A communication perception integration method and routing system based on quantum approximate optimization

Abstract

The present invention relates to a communication perception integration method and routing system based on quantum approximate optimization, comprising: S1, monitoring the monitored terminal; S2, collecting communication data packets of the monitored terminal in a static environment, and separating static influence components of the static environment channel state information caused by static obstacles; S3, collecting communication data packets of the monitored terminal in a dynamic environment, extracting dynamic environment channel state information from the communication data packets, separating difference components from the dynamic environment channel state information through static influence components, constructing the difference components into a fully connected hypergraph, using the fully connected hypergraph as input of quantum approximate optimization, obtaining a cutting problem of different frequency subcarriers of the difference components, solving a solution to the cutting problem of the subcarriers, and using the solution to the cutting problem of the subcarriers as a dynamic influence component.

Description

Technical Field

[0001] The present invention relates to the field of communication perception, and specifically refers to a communication perception integration method and routing system based on quantum approximate optimization. Background Art

[0002] In physical space, the placement of objects in the space, or the movement and actions of people in the space, will affect the reflection and diffraction of signals. Wi-Fi sensing technology can collect state information feedback in the channel to infer factors such as the Doppler frequency shift under the influence of the speed of the transmitting and receiving ends, the multipath effect caused by the disturbance of human bodies and objects in the space, and changes in the natural environment.

[0003] In the prior art, generally, by mathematically modeling the environment, through the changing characteristics of the mobile phone channel state information and the different changes in the environment, by collecting a large amount of data sets annotated according to environmental changes, and using deep learning methods to obtain high-precision perception results when the training set is rich and high-quality. However, in the scene suitable for perception tasks, in general, the channel state information obtained by the acquisition device has problems such as uneven sampling rate, high noise, and large error. Under this condition, traditional wireless perception is limited by the following four aspects when relying on the channel state information of electromagnetic waves to realize perception and calculation functions: First, the scene of the application of perception function has the characteristics of complex and diverse spatial topological structure, and also has various channel interference factors. Traditional model-based perception devices cannot describe the actual scene as a mathematical model, and cannot use the model to correspond to the precise changes of the channel, thereby reducing the accuracy of perception. Second, if there are multiple perception targets in the environment, the degree of influence of the behavior of each independent perception target on the channel is relatively uniform. The existing model-based perception system cannot meet this demand, which brings difficulties to the actual realization of the perception function. Third, in scenarios where perception functions are urgently needed, the channel state information that can be provided often lacks sufficient accuracy due to equipment problems. Its inconsistent sampling rate, packet loss, large errors and other issues further affect the construction of traditional perception models. Fourth, the portability problem is difficult to achieve for different perception scenarios applicable to perception systems. Different environments and different perception targets have different impacts on the channel in the same model, and their performance characteristics will also be different. In an environment where high-precision channel state information cannot be provided, the same model is applied based on low-precision data, and the increase in error rate means that the accuracy of perception cannot be guaranteed.

[0004] The purpose of the present invention is to design a communication perception integrated method and routing system based on quantum approximate optimization in order to solve the above problems in the prior art. Summary of the invention

[0005] In view of the problems existing in the above-mentioned prior art, the present invention provides a communication perception integration method and routing system based on quantum approximate optimization, which can effectively solve at least one problem existing in the above-mentioned prior art.

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

[0007] A method for integrating communication and perception based on quantum approximate optimization, comprising the following steps:

[0008] S1, establishing a communication connection with a monitored terminal and monitoring the monitored terminal;

[0009] S2, collecting communication data packets of the monitored terminal in a static environment, extracting static environment channel state information from the communication data packets, and separating static influence components of the static environment channel state information caused by static obstacles;

[0010] S3, collecting communication data packets of the monitored terminal in a dynamic environment, extracting dynamic environment channel state information from the communication data packets, separating difference components from the dynamic environment channel state information through static influence components, constructing the difference components into a fully connected hypergraph, using the fully connected hypergraph as input for quantum approximate optimization, obtaining a cutting problem of different frequency subcarriers of the difference components, solving a solution to the cutting problem of the subcarriers, and using the solution to the cutting problem of the subcarriers as a dynamic influence component of the dynamic environment channel state information affected by dynamic obstacles.

