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WO2023016168A1 - Procédé et appareil d'identification de signal, et support de stockage lisible par ordinateur - Google Patents

Procédé et appareil d'identification de signal, et support de stockage lisible par ordinateur Download PDF

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Publication number
WO2023016168A1
WO2023016168A1 PCT/CN2022/104966 CN2022104966W WO2023016168A1 WO 2023016168 A1 WO2023016168 A1 WO 2023016168A1 CN 2022104966 W CN2022104966 W CN 2022104966W WO 2023016168 A1 WO2023016168 A1 WO 2023016168A1
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signal
signal data
data
network
domain
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Chinese (zh)
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陈建强
李云
曹啡
徐殿平
张海军
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ZTE Corp
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ZTE Corp
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    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/10—Pre-processing; Data cleansing
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
    • G06F18/213—Feature extraction, e.g. by transforming the feature space; Summarisation; Mappings, e.g. subspace methods
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
    • G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/0464—Convolutional networks [CNN, ConvNet]

Definitions

  • the embodiments of the present application relate to but are not limited to the technical field of wireless communication, and in particular, relate to a signal identification method and device, and a computer-readable storage medium.
  • Wireless communication technology because of its mobility, high efficiency, low-cost deployment and maintenance, no need for difficult wiring engineering and maintenance measures, highly flexible networking operations, good scalability, and simple user addition and deletion operations without It will affect the overall performance of the communication network and other advantages, and has been developed rapidly in recent years.
  • ISM International Scientific Medical
  • a frequency band that does not require a license or fee that is, the ISM frequency band, has been set up, and services such as wireless metropolitan area network and wireless local area network have been generated, making more and more Many products using this frequency band have a wider range of applications in various fields such as smart home, medical health and public infrastructure.
  • interference identification methods usually need to analyze and identify based on a large amount of signal data in the area, which consumes a lot of resources and is not simple, and does not actually consider the impact of field offset caused by different signal data collection environments , so the recognition accuracy is not high.
  • Embodiments of the present application provide a signal identification method and device, and a computer-readable storage medium.
  • the embodiment of the present application provides a signal identification method, including: obtaining wireless signal data from the target domain; determining the second identification network for the target domain signal according to the wireless signal data and the first identification network for the source domain signal Identifying networks, the first identifying network is derived from the first signal data obtained from the source domain.
  • the embodiment of the present application also provides a signal identification device, including: a data acquisition module configured to acquire wireless signal data from the target domain; a data processing module configured to A first recognition network for a domain signal determines a second recognition network for a target domain signal, the first recognition network being derived from first signal data obtained from a source domain.
  • the embodiment of the present application also provides a signal identification device, including: a memory, a processor, and a computer program stored in the memory and operable on the processor, and the processor implements the computer program when executing the computer program.
  • a signal identification device including: a memory, a processor, and a computer program stored in the memory and operable on the processor, and the processor implements the computer program when executing the computer program.
  • the embodiment of the present application further provides a computer-readable storage medium storing computer-executable instructions, and the computer-executable instructions are used to execute the signal identification method in the first aspect as described above.
  • Fig. 1 is a flowchart of a signal recognition method provided by an embodiment of the present application
  • Fig. 2 is a flow chart of acquiring wireless signal data in a signal identification method provided by an embodiment of the present application
  • Fig. 3 is a flowchart of determining wireless signal data in a signal identification method provided by an embodiment of the present application
  • Fig. 4 is a flow chart of determining the second identification network in the signal identification method provided by one embodiment of the present application.
  • Fig. 5 is a flow chart of updating the first identification network in the signal identification method provided by one embodiment of the present application.
  • Fig. 6 is a flow chart after determining wireless signal data in the signal identification method provided by an embodiment of the present application.
  • Fig. 7 is a flow chart after determining the signal data to be identified in the signal identification method provided by one embodiment of the present application.
  • Fig. 8 is a schematic diagram of execution of a signal identification method provided by an embodiment of the present application.
