Conflict signal separation method and device based on three-dimensional clustering
By using a three-dimensional clustering method and leveraging the principle of channel coherence to separate concurrent signals in the LoRa protocol, the problem of data packet collisions in dynamic environments is solved, thereby improving the reliability and compatibility of information transmission.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- TSINGHUA UNIVERSITY
- Filing Date
- 2026-02-02
- Publication Date
- 2026-06-12
Smart Images
Figure CN122204249A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of wireless communication technology, and in particular to a method and apparatus for separating conflict signals based on three-dimensional clustering. Background Technology
[0002] LoRa is a low-power wide-area network communication technology designed specifically for the Internet of Things (IoT), and it is widely used due to its long-distance transmission and low power consumption characteristics.
[0003] However, in scenarios with multiple nodes transmitting concurrently, data packets are prone to collisions at the receiving end, leading to packet corruption or loss, and consequently wasting spectrum resources. The retransmission mechanism triggered by packet loss also increases energy consumption and shortens the battery life of low-power IoT devices.
[0004] Furthermore, because LoRa uses narrowband channels and has a low physical layer transmission rate, the overlap window of data packets in the time domain is relatively large, which further exacerbates the probability and impact of concurrent collisions. Summary of the Invention
[0005] The purpose of this application is to provide a collision signal separation method and apparatus based on three-dimensional clustering. By utilizing the channel coherence principle in the signal propagation process, the collision data packets in a dynamic environment can be separated, which greatly improves the reliability of information transmission.
[0006] This application provides a collision signal separation method based on three-dimensional clustering, including: Receive concurrent transmission signals; construct feature points for each symbol in the target three-dimensional space among the multiple symbols contained in the concurrent transmission signals, and establish multiple reference trajectory models with the same number of signals as the concurrent transmission signals; calculate the distance between each three-dimensional feature point and each reference trajectory model, and perform signal separation on the multiple symbols according to the distance comparison results to obtain the symbol sequence corresponding to each data packet; wherein, the target three-dimensional space is a three-dimensional space constructed based on amplitude, phase and time.
[0007] Optionally, constructing the feature points of each symbol in the target three-dimensional space among the multiple symbols contained in the concurrent transmission signal includes: demodulating and extracting features from the concurrent transmission signal to obtain the phase and amplitude IQ features of each symbol and the timestamp corresponding to each symbol; and constructing the feature points of each symbol in the target three-dimensional space based on the IQ features and the corresponding timestamp of each symbol.
[0008] Optionally, the IQ features include amplitude components and phase components; the demodulation and feature extraction of the concurrent transmission signal to obtain the phase and amplitude IQ features of each symbol in the multiple symbols includes: dividing the concurrent transmission signal into multiple demodulation windows, and performing a fast Fourier transform on the signal in each demodulation window; using a continuous interference cancellation method, sequentially extracting the amplitude and phase values at the maximum peak in each transformed demodulation window; and calculating the amplitude and phase components of the corresponding symbol based on the extracted amplitude and phase values corresponding to each demodulation window.
[0009] Optionally, establishing multiple reference trajectory models with the same number of signals as the concurrently transmitted signals includes: acquiring signal parameters for each data packet in the concurrently transmitted signals; the signal parameters for each data packet include: carrier frequency offset and sampling frequency offset; establishing multiple reference trajectory models with the same number of signals as the concurrently transmitted signals based on the acquired signal parameters for each data packet; wherein, the reference trajectory model is a rotating trajectory constructed based on the time cumulative effect of carrier frequency offset and sampling frequency offset on symbol phase; one reference trajectory model corresponds to one transmitted signal.
[0010] Optionally, obtaining the signal parameters of each data packet in the concurrent transmission signal includes: performing synchronous analysis on the preamble or known pilot symbols in the concurrent transmission signal to obtain multiple sets of different signal parameters; wherein, the multiple sets of different signal parameters are used to establish the multiple reference trajectory models.