[0011] Further, in step S2, separating the static impact component of the static environment channel state information caused by the static obstacles includes:

[0012] Components that are manifested as superposition of multipath signals with different amplitudes and phases, and drastic changes and regular reductions in signals are extracted from the static environment channel state information as the static influencing components.

[0013] Further, in step S3, separating the difference component from the dynamic environment channel state information by using the static impact component includes:

[0014] Numbering the data subcarriers in the channel state information;

[0015] Separating the subcarriers greatly affected by the static obstacles in the static impact components by using a data clustering algorithm to obtain the numbers of the subcarriers greatly affected by the static obstacles;

[0016] A data subcarrier corresponding to the subcarrier number is extracted from the dynamic environment channel state information.

[0017] Further, constructing the difference component into a fully connected hypergraph includes:

[0018] The correlation between the static impact component and the difference component is calculated, and different frequency subcarriers of the difference component are corresponded to nodes of the fully connected hypergraph according to the correlation, and edge weights in the fully connected hypergraph are assigned according to the correlation.

[0019] Further, calculating the correlation between the static impact component and the difference component includes:

[0020] performing autocorrelation operation on the static influence component and the difference component;

[0021] The autocorrelation result is weighted according to the frequency of the data subcarrier of the static influence component to obtain the correlation between the static influence component and the difference component.

[0022] Further, according to the correlation, corresponding different frequency subcarriers of the difference component to nodes of the fully connected hypergraph includes:

[0023] A preset number of different frequency subcarriers of the difference components with the lowest correlation are selected and correspond to nodes of the fully connected hypergraph.

[0024] Further, using the fully connected hypergraph as an input for quantum approximate optimization includes:

[0025] Mapping the fully connected hypergraph to a corresponding number of quantum bits;

[0026] Clustering the quantum bits into two types: dynamic influence and static influence;

[0027] The clustered quantum bits are used as input for quantum approximation optimization.

[0028] Furthermore, before using the fully connected hypergraph as an input for quantum approximate optimization, the following is performed: sparsely representing the static influence component and the difference component.

[0029] Further provided is a communication-aware integrated routing system based on quantum approximate optimization, comprising:

[0030] The monitored terminal;

[0031] A monitoring terminal, used to establish a communication connection with a monitored terminal and monitor the monitored terminal;

[0032] A computing host is used to collect communication data packets of the monitored terminal through the monitoring terminal, and implement the communication perception integration method based on quantum approximate optimization as described above during operation.

[0033] Furthermore, the monitoring terminal installs Nexmon firmware to collect and process the channel state information data packets of the monitored terminal.

[0034] Therefore, the present invention provides the following effects and / or advantages:

[0035] This application introduces a quantum approximate optimization algorithm to evolve the channel state information in a specific environment to adapt to different environments and overcome the problem of low-quality channel state information. By simulating the adiabatic evolution process of the quantum system, the objective function is constructed for a complex environment, and the changes in the channel can be interpreted from the perspective of energy. During the evolution process, the noise in the channel and the impact of objects in the static environment on the channel correspond to the changes in the channel gain coefficient, which are classified as low-dynamic static components. The gain coefficient changes caused by human body movement in the channel are divided into dynamic components. After the evolution is completed, the system stabilizes on the defined objective function, that is, the decoupling of the noise corresponding to the sub-channel, the static environment disturbance and the dynamic object disturbance is completed. This method not only enhances the adaptability of the model to different environments, avoids complex data processing and analysis, reduces the redundancy of data, but also improves the accuracy and robustness of perception tasks, and provides a new solution to deal with some application difficulties in Wi-Fi perception technology.

[0036] The present application separates the subcarriers greatly affected by static obstacles in the static impact components through a data clustering algorithm, and then extracts the data subcarriers corresponding to the subcarrier numbers in the dynamic environment channel state information according to the corresponding numbers, so that the subcarriers greatly affected by static obstacles are mapped to the subcarriers greatly affected by dynamic obstacles, and the components with large impact effects are extracted to reduce interference.

