A multi-device ad-hoc networking communication method, device, and storage medium

Abstract

The present invention relates to a multi-device ad-hoc network communication method, device, and storage medium. By collecting the magnetic field intensity vector of the device in real time and calculating the magnetic field gradient tensor, a medium polarization intensity distribution map is generated, which can accurately monitor and analyze the polarization phenomenon. When the detected polarization angle deviation exceeds the threshold, the system can respond quickly and automatically send a polarization compensation instruction to the relevant device. By extracting the multipath components and the historical polarization residual components, the failure of traditional channel estimation methods is avoided, and the prediction and compensation capabilities of the channel are optimized. The combination of the establishment of a dynamic compensation codebook and the phase pre-compensation technology uses the symbol error feedback from the receiving end to optimize the phase adjustment process and improve the compensation accuracy. By constructing a polarization memory attenuation prediction model, the channel state change can be learned and predicted in real time, providing an efficient compensation strategy for the system, adapting to the communication needs of each device, ensuring the stable transmission of signals, and improving the communication performance and reliability of the system.

Description

Technical Field

[0001] The present invention relates to a multi-device ad-hoc networking communication method, device and storage medium, belonging to the technical field of networking communication. Background Art

[0002] When ad-hoc networking devices are deployed in an atmospheric environment containing ferromagnetic particles (such as sandstorms, industrial dust), the suspended particles will produce a polarization memory phenomenon under the action of an alternating electromagnetic field. The remanent magnetization intensity of the particles will continuously affect the propagation characteristics of subsequent electromagnetic waves, forming a channel polarization memory effect with time correlation.

[0003] This effect causes traditional channel estimation methods to fail because the channel state at the current moment actually contains the polarization residue of the medium left over from the previous communication process, resulting in continuous distortion in signal propagation.

[0004] Therefore, how to mitigate the polarization memory phenomenon of device communication and improve the accuracy in signal propagation is an urgent problem to be solved currently. Summary of the Invention

[0005] In order to solve the problems existing in the above-mentioned prior art, the present invention proposes a multi-device ad-hoc networking communication method, device and storage medium.

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

[0007] On the one hand, the present invention provides a multi-device ad-hoc networking communication method, including the following steps:

[0008] Collect the magnetic field intensity vector at the location of each device in the ad-hoc network, generate a medium polarization intensity distribution map based on the magnetic field intensity vector, monitor the area in the medium polarization intensity distribution map that exceeds the preset polarization angle deviation threshold, and send a polarization compensation signal to the devices in this area;

[0009] Group the devices that receive the polarization compensation signal, perform independent analysis on each device group, extract the multipath components in the communication signal of the communication channel of the device group that exceed the preset channel coherence time threshold, and separate the channel response and the historical medium polarization residue component in the multipath components;

[0010] Construct a compensation codebook based on the historical medium polarization residue component of the current device group. The compensation codebook includes the information of the historical medium polarization residue component and the corresponding pre-distortion phase rotation amount. Query the corresponding pre-distortion phase rotation amount in the compensation codebook according to the current medium polarization residue component, perform phase compensation on the symbol to be transmitted, and then control the signal transmitter to transmit the symbol to the corresponding device group;

[0011] Calculate the error vector magnitude between the transmitted symbols received by the computing device group and the transmitted symbols sent by the signal transmitter. Update the corresponding pre-distortion phase rotation amount in the compensation codebook of the device group based on the error vector magnitude. The signal transmitter re-performs phase compensation on the transmitted symbols according to the compensation codebook and then sends them to the device group. Repeat the iteration until the error vector magnitude reaches the preset error vector magnitude threshold, and then stop the iteration to obtain the optimal pre-distortion phase rotation amount of the device group under the current medium polarization residual component.

[0012] Before communicating with any device group, the signal transmitter queries the corresponding pre-distortion phase rotation amount in the compensation codebook through the current medium polarization residual component of the device group, performs phase compensation on the transmitted symbols, and then sends them to the corresponding device group.

