LoRa signal demodulation and decoding method and system based on blind identification

Abstract

The invention discloses a LoRa signal demodulation and decoding method and system based on blind identification, and the method comprises the steps: carrying out the down-conversion processing of a LoRa signal, and generating a baseband signal corresponding to the LoRa signal; identifying linear sweep frequency characteristics of the baseband signal based on short-time Fourier transform, and judging whether the baseband signal is a CSS modulation signal according to the linear sweep frequency characteristics; if the signal is a CSS modulation signal, detecting the signal bandwidth based on spectrum estimation, and identifying a spreading factor by adopting a peak detection and zero-cross detection method; and configuring parameters of a preset LoRa demodulation and decoding module in real time according to the signal bandwidth and the spreading factor, and demodulating and decoding the LoRa signal according to the configured LoRa demodulation and decoding module to obtain LoRa communication signal content. Through development of a deep learning-based adaptive detection algorithm, blind identification and parameter estimation of LoRa signals in a complex electromagnetic environment are realized, and then robust demodulation and decoding are completed.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of LoRa signal processing, and particularly relates to a LoRa signal demodulation and decoding method and system based on blind identification. BACKGROUND

[0002] In actual wireless environment monitoring, the electromagnetic spectrum environment presents a highly complex feature. Through spectrum monitoring equipment, it can be observed that a variety of radio signals coexist, such as frequency modulation broadcast signals (88-108 MHz), short wave radio signals (3-30 MHz), various digital communication signals (such as GSM, LTE, etc.), frequency hopping communication signals (such as military radio), sweep frequency test signals, Morse code signals and burst interference generated by industrial equipment. These signals are interwoven in time domain and frequency domain, forming a dense spectrum occupation state.

[0003] In this complex electromagnetic environment, three major technical challenges are faced to achieve reliable detection and demodulation of LoRa signals: first, LoRa signals use CSS modulation technology, and its wideband chirp characteristics make the signal power spectral density low, which is easy to be overwhelmed by strong signals; second, LoRa supports a variety of configurable parameters (including frequency point, bandwidth, spreading factor, etc.), and it is difficult to achieve signal feature matching under unknown parameters; finally, burst interference in the environment may cause signal truncation or distortion. SUMMARY

[0004] The application provides a LoRa signal demodulation and decoding method and system based on blind identification, which is used to solve the technical problem that it is difficult to achieve signal feature matching under unknown parameters.

[0005] In a first aspect, the application provides a LoRa signal demodulation and decoding method based on blind identification, comprising:

[0006] Wideband spectrum scanning captures LoRa signals in a wireless environment, and performs down-conversion processing on the LoRa signals to generate a baseband signal corresponding to the LoRa signals;

[0007] Based on short-time Fourier transform, the linear sweep feature of the baseband signal is identified, and whether it is a CSS modulation signal is determined according to the linear sweep feature;

[0008] If it is a CSS modulation signal, the signal bandwidth is detected based on spectrum estimation, and the spreading factor is identified by using peak detection and zero-crossing detection methods;

[0009] The parameters of a preset LoRa demodulation and decoding module are configured in real time according to the signal bandwidth and the spreading factor, and the LoRa signal is demodulated and decoded according to the configured LoRa demodulation and decoding module, to obtain the LoRa communication signal content.

[0010] In a second aspect, the present application provides a LoRa signal demodulation and decoding system based on blind recognition, comprising:

[0011] A generating module configured to capture a LoRa signal in a wireless environment through wideband spectrum scanning, and perform down-conversion processing on the LoRa signal to generate a baseband signal corresponding to the LoRa signal;

[0012] A determining module configured to identify a linear sweep feature of the baseband signal based on short-time Fourier transform, and determine whether the baseband signal is a CSS modulation signal according to the linear sweep feature;

[0013] An identifying module configured to, if the baseband signal is a CSS modulation signal, detect a signal bandwidth based on spectrum estimation, and identify a spreading factor using a peak detection and zero-crossing detection method;

[0014] A decoding module configured to real-time configure parameters of a preset LoRa demodulation and decoding module according to the signal bandwidth and the spreading factor, and perform demodulation and decoding on the LoRa signal according to the configured LoRa demodulation and decoding module to obtain LoRa communication signal content.

[0015] In a third aspect, an electronic device is provided, comprising at least one processor, and a memory connected to the at least one processor in communication, wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the steps of the LoRa signal demodulation and decoding method based on blind recognition of any embodiment of the present application.