  • Fig. 9 is a schematic diagram of execution of the WADA algorithm provided by an embodiment of the present application.
  • Fig. 10 is a schematic diagram of a signal identification device provided by an embodiment of the present application.
  • Fig. 11 is a schematic diagram of a signal identification device provided by another embodiment of the present application.
  • the present application provides a signal identification method and device, and a computer-readable storage medium.
  • the first identification network capable of identifying the source domain signal
  • the second identification network so as to realize the domain transfer of the identification signal, is conducive to improving the accuracy of signal identification, and because the signal identification can be realized through wireless signal data, it is not necessary to analyze and identify based on a large amount of signal data in the area, thus It can save resources and reduce the difficulty of signal identification.
  • FIG. 1 is a flow chart of a signal identification method provided by an embodiment of the present application, and the signal identification method includes but not limited to step S100 and step S200 .
  • Step S100 acquiring wireless signal data from the target domain.
  • the target domain is distinguished from the source domain, wherein the source domain represents a known environment or an original environment.
  • the source domain represents a known environment or an original environment.
  • wireless signals in the source domain have been well identified; the target domain represents an unknown environment or a new environment.
  • the target domain represents an unknown environment or a new environment.
  • there are domain differences between the target domain and the source domain there are domain differences between the target domain and the source domain.
  • this may bring about differences in signal recognition, so the signal recognition of the target domain needs to be further expanded.
  • wireless signal data there may be multiple types of wireless signal data.
  • different types of wireless signal data it can be processed in a subsequent selection and adaptive manner.
  • the wireless signal data is mainly used as the The WIFI signal is described, and the corresponding data processing method is also described based on the fact that the wireless signal data is a WIFI signal, but those skilled in the art can make corresponding settings according to actual application scenarios, which is not limited in this embodiment.
  • step S100 includes but not limited to steps S110 to S130.
  • Step S110 obtaining original signal data from the target domain
  • Step S120 screening the original signal data to obtain second signal data carrying effective signals
  • Step S130 determining wireless signal data from the second signal data.
  • the invalid second signal data can be eliminated by screening the original signal data, so as to further obtain valid wireless signal data according to the second signal data, so as to ensure that the wireless signal data has a good practical effect, so as to improve the subsequent Process the reliability of wireless signal data, thereby improving the accuracy of signal recognition.
  • a wireless access point can be set to obtain the original signal data, and at least one terminal connected to the AP can be set for analysis, that is, the obtained original signal data can be obtained by the AP.
  • the signal data is transmitted to the terminal, and then the terminal is used to analyze and screen the original signal data so as to extract effective second signal data as signal samples.
  • the corresponding data carrying noise signals in the original signal data can be filtered out to obtain all data that does not contain noise signals.
  • the signal other than the noise signal is the effective signal.
  • the effective signal can have many types, including the signal that transmits the information required by the user, or is used to make the receiving device generate a preset signal after receiving the signal. Action signals, etc.
  • all the data obtained by screening are the second signal data, but the "effective signal" here is not a restrictive concept, and can be distinguished according to different actual application scenarios, not limited to the elimination of noise signals , those skilled in the art can set the distinguishing form by themselves, which is not limited in this embodiment.
  • multiple terminals can be set to match the AP, and each terminal can be called an access terminal, a user equipment (User Equipment, UE), a user unit, a user station, a mobile station, a mobile station, or a remote station , remote terminal, mobile device, user terminal, wireless communication device, user agent, or user device.
  • UE User Equipment
  • each terminal may be a cellular phone, a cordless phone, a Session Initiation Protocol (SIP) phone, a Wireless Local Loop (WLL) station, a Personal Digital Assistant (PDA), a Handheld devices with wireless communication functions, computing devices or other processing devices connected to wireless modems, vehicle-mounted devices, wearable devices, 5G networks or terminal devices in future 5G or higher networks, etc., are not specifically limited in this embodiment.