[0037] The present application corresponds the different frequency subcarriers of the difference component to the fully connected hypergraph, which can be read and calculated by quantum computing, and corresponds the different frequency subcarriers of the difference component to the nodes of the fully connected hypergraph according to the correlation, and assigns edge weights in the fully connected hypergraph according to the correlation, so that the result to be calculated in the present application is converted into a solution to the cutting problem in quantum computing, and the corresponding dynamic component is obtained by solving the cutting problem.

[0038] This application introduces a classic variational quantum optimization algorithm into the routing perception system, so as to optimize the problems that affect the perception accuracy in the perception scenario, such as the lack of high-precision channel state information and the difficulty of mathematical modeling of the environment. First of all, for the above scenarios, this system does not require traditional medium-to-large-scale antenna arrays to complete the collection of high-precision channel state information. It can only use existing commercial routers in the environment (the firmware can be modified to achieve the collection of channel state information) and low-cost and easily accessible mobile computing units to complete the collection of channel data. Low cost, convenience of hardware acquisition, and providing a certain system transplantation capability are also the advantages of the present invention.

[0039] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention are realized and obtained by the structures particularly pointed out in the description and the drawings.

[0040] It is to be understood that both the foregoing general description and the following detailed description of the present invention are exemplary and explanatory and are intended to provide further explanation of the invention as claimed. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 A schematic diagram of a flow chart provided for an embodiment of the present invention.

[0042] Figure 2 Schematic diagram of original perception data and perception data optimized by the method provided by the present invention. DETAILED DESCRIPTION

[0043] In order to facilitate understanding by those skilled in the art, the present invention is now described in further detail by way of examples:

[0044] refer to Figure 1 , a communication perception integration method based on quantum approximate optimization, comprising the following steps:

[0045] S1, establishing a communication connection with a monitored terminal and monitoring the monitored terminal;

[0046] In daily scenarios, widely distributed channel state information collection devices include commercial traditional routers, small computing units, and even specific models of personal mobile terminals, etc. They can communicate with other Internet devices in the available intranet with the help of local area networks. To perceive the required channel state information, the monitoring device (data capture device) can monitor the traffic broadcast of the monitored terminal in the local area network, thereby monitoring the monitored terminal.

[0047] In this embodiment, the carrier for monitoring the monitored terminal through the monitor can be a mobile computing unit, such as an MCU unit represented by Raspberry Pi and ESP32, a router, etc. The monitored terminal can be any device that can perform wireless communication in a local area network, such as a router, etc. The monitor first needs to be associated with a traditional router, and the specific steps can be:

[0048] In the first step, devices that support channel status information collection, such as routers and mobile computing units, are used as listeners, and the Ethernet port is connected to an available Ethernet port of the monitored terminal through an Ethernet cable. The client and the computing host performing debugging operations are connected to the LAN in the sensing environment through the access point, and the communication link is now established. At this point, the provider of the LAN will act as a DHCP server and automatically assign IP and MAC addresses to the connected computing host, client, and listener.

[0049] The second step is to ensure that the client and the listener are connected through Ethernet and that the client is on the same LAN as the computing host for further signal processing.

[0050] The third step is to enable the monitor to collect channel status information. This embodiment uses the Nexmon firmware installed on the monitor to realize the collection and processing of Wi-Fi channel status information packets. Among them, the Nexmon system is a firmware modification project for Wi-Fi devices based on Broadcom Wi-Fi chips, which is a direct adoption of existing technologies. This Nexmon firmware can specify the specific channels, ports, number of samples of data packets and address sources (specified clients) collected in the local area network.

[0051] In the fourth step, when the client, the host, and the monitor have established a basic network connection in the same local area network, they can communicate with each other through the TCP-IP protocol, which means that the data collection environment has been established and the monitored terminal can be monitored.

[0052] S2, collecting communication data packets of the monitored terminal in a static environment, extracting static environment channel state information from the communication data packets, and separating static influence components of the static environment channel state information caused by static obstacles;

[0053] Collecting the communication data packets of the monitored terminal may specifically include:

[0054] S2.1, enable the monitoring function of the system, that is, the data collection function, in the monitor, and specify the device you want to monitor.

[0055] S2.2, remotely connect to the listener from another local computer or device through the SSH protocol, and specify the corresponding key so that the listener cannot respond to other communication requests while monitoring. It is necessary to use a one-to-one mapping key to avoid requests and ensure the continuity of monitoring.