[0016] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, and the program instructions are executed by a processor to enable the processor to perform the steps of the LoRa signal demodulation and decoding method based on blind recognition of any embodiment of the present application.

[0017] The LoRa signal demodulation and decoding method and system based on blind recognition of the present application can realize blind recognition and parameter estimation of LoRa signals in a complex electromagnetic environment through the development of a deep learning-based adaptive detection algorithm, and then complete robust demodulation and decoding. This method will significantly improve the interception and analysis capabilities of LoRa signals in spectrum monitoring, emergency communication and other scenarios. BRIEF DESCRIPTION OF DRAWINGS

[0018] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiment description. Obviously, the drawings in the following description are some embodiments of the present application, and those skilled in the art can also obtain other drawings according to these drawings without creative labor.

[0019] Figure 1 A flow chart of a LoRa signal demodulation and decoding method based on blind recognition provided by an embodiment of the present application is provided.

[0020] Figure 2 A system hardware block diagram of a specific embodiment provided by an embodiment of the present application is provided.

[0021] Figure 3 A system running block diagram of a specific embodiment provided by an embodiment of the present application is provided.

[0022] Figure 4 A LoRa signal parameter blind recognition flow chart of a specific embodiment provided by an embodiment of the present application is provided.

[0023] Figure 5 A structural block diagram of a LoRa signal demodulation and decoding system based on blind recognition provided by an embodiment of the present application is provided.

[0024] Figure 6 A structural diagram of an electronic device provided by an embodiment of the present application is provided. DETAILED DESCRIPTION

[0025] To make the purpose, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative labor fall within the scope of protection of the present application.

[0026] Please refer to Figure 1 , which shows a flow chart of a LoRa signal demodulation and decoding method based on blind recognition.

[0027] As shown in Figure 1 , the LoRa signal demodulation and decoding method based on blind recognition specifically includes the following steps:

[0028] Step S101, a LoRa signal in a wireless environment is captured by wideband spectrum scanning, and the LoRa signal is down-converted to generate a baseband signal corresponding to the LoRa signal.

[0029] Step S102, a linear sweep feature of the baseband signal is identified based on short-time Fourier transform, and whether it is a CSS modulated signal is determined according to the linear sweep feature.

[0030] In this step, the signal-to-noise ratio of the baseband signal is calculated by the power spectrum density method, and when the signal-to-noise ratio is lower than a threshold, it is determined as a non-LoRa signal; the baseband signal detected by the signal-to-noise ratio is subjected to FFT frequency domain analysis to identify the linear sweep frequency characteristics of the baseband signal; and whether the baseband signal is a CSS modulation signal is determined according to the linear sweep frequency characteristics.

[0031] In step S103, if it is a CSS modulation signal, the signal bandwidth is detected based on spectrum estimation, and the peak value detection and zero-crossing detection methods are used to identify the spreading factor.

[0032] In this step, the signal bandwidth is accurately estimated by the 3dB bandwidth method; the fixed Chirp sequence characteristics of the preamble are used to measure the symbol duration and estimate the spreading factor by the peak value detection and zero-crossing detection methods.

[0033] In step S104, the parameters of the preset LoRa demodulation and decoding module are configured in real time according to the signal bandwidth and the spreading factor, and the LoRa signal is demodulated and decoded according to the configured LoRa demodulation and decoding module to obtain the LoRa communication signal content.

[0034] In summary, the method of the present application realizes blind identification and parameter estimation of LoRa signals in a complex electromagnetic environment by developing an adaptive detection algorithm based on deep learning, and then completes robust demodulation and decoding. This technology will significantly improve the interception and analysis capability of LoRa signals in spectrum monitoring, emergency communication and other scenarios.

[0035] In one specific embodiment, the LoRa signal blind identification and demodulation and decoding system is composed of three core technology modules: 1, signal capture and preprocessing; 2, parameter blind estimation; 3, adaptive demodulation and decoding.

[0036] Signal capture and preprocessing is the premise and basis of the LoRa signal blind identification and demodulation and decoding system. This module realizes wideband spectrum scanning based on the software radio architecture, monitors possible LoRa signals in the actual wireless environment, and generates baseband I / Q signals using digital down conversion processing.