  • SIP Session Initiation Protocol
  • WLL Wireless Local Loop
  • PDA Personal Digital Assistant
  • step S130 includes but not limited to step S131.
  • Step S131 determining from the second signal data that the second signal data having a wireless signal protocol stack indication bit is wireless signal data.
  • the wireless signal protocol stack defines the characteristics of communication hardware and software for coordinating work at different levels for wireless signals
  • the wireless signal protocol stack can be determined It has been set, indicating that the wireless signal exists correspondingly, so that it can be determined that the second signal data with the indication bit of the wireless signal protocol stack is wireless signal data. Therefore, the wireless signal data can be easily and reliably screened in this way, and the signal classification can be improved.
  • the wireless signal protocol stack indicator bit is the WIFI signal protocol stack indicator bit, and so on, which will not be repeated here.
  • Step S200 determining a second identification network for a signal in a target domain according to wireless signal data and a first identification network for a signal in a source domain, where the first identification network is obtained from the first signal data obtained from the source domain.
  • the source domain is a known environment, and the relevant signal can be accurately identified according to the first identification network, because the first identification network is obtained from the first signal data in the source domain, so Taking the deep convolutional neural network as an example, the qualified first recognition network is obtained by training the first signal data, and the target domain is a new environment, which is affected by the domain migration of the signal, so the first recognition network alone cannot accurately Identify related information, in order to solve the above difficulties, on the basis of the first identification network that can identify the source domain signal, cooperate with the wireless signal data obtained from the target domain to determine the second identification network that can identify the target domain signal, so as to realize the identification Signal domain migration, that is, extending the signal recognition domain from the original target domain to the current target domain, can not only identify the wireless signals in the original source domain, but also identify the wireless signals in the current target domain, thus greatly improving the signal quality.
  • the area range of identification can improve the identification accuracy for wireless signals, and because the signal identification can be realized through wireless signal data, it is not necessary to analyze and identify based on a large amount of signal data in the area, and also There is no need to add new network architecture or parameters for auxiliary identification, which can save relevant resources and further reduce the difficulty of signal identification.
  • the first signal data there may be multiple types of the first signal data, for example, it may involve signals such as Bluetooth (Bluetooth) signals, cordless phone (Cordless Phone) signals, and game controller (Game Controller) commonly used in the ISM frequency band. signal, microwave oven (Microwave Oven) signal, wireless fidelity (Wireless Fidelity, WIFI) signal, wireless analog video monitor (Video Monitor) signal and ZigBee signal etc., in other words, the first signal data in the present embodiment can be above One or more of each example, which is not limited in this embodiment.
  • signals such as Bluetooth (Bluetooth) signals, cordless phone (Cordless Phone) signals, and game controller (Game Controller) commonly used in the ISM frequency band. signal, microwave oven (Microwave Oven) signal, wireless fidelity (Wireless Fidelity, WIFI) signal, wireless analog video monitor (Video Monitor) signal and ZigBee signal etc.
  • the first signal data in the present embodiment can be above One or more of each example, which is not limited in this embodiment.
  • first signal data, the first recognition network and the second recognition network can be selected or set up according to the actual application scenario.
  • the following is mainly based on the background of deep convolutional neural network for purposes of illustration, but not limitation.
  • step S200 includes but not limited to step S210 and step S220.
  • Step S210 input the wireless signal data and the first signal data obtained from the source domain into the feature extraction network, so that the feature extraction network extracts and outputs the first sample feature corresponding to the source domain signal and the first sample feature corresponding to the target domain signal Two-sample features;
  • Step S220 updating the first identification network for the source domain signal according to the first sample feature and the second sample feature, and determining the updated first identification network as the second identification network for the target domain signal.
  • the feature extraction network can analyze the wireless signal data and the first signal data obtained from the source domain, it can output the first sample features corresponding to the source domain signal and the second sample corresponding to the target domain signal Features, that is, the signal states of the source domain and the target domain can be known separately, so that the processed first recognition network can be updated according to the signal states of the source domain and the target domain, which is equivalent to the first sample feature.
