A method for processing synaesthetic data and a routing system of a quantum approximate optimization algorithm under incomplete information

Abstract

The present invention relates to a method for processing communication-sensing data and a routing system of a quantum approximate optimization algorithm under incomplete information, comprising the following steps: S1, obtaining a communication-sensing signal at the current moment, extracting the current time-frequency resource block of the communication-sensing signal at the current moment, and calculating the current time-frequency correlation of the information boundary of the communication-sensing signal at the current moment through any two current time-frequency resource blocks; S2, constructing a connectivity graph through the current time-frequency correlation and the current time-frequency resource block, solving the connectivity graph to obtain a current adaptive graph mapping, and solving the solution of the cutting problem of any two of the current time-frequency resource blocks with respect to the adaptive graph mapping; S3, incrementing the current adaptive graph mapping through the subsequent time-frequency resource block to obtain a subsequent adaptive graph mapping, and obtaining the solution of the cutting problem of the subsequent time-frequency resource block through parameter iteration to complete the decoupling of any two current time-frequency resource blocks.

Description

Technical Field

[0001] The present invention relates to the field of communication perception, and specifically refers to a method for processing communication and sensing data and a routing system of a quantum approximate optimization algorithm under incomplete information. Background Art

[0002] In the physical space, the placement of objects in the space, or the movement and actions of people in the space, etc. will affect the reflection, diffraction, etc. of signals. Communication perception technology can infer factors such as Doppler frequency shift affected by the speed of the transceiver at both ends of the channel, multipath effects caused by human body and object perturbations in the space, and natural environment changes by collecting the feedback of the state information in the channel.

[0003] In the prior art, for example, in the existing application with the publication number CN119402831B and the name of a method and routing system for communication and sensing integration based on quantum approximate optimization, a carrier - to - carrier channel correlation model is constructed and used to distinguish carriers suitable for sensing. However, for an incomplete channel, the information collected by this channel is not enough to complete all the required data for the above - mentioned prior art tasks. This situation does not consider the application scenario of an incomplete channel where accurate channel state information cannot be obtained, and it is impossible to clarify the information boundary between communication and sensing information in the problem of homogeneous decoupling. Therefore, the sub - carrier decoupling method corresponding to sensing information and communication information in the above - mentioned prior art will no longer be applicable.

[0004] The purpose of the research of the present invention is to design a method for processing communication and sensing data and a routing system of a quantum approximate optimization algorithm under incomplete information for the problems existing in the above - mentioned prior art. Summary of the Invention

[0005] Aiming at the problems existing in the above - mentioned prior art, the present invention provides a method for processing communication and sensing data and a routing system of a quantum approximate optimization algorithm under incomplete information, which can effectively solve at least one of the problems existing in the above - mentioned prior art.

[0006] The technical solution of the present invention is as follows:

[0007] A method for processing communication and sensing data of a quantum approximate optimization algorithm under incomplete information includes the following steps:

[0008] S1. Obtain the communication and sensing signal at the current moment, extract the current time - frequency resource block of the communication and sensing signal at the current moment, and calculate the current time - frequency correlation of the information boundary of the communication and sensing signal at the current moment through any two current time - frequency resource blocks;

[0009] S2. Construct a connected graph through the current time - frequency correlation and the current time - frequency resource block, solve the connected graph to obtain the current adaptive graph mapping, and solve the solution of the cutting problem of any two of the current time - frequency resource blocks with respect to the adaptive graph mapping;

[0010] S3. Obtain the integrated sensing and communication signal at a subsequent moment, extract the subsequent time-frequency resource block of the integrated sensing and communication signal at the subsequent moment, increment the current adaptive graph mapping through the subsequent time-frequency resource block to obtain a subsequent adaptive graph mapping, and obtain the solution to the cutting problem of the subsequent time-frequency resource block through parameter iteration to complete the decoupling of any two current time-frequency resource blocks.

[0011] Further, calculating the current time-frequency correlation of the information boundary of the integrated sensing and communication signal at the current moment through any two current time-frequency resource blocks includes:

[0012] Calculate the correlation score of any two current time-frequency resource blocks;

[0013] Calculate the sum of all correlation scores, and take the sum of all correlation scores as the current time-frequency correlation.