[0013] Preferably, the method further includes the following steps:

[0014] Construct a polarization memory decay prediction model based on a machine learning model. Construct a training sample set based on the historical polarization residual components of the device group. Train the polarization memory decay prediction model through the training sample set. The trained polarization memory decay prediction model outputs the polarization memory decay rate corresponding to the device group.

[0015] Calculate the future medium polarization residual component of the current device group through the polarization memory decay rate. Query the pre-distortion phase rotation amount in the compensation codebook of the device group based on the future medium polarization residual component. Perform phase pre-compensation on the future transmitted signal of the signal transmitter based on the queried pre-distortion phase rotation amount.

[0016] Preferably, the steps for constructing the medium polarization intensity distribution map are as follows:

[0017] Filter the magnetic field intensity vector of each device in the ad hoc network through the Kalman filtering algorithm.

[0018] Fuse the magnetic field intensity vector of each device and its corresponding position coordinates to obtain the magnetic field intensity distribution data of the ad hoc network devices.

[0019] Generate a medium polarization intensity distribution map through a spatial interpolation algorithm based on the magnetic field intensity distribution data, the magnetic field gradient tensor, and the device topology information.

[0020] Preferably, the specific steps for monitoring the area in the medium polarization intensity distribution map that exceeds the preset polarization angle deviation threshold are as follows:

[0021] Divide the medium polarization intensity distribution map into the same number of areas as the number of devices according to the device positions.

[0022] Calculate the polarization angles of each area in the medium polarization intensity distribution map, as shown in the following formula:

[0023] ;

[0024] Wherein: represents the polarization angle of any region; represents the dielectric polarization intensity of this region in the direction; represents the dielectric polarization intensity of this region in the direction;

[0025] If the polarization angle of the current region is greater than the preset polarization angle deviation threshold, a polarization compensation signal is sent to the device in this region.

[0026] Preferably, the specific steps for grouping the devices that receive the polarization compensation signal are as follows:

[0027] Collect the polarization parameters of the devices that receive the polarization compensation signal, and the polarization parameters include polarization angle, polarization mode, and degree of polarization;

[0028] For any two devices, calculate the polarization orthogonality between the devices according to their polarization parameters, as shown in the following formula:

[0029] ;

[0030] Wherein: represents the polarization orthogonality between device and device ; represents the polarization angle of device ; represents the polarization angle of device ; represents the maximum range of the polarization angle;

[0031] Construct a device status correlation matrix based on the polarization orthogonality between the devices, as shown in the following formula:

[0032] ;

[0033] Wherein: represents the device status correlation matrix; represents the total number of devices;

[0034] Sort the devices according to the order of the polarization orthogonality in the device status correlation matrix, and then use the graph theory optimal matching algorithm to group the devices.

[0035] Preferably, analyze the communication signals received by the device group, analyze the time interval at which the communication signal components on different paths arrive at the device group, extract the communication signal components with a time interval greater than the preset channel coherence time threshold, and combine them into multipath components;

[0036] Construct a dual-channel filter based on a filtering algorithm. One channel is used to filter the channel response in the multipath components, and the other channel is used to filter the historical medium polarization residual components in the multipath components to obtain the separated channel response and historical medium polarization residual components.

[0037] Preferably, the specific calculation steps for the pre-distortion phase rotation amount in the compensation codebook are as follows:

[0038] Calculate the normalized ratio of the channel response and the historical medium polarization residual components in the multipath components, as shown in the following formula:

[0039] ;

[0040] Where: represents the normalized ratio of the channel response and the historical medium polarization residual components in the multipath components at time represents the normalized channel response in the multipath components at time represents the normalized historical medium polarization residual components in the multipath components at time

[0041] Convert the normalized ratio of the channel response and the historical medium polarization residual components in the multipath components into a pre-distortion phase rotation amount through a non-linear function, as shown in the following formula:

[0042] ;

[0043] Where: represents the pre-distortion phase rotation amount at time represents the scaling factor; represents the non-linear function.

