A long-distance reflection communication method and system under extremely weak signal-to-noise ratio conditions

Abstract

This invention discloses a method and system for long-distance reflection communication under extremely weak signal-to-noise ratio conditions. The method includes the following steps: S1, performing signal enhancement and preamble detection on the original signal to identify and detect the reflected signal; S2, preprocessing the reflected signal detected in step 1; S3, decoding the processed reflected signal in step 2 to generate a new signal. This invention can enhance the signal-to-noise ratio, reduce the decoding signal-to-noise ratio threshold, and extend the battery life of communication equipment.

Description

Technical Field

[0001] This invention relates to the field of reflection communication, and in particular to a reflection communication method and system for long-distance, extremely weak signal-to-noise ratio communication. Background Technology

[0002] Reflection communication, also known as backscatter communication, differs from previous communication methods, which are mostly considered active communication methods. In these methods, the sender actively generates electromagnetic waves and modulates and transmits signals based on these waves. Backscatter communication employs a different approach: the sender does not need to actively generate a signal; instead, it communicates by reflecting electromagnetic waves generated by other devices.

[0003] Backscattering is typically achieved by the following basic idea: the transmitter controls its antenna to switch between two states: complete signal absorption and complete signal reflection. Consequently, the reflected signal will have different amplitudes, which can be used to represent different information.

[0004] When an electromagnetic wave encounters the boundary between two media with different impedances during propagation, the wave will be absorbed or reflected to some extent. Therefore, information transmission can be achieved simply by switching the impedance at the antenna. Reflective communication technology can reduce the power consumption of radio frequency devices by several orders of magnitude, thus offering significant advantages in various Internet of Things (IoT) applications.

[0005] If an excitation signal Sin is sent to a reflective communication device, its reflected signal Sout can be described by the following formula:

[0006]

[0007] Where Za and Zc represent the impedance of the antenna (typically 50Ω50Ω) and the impedance of the circuit connected to the antenna, respectively.

[0008] In long-distance reflection communication, the problem of extremely low signal-to-noise ratio leading to decoding failure is an issue that urgently needs to be addressed in existing technologies. Summary of the Invention

[0009] To address the existing problems, this invention provides a reflection communication method and system for long-distance communication with extremely weak signal-to-noise ratio, the specific solution of which is as follows:

[0010] A long-distance reflection communication method under extremely weak signal-to-noise ratio conditions includes the following steps:

[0011] S1. Perform signal enhancement and preamble detection on the original signal to identify and detect the reflected signal;

[0012] S2. Preprocess the reflection signal detected in step 1;

[0013] S3. Decode the reflected signal after step 2 to generate a new signal.

[0014] Preferably, in step 1, a signal superposition algorithm is used for signal enhancement, and the Minimum Mean Square Error (MMSE) algorithm is used for leader detection.

[0015] Preferably, the preprocessing in step 2 includes the following steps:

[0016] S21. The redundant symbols in the packet preamble of the reflected signal detected in step 1 are used to compensate for the offset in the frequency domain and time domain, thereby generating time-aligned and offset-free symbols in the packet payload.

[0017] S22. Convert the symbols generated in step 21 into a dual-channel spectrum;

[0018] S23. Eliminate channel noise in the dual-channel spectrum to obtain a mask spectrum;

[0019] S24. Decode the mask spectrum to generate a new reflection signal.

[0020] Preferably, the STFT short-time Fourier transform algorithm is used to convert the signal in step 22.

[0021] The present invention also discloses a computer-readable storage medium containing a computer program, which, when executed, performs the aforementioned long-distance reflection communication method under extremely weak signal-to-noise ratio.

[0022] The present invention also discloses a computer system, including a processor and a storage medium, wherein a computer program is stored on the storage medium, and the processor reads from the storage medium and runs the computer program to execute the reflection communication method under extremely low signal-to-noise ratio over long distances as described above.

[0023] Preferably, a system for a long-distance reflection communication method under extremely weak signal-to-noise ratio includes a signal enhancement module, a preamble detection module, an offset recovery module, a symbol conversion module, an enable mask filtering module, and a decoder module.

[0024] The signal enhancement module is used to enhance the original signal in order to facilitate the identification of the reflected signal;

[0025] The leading detection module is used to filter and detect the required reflected signals;

[0026] The offset recovery module is used to compensate for the offset in the frequency domain and time domain by using redundant symbols in the packet preamble, thereby generating time-aligned and offset-free symbols in the packet payload.

[0027] The symbol conversion module is used to convert time-aligned and offset-free symbols into dual-channel spectra;

[0028] The enable mask filtering module can be a DNN filter, used to eliminate channel noise in the dual-channel spectrum, thereby obtaining the mask spectrum.