[0056] S2.3, collect communication data packets through the host control monitor. This step requires configuring the MAC address of the collected device in the monitor, and setting the channel and channel bandwidth in the monitoring channel parameters. These collected data packets are stored in the storage area of ​​the monitor.

[0057] S2.4, the host retrieves the collected communication data packets from the monitor storage area and processes the collected communication data packets, which contain channel state information (CSI).

[0058] The following describes how to extract the static environment channel state information from the communication data packet and separate the static impact component of the static environment channel state information caused by static obstacles.

[0059] The environment where the monitored terminal is located generally includes the following situations: the first is the line-of-sight path component, that is, the client signal directly reaches the monitor without any obstruction or the component that is not greatly affected by the obstruction. The second is the non-line-of-sight multipath component, that is, there are static obstacles between the client and the monitored terminal, such as tables, cabinets, etc. The third is environmental noise. The fourth is that there are dynamic obstacles between the client and the monitored terminal, such as human movement.

[0060] Channel state information (CSI) is used to describe the change of channel state over time, so there is a corresponding CSI value at each time point. Since the channel state information (CSI) carried by each data subcarrier has a corresponding phase value and an amplitude value at each time point, the channel state information (CSI) carried by 234 data subcarriers has a total of 234 phase values ​​and 234 amplitude values ​​at each time point.

[0061] In this embodiment, data collection is first performed in a static environment, that is, the environment in which the monitored terminal is located does not contain dynamic obstacles. In this case, since the subcarrier propagates freely in the wireless link, this embodiment separates the first to third environmental conditions mentioned above through different channel correlations and statistical features. Specifically, since the channel in a relatively static environment is relatively stable, the environmental components reflected by the channel information can be divided into three types: the first is the line-of-sight path component, that is, the client signal directly reaches the monitor without containing components that are not greatly affected by occlusion or occlusion factors. The second is the non-line-of-sight multipath component. This signal component is manifested as: different carriers pass through obstacles (natural entities in the environment) resulting in different propagation paths and different arrival times. The performance on the receiver is mostly multipath signal superposition with different amplitudes and phases, and the signal changes dramatically and the regularity decreases. The third is environmental noise, which generally has a specific probability distribution in a specific environment and can generally be considered as a Gaussian distribution. Therefore, according to this statistical feature, the components of the second situation can be extracted.

[0062] S3, collecting communication data packets of the monitored terminal in a dynamic environment, extracting dynamic environment channel state information from the communication data packets, separating difference components from the dynamic environment channel state information through static influence components, constructing the difference components into a fully connected hypergraph, using the fully connected hypergraph as input for quantum approximate optimization, obtaining a cutting problem of different frequency subcarriers of the difference components, solving a solution to the cutting problem of the subcarriers, and using the solution to the cutting problem of the subcarriers as a dynamic influence component of the dynamic environment channel state information affected by dynamic obstacles.

[0063] Specifically, this embodiment introduces quantum approximate optimization and uses the principle of quantum thermal insulation evolution to distinguish and separate the dynamic components in the coupled environmental signals. In this step, the data is collected in a dynamic environment, which contains interference such as human movement, animal movement, and human action. The situation is very complicated, and the correlation between the subcarriers of the orthogonal frequency division multiple access waveform at the bottom of the physical layer of the WiFi protocol family based on 802.11 is complex. Therefore, the method of separating the static influencing components in S2 is not suitable for the different multipath components contained in the second component in a complex environment. At the same time, the orthogonality of the frequency division here also provides rationality for the subsequent construction of the hypergraph as an independent node.

[0064] Therefore, the method for separating the static influencing components in S2 is not suitable for the different multipath components contained in the second component in a complex environment. First, in this step, the communication data packets collected from the monitored terminal in a static environment and the communication data packets collected from the monitored terminal in a dynamic environment can be compared to obtain the difference components. Then, this step constructs the difference components into a fully connected hypergraph, which can be used as the input of the quantum approximate optimization algorithm and thus calculated by the optimization algorithm. After the quantum approximate optimization algorithm is performed, a cutting problem can be obtained. Since the subcarriers of the communication data packets in the area are cutting problems, the optimization result is obtained by solving the solution to the cutting problem. After the optimization is completed, the final quantum circuit is used to measure the state of the quantum bit, and the segmentation result is obtained according to the binary state. This result can be used as the dynamic influencing component.