[0011] Optionally, the step of calculating the distance between each three-dimensional feature point and each reference trajectory model, and performing signal separation on the multiple symbols based on the distance comparison results to obtain the symbol sequence corresponding to each data packet includes: calculating the minimum Euclidean distance from each three-dimensional feature point to each reference trajectory model; assigning each three-dimensional feature point to the trajectory cluster corresponding to the reference trajectory model with the minimum Euclidean distance, and aggregating the three-dimensional feature points assigned to the same trajectory cluster to obtain the symbol sequence corresponding to each data packet.
[0012] This application also provides a collision signal separation device based on three-dimensional clustering, comprising: The system includes a signal receiving module for receiving concurrently transmitted signals; a construction module for constructing feature points of each symbol in the target three-dimensional space among the multiple symbols contained in the concurrently transmitted signals, and establishing multiple reference trajectory models with the same number of signals as the concurrently transmitted signals; and a signal separation module for calculating the distance between each three-dimensional feature point and each reference trajectory model, and performing signal separation on the multiple symbols based on the distance comparison results to obtain the symbol sequence corresponding to each data packet; wherein the target three-dimensional space is a three-dimensional space constructed based on amplitude, phase, and time.
[0013] Optionally, the construction module is specifically used to demodulate and extract features from the concurrent transmission signal to obtain the phase and amplitude IQ features of each symbol among multiple symbols and the timestamp corresponding to each symbol; the construction module is also specifically used to construct feature points of each symbol in the target three-dimensional space based on the IQ features and the corresponding timestamp of each symbol.
[0014] Optionally, the construction module is specifically used to divide the concurrent transmission signal into multiple demodulation windows and perform a fast Fourier transform on the signal in each demodulation window; the construction module is also specifically used to use a continuous interference cancellation method to sequentially extract the amplitude and phase values at the maximum peak in each transformed demodulation window; the construction module is also specifically used to calculate the amplitude and phase components of the corresponding symbol based on the extracted amplitude and phase values corresponding to each demodulation window.
[0015] Optionally, the construction module is specifically used to obtain the signal parameters of each data packet in the concurrent transmission signal; the signal parameters of the data packet include: carrier frequency offset and sampling frequency offset; the construction module is further used to establish multiple reference trajectory models with the same number of signals as the concurrent transmission signal based on the obtained signal parameters of each data packet; wherein, the reference trajectory model is: a gyratory trajectory constructed based on the time cumulative effect of carrier frequency offset and sampling frequency offset on symbol phase; one transmission signal corresponds to one reference trajectory model.
[0016] Optionally, the construction module is further configured to perform synchronous analysis on the preamble or known pilot symbols in the concurrent transmission signal to obtain multiple sets of different signal parameters; wherein, the multiple sets of different signal parameters are used to establish the multiple reference trajectory models.
[0017] Optionally, the signal separation module is specifically used to calculate the minimum Euclidean distance from each three-dimensional feature point to each reference trajectory model; the signal separation module is also specifically used to assign each three-dimensional feature point to the trajectory cluster corresponding to the reference trajectory model with the minimum Euclidean distance, and to aggregate the three-dimensional feature points assigned to the same trajectory cluster to obtain the symbol sequence corresponding to each data packet.
[0018] This application also provides a computer program product, including a computer program / instructions that, when executed by a processor, implement the steps of the conflict signal separation method based on three-dimensional clustering as described above.
[0019] This application also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of any of the above-described conflict signal separation methods based on three-dimensional clustering.
[0020] This application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the conflict signal separation method based on three-dimensional clustering as described above.