  • the influence of the second sample feature is further considered to obtain an updated first recognition network, that is, to determine the second recognition network so that the second recognition network can recognize signals in the target domain.
  • step S220 “update the first recognition network for the source domain signal according to the first sample feature and the second sample feature" in step S220 includes but not limited to step S221 and step S222.
  • Step S221 in the case that the first sample feature and the second sample feature are mutually mapped between the source domain and the target domain, determine the first loss function according to the mapping condition, and the mapping condition is used to characterize the first sample before and after mutual mapping
  • the data categories corresponding to the feature and the second sample feature are unchanged;
  • Step S222 updating the first recognition network for the source domain signal according to the first loss function and the preset second loss function.
  • first sample features and second sample features there can be multiple first sample features and second sample features and the numbers can be consistent, and each first sample feature and each second sample feature can correspond to each other, which is beneficial to the first sample
  • the features and the second sample features are mutually mapped to each other.
  • the mapping condition characterizes cycle consistency (Cycle Consistency, CC), indicating that in the case where the first sample feature and the second sample feature are mutually mapped between the source domain and the target domain, regardless of the first sample
  • the feature is mapped to the second sample feature several times, or the second sample feature is mapped to the first sample feature several times, and the category of the first sample feature or the second sample feature before and after the mapping will not change, for example,
  • the category of the first sample feature is the category corresponding to the Bluetooth signal, then the category of the first sample feature after mapping is still the category corresponding to the Bluetooth signal, or, the category of the first sample feature obeys a uniform distribution, then after mapping
  • the transformed data category also obeys the uniform distribution, and the first loss function is determined by mapping conditions, and the first recognition network is updated based on the first loss function and the preset second loss function, so that the first sample features and the second sample
  • the variation of features after data processing will not be too large, which can reduce the error of data processing and improve the data correlation between
  • the above-mentioned first loss function can be minimized by simply associating the target domain Sample data implementation, that is, it may only mine a small part of the common features between the two domains, but cannot characterize the more general similarity features, so the introduction of the preset second loss function can better characterize The characteristics of domain migration can achieve the effect of reducing errors, which is conducive to further improving the accuracy of signal recognition.
  • “mutual mapping” refers to the round-trip mapping between the first sample feature and the second sample feature between the source domain and the target domain, or called circular mapping.
  • a first sample feature can be continuously A second sample feature is mapped from the source domain to the target domain, and then the second sample feature is mapped from the target domain back to another first sample feature in the source domain, which is equivalent to the first sample feature and the second sample feature
  • the two-sample feature completes a basic mutual mapping process between the source domain and the target domain.
  • the above mutual mapping process can also be performed multiple times, but in any case it can be called mutual mapping.
  • a second sample feature can be Continuously map from the target domain to a certain first sample feature in the source domain, and then the first sample feature is mapped from the source domain back to another second sample feature in the target domain.
  • the principle is the same as that described above. The principle of the mapping process is similar and will not be repeated here.
  • the second loss function may include but not limited to at least one of the following types:
  • a second label loss function associated with the wireless signal data is A second label loss function associated with the wireless signal data.
  • the integrity loss function can make up for the possible lack of relevance of the first loss function.
  • the integrity loss function consists of the uniform distribution of sample data on the target domain and accessing the target from any sample data on the source domain.
  • the cross-entropy definition between the probability of the sample data on the domain, when there is a difference in the data category distribution of the source domain and the target domain, the weight for the integrity loss function can be appropriately reduced.
  • the first label loss function and the second label loss function characterize the intra-domain correlation, as a supplement to the inter-domain correlation function, which can further improve the loss adjustment for domain transfer.