[0037] Parameter blind identification is the key and core of the LoRa signal blind identification and demodulation and decoding system. First, the CSS modulation characteristics are identified by STFT to determine whether it is a LoRa modulation type, second, the signal bandwidth is detected based on spectrum estimation, and finally the peak value detection and zero-crossing detection methods are used to identify the spreading factor.

[0038] The adaptive demodulation decoding is a result of the LoRa signal blind identification and demodulation decoding system. According to signal capture and parameter blind identification, the LoRa demodulation decoding chip parameters are configured in real time, and finally the powerful demodulation capability of the LoRa demodulation decoding chip is used to successfully capture and restore the content of the LoRa communication signal.

[0039] The hardware block diagram of the LoRa signal demodulation decoding system is shown in Figure 2 The hardware block diagram of the LoRa signal demodulation decoding system is shown in

[0040] The LoRa signal capture demodulation decoding workflow is shown in Figure 3

[0041] In the signal capture and preprocessing system, the front-end radio frequency receiving unit first performs low-noise amplification, band-pass filtering and down-conversion processing on the air interface radio frequency signal, and converts it into a baseband signal suitable for digital processing. Subsequently, the system dynamically generates a local reference signal copy (i.e. background template) in the digital signal processor (DSP) of the control mainboard according to the prior characteristics of the target signal (such as the spreading code sequence, the modulation format or the specific synchronization header). The received baseband signal and the local template are matched and filtered by sliding cross-correlation operation, realizing the joint search detection of signal existence, code phase and carrier frequency offset. When the correlation operation output peak significantly exceeds the preset detection threshold (representing that the signal energy exceeds the statistical characteristics of background noise and interference), it is determined that a potential abnormal signal is captured, and its instantaneous center frequency and time offset are accurately locked. The capture state information triggers the FPGA coprocessor unit to start the parameter blind identification module. This module implements non-cooperative parameter estimation for unknown signals, using high-order cyclic spectrum analysis, signal cyclic stationary characteristic detection or maximum likelihood estimation techniques to blindly identify key physical layer parameters (such as symbol rate, spreading factor, bandwidth, etc.). For signals identified as LoRa modulation type in the identification results, the estimated accurate parameter set (including spreading factor SF, bandwidth BW, coding rate CR, etc.) is input to the LoRa dedicated demodulation module. The module performs coherent or non-coherent demodulation with adaptive parameters, and completes channel decoding, finally realizing robust information bit stream recovery.

[0042] The parameter blind identification runs in the PS end of ZYNQ, and the LoRa signal parameter identification flow chart is shown in Figure 4

[0043] ​​First, the power spectrum density method is used to calculate the signal-to-noise ratio of the baseband signal after capture. When the signal-to-noise ratio is lower than the threshold, it is determined to be a non-LoRa signal. Then, the baseband signal passing through the signal-to-noise ratio detection is analyzed in the frequency domain by FFT, and the linear sweep feature of the LoRa signal is identified. Finally, if the signal is a CSS modulated signal, the 3dB bandwidth method is used to accurately estimate the signal bandwidth, and the fixed Chirp sequence feature of the preamble is used to measure the symbol duration by peak detection and zero-crossing detection method, and estimate the signal spreading factor.

[0044] The adaptive demodulation and decoding is realized based on the Semtech SX1276 radio frequency transceiver chip. The SX1276 chip is a radio frequency transceiver based on LoRa spread spectrum modulation and demodulation technology developed by Semtech Company, with a bandwidth range of 7.8-500kHz and a spreading factor of 6-12, covering all available frequency bands. After capturing the LoRa signal, the processor sends the estimated parameters such as frequency, bandwidth and spreading factor to the SX1276 through the SPI interface, and configures it as a receiver mode. The SX1276 chip automatically completes the demodulation and decoding of the LoRa signal.

[0045] Please refer to Figure 5 , which shows the structure block diagram of a LoRa signal demodulation and decoding system based on blind identification according to the present application.

[0046] As shown in Figure 5 , the LoRa signal demodulation and decoding system 200 includes a generation module 210, a determination module 220, an identification module 230 and a decoding module 240.