[0021] The collision signal separation method and apparatus based on three-dimensional clustering provided in this application first receive concurrently transmitted signals; then, it constructs feature points for each symbol in a target three-dimensional space among multiple symbols contained in the concurrently transmitted signals, and establishes multiple reference trajectory models with the same number of signals as the concurrently transmitted signals; finally, it calculates the distance between each three-dimensional feature point and each reference trajectory model, and performs signal separation on the multiple symbols based on the distance comparison results to obtain the symbol sequence corresponding to each data packet; wherein, the target three-dimensional space is a three-dimensional space constructed based on amplitude, phase, and time. Thus, by utilizing the channel coherence principle in the signal propagation process, the separation of collision data packets in a dynamic environment is achieved, greatly improving the reliability of information transmission. Attached Figure Description
[0022] To more clearly illustrate the technical solutions in this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0023] Figure 1 This is a flowchart illustrating the conflict signal separation method based on three-dimensional clustering provided in this application; Figure 2This is a schematic diagram illustrating the ideal situation and the actual observed signal phase change trend provided in this application; Figure 3 This is a schematic diagram of the distribution and clustering of collision signals in three-dimensional space provided in this application; Figure 4 This is a schematic diagram of the collision signal separation device based on three-dimensional clustering provided in this application; Figure 5 This is a schematic diagram of the structure of the electronic device provided in this application. Detailed Implementation
[0024] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.
[0025] The terms "first," "second," etc., used in the specification and claims of this application are used to distinguish similar objects and not to describe a specific order or sequence. It should be understood that such use of data can be interchanged where appropriate so that embodiments of this application can be implemented in orders other than those illustrated or described herein, and the objects distinguished by "first," "second," etc., are generally of the same class and the number of objects is not limited; for example, a first object can be one or more. Furthermore, "and / or" in the specification and claims indicates at least one of the connected objects, and the character " / " generally indicates that the preceding and following objects are in an "or" relationship. All actions involving the acquisition of signal information or data in this application are performed in accordance with the relevant data protection laws and policies of the country where the application is located and with authorization from the owner of the relevant device.
[0026] While LoRa concurrent decoding schemes in related technologies can achieve collision separation in static environments, they do not fully consider the time-varying signal characteristics caused by dynamic environmental changes and node movement. In other words, such dynamic scenarios lead to random fluctuations in the signal's frequency domain distribution, time-domain arrival characteristics, or coded recognition features, significantly reducing the separation accuracy of existing schemes and making them unsuitable for practical applications. Furthermore, some schemes require modifications to existing hardware architectures or modulation / demodulation mechanisms, making them incompatible with commercial LoRa devices and further limiting their deployment feasibility.
[0027] To address the aforementioned technical problems in related technologies, this application provides a collision signal separation method based on three-dimensional clustering for dynamic scenarios such as environmental changes and node movement under LoRa multi-node concurrent transmission. This method utilizes the channel coherence principle during signal propagation to separate conflicting data packets in dynamic environments. Furthermore, this method requires only a single gateway for processing, eliminates the need for additional hardware support, and is fully compatible with existing commercial LoRa devices.
[0028] The conflict signal separation method based on three-dimensional clustering provided in this application will be described in detail below with reference to the accompanying drawings, through specific embodiments and application scenarios.
[0029] like Figure 1 As shown in the embodiment of this application, a collision signal separation method based on three-dimensional clustering is provided. This method may include the following steps 101 to 103: Step 101: Receive concurrent transmission signals.
[0030] For example, the aforementioned concurrent transmission signals are discrete signals received by the LoRa device. The transmitted signal is a LoRa modulated signal.
[0031] Step 102: Construct the feature points of each symbol in the target three-dimensional space of the multiple symbols contained in the concurrent transmission signal, and establish multiple reference trajectory models with the same number of signals as the concurrent transmission signal.
[0032] The target three-dimensional space is a three-dimensional space constructed based on amplitude, phase, and time.
[0033] For example, after receiving the above concurrent transmission signals, it is necessary to map each symbol into the target three-dimensional space and establish a reference trajectory model corresponding to each transmission signal.
[0034] Specifically, for the construction of feature points of a symbol in the target three-dimensional space, step 102 above may further include the following steps 102a and 102b: Step 102a: Demodulate and extract features from the concurrent transmission signal to obtain the phase and amplitude IQ features of each symbol and the timestamp corresponding to each symbol.