[0044] Preferably, update the corresponding pre-distortion phase rotation amount in the compensation codebook of the device group based on the error vector magnitude, as shown in the following formula:

[0045] ;

[0046] ;

[0047] Where: represents the error vector magnitude; represents the total number of symbols received by the device group; represents the th symbol value received by the device group; represents the th original value of the symbol; represents the Updated value of the pre-distortion phase rotation amount after the Indicates the Pre-distortion phase rotation amount at the Indicates the learning rate.

[0048] On the other hand, the present invention also provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, the method described in the present invention is implemented.

[0049] On yet another aspect, the present invention also provides a computer-readable storage medium, on which a computer program is stored. When the program is executed by a processor, the method described in the present invention is implemented.

[0050] The present invention has the following beneficial effects:

[0051] 1. By collecting the magnetic field intensity vector of the device in real time and calculating the magnetic field gradient tensor, the present invention generates a medium polarization intensity distribution map, which can accurately monitor and analyze the polarization phenomenon.

[0052] 2. The present invention combines the establishment of a dynamic compensation codebook with phase pre-compensation technology, and uses the symbol error feedback from the receiving end to optimize the phase adjustment process, forming a closed-loop feedback control to improve the compensation accuracy.

[0053] 3. By constructing a polarization memory attenuation prediction model, the present invention can learn and predict the channel state change in real time, provide an efficient compensation strategy for the system, adapt to the communication requirements of each device, ensure the stable transmission of signals, and improve the communication performance and reliability of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 Is a flowchart of the method of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0055] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0056] It should be understood that the step numbers used in the text are only for convenience of description and do not limit the execution order of the steps.

[0057] It should be understood that the terms used in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the specification of the present invention and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include the plural forms.

[0058] The terms "comprising" and "including" indicate the presence of the described features, wholes, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or their combinations.

[0059] The term "and / or" refers to any combination and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0060] Embodiment 1:

[0061] See Figure 1 , a multi-device ad-hoc network communication method, comprising the following steps:

[0062] Collect the magnetic field intensity vectors at the locations of each device in the ad-hoc network, generate a medium polarization intensity distribution map based on the magnetic field intensity vectors, monitor the regions in the medium polarization intensity distribution map that exceed the preset polarization angle deviation threshold, and send polarization compensation signals to the devices in this region through a dedicated control channel;

[0063] In this embodiment, each device is equipped with a three-axis magnetometer for collecting the magnetic field intensity vectors at its location. These devices are deployed in a coverage area. Each device is at a fixed spatial position and can monitor the magnetic field intensity information at its location in real time. Each device uses a wireless communication protocol (such as LoRaWAN, Wi-Fi or Zigbee) to broadcast the collected polarized magnetic field data to adjacent device nodes in real time. These data packets contain not only the magnetic field intensity vectors but also the spatial coordinate information of the devices;

[0064] Group the devices that receive the polarization compensation signals, perform independent analysis on each device group, extract the multipath components in the communication signals of the communication channels of the device groups that exceed the preset channel coherence time threshold (the channel coherence time refers to the time interval between the signals arriving at the receiving end through multiple paths due to the historical polarization residual memory during the signal propagation process. The channel coherence time threshold is set comprehensively based on the communication environment conditions and the moving speed of the devices, and the default setting is 10 ms), and perform time-domain separation and decoupling on the channel response and the historical medium polarization residual components in the multipath components;

[0065] Since the devices in the device group are still in a communication state at this time, in order to effectively communicate and perform polarization compensation between devices, mutually orthogonal reference signal sequences are embedded at preset positions in the underlying frame structure of the device group communication protocol. The reference signal sequences (such as Zadoff-Chu sequences) are used to synchronize the communication signals between devices and, at the same time, serve as a reference benchmark for the signals to help the receiving end accurately identify and demodulate the signals. The embedded reference signal sequences should ensure orthogonality, that is, the signals used between devices are independent of each other and will not interfere with each other;

[0066] Based on the historical medium polarization residual components of the current device group, a compensation codebook is constructed. The compensation codebook includes the medium polarization residual component information at historical moments and the corresponding pre-distortion phase rotation amounts (by phase rotation, the angle of the signal can be adjusted to make it closer to the desired phase). According to the current medium polarization residual component, the corresponding pre-distortion phase rotation amount is queried in the compensation codebook, and the symbol to be transmitted is phase-compensated and then the signal transmitter is controlled to transmit the symbol to the corresponding device group;