[0014] Further, calculating the correlation score of any two current time-frequency resource blocks includes:

[0015] Construct the current time-frequency resource block into a set , where t represents the time direction, k represents the carrier coefficient direction, T represents the total number of time series, and K represents the total number of carrier coefficients;

[0016] Calculate the complex channel response of the current time-frequency resource block , and construct the eigenvector of the complex channel response ; of the complex channel response ;

[0017] Perform a linear transformation on the eigenvector in the weight matrix to obtain the time-domain coefficient and the frequency-domain coefficient , where , , , are the corresponding weight matrices;

[0018] Calculate the correlation score of any two current time-frequency resource blocks through the following formula :

[0019] , where d represents the dimension of the data, is the frequency-domain coefficient of a resource block different from , represents the serial numbers of current time-frequency resource blocks different from , n represents the index, and N represents the total number of times to calculate the correlation score.

[0020] Further, constructing a connectivity graph through the current time-frequency correlation and the current time-frequency resource block includes:

[0021] Set As the connectivity graph A set V of qubits, and taking the sum of the correlation scores as the connectivity graph Connection between qubits in the set V of qubits.

[0022] Further, solving the connectivity graph to obtain the current adaptive graph mapping includes:

[0023] Constructing a minimum cut problem for the edge weights of the connectivity graph;

[0024] Converting the minimum cut problem into a maximum cut problem, and taking the maximum cut problem as the current adaptive graph mapping.

[0025] Further, converting the minimum cut problem into a maximum cut problem includes:

[0026] Obtaining the maximum information boundary of the current edge weights , where ;

[0027] Through Adjusting the maximum information boundary of the current edge weights to obtain the updated boundary of the edge weights , through the updated boundary of the edge weights Constructing a maximum cut problem for the corresponding edge weights and nodes, where is an all-ones matrix.

[0028] Further, solving the solution of the cut problem for the adaptive graph mapping includes:

[0029] Mapping the nodes of the current adaptive graph mapping to the ground state quantum state, and the edge weights in the adaptive graph mapping E are determined by and mapped to the corresponding problem Hamiltonian;

[0030] Constructing all the nodes and weights of the current adaptive graph mapping into a fully connected hypergraph, taking the fully connected hypergraph as the input of the quantum approximate optimization, obtaining the cut problem for any two current time-frequency resource blocks of the current adaptive graph mapping, and solving the solution of the cut problem for any two current time-frequency resource blocks of the adaptive graph mapping.

[0031] Further, incrementing the current adaptive graph mapping through the subsequent time-frequency resource block includes:

[0032] Calculating the subsequent time-frequency correlation of the information boundary of the communication-sensing signal at the subsequent moment through any two subsequent time-frequency resource blocks;

[0033] Construct a connectivity graph through the subsequent time-frequency correlation and the subsequent time-frequency resource block, and solve the connectivity graph to obtain a subsequent adaptive graph mapping.

[0034] Obtain the subsequent maximum information boundary for constructing the connectivity graph of the subsequent time-frequency resource block, and increment the nodes and weights of the current adaptive graph mapping through the subsequent maximum information boundary to obtain a subsequent adaptive graph mapping.

[0035] Further, obtaining the solution to the cutting problem of the subsequent time-frequency resource block through parameter iteration includes:

[0036] S31, solve the optimal parameters corresponding to the solution to the cutting problem of the current adaptive graph mapping;

[0037] S32, set the initial parameters of the cutting problem of the subsequent adaptive graph mapping to the optimal parameters;

[0038] S33, solve the solution to the cutting problem of the subsequent adaptive graph mapping to obtain the decoupling of any two current time-frequency resource blocks.

[0039] Further provide a routing system for a quantum approximate optimization algorithm under incomplete information, including:

[0040] A monitored terminal;

[0041] A monitoring terminal, used to establish a communication connection with the monitored terminal and monitor the monitored terminal;

[0042] A computing host, used to collect communication data packets of the monitored terminal through the monitoring terminal, and implement the communication and sensing data processing method of a quantum approximate optimization algorithm under incomplete information when working.

[0043] Therefore, the present invention provides the following effects and / or advantages:

[0044] This application constructs a fully connected graph corresponding to the inter-carrier sub-frequency channel correlation by combining the interference and superposition characteristics of quantum states, and couples and models the communication and sensing information of the current adaptive graph mapping as a maximum cut problem. Then, the optimization problem is solved through a variational quantum circuit to achieve efficient separation of sensing information in a homogeneous channel. This method can accurately identify sub-carriers carrying sensing information in the sub-carrier domain of communication and sensing integration, improve the robustness and accuracy of sensing tasks, and enhance the construction ability of a multi-objective and multi-task sensing network.