[0029] The decoder module is a spectrum-based decoder module, which may be a DNN decoder, used to decode the mask spectrum to generate a new signal.

[0030] The beneficial effects of this invention are as follows:

[0031] This invention can enhance the signal-to-noise ratio, reduce the decoding signal-to-noise ratio threshold, and extend the battery life of communication devices. Attached Figure Description

[0032] To more clearly illustrate the technical solutions in the embodiments of the present invention 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 the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0033] Figure 1 This is a flowchart of the method of the present invention. Detailed Implementation

[0034] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0035] like Figure 1 A reflection communication method for long-distance communication under extremely weak signal-to-noise ratio includes the following steps:

[0036] S1. The original signal is enhanced and a leader is detected to identify and detect the reflected signal; signal enhancement is performed using a signal superposition algorithm and leader detection is performed using the Minimum Mean Square Error (MMSE) algorithm.

[0037] S2. Preprocess the reflection signal detected in step 1;

[0038] The preprocessing includes the following steps:

[0039] S21. Use the redundant symbols in the packet preamble of the reflected signal detected in step 1 to compensate for the offset in the frequency domain and time domain, thereby generating time-aligned and offset-free symbols in the packet payload.

[0040] S22. Convert the symbols generated in step 21 into a dual-channel spectrum; wherein, the STFT short-time Fourier transform algorithm is used for signal conversion;

[0041] S23. Eliminate channel noise in the dual-channel spectrum to obtain the mask spectrum;

[0042] S24. Decode the mask spectrum to generate a new reflected signal.

[0043] S3. Decode the reflected signal after step 2 to generate a new signal.

[0044] A system for a long-distance, extremely weak signal-to-noise ratio reflection communication method includes a signal enhancement module, a preamble detection module, an offset recovery module, a symbol conversion module, an enable mask filtering module, and a decoder module.

[0045] Signal enhancement modules are used to enhance the original signal in order to facilitate the identification of reflected signals.

[0046] The lead detection module is used to filter and detect the desired reflected signals.

[0047] The offset recovery module is used to compensate for offsets in the frequency and time domains by utilizing redundant symbols in the packet preamble, thereby generating time-aligned and offset-free symbols in the packet payload.

[0048] The symbol conversion module is used to convert time-aligned and offset-free symbols into dual-channel spectra.

[0049] The mask filtering module can be enabled by using a DNN filter to eliminate channel noise in the dual-channel spectrum, thereby obtaining the mask spectrum.

[0050] The decoder module is a spectrum-based decoder module, which can be a DNN decoder, used to decode the mask spectrum to generate new signals.

[0051] This invention solves the problem of decoding failure caused by extremely low signal-to-noise ratio (SNR) in long-distance reflection communication by employing a deep neural network (DNN) method to leverage the feature extraction capabilities of deep learning to support ultra-low SNR reflection communication. Using the amplitude and phase spectrograms as input, a DNN filter is first used to extract multi-dimensional features to capture clean chirped symbols. Secondly, a spectrogram-based DNN decoder is used to accurately decode these chirped signals. Finally, high-quality chirped symbols are generated from the received signal. This invention can enhance the SNR, reduce the decoding SNR threshold, and extend the battery life of communication equipment.

[0052] The present invention also discloses a computer-readable storage medium containing a computer program, which, when executed, performs the aforementioned long-distance reflection communication method under extremely weak signal-to-noise ratio.

[0053] The present invention also discloses a computer system, including a processor and a storage medium, wherein a computer program is stored on the storage medium, and the processor reads from the storage medium and runs the computer program to execute a reflection communication method under long-distance and extremely weak signal-to-noise ratio.

[0054] Those skilled in the art will further appreciate that the various illustrative logic blocks, modules, circuits, and algorithm steps described in conjunction with the embodiments disclosed herein can be implemented as electronic hardware, computer software, or a combination of both. To clearly illustrate this interchangeability between hardware and software, the various illustrative components, blocks, modules, circuits, and steps are described above in a generalized manner in terms of their functionality. Whether such functionality is implemented as hardware or software depends on the specific application and the design constraints imposed on the overall system. Those skilled in the art may implement the described functionality in different ways for each specific application, but such implementation decisions should not be construed as departing from the scope of the invention.

[0055] The various illustrative logic blocks, modules, and circuits described in conjunction with the embodiments disclosed herein can be implemented or performed using a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. The general-purpose processor may be a microprocessor, but in alternatives, it may be any conventional processor, controller, microcontroller, or state machine. The processor may also be implemented as a combination of computing devices, such as a combination of a DSP and a microprocessor, multiple microprocessors, one or more microprocessors cooperating with a DSP core, or any other such configuration.