[0067] Calculate the error vector magnitude between the transmitted symbol received by the device group and the transmitted symbol sent by the signal transmitter. Based on the error vector magnitude, update the corresponding pre-distortion phase rotation amount in the compensation codebook of the device group. The signal transmitter performs phase compensation on the transmitted symbol again according to the compensation codebook and then sends it to the device group. Repeat the iteration until the error vector magnitude reaches the preset error vector magnitude threshold, and then stop the iteration to obtain the optimal pre-distortion phase rotation amount of the device group under the current medium polarization residual component;

[0068] Before communicating with any device group, the signal transmitter queries the corresponding pre-distortion phase rotation amount in the compensation codebook based on the current medium polarization residual component of the device group, performs phase compensation on the transmitted symbol, and then sends it to the corresponding device group.

[0069] As a preferred implementation manner of this embodiment, the method further includes the following steps:

[0070] Based on machine learning models (such as online gradient descent, support vector machines, recurrent neural networks (RNN), etc.), a polarization memory decay prediction model is constructed. Based on the historical polarization residual components of the device group, a training sample set is constructed (the training sample set also includes environmental parameters, specifically external factors such as temperature, humidity, and air pressure that affect channel attenuation). The polarization memory decay prediction model is trained through the training sample set. The trained polarization memory decay prediction model outputs the polarization memory decay rate corresponding to the device group. The polarization memory decay rate reflects the attenuation degree of the polarization characteristics of the signal over time due to changes in the propagation environment;

[0071] Based on the error feedback between devices, the system activates the collaborative correction mechanism, and adjusts the parameters of the polarization memory attenuation prediction model through the mutual feedback information of multiple devices. Each device updates the parameters in the polarization memory attenuation prediction model according to the feedback error of adjacent nodes, gradually improving the prediction accuracy;

[0072] The correction process can be achieved by methods such as weighted average or error minimization until the prediction accuracy rate of the polarization memory attenuation prediction model reaches the set index;

[0073] Calculate the future medium polarization residual component of the current device group through the polarization memory attenuation rate, query the pre-distortion phase rotation amount in the device group compensation codebook based on the future medium polarization residual component, and perform phase pre-compensation on the future transmission signal of the signal transmitter end based on the queried pre-distortion phase rotation amount. Specifically:

[0074] Encapsulate the prediction result into the header control field (the header control field is the management information part of the communication protocol bottom layer frame (physical layer or link layer), used to carry control information and synchronization signals);

[0075] The signal transmitter end obtains the polarization prediction residual attenuation rate between the next moment and the receiving end device in the header control field, calculates the predicted polarization residual component in real time, and retrieves the phase pre-compensation amount in the dynamic compensation codebook based on the predicted polarization residual component to perform phase pre-compensation on the transmitted symbol of the transmitter end.

[0076] As a preferred implementation manner of this embodiment, the construction steps of the medium polarization intensity distribution map (this distribution map reflects the magnetic field intensity distribution at different positions and calculates the polarization intensity of the medium therefrom) are as follows:

[0077] Filter the magnetic field intensity vector of each device in the ad hoc network through the Kalman filter algorithm;

[0078] Fuse the magnetic field intensity vector of each device and its corresponding position coordinates to obtain the magnetic field intensity distribution data of the ad hoc network devices;

[0079] Generate the medium polarization intensity distribution map through the spatial interpolation algorithm based on the magnetic field intensity distribution data, the magnetic field gradient tensor, and the device topology information;

[0080] The magnetic field intensity vector of each device and its position coordinates are continuously updated. The filtering process ensures that the magnetic field data collected from multiple device nodes is weighted and fused (the weights are set based on the signal transmission stability), reducing noise and uncertainty, and obtaining a relatively accurate magnetic field intensity estimate.