[0045] In the present application, the quantum approximate optimization algorithm constructed by the previous project team cannot describe the relationships between all carriers in an incomplete channel for the fully connected graph of inter-carrier correlation. Combining the above content, for the problem of carrier decoupling in an incomplete channel, two aspects are mainly considered: determining the boundary of the sensing information in the communication link and overcoming the incompleteness of the correlation graph. A correlation model is established based on the time-frequency characteristics jointly determined by the time-domain symbols and the frequency-domain subcarrier coefficients to determine the information boundary, and an incremental quantum approximate optimization algorithm that can update and solve the fully connected graph is applied, thereby effectively solving the problem of decoupling homogeneous communication sensing carriers with incomplete information.

[0046] In the present application, the current time-frequency correlation is first constructed, then a connectivity graph is constructed based on the time-frequency correlation and the time-frequency resource blocks, and the connectivity graph is solved to obtain the current adaptive graph mapping. Then, the current adaptive graph mapping is incremented by the sensing and communication signals at subsequent moments. Finally, the optimal parameters based on the current adaptive graph mapping are used to iteratively solve the subsequent adaptive graph mapping and the solution to the cutting problem of the subsequent time-frequency resource blocks, thereby completing the decoupling of any two current time-frequency resource blocks.

[0047] Other features and advantages of the present invention will be described in the subsequent specification, and in part, will be obvious from the specification, or will be understood by implementing the present invention. The objectives and other advantages of the present invention are achieved and obtained by the structures specifically pointed out in the specification and the drawings.

[0048] It should be understood that the above summary and the following detailed description of the present invention are exemplary and explanatory, and are intended to provide further explanation of the present invention as claimed. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 A flowchart provided for one embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0050] For the convenience of those skilled in the art to understand, the embodiments will be further described in detail for the present invention as follows:

[0051] Reference Figure 1 , a method for processing sensing and communication data based on a quantum approximate optimization algorithm under incomplete information, includes the following steps:

[0052] S1, obtaining the sensing and communication signal at the current moment, extracting the current time-frequency resource block of the sensing and communication signal at the current moment, and calculating the current time-frequency correlation of the information boundary of the sensing and communication signal at the current moment through any two current time-frequency resource blocks;

[0053] Further, calculating the current time-frequency correlation of the information boundary of the sensing and communication signal at the current moment through any two current time-frequency resource blocks includes:

[0054] Calculate the correlation score of any two current time-frequency resource blocks;

[0055] Calculate the sum of all correlation scores, and take the sum of all correlation scores as the current time-frequency correlation.

[0056] Furthermore, calculating the correlation score of any two current time-frequency resource blocks includes:

[0057] Construct the current time-frequency resource block into a set , where t represents the time direction, k represents the carrier coefficient direction, T represents the total number of time series, and K represents the total number of carrier coefficients;

[0058] Calculate the complex channel response of the current time-frequency resource block , and construct the eigenvector of the complex channel response ; of the complex channel response ;

[0059] Perform a linear transformation on the eigenvector in the weight matrix to obtain the time-domain coefficient and the frequency-domain coefficient , where , , , are the corresponding weight matrices;

[0060] Calculate the correlation score of any two current time-frequency resource blocks through the following formula :

[0061] , where d represents the dimension of the data, is the frequency-domain coefficient of a different resource block from , is expressed as the sum of and the serial number of a different current time-frequency resource block, n represents the index, and N represents the total number of times to calculate the correlation score.

[0062] Specifically, in this step, first establish a time-frequency correlation model for determining the information boundary of the communication-sensing signal under incomplete information. Assume that the communication-sensing signal has T time-domain symbols and K subcarriers within a time slot, then the formed time-frequency resource block set is where t represents the time direction and k represents the carrier coefficient direction. For each time-frequency resource block , estimate its complex channel response through the pilot signal and construct the eigenvector .

