angles <30 degrees; All conflicting node pairs are organized into a conflicting node pair list, and a three-dimensional conflict detection report is generated.

[0042] S3.4: Processing the three-dimensional conflict detection report through the conflict resolution engine to generate a conflict-free ARU allocation matrix; It should be noted that the list of conflicting node pairs in the three-dimensional conflict detection report is read; for time domain conflicts, the time slot offset adjuster is called to perform random perturbation of the time slot offset; for frequency domain conflicts, the frequency point reallocator is called to perform suboptimal clean frequency band selection; for spatial domain conflicts, the beam optimizer is called to perform main lobe fine-tuning and zero-steering enhancement to generate adjusted resource parameters; based on the adjusted resource parameters, the node resource records are reconstructed through resource reorganization, and triple real-time verification is performed through conflict elimination verification to generate a conflict-free ARU allocation matrix.

[0043] S4: Split the service flow into atomic data blocks, use the Hungarian algorithm to map the atomic data blocks to the conflict-free ARU allocation matrix, and generate a three-dimensional resource scheduling table; S4.1: Split the service flow into atomic data blocks through a fixed block algorithm to generate an atomic data block set; Furthermore, the service flow is processed by a fixed blocking algorithm, and the number of blocks is calculated according to the single ARU capacity value to generate atomic data blocks. The timestamp marker is called to add a generation timestamp for each atomic data block. The transmission deadline is calculated for each atomic data block in combination with the service priority and the maximum allowed delay value. A set of atomic data blocks with priority labels is generated.

[0044] S4.2: Based on the atomic data block set, a data block cost matrix is constructed, and the optimal mapping relationship is obtained through the Hungarian algorithm optimal matching; Furthermore, the timestamp, transmission deadline and service priority in the atomic data block set are obtained; the time slot start time, frequency band position and beam index parameters in the conflict-free ARU allocation matrix are read; the Euclidean distance between the data block source node position and the ARU node position is obtained, and the delay margin value is calculated in combination with the atomic data block deadline and the time slot start time; the weighted comprehensive evaluation model is applied to integrate the Euclidean distance, delay margin value and service priority to generate a cost value; the two-dimensional matrix is filled with atomic data block indices as rows and ARU resource indices as columns to generate a data block cost matrix.

[0045] It should be noted that service priorities include tactical command stream priority, video stream priority, and sensor stream priority: Tactical instruction stream priority: Parse the QoS field in the data packet header through deep packet inspection, extract the urgency level (such as levels 0 - 3), and directly map the urgency level to the tactical instruction stream priority; Video stream priority: Combine the stream type identifier and resolution factor in the packet header QoS field, calculate the priority weight through a weighted formula, and generate the video stream priority; Sensor stream priority: Based on the validity period field and sampling frequency in the data packet header, perform an inverse function conversion to generate the sensor stream priority; The generated priority label is an integer value from 0 to 7, which is used for the transmission queue sorting during the atomic data block packaging and the cost matrix calculation during the Hungarian algorithm mapping, ensuring that high - priority services can obtain low - latency resource allocation first.

[0046] Based on the data block cost matrix, perform row reduction operations and column reduction operations, and use the minimum straight - line covering method to mark the positions of independent zero elements in the data block cost matrix; when the number of covering straight lines is equal to the order of the data block cost matrix, output the initial matching scheme; when the number of covering straight lines is less than the matrix order, locate the minimum value in the uncovered area; subtract the minimum value from all elements in the uncovered row and add the minimum value to all elements in the covered column; re - perform the zero - element covering test until the minimum value in the uncovered area is located when the number of covering straight lines is less than the matrix order; detect the conflict items where the end time of the time slot in the mapping relationship is later than the deadline of the atomic data block; call the standby ARU resource record to re - allocate the conflict atomic data blocks to generate the optimal mapping relationship table.

[0047] S4.3: Map the atomic data blocks to the conflict - free ARU allocation matrix through the optimal mapping relationship to generate a three - dimensional resource scheduling table; Furthermore, analyze the correspondence between the atomic data block identifier and the ARU resource identifier in the optimal mapping relationship table; retrieve the time - slot window parameters, frequency - band channel parameters, and beam index parameters of the corresponding ARU resources from the conflict - free ARU allocation matrix; extract the source node identifier, priority parameter, and deadline parameter from the atomic data block set; re - organize the resource allocation sequence in ascending order of the time - slot start time through the time - axis sorting algorithm; apply the frequency - domain coding converter to convert the center frequency point of the sub - carrier group into a physical channel number; integrate the beam index codebook to generate a spatial - dimension control instruction set; encapsulate the time - slot window range, physical channel number, beam index value, and atomic data block feature parameters to generate a structured scheduling record; add a scheduling period identifier and calculate the cyclic redundancy check code for the complete scheduling table; output the binary data stream of the three - dimensional resource scheduling table with the check code.