[0081] As a preferred implementation manner of this embodiment, the specific steps for monitoring the area in the medium polarization intensity distribution map that exceeds the preset polarization angle deviation threshold are as follows:

[0082] Divide the dielectric polarization intensity distribution map into the same number of regions as the number of devices according to the device positions;

[0083] Calculate the polarization angles of each region in the dielectric polarization intensity distribution map, as shown in the following formula:

[0084] ;

[0085] Where: represents the polarization angle of any region; represents the dielectric polarization intensity of this region in the direction; represents the dielectric polarization intensity of this region in the direction;

[0086] If the polarization angle of the current region is greater than the preset polarization angle deviation threshold, send a polarization compensation signal to the device in this region.

[0087] As a preferred implementation manner of this embodiment, the specific steps for grouping the devices that receive the polarization compensation signal are as follows:

[0088] Collect the polarization parameters of the devices that receive the polarization compensation signal, and the polarization parameters include polarization angle, polarization mode, and degree of polarization;

[0089] For any two devices, calculate the polarization orthogonality between the devices according to their polarization parameters, as shown in the following formula:

[0090] ;

[0091] Where: represents the polarization orthogonality between device and device ; represents the polarization angle of device ; represents the polarization angle of device ; represents the maximum range of the polarization angle;

[0092] Construct a device status correlation matrix based on the polarization orthogonality between the devices, as shown in the following formula:

[0093] ;

[0094] Where: represents the device status correlation matrix; represents the total number of devices;

[0095] The devices are sorted according to the magnitude order of polarization orthogonality in the device status association matrix, and then the graph theory optimal matching algorithm is used to group the devices. All devices in the same device group use the same time slot for data transmission;

[0096] As a preferred implementation manner of this embodiment, the communication signals received by the device group are analyzed, the time intervals for the communication signal components on different paths to reach the device group are analyzed, and the communication signal components with time intervals greater than the preset channel coherence time threshold are extracted and combined into multipath components;

[0097] A dual-channel filter is constructed based on the Kalman filter or the adaptive filter algorithm. One channel is used to filter the channel response in the multipath components, and the other channel is used to filter the historical medium polarization residual components in the multipath components to obtain the separated channel response and the historical medium polarization residual components;

[0098] When filtering, the suppression weight of the historical polarization residual components is set, which depends on the analysis results of historical data and the status of the current channel. When the historical polarization effect is strong, a higher suppression weight needs to be set. However, to prevent distortion caused by excessive suppression, the suppression ratio is required not to exceed 50%;

[0099] As a preferred implementation manner of this embodiment, the specific calculation steps of the pre-distortion phase rotation amount in the compensation codebook are as follows:

[0100] Calculate the normalized ratio of the channel response and the historical medium polarization residual components in the multipath components, as shown in the following formula:

[0101] ;

[0102] Where: represents the normalized ratio of the channel response and the historical medium polarization residual components in the multipath components at time represents the normalized channel response in the multipath components at time represents the normalized historical medium polarization residual components in the multipath components at time

[0103] The normalized ratio of the channel response and the historical medium polarization residual components in the multipath components is converted into the pre-distortion phase rotation amount through a non-linear function, as shown in the following formula:

[0104] ;

[0105] Where: represents the pre-distortion phase rotation amount at time represents the scaling factor; Represents a non-linear function, such as the Sigmoid function, etc.

[0106] As a preferred implementation of this embodiment, the corresponding pre-distortion phase rotation amount in the compensation codebook of the device group is updated based on the error vector magnitude, as shown in the following formula:

[0107] ;

[0108] ;

[0109] Where: Represents the error vector magnitude; Represents the total number of symbols received by the device group; Represents the th symbol value received by the device group; Represents the th original value of the symbol; Represents the th updated value of the pre-distortion phase rotation amount after iteration; Represents the th pre-distortion phase rotation amount during iteration; Represents the learning rate; Represents the gradient of the EVM with respect to the pre-distortion phase rotation amount;

[0110] Monitor the phase pre-compensation process, and trigger the modulation order reduction protection mechanism when the rotation angle exceeds the gradient threshold of the set multi-level angle (such as 10 degrees, 1 degree, 0.1 degree), and switch to the low-order modulation format to suppress the spread of non-linear distortion.