[0063] Due to the lack of high-precision channel state information in the incomplete channel, The channel cannot be accurately characterized, so a weighted time-frequency correlation mechanism is introduced to capture the subtle channel differences between different resource blocks to determine the information boundary. The information boundary is defined as the maximum boundary where the data types of sensing information and communication information can be distinguished in the joint communication and sensing design. For each resource block feature vector , a sensitivity factor S is set to meet the joint communication and sensing design tasks of the model for different resource allocations. When S = n , a linear transformation is performed on n weight matrices to obtain the time-domain coefficient and the frequency-domain coefficient : , , where , are known matrices.

[0064] Subsequently, for any two resource block nodes, the correlation score on the h weight matrix is calculated as follows:

[0065] , where d is the data dimension of the resource block, is expressed as the frequency-domain coefficient of a different time-frequency resource block from ;

[0066] After fusing them, the numerical value for constructing the edge weights between nodes of the graph problem and the information boundary between the communication and sensing carrier resource blocks is described as:

[0067] ;

[0068] In the process of homogeneous channel decoupling, it provides the information boundary between the carrier resource blocks corresponding to communication and sensing under incomplete information. Therefore it can be used to calculate the strength of the correlation between two current time-frequency resource blocks, thereby providing a basis for decoupling.

[0069] S2, construct a connected graph through the current time-frequency correlation and the current time-frequency resource block, solve the connected graph to obtain the current adaptive graph mapping, and solve the solution to the cutting problem of any two of the current time-frequency resource blocks regarding the adaptive graph mapping;

[0070] Furthermore, constructing a connected graph through the current time-frequency correlation and the current time-frequency resource block includes:

[0071] Set as the set V of qubits of the connected graph , and use the sum of the correlation scores as the connected graph Set V of qubits, connections between qubits.

[0072] Furthermore, solving the connected graph to obtain the current adaptive graph mapping includes:

[0073] Constructing the minimum cut problem of the edge weights of the connected graph;

[0074] Converting the minimum cut problem into a maximum cut problem and taking the maximum cut problem as the current adaptive graph mapping.

[0075] Furthermore, converting the minimum cut problem into a maximum cut problem includes:

[0076] Obtaining the maximum information boundary of the current edge weights , where ;

[0077] By Adjusting the maximum information boundary of the current edge weights to obtain the updated boundary of the edge weights , and constructing a maximum cut problem through the nodes corresponding to the updated boundary of the edge weights , where is a matrix of all 1s.

[0078] Furthermore, solving the solution to the cut problem regarding the adaptive graph mapping includes:

[0079] Mapping the nodes of the current adaptive graph mapping to the ground state quantum state, and the edge weights in the adaptive graph mapping E are determined by and mapped to the corresponding problem Hamiltonian;

[0080] Constructing a fully connected hypergraph from all the nodes and weights of the current adaptive graph mapping, taking the fully connected hypergraph as the input of quantum approximate optimization to obtain the cut problem of any two current time-frequency resource blocks regarding the current adaptive graph mapping, and solving the solution to the cut problem of any two current time-frequency resource blocks regarding the adaptive graph mapping.

[0081] This step mainly specifically realizes constructing an adaptive graph mapping based on the time-frequency correlation model of communication-sensing carriers under incomplete information.

[0082] First, set the time-frequency resource blocks in all time-frequency correlation models as independent nodes. Since each node is assigned a communication task or a sensing task in the communication-sensing joint design system, therefore, whether at the data flow level or the wireless link transmission level, the distinguishability between nodes should be maximally ensured to ensure that the communication-sensing task carriers are non-coupled, that is, the correlation between any two of them is relatively low. Through the correlation between any two can be determined.

[0083] Next, the homogeneous synaesthesia channel is decoupled using the cut problem, and an information boundary connectivity graph is introduced , where and are the set of synaesthesia nodes and the correlation weight value between nodes. The set serves as the set V of qubits of the connectivity graph , and the sum of the correlation scores is used as the connection between qubits in the set V of qubits of the connectivity graph . Since the edge weight value represents the correlation between time-frequency resource blocks, the minimum cut problem needs to be used to decouple the two groups with low correlation. However, in the application of the wide-coverage synaesthesia system, the minimum cut problem does not consider the node scale, and there is a situation where only an isolated node is considered in the cut scheme while ignoring other nodes, which does not conform to the ultimate purpose of this embodiment. Therefore, the minimum cut problem is transformed into the maximum cut problem to comprehensively analyze all synaesthesia nodes within the wide-coverage range. Considering transforming the minimum cut problem into the maximum cut problem, specifically: obtaining the maximum information boundary , assuming is a matrix of all 1s, then the updated edge weight is finally obtained as: . Since the above is to calculate the correlation score between one time-frequency resource block and other time-frequency resource blocks. At this time, calculate the correlation score between any time-frequency resource block and other time-frequency resource blocks to obtain multiple correlation scores, and then obtain the maximum value of the correlation scores as the maximum information boundary, or round up the maximum value of the correlation scores as the maximum information boundary. Therefore, it is obtained through calculation.