  • the framework used in the embodiments of the present application for signal recognition is simple and reliable, easy to implement and process, and is applicable to data collection scenarios in different environments, using the limited wireless signal data and the first signal data through the same feature extraction Network, to achieve the purpose of constructing the feature vectors of two domain samples, and at the same time construct the predicted label cycle consistency of source domain sample features and target domain sample features through inter-domain conversion, combined with integrity regularization requirements and traditional supervised loss as a total
  • the loss function updates the first recognition network to obtain the second recognition network.
  • the second recognition network can not only inherit the invariance of the source domain to the domain offset, but also achieve high recognition of wireless signals in the target domain, improving Domain generalization in the source domain and signal test performance in the target domain.
  • step S132 is also included but not limited to.
  • Step S132 determining from the second signal data that the second signal data except the wireless signal data is the signal data to be identified in the target domain.
  • the wireless signal data is mainly used for data processing so that the second identification network can reliably identify the signal, it is not necessary to re-identify the determined wireless signal data , and because the second signal data is unknown data obtained from the target domain, the second signal data other than the wireless signal data can be used as the signal data to be identified, so as to identify the signal data to be identified, thereby Verify the effect of domain transfer from the source domain to the target domain.
  • wireless signal data may also be identified multiple times, which is not limited in this embodiment.
  • step S133 is also included but not limited to.
  • Step S133 inputting the signal data to be recognized to the second recognition network, so that the second recognition network outputs a signal recognition result for the signal data to be recognized.
  • the second signal data other than the wireless signal data is used as the signal data to be identified to prevent the wireless signal data from affecting the identification, and the signal data to be identified is identified through the second identification network, which can be well detected.
  • the construction effect of the second identification network further verifies the domain transfer effect from the source domain to the target domain.
  • FIG. 8 a schematic diagram of an execution flow for realizing signal identification is given.
  • the original signal data is obtained based on the first AP; then, the first terminal extracts the effective part of the original signal data to obtain the second signal data; then, the second signal data is detected, that is, for the second signal data Extract the WIFI signal protocol stack indicator bit to distinguish the WIFI signal sample from the rest of the interference signal samples in the target domain;
  • the adaptive correction algorithm (hereinafter referred to as "WADA algorithm") realizes the adaptive correction of the first recognition network in the target domain environment, and obtains the second recognition network.
  • the WADA algorithm is Domain Adaptation (Domain Adaptation) in transfer learning.
  • the average accuracy rate of the corrected recognition network for various interference signals in the target domain is more than 90%, while the second recognition network can maintain the accuracy of various interference signals in the source domain.
  • the average accuracy rate also remains above 90%, which can meet the accuracy requirements of signal recognition.
  • FIG. 9 a schematic diagram of the principle of the WADA algorithm is given.
  • the network structure corresponding to the WADA algorithm includes a feature extraction network convolutional neural architecture and a category prediction network fully connected architecture.
  • the basic principles are as follows:
  • association mapping is performed for the sample features of the source domain and the target domain:
  • H(T, P aba ) means cross entropy
  • class() indicates the category of the sample.
  • the overall loss function also includes the labeled cross-entropy loss L cla1 of the source domain and the labeled loss L cla2 of the WIFI data of the target domain.
  • ⁇ 1 , ⁇ 2 ⁇ [0,1] are weight parameters.
  • the first recognition network for the source domain becomes the second recognition network that can adapt to the current target domain environmental data through the correction of WIFI signal samples, that is, the category prediction network is fully connected as shown in Figure 9 Architecture, where the fully connected architecture of the category prediction network is a convolutional network model, including several sequentially connected convolutional layers, batch normalization layers, and pooling layers, and then passes through several fully connected layers after global average pooling , and finally output the category prediction results for the current target domain environment data.
  • the fully connected architecture of the category prediction network is a convolutional network model, including several sequentially connected convolutional layers, batch normalization layers, and pooling layers, and then passes through several fully connected layers after global average pooling , and finally output the category prediction results for the current target domain environment data.