[0065] In summary, since the channel in a relatively static environment is relatively stable, the above-mentioned linear operation of separating the corresponding dimensions by correlation of different subcarriers is also reasonable. However, in an environment with entity intervention, that is, in a dynamic scenario where the actual perception function is applied, especially for static influencing components, changes caused by dynamic entity intervention, and changes caused by natural obstacles in the environment, their statistical characteristics are similar, resulting in simple linear operations that cannot directly separate different components. At the same time, this is a non-deterministic polynomial problem (NP-HARD), which cannot be solved efficiently by classical algorithms. Therefore, this embodiment introduces a quantum approximate optimization algorithm to provide a new optimization strategy. This method can distinguish and separate the subcarriers in the static influencing components that are affected by static and dynamic entities in the environment and produce different changes due to frequency selectivity. Since the separated subcarriers have richer information, the practical application limitations caused by poor data accuracy can be optimized while reducing information redundancy.

[0066] Next, we will introduce how to implement the specific details of each step.

[0067] Further, in step S2, separating the static impact component of the static environment channel state information caused by the static obstacles includes:

[0068] Components that are manifested as superposition of multipath signals with different amplitudes and phases, and drastic changes and regular reductions in signals are extracted from the static environment channel state information as the static influencing components.

[0069] The statistical characteristics of the static influence component are analyzed in Shanshu and are manifested as follows: different carriers pass through obstacles (natural entities in the environment) resulting in different propagation paths and different arrival times. The performance at the receiver is mostly the superposition of multipath signals with different amplitudes and phases, and the signal changes dramatically and the regularity decreases. The characteristics of the other first and third cases are quite different, so the static influence component can be separated by statistical characteristics.

[0070] Further, in step S3, separating the difference component from the dynamic environment channel state information by using the static impact component includes:

[0071] Numbering the data subcarriers in the channel state information;

[0072] Separating the subcarriers greatly affected by the static obstacles in the static impact components by using a data clustering algorithm to obtain the numbers of the subcarriers greatly affected by the static obstacles;

[0073] A data subcarrier corresponding to the subcarrier number is extracted from the dynamic environment channel state information.

[0074] First, the original data in the channel state information is 234 data subcarriers in the OFDM system excluding the zero-frequency subcarrier and the pilot subcarrier. The subcarriers may be numbered according to the 802.11ac Wi-Fi technical standard, from negative to positive, and the order mentioned in the subsequent processing is also based on the numbering from negative to positive. The removed subcarrier numbers are: zero-frequency subcarrier: index 0; pilot subcarrier: index ±7, ±21, ±43, ±57; guard subcarrier: index -128 to -123 and 123 to 127.

[0075] Then, first, in a selected environment, keep the environment static (except for the entity of the environment itself, no entity is added) to collect the channel state information of the scene. The 234 phase values ​​of the channel state information carried by the 234 data subcarriers after basic processing are correlated and evaluated in sequence according to the number, and the subcarriers propagate freely in the wireless link. Since the channel in a relatively static environment is relatively stable, the above correlation is performed on 234 different subcarriers to separate the static influence components. Since subcarriers of different frequencies have different effects on static obstacles, the degree of obvious signal changes they reflect is also different. By analyzing the static influence components, the subcarrier numbers that are most affected by static obstacles are extracted, and the subcarriers corresponding to the numbers will be more obvious when they are affected by dynamic influences.

[0076] Optionally, data preprocessing is performed on the phase and amplitude of the channel state information carried by the 234 data subcarriers respectively.

[0077] Furthermore, in step S3, constructing the difference components into a fully connected hypergraph includes:

[0078] The correlation between the static impact component and the difference component is calculated, and different frequency subcarriers of the difference component are corresponded to nodes of the fully connected hypergraph according to the correlation, and edge weights in the fully connected hypergraph are assigned according to the correlation.

[0079] The correlation between the static impact component and the difference component is calculated, so that several subcarriers with the smallest correlation coefficient can be screened out as hypergraph nodes.