[0084] At this time, solving the maximum cut problem can obtain the solution of the current adaptive graph mapping for the connectivity graph.

[0085] S3. Obtain the synaesthesia signal at the subsequent moment, extract the subsequent time-frequency resource block of the synaesthesia signal at the subsequent moment, increment the current adaptive graph mapping through the subsequent time-frequency resource block to obtain the subsequent adaptive graph mapping, and obtain the solution of the cut problem of the subsequent time-frequency resource block through parameter iteration to complete the decoupling of any two current time-frequency resource blocks.

[0086] Furthermore, incrementing the current adaptive graph mapping through the subsequent time-frequency resource block includes:

[0087] Calculate the subsequent time-frequency correlation of the information boundary of the synaesthesia signal at the subsequent moment through any two subsequent time-frequency resource blocks;

[0088] Construct a connectivity graph through the subsequent time-frequency correlation and the subsequent time-frequency resource block, and solve the connectivity graph to obtain the subsequent adaptive graph mapping;

[0089] Obtain the subsequent maximum information boundary for constructing a connected graph of subsequent time-frequency resource blocks, and increment the nodes and weights of the current adaptive graph mapping through the subsequent maximum information boundary to obtain a subsequent adaptive graph mapping.

[0090] The purpose of this step is to formulate an incremental quantum approximate optimization algorithm to adaptively solve the strategy of the non-complete communication-sensing node connection graph. Specifically, for the decoupling of communication-sensing carriers under non-complete information in wide coverage, since the minimum cut problem cannot consider multi-node networks, the polynomial solution problem is transformed into the NP-hard problem (Non-deterministic Polynomial Hard) of the maximum cut problem, and a quantum heuristic algorithm, quantum approximate optimization, is used to solve this problem.

[0091] First, map the nodes in the communication-sensing carrier connection graph to the ground state quantum state, and the edge weights in the graph problem E are determined by and mapped to the corresponding problem Hamiltonian. The ground state energy of the quantum system is solved in the quantum state space formed for the maximum cut problem by applying an influence to the ground state quantum through a quantum rotation gate. The specific method is a prior art, and reference can be made to the existing application with the publication number CN119402831B and the name "A Method for Communication Sensing Integration Based on Quantum Approximate Optimization, Routing System".

[0092] Due to the non-completeness of information, this evolution process requires data update, resulting in an adaptive process. The specific steps are as follows:

[0093] Step 1, construct the simplest maximum cut problem model, that is, the initial communication-sensing carrier connection graph under non-complete information. Here, define the hybrid Hamiltonian as , and define the problem Hamiltonian as . Set all initial quantum states to uniform superposition states, construct the alternating action operation of the hybrid Hamiltonian and the problem Hamiltonian , and optimize the parameters with the help of a classical optimizer to find the optimal parameters in the quantum circuit, so that the measured cut expectation value reaches the maximum. Finally, after multiple layers of alternating evolution in the quantum circuit, measure the quantum state to obtain the cut problem of any two time-frequency resource blocks of the adaptive graph mapping, and solve the solution of the cut problem of any two time-frequency resource blocks of the adaptive graph mapping, that is, maximize the sum of the cross-group edge weights in the updated graph and the corresponding communication-sensing carrier decoupling scheme.

[0094] Step 2: Node update of the adaptive graph under incomplete information. Since the information between carriers is not obtained all at once under incomplete channel conditions, the graph structure and edge weights will be gradually improved as additional time-frequency information is supplemented. Extract the subsequent time-frequency resource blocks of the communication and sensing signal at the subsequent moment, then perform the same steps as S1 - S2, calculate the maximum information boundary C1 of the subsequent time-frequency resource blocks, and then increment the nodes and weights mapped by the current adaptive graph through the subsequent maximum information boundary to obtain the subsequent adaptive graph mapping.