[0035] The IQ features include amplitude components and phase components.
[0036] Step 102b: Based on the IQ features of each symbol and the corresponding timestamp, construct the feature points of each symbol in the target 3D space.
[0037] For example, for the received discrete signal After dividing the demodulation window, an N-point FFT transform is performed. Using the SIC method, the amplitude and phase values at the maximum peak within the window are extracted sequentially to obtain the two-dimensional IQ features of the symbol. Then, combine the timestamps corresponding to the symbols. By fusing two-dimensional IQ features with the time dimension, a three-dimensional feature vector is constructed. That is, the feature points of the above symbols in the target's three-dimensional space.
[0038] Specifically, step 102a above may also include steps 102a1 to 102a3: Step 102a1: Divide the concurrent transmission signal into multiple demodulation windows, and perform a fast Fourier transform on the signal in each of the multiple demodulation windows.
[0039] Step 102a2: Using the continuous interference cancellation method, extract the amplitude and phase values at the maximum peak in each transformed demodulation window in sequence.
[0040] Step 102a3: Based on the amplitude and phase values corresponding to each demodulation window, calculate the amplitude and phase components of the corresponding symbol.
[0041] For example, in this embodiment of the application, a Fast Fourier Transform (FFT) is performed in each demodulation window, and a Continuous Interference Cancellation (SIC) method is used to sequentially extract the phase value and the amplitude value of the symbol at the maximum peak in the window. Let the discrete received signal in the current demodulation window be... Perform an N-point FFT transformation on it, and the transformation formula is as follows: (Formula 1) Where N is the number of FFT points, n is the discrete-time index, and m is the frequency index.
[0042] After that, through Extract the amplitude value, through Extract the phase value. max is the frequency index corresponding to the maximum peak.
[0043] Specifically, regarding the construction of the reference trajectory model, step 102 above may further include the following steps 102c and 102d: Step 102c: Obtain the signal parameters of each data packet in the concurrent transmission signal.
[0044] The signal parameters of the data packet include: carrier frequency offset and sampling frequency offset.
[0045] Step 102d: Based on the signal parameters of each data packet obtained, establish multiple reference trajectory models with the same number of signals as the concurrently transmitted signals.
[0046] The reference trajectory model is a rotating trajectory constructed based on the time cumulative effect of carrier frequency offset and sampling frequency offset on symbol phase; one transmission signal corresponds to one reference trajectory model.
[0047] For example, before proceeding with subsequent calculations, it is necessary to establish a reference trajectory model corresponding to the number of concurrent signals based on the spiral trajectory expression of each data packet and its unique amplitude, CFO, SFO, and other parameters.
[0048] Specifically, step 102c above may also include the following step 102c1: Step 102c1: Perform synchronous analysis on the preamble or known pilot symbols in the concurrent transmission signal to obtain multiple sets of different signal parameters.
[0049] The multiple sets of different signal parameters are used to establish the multiple reference trajectory models.
[0050] For example, such as Figure 2 As shown, during the channel coherence time, the signal amplitude remains stable, while the effects of carrier frequency offset (CFO) and sampling frequency offset (SFO) on the symbol phase accumulate over time, resulting in different phase values of different data packets exhibiting differentiated characteristics in the IQ plane, rotating around a center with their respective fixed radii. The signal then evolves into a unique spiral trajectory in the IQ-time three-dimensional space, which can be characterized by the following formula: (Formula 2) in, The initial frequency of the symbol. For signal amplitude, For frequency modulation slope, Symbol period, symbol index Determined by demodulation timing, t For time variables, For carrier frequency offset, For sampling frequency offset, For channel phase, I The amplitude value. Phase value. Frequency modulation slope. k Determined by bandwidth (BW) and symbol period. Carrier frequency offset, sampling frequency offset, and channel phase can be extracted and measured through the preamble synchronization process and by relying on pilot symbols of a known sequence. Time variable. t It can be obtained through synchronization with the gateway's local clock.