[0111] Embodiment 2:

[0112] This embodiment proposes an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, it implements the method described in any embodiment of the present invention.

[0113] Embodiment 3:

[0114] This embodiment proposes a computer-readable storage medium, on which a computer program is stored. When the program is executed by a processor, it implements the method described in any embodiment of the present invention.

[0115] In the embodiments of the present application, "at least one" means one or more, and "a plurality" means two or more. "And / or" describes the association relationship of associated objects, indicating that three relationships can exist. For example, A and / or B can represent the situations of A existing alone, A and B existing simultaneously, and B existing alone. Wherein A and B can be singular or plural. The character " / " generally represents an "or" relationship between the associated objects before and after. "At least one of the following" and its similar expressions refer to any combination of these items, including any combination of single items or plural items. For example, at least one of a, b, and c can represent: a, b, c, a and b, a and c, b and c, or a, b, and c, where a, b, and c can be single or multiple.

[0116] Those of ordinary skill in the art can realize that the various units and algorithm steps described in the embodiments disclosed herein can be implemented by a combination of electronic hardware, computer software, and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods for each specific application to implement the described functions, but such implementation should not be considered to exceed the scope of the present application.

[0117] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the systems, devices, and units described above can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0118] In several embodiments provided by the present application, if any function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art or a part of this technical solution can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present application. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (hereinafter referred to as ROM), random access memories (hereinafter referred to as RAM), magnetic disks, or optical discs that can store program codes.

[0119] The above are only the embodiments of the present invention and do not limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made by using the content of the specification and drawings of the present invention, or directly or indirectly applied in other related technical fields, shall be equally included in the patent protection scope of the present invention.

Claims

1. A multi-device self-organizing network communication method, characterized in that: The following steps are involved: Collect the magnetic field strength vector of each device in the ad hoc network, generate a medium polarization intensity distribution map based on the magnetic field strength vector, monitor the area in the medium polarization intensity distribution map that exceeds a preset polarization angle deviation threshold, and send a polarization compensation signal to the device in the area; The devices receiving the polarization compensation signal are grouped, and each device group is analyzed independently to extract the multipath components exceeding the preset channel coherence time threshold in the communication signal of the communication channel of the device group, and separate the channel response and the historical medium polarization residual component in the multipath components; A compensation codebook is constructed based on the historical medium polarization residual component of the current device group. The compensation codebook includes the medium polarization residual component information at the historical moment and the corresponding pre-distortion phase rotation amount. According to the current medium polarization residual component, the corresponding pre-distortion phase rotation amount is queried in the compensation codebook to perform phase compensation on the symbols to be transmitted, and then the signal transmitter is controlled to transmit the symbols to the corresponding device group; The specific calculation steps of the pre-distortion bit rotation amount in the compensation codebook are: Calculate the normalized ratio of the channel response in the multipath component to the historical medium polarization residual component, as shown in the following formula: ； in: express The normalized ratio of the channel response in the multipath component at the time instant and the residual component of the historical medium polarization; Represents the normalized The channel response in the multipath component at time t; Represents the normalized The historical medium polarization residual component in the multipath component at the moment; The normalized ratio of the channel response in the multipath component and the residual component of the historical medium polarization is converted into the predistortion phase rotation through a nonlinear function, as shown in the following formula: ； in: express The amount of predistortion position rotation at time; represents the scaling factor; represents a nonlinear function; Calculate the error vector amplitude between the transmission symbol received by the device group and the transmission symbol sent by the signal transmitting end, update the corresponding pre-distortion phase rotation amount in the compensation code book of the device group based on the error vector amplitude, and the signal transmitting end performs phase compensation on the transmission symbol again according to the compensation code book and sends it to the device group, repeat the iteration until the error vector amplitude reaches the preset error vector amplitude threshold, stop the iteration, and obtain the optimal pre-distortion phase rotation amount of the device group under the current medium polarization residual component; Before the signal transmitter communicates with any device group, it searches the compensation codebook for the corresponding predistortion phase rotation amount through the current medium polarization residual component of the device group, performs phase compensation on the transmission symbol, and then sends it to the corresponding device group.