[0048] S5: Perform anti - interference scheduling according to the three - dimensional resource scheduling table, collect the bit error rate, end - to - end delay, and throughput metrics, verify the interference suppression effect and optimize the scheduling to generate an optimized anti - interference scheduling scheme; Read the time slot window range, physical channel number, and beam index value parameters in the three-dimensional resource scheduling table as the three-dimensional resource scheduling table parameters; control the radio transceiver to perform data transmission operations according to the three-dimensional resource scheduling table parameters; collect the bit error rate through the bit error rate detection circuit at the receiving end; calculate the end-to-end delay through the timestamp comparison method; count the throughput through the packet counter; input the bit error rate, end-to-end delay, and throughput into the threshold comparator to compare with the preset performance threshold; activate the scheduling optimization engine when any index exceeds the preset performance threshold; the scheduling optimization engine adjusts the time slot offset, frequency band position, and beam index parameters in the conflict-free ARU allocation matrix; and regenerate the optimized anti-interference scheduling scheme.

[0049] It should be noted that the service priority and the maximum allowable delay in the service flow are read; the corresponding bit error rate threshold is obtained by querying the quality of service level specification table according to the service priority; the end-to-end delay threshold is calculated based on the maximum allowable delay; and the throughput threshold is derived by combining the theoretical maximum throughput of the physical layer with the service priority.

[0050] This embodiment also provides a wireless ad hoc network adaptive anti-interference scheduling system, including: a data acquisition module, a metabolic calculation module, a matrix construction module, a service mapping module, and an execution optimization module; The data acquisition module is used to collect the service flow in the wireless ad hoc network in real time and monitor the data environment parameters, where the data environment parameters include the interference intensity and the network load rate; The metabolic calculation module is used to calculate the basic resource rate using the Michaelis-Menten equation according to the interference intensity and dynamically adjust the parameters of the Michaelis-Menten equation according to the network load rate to generate the dynamic total resources; The matrix construction module is used to construct an orthogonal ARU matrix according to the dynamic total resources and generate a conflict-free ARU allocation matrix by constraining the ARU correlation coefficients of adjacent nodes through the Hadamard matrix; The service mapping module is used to split the service flow into atomic data blocks and map the atomic data blocks to the conflict-free ARU allocation matrix using the Hungarian algorithm to generate a three-dimensional resource scheduling table; The execution optimization module is used to perform anti-interference scheduling according to the three-dimensional resource scheduling table, collect the bit error rate, end-to-end delay, and throughput metrics, verify the interference suppression effect and perform scheduling optimization to generate an optimized anti-interference scheduling scheme.

[0051] This embodiment also provides a computer device applicable to the case of the wireless ad hoc network adaptive anti-interference scheduling method, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the wireless ad hoc network adaptive anti-interference scheduling method proposed in the above embodiment.

[0052] The computer device can be a terminal, which includes a processor, a memory, a communication interface, a display screen, and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The communication interface of the computer device is used to communicate with external terminals in a wired or wireless manner, and the wireless manner can be implemented through WIFI, a carrier network, NFC (Near Field Communication), or other technologies. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen. The input device of the computer device can be a touch layer covering the display screen, or buttons, a trackball, or a touchpad provided on the outer shell of the computer device. It can also be an external keyboard, touchpad, or mouse, etc.

[0053] This embodiment also provides a storage medium, on which a computer program is stored. When the program is executed by a processor, it implements the wireless ad-hoc network adaptive anti-interference scheduling method proposed in the above embodiment; the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM for short), Electrically Erasable Programmable Read-Only Memory (EEPROM for short), Erasable Programmable Read Only Memory (EPROM for short), Programmable Red-Only Memory (PROM for short), Read-Only Memory (ROM for short), magnetic memory, flash memory, a magnetic disk, or an optical disc.

[0054] In summary, in the present invention, by dynamically modeling the resource rate using the Michaelis-Menten equation and adjusting the parameters in real time in combination with the network load rate, the accuracy of resource allocation and the system robustness are improved; at the same time, the orthogonality constraint of the ARU correlation of adjacent nodes is carried out using the Hadamard matrix, and a conflict-free resource allocation matrix is constructed, reducing the probability of multi-dimensional resource conflicts and improving the spectrum reuse efficiency and network stability.

[0055] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and all of them should be covered by the scope of the claims of the present invention.

Claims

1. A wireless ad-hoc network adaptive anti-jamming scheduling method, characterized in that: including Real-time collect traffic flows in a wireless ad-hoc network and monitor data environment parameters, where the data environment parameters include interference intensity and network load rate; Calculate the basic resource rate using the Michaelis-Menten equation according to the interference intensity, and dynamically adjust the parameters of the Michaelis-Menten equation according to the network load rate to generate the total dynamic resources; Construct an orthogonal ARU matrix according to the total dynamic resources, and generate a conflict-free ARU allocation matrix by constraining the ARU correlation coefficients of adjacent nodes through a Hadamard matrix; Split the traffic flow into atomic data blocks, and use the Hungarian algorithm to map the atomic data blocks to the conflict-free ARU allocation matrix to generate a three-dimensional resource scheduling table; Execute anti-interference scheduling according to the three-dimensional resource scheduling table, collect bit error rate, end-to-end delay and throughput metrics, verify the interference suppression effect and optimize the scheduling to generate an optimized anti-interference scheduling scheme.