[0051] For example, based on Figure 2 ,like Figure 3 As shown, by extracting and calculating the above parameters, the spiral trajectory of the corresponding signal in the IQ-time three-dimensional space (i.e., the above target three-dimensional space) can be obtained.
[0052] Step 103: Calculate the distance between each three-dimensional feature point and each reference trajectory model, and perform signal separation on the multiple symbols based on the distance comparison results to obtain the symbol sequence corresponding to each data packet.
[0053] For example, based on the extracted amplitude and phase parameters and the generated reference trajectory model, the minimum Euclidean distance from each three-dimensional feature vector to each reference trajectory can be calculated, the distance metric between the feature point and each trajectory can be determined, and thus the separation of concurrent transmission signals can be completed.
[0054] Specifically, step 103 above may also include the following steps 103a1 and 103a2: Step 103a1: Calculate the minimum Euclidean distance from each 3D feature point to each reference trajectory model.
[0055] Step 103a2: Assign each three-dimensional feature point to the trajectory cluster corresponding to the reference trajectory model with the smallest Euclidean distance, and aggregate the three-dimensional feature points assigned to the same trajectory cluster to obtain the symbol sequence corresponding to each data packet.
[0056] For example, by Feature points are assigned to the nearest trajectory cluster. For feature points i With the The distance between the trajectories. Feature points assigned to the same trajectory cluster are aggregated to classify and separate conflicting data packets, resulting in independent symbol sequences for each data packet.
[0057] For example, the clustering process is as follows: First, the IQ features extracted from each symbol using FFT are... With corresponding timestamp Fusion to construct a three-dimensional feature vector Simultaneously, based on the spiral trajectory expression of each data packet, a reference trajectory model corresponding to the number of signals is established (each trajectory is defined by its unique amplitude, CFO, SFO, and other parameters). During clustering, the minimum Euclidean distance from each three-dimensional feature vector to each reference trajectory needs to be calculated, and feature points are assigned to the nearest trajectory cluster, i.e., through... Complete the classification. For example... Figure 3As shown, since the symbol feature points of the same data packet are continuously distributed along their own spiral trajectory, while different data packets form separate trajectory clusters due to parameter differences, this clustering method can achieve the separation and decoding of conflict signals.
[0058] The collision signal separation method based on three-dimensional clustering provided in this application incorporates the time dimension as one of the features into the clustering model. This allows the symbol feature points of the same data packet to continuously evolve along their own spiral trajectory within the channel coherence time. This preserves the characteristics of stable amplitude and regular phase accumulation of the signal within the coherence time, while also capturing subtle changes in parameters over time through the dynamic trend of the trajectory. This deep utilization of time features enables it to adapt to time-varying interference such as phase drift and frequency shift in scenarios of dynamic channel changes. Even under the influence of multipath fading or sudden noise, robust clustering can still be achieved based on the overall coherence of the trajectory, significantly improving the robustness of collision signal separation and decoding based on three-dimensional clustering.
[0059] The collision signal separation method based on three-dimensional clustering provided in this application first receives concurrently transmitted signals; then, it constructs feature points for each symbol in the target three-dimensional space among multiple symbols contained in the concurrently transmitted signals, and establishes multiple reference trajectory models with the same number of signals as the concurrently transmitted signals; finally, it calculates the distance between each three-dimensional feature point and each reference trajectory model, and performs signal separation on the multiple symbols based on the distance comparison results to obtain the symbol sequence corresponding to each data packet; wherein, the target three-dimensional space is a three-dimensional space constructed based on amplitude, phase, and time. In this way, by utilizing the channel coherence principle in the signal propagation process, the separation of collision data packets in a dynamic environment is achieved, greatly improving the reliability of information transmission.
[0060] It should be noted that the conflict signal separation method based on three-dimensional clustering provided in this application embodiment can be executed by a conflict signal separation device based on three-dimensional clustering, or by a control module within that device for executing the conflict signal separation method based on three-dimensional clustering. This application embodiment uses the execution of the conflict signal separation method based on three-dimensional clustering by a conflict signal separation device as an example to illustrate the conflict signal separation device based on three-dimensional clustering provided in this application embodiment.