[0095] Based on the above, a local update mechanism and a masking mechanism can be introduced. When new time-frequency carrier information or more refined correlation data is obtained, check the impact of the additional information on the size of the solution space during the evolution process. If the solution space remains unchanged, mask it so as not to affect other nodes, and finally form a new edge weight matrix and a new problem Hamiltonian. Specifically, if the maximum information boundary C1 of the subsequent time-frequency resource blocks is less than the preset threshold, discard the current update of this node.

[0096] Furthermore, obtaining the solution to the cutting problem of the subsequent time-frequency resource blocks through parameter iteration includes:

[0097] S31, solve the optimal parameters corresponding to the solution of the cutting problem of the current adaptive graph mapping;

[0098] S32, set the initial parameters of the cutting problem of the subsequent adaptive graph mapping to the optimal parameters;

[0099] S33, solve the solution of the cutting problem of the subsequent adaptive graph mapping to obtain the decoupling of any two current time-frequency resource blocks.

[0100] At this time, specifically, add after the above Step 2:

[0101] Step 3: Application of the incremental quantum approximate optimization algorithm. First, solve the optimal parameters corresponding to the solution of the cutting problem of the current adaptive graph mapping , and then set the initial parameters of the cutting problem of the subsequent adaptive graph mapping to . Under the new information, use a finite number of iterative optimizations to adjust the parameters to ensure that the quantum circuit state still corresponds to a cutting solution close to the global optimum. The updated quantum circuit will output a new maximum cut value, thus corresponding to the optimal communication and sensing carrier decoupling based on the information boundary under incomplete information, and obtaining the decoupling of any two current time-frequency resource blocks.

[0102] And when the improvement in the cut value brought by the subsequent adaptive graph mapping is lower than the predetermined threshold or the maximum number of iterations is reached, stop the update and output the current optimal solution.

[0103] Further provided is a routing system for a quantum approximate optimization algorithm under incomplete information, including:

[0104] A monitored terminal;

[0105] A monitoring terminal, configured to establish a communication connection with the monitored terminal and monitor the monitored terminal;

[0106] A computing host, configured to collect communication data packets of the monitored terminal through the monitoring terminal, and implement the method for processing communication and sensing data of the quantum approximate optimization algorithm under incomplete information when working.

[0107] Those skilled in the art should understand that the embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0108] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to the embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, and the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices generate means for implementing the functions specified in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0109] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory generate a manufactured article including an instruction device that implements the functions specified in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0110] Although the preferred embodiments of the present invention have been described, those skilled in the art can make additional changes and modifications once they learn the basic creative concepts. Therefore, the appended claims are intended to be construed as including the preferred embodiments and all changes and modifications falling within the scope of the present invention.

[0111] In the description of this specification, the description referring to terms such as "one embodiment", "some embodiments", "examples", "specific examples", or "some examples", etc. means that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic expressions of the above terms should not be understood as necessarily referring to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in a suitable manner in any one or more embodiments or examples. In addition, without contradiction, those skilled in the art can combine and combine the different embodiments or examples described in this specification and the features of different embodiments or examples.