2. A multi-device ad hoc network communication method according to claim 1, characterized in that: The method further comprises the following steps: A polarization memory decay prediction model is constructed based on a machine learning model, a training sample set is constructed based on the historical polarization residual component of the device group, the polarization memory decay prediction model is trained through the training sample set, and the trained polarization memory decay prediction model outputs the polarization memory decay rate corresponding to the device group; The future medium polarization residual component of the current device group is calculated by the polarization memory attenuation rate, and the pre-distortion phase rotation amount is queried in the device group compensation codebook based on the future medium polarization residual component. Based on the queried pre-distortion phase rotation amount, phase pre-compensation is performed on the future transmitted signal of the signal transmitting end.

3. A multi-device ad hoc network communication method according to claim 1, characterized in that: The steps of constructing the medium polarization intensity distribution map are: The magnetic field strength vector of each device in the ad hoc network is filtered by the Kalman filter algorithm; The magnetic field intensity vector of each device and its corresponding position coordinates are merged to obtain the magnetic field intensity distribution data of the ad hoc network device; Based on the magnetic field intensity distribution data, magnetic field gradient tensor and device topology information, the medium polarization intensity distribution map is generated through the spatial interpolation algorithm.

4. A multi-device ad hoc network communication method according to claim 1, characterized in that: The specific steps of monitoring the area in the medium polarization intensity distribution diagram that exceeds the preset polarization angle deviation threshold are: Divide the medium polarization intensity distribution diagram into regions equal to the number of devices according to the device locations; Calculate the polarization angle of each area in the medium polarization intensity distribution diagram, as shown in the following formula: ； in: represents the polarization angle of any region; Indicates that the area is The dielectric polarization intensity in the direction; Indicates that the area is The dielectric polarization intensity in the direction; If the polarization angle of the current area is greater than the preset polarization angle deviation threshold, a polarization compensation signal is sent to the device in the area.

5. A multi-device ad hoc network communication method according to claim 1, characterized in that: The specific steps of grouping the devices that receive the polarization compensation signal are as follows: Collecting polarization parameters of a device that receives a polarization compensation signal, wherein the polarization parameters include a polarization angle, a polarization mode, and a degree of polarization; For any two devices, the polarization orthogonality between the devices is calculated based on their polarization parameters, as shown in the following formula: ； in: Indicates the device With equipment Polarization orthogonality between them; Indicates the device The polarization angle of Indicates the device The polarization angle of Indicates the maximum range of polarization angle; The device state association matrix is ​​constructed based on the polarization orthogonality between devices, as shown in the following formula: ； in: represents the device state association matrix; Indicates the total number of devices; After sorting the devices according to the order of polarization orthogonality in the device state association matrix, the devices are grouped using the graph theory optimal matching algorithm.

6. A multi-device ad hoc network communication method according to claim 5, characterized in that: Analyze the communication signals received by the device group, analyze the time intervals of the communication signal components of different paths arriving at the device group, extract the communication signal components whose time intervals are greater than a preset channel coherence time threshold, and combine them into multipath components; A dual-path filter is constructed based on the filtering algorithm, one path is used to filter the channel response in the multipath component, and the other path is used to filter the historical medium polarization residual component in the multipath component, so as to obtain the separated channel response and the historical medium polarization residual component.

7. A multi-device ad hoc network communication method according to claim 1, characterized in that: The corresponding pre-distortion bit rotation amount in the compensation codebook of the device group is updated based on the error vector magnitude, as shown in the following formula: ； ； in: represents the error vector magnitude; Indicates the total number of symbols received by the device group; Indicates the first symbol value; Indicates The original value of the symbol; Indicates The updated value of the predistortion position rotation after iterations; Indicates The amount of predistortion bit rotation at the iteration; Represents the learning rate.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the method according to any one of claims 1 to 7 is implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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