[0061] It should be noted that the conflict signal separation methods based on three-dimensional clustering shown in the accompanying drawings of the embodiments of this application are all illustrated by way of example with reference to one of the accompanying drawings of the embodiments of this application. In specific implementation, the conflict signal separation methods based on three-dimensional clustering shown in the accompanying drawings of the above methods can also be implemented in conjunction with any other accompanying drawings shown in the above embodiments, which will not be elaborated here.
[0062] The collision signal separation device based on three-dimensional clustering provided in this application is described below. The collision signal separation method based on three-dimensional clustering described below can be referred to in correspondence with the collision signal separation method described above.
[0063] Figure 4 This is a schematic diagram of the collision signal separation device based on three-dimensional clustering provided in the embodiments of this application, as shown below. Figure 4 As shown, it specifically includes: The signal receiving module 401 is used to receive concurrently transmitted signals; the construction module 402 is used to construct the feature points of each symbol in the target three-dimensional space of the multiple symbols contained in the concurrently transmitted signals, and to establish multiple reference trajectory models with the same number of signals as the concurrently transmitted signals; the signal separation module 403 is used to calculate the distance between each three-dimensional feature point and each reference trajectory model, and to perform signal separation on the multiple symbols according to the distance comparison results to obtain the symbol sequence corresponding to each data packet; wherein, the target three-dimensional space is a three-dimensional space constructed based on amplitude, phase and time.
[0064] Optionally, the construction module 402 is specifically used to demodulate and extract features from the concurrent transmission signal to obtain the phase and amplitude IQ features of each symbol among multiple symbols and the timestamp corresponding to each symbol; the construction module 402 is also specifically used to construct feature points of each symbol in the target three-dimensional space based on the IQ features and the corresponding timestamp of each symbol.
[0065] Optionally, the construction module 402 is specifically used to divide the concurrent transmission signal into multiple demodulation windows and perform a fast Fourier transform on the signal in each of the multiple demodulation windows; the construction module 402 is also specifically used to use a continuous interference cancellation method to sequentially extract the amplitude and phase values at the maximum peak in each transformed demodulation window; the construction module 402 is also specifically used to calculate the amplitude and phase components of the corresponding symbol based on the extracted amplitude and phase values corresponding to each demodulation window.
[0066] Optionally, the construction module 402 is specifically used to obtain the signal parameters of each data packet in the concurrent transmission signal; the signal parameters of the data packet include: carrier frequency offset and sampling frequency offset; the construction module 402 is further used to establish multiple reference trajectory models with the same number of signals as the concurrent transmission signal based on the obtained signal parameters of each data packet; wherein, the reference trajectory model is: a gyratory trajectory constructed based on the time cumulative effect of carrier frequency offset and sampling frequency offset on symbol phase; one transmission signal corresponds to one reference trajectory model.
[0067] Optionally, the construction module 402 is further configured to perform synchronous analysis on the preamble or known pilot symbols in the concurrent transmission signal to obtain multiple sets of different signal parameters; wherein, the multiple sets of different signal parameters are used to establish the multiple reference trajectory models.
[0068] Optionally, the signal separation module 403 is specifically used to calculate the minimum Euclidean distance from each three-dimensional feature point to each reference trajectory model; the signal separation module 403 is also specifically used to assign each three-dimensional feature point to the trajectory cluster corresponding to the reference trajectory model with the minimum Euclidean distance, and to aggregate the three-dimensional feature points assigned to the same trajectory cluster to obtain the symbol sequence corresponding to each data packet.