2. The wireless ad-hoc network adaptive anti-interference scheduling method according to claim 1, wherein: The traffic flow includes tactical instruction flow, video flow and sensor flow.

3. The wireless ad-hoc network adaptive anti-jamming scheduling method according to claim 2, characterized in that: For the step of monitoring the data environment parameters, it is as follows specifically Collect broadband noise interference, impulse interference and co-channel interference, and obtain the interference intensity through weighted summation; Generate the network load rate by quantifying the traffic resource requirements and combining the physical layer capacity and processing delay; The traffic resource requirements include tactical instruction flow requirements, video flow requirements and sensor flow requirements.

4. The wireless ad hoc network adaptive anti-jamming scheduling method according to claim 3, wherein: For the step of calculating the basic resource rate using the Michaelis-Menten equation according to the interference intensity and dynamically adjusting the parameters of the Michaelis-Menten equation according to the network load rate to generate the total dynamic resources, it is as follows specifically Read the total physical layer capacity of the wireless ad-hoc network, and initialize the parameters of the Michaelis-Menten equation in combination with the historical average interference intensity; According to the initialized parameters of the Michaelis-Menten equation, combine the interference intensity and network load rate to calculate the basic resource rate; Adopt a network load rate threshold trigger mechanism to adjust the parameters of the Michaelis-Menten equation, and obtain the total dynamic resources through the adjusted parameters of the Michaelis-Menten equation.

5. The wireless ad-hoc network adaptive anti-jamming scheduling method according to claim 4, characterized in that: For the step of constructing an orthogonal ARU matrix according to the total dynamic resources and generating a conflict-free ARU allocation matrix by constraining the ARU correlation coefficients of adjacent nodes through a Hadamard matrix, it is as follows specifically Process the total dynamic resources through a resource quantization converter, and obtain the number of ARU units in combination with the ceiling function; Based on the number of ARU units, obtain a list of time slot offsets, a subcarrier group allocation table and a beam index codebook respectively through resource block allocation and spatial domain beamforming, and generate an initial ARU matrix through assembly; Based on the number of nodes in the initial ARU matrix, match the Hadamard basis matrix to generate an orthogonalized ARU matrix, and generate a three-dimensional conflict detection report through three-dimensional conflict joint detection; Process the three-dimensional conflict detection report through a conflict resolution engine to generate a conflict-free allocation matrix.

6. The wireless ad-hoc network adaptive anti-jamming scheduling method according to claim 5, characterized in that: The three-dimensional conflict joint detection includes time domain conflict detection, frequency domain conflict detection and spatial domain conflict detection.

7. The wireless ad-hoc network adaptive anti-jamming scheduling method according to claim 1, characterized in that: For the step of splitting the traffic flow into atomic data blocks, using the Hungarian algorithm to map the atomic data blocks to the conflict-free ARU allocation matrix to generate a three-dimensional resource scheduling table, it is as follows specifically Split the traffic flow into atomic data blocks through a fixed block algorithm to generate a set of atomic data blocks; Based on the set of atomic data blocks, construct a data block cost matrix, and obtain the optimal mapping relationship through the optimal matching of the Hungarian algorithm Map the atomic data blocks to the conflict-free ARU allocation matrix through the optimal mapping relationship to generate a three-dimensional resource scheduling table.

8. A wireless ad-hoc network adaptive anti-jamming scheduling system, based on the wireless ad-hoc network adaptive anti-jamming scheduling method according to any one of claims 1 to 7, characterized in that: It includes a data acquisition module, a metabolic calculation module, a matrix construction module, a service mapping module, and an execution optimization module; The data acquisition module is used to collect service flows in the wireless ad hoc network in real time and monitor data environment parameters, where the data environment parameters include interference intensity and network load rate; The metabolic calculation module is used to calculate the basic resource rate using the Michaelis-Menten equation according to the interference intensity, and dynamically adjust the parameters of the Michaelis-Menten equation according to the network load rate to generate the total dynamic resources; The matrix construction module is used to construct an orthogonal ARU matrix according to the total dynamic resources, and generate a conflict-free ARU allocation matrix by constraining the ARU correlation coefficient of adjacent nodes through the Hadamard matrix; The service mapping module is used to split the service flow into atomic data blocks, and map the atomic data blocks to the conflict-free ARU allocation matrix using the Hungarian algorithm to generate a three-dimensional resource scheduling table; The execution optimization module is used to perform anti-interference scheduling according to the three-dimensional resource scheduling table, collect bit error rate, end-to-end delay, and throughput metrics, verify the interference suppression effect and optimize the scheduling to generate an optimized anti-interference scheduling scheme.

9. A computer device, comprising a memory and a processor, the memory storing a computer program, characterized in that: When the processor executes the computer program, it implements the steps of the wireless ad hoc network adaptive anti-interference scheduling method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the steps of the wireless ad hoc network adaptive anti-interference scheduling method according to any one of claims 1 to 7.

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