Claims

1. A method for processing synaesthetic data of a quantum approximate optimization algorithm under incomplete information, characterized in that: It includes the following steps: S1, Obtain the synaesthesia signal at the current moment, extract the current time-frequency resource block of the synaesthesia signal at the current moment, and calculate the current time-frequency correlation of the information boundary of the synaesthesia signal at the current moment through any two current time-frequency resource blocks; S2, Construct a connectivity graph through the current time-frequency correlation and the current time-frequency resource block, solve the connectivity graph to obtain the current adaptive graph mapping, and solve the solution of the cutting problem of any two of the current time-frequency resource blocks with respect to the adaptive graph mapping; S3, Obtain the synaesthesia signal at a subsequent moment, extract the subsequent time-frequency resource block of the synaesthesia signal at the subsequent moment, increment the current adaptive graph mapping through the subsequent time-frequency resource block to obtain a subsequent adaptive graph mapping, and obtain the solution of the cutting problem of the subsequent time-frequency resource block through parameter iteration to complete the decoupling of any two current time-frequency resource blocks; Calculating the current time-frequency correlation of the information boundary of the synaesthesia signal at the current moment through any two current time-frequency resource blocks includes: Calculate the correlation score of any two current time-frequency resource blocks; Calculate the sum of all correlation scores, and use the sum of all correlation scores as the current time-frequency correlation; Calculating the correlation score of any two current time-frequency resource blocks includes: Construct the current time-frequency resource block as a set , where t represents the time direction, k represents the carrier coefficient direction, T represents the total number of time series, and K represents the total number of carrier coefficients; Calculate the current time-frequency resource block of the complex channel response , and construct the eigenvector of the complex channel response ; ; For the eigenvector perform a linear transformation on the weight matrix to obtain the time-domain coefficients and the frequency-domain coefficients , where , , 、 are the corresponding weight matrices; Calculate the correlation score of any two current time-frequency resource blocks through the following formula : , where d represents the dimension of the data, is related to the frequency-domain coefficients of different resource blocks, is expressed as the sum of the sequence numbers of different current time-frequency resource blocks, n represents the index, and N represents the total number of times to calculate the correlation score; Constructing a connectivity graph through the current time-frequency correlation and the current time-frequency resource block includes: Set As a connected graph For the set V of qubits, taking the sum of the correlation scores as a connected graph Inter - qubit connections in the set V of qubits 2. The method for processing communication-sensing data of a quantum approximate optimization algorithm under incomplete information according to claim 1, wherein: Solving the connectivity graph to obtain the current adaptive graph mapping includes: Construct the minimum cut problem of the edge weights of the connectivity graph; Convert the minimum cut problem into a maximum cut problem, and use the maximum cut problem as the current adaptive graph mapping.

3. The method for processing the synesthesia data of the quantum approximate optimization algorithm under incomplete information according to claim 2, characterized in that: Converting the minimum cut problem into a maximum cut problem includes: Obtain the maximum information boundary of the current edge weight , where ; By adjusting the maximum information boundary of the current edge weight to obtain the boundary of the updated edge weight , and constructing a maximum cut problem through the boundary of the updated edge weight corresponding edge weights and nodes, where is a matrix of all 1s 4. The general perception data processing method of the quantum approximate optimization algorithm under incomplete information according to claim 3, characterized in that: Solving the solution of the cutting problem with respect to the adaptive graph mapping includes: Map the nodes of the current adaptive graph mapping to the ground state quantum state, and the edge weights in the adaptive graph mapping E are determined by and mapped to the corresponding problem Hamiltonian; Construct all nodes and weights of the current adaptive graph mapping into a fully connected hypergraph, use the fully connected hypergraph as the input of quantum approximate optimization to obtain the cutting problem of any two current time-frequency resource blocks with respect to the current adaptive graph mapping, and solve the solution of the cutting problem of any two current time-frequency resource blocks with respect to the adaptive graph mapping.

5. The method for processing synaesthesia data of a quantum approximate optimization algorithm under incomplete information according to claim 1, characterized in that: Incrementing the current adaptive graph mapping through the subsequent time-frequency resource block includes: Calculate the subsequent time-frequency correlation of the information boundary of the synaesthesia signal at the subsequent moment through any two subsequent time-frequency resource blocks; Construct a connectivity graph through the subsequent time-frequency correlation and the subsequent time-frequency resource block, and solve the connectivity graph to obtain a subsequent adaptive graph mapping; Obtain the subsequent maximum information boundary for constructing the connectivity graph of the subsequent time-frequency resource block, and increment the nodes and weights of the current adaptive graph mapping through the subsequent maximum information boundary to obtain a subsequent adaptive graph mapping.

6. The method for processing synaesthesia data of a quantum approximate optimization algorithm under incomplete information according to claim 5, characterized in that: Obtaining the solution of the cutting problem of the subsequent time-frequency resource block through parameter iteration includes: S31, Solve the optimal parameter corresponding to the solution of the cutting problem of the current adaptive graph mapping; S32, Set the initial parameter of the cutting problem of the subsequent adaptive graph mapping as the optimal parameter; S33, Solve the solution of the cutting problem of the subsequent adaptive graph mapping to obtain the decoupling of any two current time-frequency resource blocks.

7. A routing system for a quantum approximate optimization algorithm under incomplete information, characterized in that: It includes: Monitored terminal A monitoring terminal, which is used to establish a communication connection with the monitored terminal and monitor the monitored terminal; A computing host, which is used to collect communication data packets of the monitored terminal through the monitoring terminal, and implements a communication perception data processing method of a quantum approximate optimization algorithm under incomplete information as described in any one of claims 1-6 during operation.

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