[0069] The collision signal separation device based on three-dimensional clustering provided in this application first receives concurrently transmitted signals; then, it constructs feature points for each symbol in the target three-dimensional space among multiple symbols contained in the concurrently transmitted signals, and establishes multiple reference trajectory models with the same number of signals as the concurrently transmitted signals; finally, it calculates the distance between each three-dimensional feature point and each reference trajectory model, and performs signal separation on the multiple symbols based on the distance comparison results to obtain the symbol sequence corresponding to each data packet; wherein, the target three-dimensional space is a three-dimensional space constructed based on amplitude, phase, and time. Thus, by utilizing the channel coherence principle in the signal propagation process, the separation of conflicting data packets in a dynamic environment is achieved, greatly improving the reliability of information transmission.
[0070] Figure 5 An example is a schematic diagram of the physical structure of an electronic device, such as... Figure 5As shown, the electronic device may include a processor 510, a communications interface 520, a memory 530, and a communication bus 540. The processor 510, communications interface 520, and memory 530 communicate with each other via the communication bus 540. The processor 510 can call logical instructions in the memory 530 to execute a collision signal separation method based on three-dimensional clustering. This method includes: first, receiving concurrently transmitted signals; then, constructing feature points for each symbol in the target three-dimensional space among multiple symbols contained in the concurrently transmitted signals, and establishing multiple reference trajectory models with the same number of signals as the concurrently transmitted signals; finally, calculating the distance between each three-dimensional feature point and each reference trajectory model, and performing signal separation on the multiple symbols based on the distance comparison results to obtain the symbol sequence corresponding to each data packet; wherein the target three-dimensional space is a three-dimensional space constructed based on amplitude, phase, and time. Thus, by utilizing the channel coherence principle during signal propagation, the separation of collision data packets in a dynamic environment is achieved, greatly improving the reliability of information transmission.
[0071] Furthermore, the logical instructions in the aforementioned memory 530 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the 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 to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.
[0072] On the other hand, this application also provides a computer program product, which includes a computer program stored on a computer-readable storage medium. The computer program includes program instructions, and when the program instructions are executed by a computer, the computer can execute the collision signal separation method based on three-dimensional clustering provided by the above methods. This method includes: first, receiving concurrently transmitted signals; then, constructing feature points for each symbol in a target three-dimensional space among multiple symbols contained in the concurrently transmitted signals, and establishing multiple reference trajectory models with the same number of signals as the concurrently transmitted signals; finally, calculating the distance between each three-dimensional feature point and each reference trajectory model, and performing signal separation on the multiple symbols based on the distance comparison results to obtain a symbol sequence corresponding to each data packet; wherein, the target three-dimensional space is a three-dimensional space constructed based on amplitude, phase, and time. Thus, by utilizing the channel coherence principle during signal propagation, the separation of collision data packets in a dynamic environment is achieved, greatly improving the reliability of information transmission.
[0073] Furthermore, this application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, performs the aforementioned collision signal separation methods based on three-dimensional clustering. The method includes: first, receiving concurrently transmitted signals; then, constructing feature points for each symbol in a target three-dimensional space among multiple symbols contained in the concurrently transmitted signals, and establishing multiple reference trajectory models with the same number of signals as the concurrently transmitted signals; finally, calculating the distance between each three-dimensional feature point and each reference trajectory model, and performing signal separation on the multiple symbols based on the distance comparison results to obtain a symbol sequence corresponding to each data packet; wherein the target three-dimensional space is a three-dimensional space constructed based on amplitude, phase, and time. Thus, by utilizing the channel coherence principle during signal propagation, the separation of conflicting data packets in a dynamic environment is achieved, greatly improving the reliability of information transmission.
[0074] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.
[0075] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.
[0076] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.
Claims
1. A collision signal separation method based on three-dimensional clustering, characterized in that, include: Receive concurrent transmission signals; Construct feature points for each symbol in the target three-dimensional space among the multiple symbols contained in the concurrent transmission signal, and establish multiple reference trajectory models with the same number of signals as the concurrent transmission signal; Calculate the distance between each three-dimensional feature point and each reference trajectory model, and perform signal separation on the multiple symbols based on the distance comparison results to obtain the symbol sequence corresponding to each data packet; The target three-dimensional space is a three-dimensional space constructed based on amplitude, phase, and time.