[0047] The generation module 210 is configured to capture the LoRa signal in the wireless environment by wideband spectrum scanning, and to generate a baseband signal corresponding to the LoRa signal by down-conversion processing of the LoRa signal. The determination module 220 is configured to identify the linear sweep feature of the baseband signal based on short-time Fourier transform, and to determine whether it is a CSS modulated signal according to the linear sweep feature. The identification module 230 is configured to detect the signal bandwidth based on spectrum estimation if it is a CSS modulated signal, and to identify the spreading factor by peak detection and zero-crossing detection method. The decoding module 240 is configured to real-time configure the parameters of the preset LoRa demodulation and decoding module according to the signal bandwidth and the spreading factor, and to demodulate and decode the LoRa signal by the configured LoRa demodulation and decoding module to obtain the LoRa communication signal content.

[0048] It should be understood that Figure 5 the modules described in the specification correspond to each step in the method described with reference to Figure 1 . Therefore, the operations and features described above for the method and the corresponding technical effects are also applicable to Figure 5modules in the system, and will not be described here again.

[0049] In some other embodiments, the present application also provides a computer readable storage medium, which stores a computer program, and the program instructs a processor to execute a LoRa signal demodulation and decoding method based on blind identification in any of the above method embodiments when executed by the processor.

[0050] As an implementation form, the computer readable storage medium of the present application stores computer executable instructions, and the computer executable instructions are configured to:

[0051] The wideband spectrum scanning acquires a LoRa signal in a wireless environment, and performs a down-conversion processing on the LoRa signal to generate a baseband signal corresponding to the LoRa signal;

[0052] The short-time Fourier transform is used to identify a linear sweep feature of the baseband signal, and the linear sweep feature is used to determine whether the signal is a CSS modulation signal;

[0053] If the signal is a CSS modulation signal, a spectrum estimation is used to detect a signal bandwidth, and a peak detection and zero-crossing detection method is used to identify a spreading factor;

[0054] The parameters of a preset LoRa demodulation and decoding module are configured in real time according to the signal bandwidth and the spreading factor, and the LoRa signal is demodulated and decoded according to the configured LoRa demodulation and decoding module to obtain a LoRa communication signal content.

[0055] The computer readable storage medium can include a program storage area and a data storage area, wherein the program storage area can store an operating system and at least one application required by a function; and the data storage area can store data created according to the use of the LoRa signal demodulation and decoding system based on blind identification. In addition, the computer readable storage medium can include a high-speed random access memory, and can also include a memory such as at least one magnetic disk storage device, a flash memory device, or other non-volatile solid-state memory device. In some embodiments, the computer readable storage medium can optionally include a memory remotely arranged relative to the processor, and these remote memories can be connected to the LoRa signal demodulation and decoding system based on blind identification through a network. Examples of the above network include but are not limited to the Internet, an intranet, a local area network, a mobile communication network, and a combination thereof.

[0056] Figure 6 is a structural schematic diagram of an electronic device provided by the embodiment of the present application, as Figure 6As shown, the device includes a processor 310 and a memory 320. The electronic device can also include an input device 330 and an output device 340. The processor 310, the memory 320, the input device 330 and the output device 340 can be connected by a bus or other means, Figure 6 The memory 320 is the computer readable storage medium described above. The processor 310 performs various functional applications and data processing of the server by running the non-volatile software programs, instructions and modules stored in the memory 320, that is, implements the LoRa signal demodulation and decoding method based on blind identification of the method embodiments described above. The input device 330 can receive input digital or character information, and generate key signal input related to user settings and function control of the LoRa signal demodulation and decoding system based on blind identification. The output device 340 can include a display device such as a display screen.

[0057] The electronic device described above can perform the method provided by the embodiments of the application, and has the corresponding function modules and beneficial effects of performing the method. Technical details not described in detail in the present embodiment can be referred to the method provided by the embodiments of the application.

[0058] As an implementation, the electronic device described above is applied to the LoRa signal demodulation and decoding system based on blind identification, and is used for a client, including: at least one processor; and a memory in communication connection with the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to:

[0059] Capture the LoRa signal in the wireless environment by wideband spectrum scanning, and generate a baseband signal corresponding to the LoRa signal by down-conversion processing on the LoRa signal;

[0060] Identify the linear sweep feature of the baseband signal based on short-time Fourier transform, and determine whether it is a CSS modulation signal according to the linear sweep feature;

[0061] If it is a CSS modulation signal, detect the signal bandwidth based on spectrum estimation, and identify the spreading factor by using peak detection and zero-crossing detection methods;

[0062] Real-time configure the parameters of a preset LoRa demodulation and decoding module according to the signal bandwidth and the spreading factor, and demodulate and decode the LoRa signal according to the configured LoRa demodulation and decoding module to obtain the LoRa communication signal content.