[0080] Further, calculating the correlation between the static impact component and the difference component includes:

[0081] performing autocorrelation operation on the static influence component and the difference component;

[0082] The autocorrelation result is weighted according to the frequency of the data subcarrier of the static influence component to obtain the correlation between the static influence component and the difference component.

[0083] Specifically, in this step, all difference components With static impact components Perform autocorrelation operation and assign the correlation coefficient as weight Wi to In order to expand the difference, according to the weight Specifically, the closer the carrier number is, the higher the weighting coefficient is, and at this time, the difference between the two can be magnified, so that the degree of separation of the correlation is increased.

[0084] Further, according to the correlation, corresponding different frequency subcarriers of the difference component to nodes of the fully connected hypergraph includes:

[0085] A preset number of different frequency subcarriers of the difference components with the lowest correlation are selected and correspond to nodes of the fully connected hypergraph.

[0086] In this embodiment, subcarriers with the smallest correlation coefficient less than or equal to 16 are selected as hypergraph nodes. The edge weight between nodes is the difference between the correlation coefficients between adjacent nodes.

[0087] Further, using the fully connected hypergraph as an input for quantum approximate optimization includes:

[0088] Mapping the fully connected hypergraph to a corresponding number of quantum bits;

[0089] Clustering the quantum bits into two types: dynamic influence and static influence;

[0090] The clustered quantum bits are used as input for quantum approximation optimization.

[0091] In this step, the above X subcarriers are mapped to X qubits, and the variables are defined As the category of binary quantum bits, 0 and 1 correspond to the quantum bits representing static influence and dynamic influence respectively. The K-means data clustering algorithm is used to separate the carriers greatly affected by the multipath component from the dynamic environment channel state information.

[0092] The clustered quantum bit input as the input of quantum approximation optimization can be specifically as follows:

[0093] The cost Hamiltonian and the mixed Hamiltonian of the cutting problem are constructed as follows: . in, is the Z operator for the ith and jth qubits, is the weight of the edge (i, j) of the hypergraph, and E is the set of nodes in the hypergraph. The weight value is defined as the difference between the weighted sum of the autocorrelation coefficients between subcarriers in the static environment and the dynamic environment. , X is the number of quantum bits, where the sum of X operations on X quantum bits is calculated. Then, in order to construct the quantum gate circuit, all quantum bits are initialized to a uniform superposition state. ,Right now

[0094] ;

[0095] Here H is the gate structure used to entangle two quantum states. is the tensor product, is the ground state 0.

[0096] The uniform superposition state is used as the input of quantum approximate optimization. Each layer of the quantum gate circuit structure applies a problem Hamiltonian and a mixed Hamiltonian evolution to the input: and , where β and γ are classical parameters, e is the natural index, relying on the optimization of classical algorithms, the present invention uses the quasi-Newton method to optimize and . That is, maximize the expected value: The optimization steps are given below:

[0097] 1. Initialization parameters: Randomly select the initial and β value.

[0098] 2. Run the quantum circuit: Run the QAOA circuit according to the current parameters, measure the quantum bits, and estimate ⟨𝐶>.

[0099] 3. Update parameters: Use the optimizer to update according to the measurement results and β.

[0100] 4. Iteration: Repeat the above process until convergence or the predetermined number of iterations is reached.

[0101] Furthermore, before using the fully connected hypergraph as an input for quantum approximate optimization, the following is performed: sparsely representing the static influence component and the difference component.

[0102] Since the data will be expressed in a sparse manner, the frequency offset (CFO) and clock error caused by the device will not be considered. The quantum optimization algorithm introduced in this embodiment, since the basic unit of the quantum algorithm is the quantum bit, theoretically the quantum bit processed by the quantum computer can be very large, but in reality, the number of quantum bits for systems with medium noise scale is generally about 100 quantum bits. Using quantum optimization algorithms to process classical data requires considering a certain time cost, and in practical applications, consider mapping data to fewer quantum bits.