2. The method according to claim 1, characterized in that, The construction of feature points for each of the multiple symbols contained in the concurrent transmission signal in the target three-dimensional space includes: The concurrent transmission signal is demodulated and its features are extracted to obtain the phase and amplitude IQ features of each symbol and the timestamp corresponding to each symbol. Based on the IQ features of each symbol and the corresponding timestamp, feature points of each symbol in the target 3D space are constructed.
3. The method according to claim 2, characterized in that, The IQ features include: amplitude components and phase components; The demodulation and feature extraction of the concurrent transmission signal to obtain the phase and amplitude IQ features of each symbol in the multiple symbols includes: The concurrent transmission signal is divided into multiple demodulation windows, and a fast Fourier transform is performed on the signal in each of the multiple demodulation windows. A continuous interference cancellation method is used to sequentially extract the amplitude and phase values at the maximum peak in each transformed demodulation window; Based on the amplitude and phase values extracted for each demodulation window, the amplitude and phase components of the corresponding symbol are calculated.
4. The method according to claim 1, characterized in that, The establishment of multiple reference trajectory models with the same number of signals as the concurrently transmitted signals includes: Obtain the signal parameters of each data packet in the concurrent transmission signal; the signal parameters of the data packet include: carrier frequency offset and sampling frequency offset; Based on the signal parameters of each data packet, establish multiple reference trajectory models with the same number of signals as the concurrently transmitted signals; The reference trajectory model is a rotating trajectory constructed based on the time cumulative effect of carrier frequency offset and sampling frequency offset on symbol phase; one transmission signal corresponds to one reference trajectory model.
5. The method according to claim 4, characterized in that, The step of obtaining the signal parameters of each data packet in the concurrent transmission signal includes: Synchronous analysis is performed on the preamble or known pilot symbols in the concurrent transmission signal to obtain multiple sets of different signal parameters; The multiple sets of different signal parameters are used to establish the multiple reference trajectory models.
6. The method according to claim 1, characterized in that, The calculation of the distance between each 3D feature point and each reference trajectory model, and the signal separation of the multiple symbols based on the distance comparison results, to obtain the symbol sequence corresponding to each data packet, includes: Calculate the minimum Euclidean distance from each 3D feature point to each reference trajectory model; Each 3D feature point is assigned to the trajectory cluster corresponding to the reference trajectory model with the smallest Euclidean distance, and the 3D feature points assigned to the same trajectory cluster are aggregated to obtain the symbol sequence corresponding to each data packet.
7. The method according to claim 1 or 4, characterized in that, The reference trajectory model is obtained based on the following formula: in, The initial frequency of the symbol. For signal amplitude, For frequency modulation slope, Symbol period, symbol index Determined by demodulation timing, t For time variables, For carrier frequency offset, For sampling frequency offset, For channel phase, I The amplitude value. This is the phase value.
8. A collision signal separation device based on three-dimensional clustering, characterized in that, The device includes: The signal receiving module is used to receive concurrently transmitted signals; The construction module is used to construct the feature points of each symbol in the target three-dimensional space of the multiple symbols contained in the concurrent transmission signal, and to establish multiple reference trajectory models with the same number of signals as the concurrent transmission signal; The signal separation module is used to calculate the distance between each three-dimensional feature point and each reference trajectory model, and to perform signal separation on the multiple symbols based on the distance comparison results to obtain the symbol sequence corresponding to each data packet; The target three-dimensional space is a three-dimensional space constructed based on amplitude, phase, and time.
9. An electronic device, characterized in that, It includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of the collision signal separation method based on three-dimensional clustering as described in any one of claims 1 to 7.
10. A computer-readable storage medium, characterized in that, It stores a computer program that, when executed by a processor, implements the steps of the conflict signal separation method based on three-dimensional clustering as described in any one of claims 1 to 7.