  • an embodiment of the present application also provides a signal identification device, which includes:
  • the data acquisition module 100 is configured to acquire wireless signal data from the target domain
  • the data processing module 200 is configured to determine a second identification network for the target domain signal according to the wireless signal data and the first identification network for the source domain signal, and the first identification network is obtained from the first signal data obtained from the source domain.
  • the data processing module 200 can determine the signal that can identify the target domain signal based on the wireless signal data and the first identification network for the source domain signal.
  • the second identification network thereby realizing the field migration of the identification signal, is conducive to improving the accuracy of signal identification, and, because the data processing module 200 can cooperate to realize signal identification through wireless signal data, that is, there is no need to perform signal identification based on a large amount of signal data in the area. Analysis and recognition, therefore, for the data processing module 200, resources can be saved and the difficulty of signal recognition can be reduced.
  • an embodiment of the present application also provides a signal recognition device, which includes: a memory, a processor, and a computer program stored in the memory and operable on the processor.
  • the processor and memory can be connected by a bus or other means.
  • the non-transitory software programs and instructions required to realize the signal recognition methods of the above-mentioned embodiments are stored in the memory, and when executed by the processor, the signal recognition methods of the above-mentioned embodiments are executed, for example, the above-described implementation of the above-described FIG. 1 Method steps S100 to S200, method steps S110 to S130 in FIG. 2 , method steps S131 in FIG. 3 , method steps S210 to S220 in FIG. 4 , method steps S221 to S222 in FIG. 5 , method steps in FIG. 6 S132 or method step S133 in FIG. 7 .
  • the device embodiments described above are only illustrative, and the units described as separate components may or may not be physically separated, that is, they may be located in one place, or may be distributed to multiple network units. Part or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment.
  • an embodiment of the present application also provides a computer-readable storage medium, the computer-readable storage medium stores computer-executable instructions, and the computer-executable instructions are executed by a processor or a controller, for example, by the above-mentioned Execution by a processor in the device embodiment can cause the processor to execute the signal identification method in the above embodiment, for example, execute the method steps S100 to S200 in FIG. 1 and the method steps S110 to S130 in FIG. 2 described above. , method step S131 in FIG. 3 , method steps S210 to S220 in FIG. 4 , method steps S221 to S222 in FIG. 5 , method step S132 in FIG. 6 or method step S133 in FIG. 7 .
  • the embodiment of the present application includes: obtaining wireless signal data from the target domain; determining a second identification network for the target domain signal according to the wireless signal data and the first identification network for the source domain signal, and the first identification network is obtained from the first identification network obtained from the source domain.
  • a signal data is obtained.
  • the second identification network capable of identifying the target domain signal can be determined by cooperating with the wireless signal data obtained from the target domain, thereby realizing identification
  • the domain migration of signals is conducive to improving the accuracy of signal identification, and since signal identification can be realized through wireless signal data, there is no need to analyze and identify based on a large amount of signal data in the area, which can save resources and reduce the difficulty of signal identification.
  • Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disk (DVD) or other optical disk storage, magnetic cartridges, tape, magnetic disk storage or other magnetic storage devices, or can Any other medium used to store desired information and which can be accessed by a computer.
  • communication media typically embodies computer readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave or other transport mechanism, and may include any information delivery media .

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Abstract

L'invention concerne un procédé et un appareil d'identification de signal, et un support de stockage lisible par ordinateur. Le procédé d'identification de signal consiste à : obtenir des données de signal sans fil à partir d'un domaine cible (S100) ; et selon les données de signal sans fil et un premier réseau d'identification pour un signal de domaine source, déterminer un second réseau d'identification pour un signal de domaine cible, le premier réseau d'identification étant obtenu à partir de premières données de signal obtenues à partir d'un domaine source (S200).
PCT/CN2022/104966 2021-08-10 2022-07-11 Procédé et appareil d'identification de signal, et support de stockage lisible par ordinateur Ceased WO2023016168A1 (fr)

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