[0103] The presence of a moving entity (entity includes a perceived object, a perceived target, which may be a person, an object, an animal, etc.) in the perception scene of the present invention or an entity in the perception area of ​​the present invention will cause changes in the channel state information at one or more time points. Therefore, in data processing and subsequent analysis, not only the data packet at one time point should be processed, but also all data in the overall perception time range should be processed. However, one time point cannot fully describe continuous actions in the time domain, and too long a time will lead to the loss of perception opportunities and a decrease in signal processing efficiency. Therefore, multiple time points are analyzed together and sparsely represented.

[0104] The specific steps of the sparse expression rule of this embodiment are introduced below:

[0105] First of all, it should be stated that the sparseness process is performed on all perception data sets collected in the overall time range, and continuously tracks CSI changes on the entire time axis. Take a perception cycle as an example, that is, a perception cycle of a metasequence is X seconds, and a perception cycle contains M CSI phase values ​​corresponding to M time points that have been pre-processed. Because the data is collected in chronological order, these M CSI phase values ​​are also arranged in chronological order. Therefore, in the subsequent processing, the first CSI phase value represents the perception starting point, and the last CSI phase value represents the last time point in the continuous range of a metasequence, and so on.

[0106] Cluster the M CSI phases in each group of N sub-frequencies (e.g. N=234 after removing pilot and blank frequencies in 802.11ac), with the number of CSI phase values ​​in each group being M. The grouping is as follows (note that basic processing has been done): The first group, corresponding to the first component, first sets the number of clusters K=2, and finds the first five vectors with the smallest variance as the clustering input . Apply K-means clustering algorithm to other one-dimensional vectors When and When they are not in the same cluster, they are divided into the second and third components, namely NLOS and noise, and the rest are the first component. The second and third components are the subcarriers that are more affected by noise after removing the first component, and the present embodiment uses the ICA algorithm to separate them. First, the signal mean of the first component is removed from the second and third components to reduce the data dimension, and the Gaussian distribution is fitted to the remaining subcarrier column vectors. The fitting threshold g is set. After classification by the fastICA algorithm, those greater than g are classified as the third component, and the rest are the second component. The vector matrices of the three components are separated, only the components themselves have values, and the rest are 0. So far, the sparse CSI has been obtained.

[0107] Further provided is a communication-aware integrated routing system based on quantum approximate optimization, comprising:

[0108] The monitored terminal;

[0109] A monitoring terminal, used to establish a communication connection with a monitored terminal and monitor the monitored terminal;

[0110] A computing host is used to collect communication data packets of the monitored terminal through the monitoring terminal, and implement the communication perception integration method based on quantum approximate optimization as described above during operation.

[0111] Furthermore, the monitoring terminal installs Nexmon firmware to collect and process the channel state information data packets of the monitored terminal.

[0112] After the wireless link is established, the host (loaded with any system that can be connected to the monitor via Ethernet) can collect and process data packets and extract and analyze perception information. The present invention uses Nexmon to enable the monitor and monitor the specified port, IP address and corresponding MAC address. In this embodiment, a router (ASUSac86u) can be used to mount a USB flash drive and enable the Server Messages Block (samba) service. The host can directly access the file through it without frequently operating the storage area of ​​the monitor. Once the collection process is carried out, the present invention can build a perception link with the device of the collected data packet under the condition of maintaining a communication state with the device to form a perception area. At this time, the collected data packet contains various state change information in the perception scene (the collected device can be other modified routers and mobile computing units, or the mobile computing unit itself for collection, or daily communication devices such as mobile phones and PCs, and multiple devices can be collected at the same time to form a wide-coverage communication perception integrated system).

[0113] Experimental Results

[0114] According to the method or system provided in this embodiment, a group of subcarriers that characterize the dynamic impact of the environment can be obtained by using the quantum approximate optimization results (cutting scheme), thereby completing the decoupling of the environment. For subsequent judgment, only the subcarriers separated by evolution need to be analyzed, which reduces the data dimension and data accuracy requirements for WiFi perception.

[0115] refer to Figure 2 , a listener M is placed in an environment E_d where there is frequent flow of people. For a specified client, the phase value of the Wi-Fi channel state information based on the 802.11ac protocol is displayed in the sub-picture above the picture. In the current environment, there are many entities and the environment is complex. At the same time, the multi-carrier brought by 80Mhz also leads to a large data dimension. Based on the quantum approximate optimization algorithm, through the separation of dynamic influence components introduced in steps S1-S3, it is shown that it has excellent results. After evolution in the system of the present invention, it is obtained Figure 2 The carrier data of the lower sub-image in the figure has a greatly reduced dimension of perception data and is highly sensitive to moving objects. Therefore, the method or system provided in this embodiment provides an optimization strategy for perception tasks in complex environments.