[0056] The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of both. The software module may reside in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium known in the art. An exemplary storage medium is coupled to a processor such that the processor can read and write information to / from the storage medium. In an alternative, the storage medium may be integrated into the processor. The processor and storage medium may reside in an ASIC. The ASIC may reside in a user terminal. In an alternative, the processor and storage medium may reside as discrete components in the user terminal.

[0057] In one or more exemplary embodiments, the described functionality may be implemented in hardware, software, firmware, or any combination thereof. If implemented in software as a computer program product, the functionality may be stored or transmitted as one or more instructions or code on or through a computer-readable medium. A computer-readable medium includes both computer storage media and communication media, encompassing any medium that facilitates the transfer of a computer program from one location to another. A storage medium may be any available medium accessible to a computer. By way of example and not limitation, such a computer-readable medium may include RAM, ROM, EEPROM, CD-ROM or other optical disc storage, disk storage or other magnetic storage devices, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and is accessible to a computer. Any connection is also legitimately referred to as a computer-readable medium. For example, if the software is transmitted from a website, server, or other remote source using coaxial cable, fiber optic cable, twisted pair, digital subscriber line (DSL), or wireless technologies such as infrared, radio, and microwave, then the coaxial cable, fiber optic cable, twisted pair, DSL, or wireless technologies such as infrared, radio, and microwave are included in the definition of a medium. As used in this article, disk and disc include compact discs (CDs), laser discs, optical discs, digital multi-purpose discs (DVDs), floppy disks, and Blu-ray discs. Disks typically reproduce data magnetically, while discs reproduce data optically using lasers. Combinations of these should also be included within the scope of computer-readable media.

[0058] The prior description of this disclosure is provided to enable any person skilled in the art to make or use this disclosure. Various modifications to this disclosure will be apparent to those skilled in the art, and the general principles defined herein may be applied to other variations without departing from the spirit or scope of this disclosure. Therefore, this disclosure is not intended to be limited to the examples and designs described herein, but should be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0059] Although the present invention 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; and these 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 the present invention.

Claims

1. A reflection communication method for long-distance communication with extremely weak signal-to-noise ratio, characterized in that, Includes the following steps: S1. Perform signal enhancement and preamble detection on the original signal to identify and detect the reflected signal; S2. Preprocess the reflection signal detected in step 1; S3. Decode the reflected signal processed in step 2 to generate a new signal; The preprocessing in step 2 includes the following steps: S21. The redundant symbols in the packet preamble of the reflected signal detected in step 1 are used to compensate for the offset in the frequency domain and time domain, thereby generating time-aligned and offset-free symbols in the packet payload. S22. Convert the symbols generated in step 21 into a dual-channel spectrum; S23. Eliminate channel noise in the dual-channel spectrum to obtain a mask spectrum; S24. Decode the mask spectrum to generate a new reflection signal.

2. The method according to claim 1, characterized in that: In step 1, a signal superposition algorithm is used to enhance the signal, and the Minimum Mean Square Error (MMSE) algorithm is used for leader detection.

3. The method according to claim 1, characterized in that: In step 22, the STFT short-time Fourier transform algorithm is used to convert the signal.

4. A computer-readable storage medium, characterized in that: The medium contains a computer program, which, when run, executes the long-distance, extremely weak signal-to-noise ratio reflection communication method as described in any one of claims 1 to 3.

5. A computer system, characterized in that: It includes a processor and a storage medium, on which a computer program is stored, and the processor reads from the storage medium and runs the computer program to execute the long-distance reflection communication method under extremely weak signal-to-noise ratio as described in any one of claims 1 to 3.

6. A system for a long-distance reflection communication method under extremely weak signal-to-noise ratio as described in any one of claims 1-3, characterized in that: It includes a signal enhancement module, a preamble detection module, an offset recovery module, a symbol conversion module, an enable mask filtering module, and a decoder module; The signal enhancement module is used to enhance the original signal in order to facilitate the identification of the reflected signal; The leading detection module is used to filter and detect the required reflected signals; The offset recovery module is used to compensate for the offset in the frequency domain and time domain by using redundant symbols in the packet preamble, thereby generating time-aligned and offset-free symbols in the packet payload. The symbol conversion module is used to convert time-aligned and offset-free symbols into dual-channel spectra; The enable mask filtering module uses a DNN filter to eliminate channel noise in the dual-channel spectrum, thereby obtaining a mask spectrum. The decoder module is a spectrum-based decoder module, which uses a DNN decoder to decode the mask spectrum and generate a new signal.

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