[0063] The device embodiments described above are merely illustrative, wherein the units illustrated as separate components can or can not be physically separate, and the components illustrated as units can or can not be physical units, i.e., can be located in one place, or can be distributed to multiple network units. Part or all of the modules can be selected to achieve the purposes of the embodiments according to actual needs. Those skilled in the art can understand and implement without creative labor.

[0064] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, and not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that the technical solutions recorded in the foregoing embodiments can still be modified, or some technical features can be replaced by equivalents; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A LoRa signal demodulation and decoding method based on blind recognition, characterized in that, The method comprises: capturing a LoRa signal in a wireless environment through wideband spectrum scanning, and performing down-conversion processing on the LoRa signal to generate a baseband signal corresponding to the LoRa signal; identifying a linear sweep feature of the baseband signal based on short-time Fourier transform, and determining whether the baseband signal is a CSS modulation signal according to the linear sweep feature; if the baseband signal is a CSS modulation signal, detecting a signal bandwidth based on spectrum estimation, and identifying a spreading factor by using peak detection and zero-crossing detection methods; configuring parameters of a preset LoRa demodulation and decoding module in real time according to the signal bandwidth and the spreading factor, and performing demodulation and decoding on the LoRa signal according to the configured LoRa demodulation and decoding module to obtain LoRa communication signal content.

2. The LoRa signal demodulation and decoding method based on blind recognition according to claim 1, characterized in that, The down-conversion processing on the LoRa signal to generate a baseband signal corresponding to the LoRa signal comprises: performing low-noise amplification, band-pass filtering and down-conversion processing on the LoRa signal to obtain a baseband signal suitable for digital output. 3.The LoRa signal demodulation and decoding method based on blind recognition according to claim 1, characterized in that, The identifying a linear sweep feature of the baseband signal based on short-time Fourier transform, and determining whether the baseband signal is a CSS modulation signal according to the linear sweep feature comprises: calculating a signal-to-noise ratio of the baseband signal by using a power spectral density method, and determining that the baseband signal is not a LoRa signal when the signal-to-noise ratio is lower than a threshold; performing FFT frequency domain analysis on the baseband signal detected by the signal-to-noise ratio to identify a linear sweep feature of the baseband signal; determining whether the baseband signal is a CSS modulation signal according to the linear sweep feature.

4. The LoRa signal demodulation and decoding method based on blind recognition according to claim 1, characterized in that, The detecting a signal bandwidth based on spectrum estimation, and identifying a spreading factor by using peak detection and zero-crossing detection methods comprises: accurately estimating the signal bandwidth by using a 3dB bandwidth method; measuring a symbol duration and estimating a spreading factor by using peak detection and zero-crossing detection methods based on a fixed Chirp sequence feature of a preamble.

5. The LoRa signal demodulation and decoding method based on blind recognition according to claim 1, characterized in that, After determining whether the baseband signal is a CSS modulation signal according to the linear sweep feature, the method further comprises: if the baseband signal is not a CSS modulation signal, stopping demodulation and decoding of the baseband signal. 6.A LoRa signal demodulation and decoding system based on blind recognition, characterized in that, The method comprises: a generating module configured to capture a LoRa signal in a wireless environment through wideband spectrum scanning, and perform down-conversion processing on the LoRa signal to generate a baseband signal corresponding to the LoRa signal; a determining module configured to identify a linear sweep feature of the baseband signal based on short-time Fourier transform, and determine whether the baseband signal is a CSS modulation signal according to the linear sweep feature; an identifying module configured to, if the baseband signal is a CSS modulation signal, detect a signal bandwidth based on spectrum estimation, and identify a spreading factor by using peak detection and zero-crossing detection methods; a decoding module configured to configure parameters of a preset LoRa demodulation and decoding module in real time according to the signal bandwidth and the spreading factor, and perform demodulation and decoding on the LoRa signal according to the configured LoRa demodulation and decoding module to obtain LoRa communication signal content.

7. An electronic device, comprising: The method comprises: at least one processor, and a memory connected with the at least one processor in communication, wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method of any one of claims 1 to 5.

8. A computer-readable storage medium having stored thereon a computer program, characterized in that, The program, when executed by a processor, implements the method of any one of claims 1 to 5. The program, when executed by a processor, implements the method of any one of claims 1 to 5.

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