[0116] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may 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.

[0117] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as 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 a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0118] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0119] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0120] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms should not be understood as necessarily being directed to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, unless they are contradictory.

Claims

1. A communication and perception integration method based on quantum approximate optimization, characterized by: The following steps are involved: S1, establishing a communication connection with a monitored terminal and monitoring the monitored terminal; S2, collecting communication data packets of the monitored terminal in a static environment, extracting static environment channel state information from the communication data packets, and separating static influence components of the static environment channel state information caused by static obstacles; S3, collecting communication data packets of the monitored terminal in a dynamic environment, extracting dynamic environment channel state information from the communication data packets, separating difference components from the dynamic environment channel state information through static influence components, constructing the difference components into a fully connected hypergraph, using the fully connected hypergraph as an input for quantum approximate optimization, obtaining a cutting problem of different frequency subcarriers of the difference components, solving a solution to the cutting problem of the subcarriers, and using the solution to the cutting problem of the subcarriers as a dynamic influence component of the dynamic environment channel state information affected by dynamic obstacles; Separating the difference component from the dynamic environment channel state information by the static impact component includes: Numbering the data subcarriers in the channel state information; Separating the subcarriers greatly affected by the static obstacles in the static impact components by using a data clustering algorithm to obtain the numbers of the subcarriers greatly affected by the static obstacles; Extracting the data subcarrier corresponding to the subcarrier number from the dynamic environment channel state information; Constructing the difference components into a fully connected hypergraph includes: The correlation between the static impact component and the difference component is calculated, and different frequency subcarriers of the difference component are corresponded to nodes of the fully connected hypergraph according to the correlation, and edge weights in the fully connected hypergraph are assigned according to the correlation.

2. According to claim 1, a communication perception integration method based on quantum approximate optimization is characterized in that: In step S2, separating the static impact component of the static environment channel state information caused by the static obstacles includes: Components that are manifested as superposition of multipath signals with different amplitudes and phases, and drastic changes and regular reductions in signals are extracted from the static environment channel state information as the static influencing components.

3. The method for integrating communication and perception based on quantum approximate optimization according to claim 1, characterized in that: Calculating the correlation between the static impact component and the difference component includes: performing autocorrelation operation on the static influence component and the difference component; The autocorrelation result is weighted according to the frequency of the data subcarrier of the static influence component to obtain the correlation between the static influence component and the difference component.

4. The method for integrating communication and perception based on quantum approximate optimization according to claim 3, characterized in that: According to the correlation, corresponding different frequency subcarriers of the difference component to nodes of the fully connected hypergraph includes: A preset number of different frequency subcarriers of the difference components with the lowest correlation are selected and correspond to nodes of the fully connected hypergraph.

5. The method for integrating communication and perception based on quantum approximate optimization according to claim 4, characterized in that: Using the fully connected hypergraph as input for quantum approximate optimization includes: Mapping the fully connected hypergraph to a corresponding number of quantum bits; Clustering the quantum bits into two types: dynamic influence and static influence; The clustered quantum bits are used as input for quantum approximation optimization.

6. The method for integrating communication and perception based on quantum approximate optimization according to claim 1, characterized in that: Before using the fully connected hypergraph as an input for quantum approximate optimization, the following is performed: sparsely representing the static influence component and the difference component.

7. A communication-aware integrated routing system based on quantum approximate optimization, characterized in that: include: The monitored terminal; A monitoring terminal, used to establish a communication connection with a monitored terminal and monitor the monitored terminal; A computing host is used to collect communication data packets of the monitored terminal through the monitoring terminal, and implements a communication perception integration method based on quantum approximate optimization as described in any one of claims 1 to 6 when working.

8. According to claim 7, a communication-aware integrated routing system based on quantum approximate optimization is characterized in that: The monitoring terminal is installed with Nexmon firmware to collect and process the channel status information data packets of the monitored terminal.

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