CN111865848B - Signal modulation format identification method and system - Google Patents
Signal modulation format identification method and system Download PDFInfo
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- CN111865848B CN111865848B CN202010548552.1A CN202010548552A CN111865848B CN 111865848 B CN111865848 B CN 111865848B CN 202010548552 A CN202010548552 A CN 202010548552A CN 111865848 B CN111865848 B CN 111865848B
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L27/00—Modulated-carrier systems
- H04L27/0012—Modulated-carrier systems arrangements for identifying the type of modulation
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L27/00—Modulated-carrier systems
- H04L27/32—Carrier systems characterised by combinations of two or more of the types covered by groups H04L27/02, H04L27/10, H04L27/18 or H04L27/26
- H04L27/34—Amplitude- and phase-modulated carrier systems, e.g. quadrature-amplitude modulated carrier systems
- H04L27/345—Modifications of the signal space to allow the transmission of additional information
- H04L27/3461—Modifications of the signal space to allow the transmission of additional information in order to transmit a subchannel
- H04L27/3483—Modifications of the signal space to allow the transmission of additional information in order to transmit a subchannel using a modulation of the constellation points
Abstract
A signal modulation format recognition method and system relates to the communication application field, including: the receiving end subtracts every two of N symbols obtained by receiving and processing N original receiving signals to obtain a plurality of pseudo signal vectors; obtaining a pseudo signal constellation diagram according to the pseudo signal corresponding to the pseudo signal vector, and generating images with uniform sizes from the pseudo signal constellation diagram; identifying a modulation format adopted by an original receiving signal corresponding to the image through a deep learning network; various high-order modulation formats are identified through a small amount of received signals, and a high identification success rate can be obtained under the condition that a channel is rapidly changed or the data volume is limited.
Description
Technical Field
The present invention relates to the field of communication applications, and in particular, to a method and a system for identifying a signal modulation format.
Background
To cope with the problem of an increasing shortage of spectrum resources, optical wireless technologies such as visible light, millimeter wave, and the like are more likely to be used in indoor communication scenarios. In a typical optical wireless communication scenario, the SNR (Signal Noise Ratio) of a channel is not flat, and factors such as multiple users, channel fading, non-line-of-sight, and the like further increase the complexity of the system. In order to effectively utilize and manage channels, techniques such as channel equalization, DMT (Discrete Multi Tone) and the like are generally used to simultaneously serve a plurality of users, thereby increasing the communication capacity of the system. Techniques such as Bit-loading are also used to cope with channel fading, and the minimum Bit error rate is achieved under different SNR conditions by dynamically adjusting the modulation format of each subcarrier. Therefore, under complicated channel conditions in optical wireless communication, the modulation format of the dynamically identified signal at the receiving end is very important for applications such as spectrum management, interference identification, and eavesdropping, and becomes an important research problem in optical wireless communication.
Conventional modulation format classification methods can be classified into two broad categories: one is based on Maximum Likelihood (ML) algorithms, and one is based on feature classification methods. The maximum likelihood algorithm compares the similarity of the received signal and the known modulation format signal to judge the modulation format; the feature classification method extracts signal features from the signal observation values to make decisions. Among the existing feature classification methods, various methods such as Support Vector Machines (SVMs), cumulants (Cumulants), artificial Neural Networks (ANN), and the like can obtain higher recognition accuracy. The methods based on feature extraction all need enough samples to accurately reflect the statistical features, but in practical application, more received signals increase the calculation amount and signal delay, and in the channel conditions of complicated change of optical wireless, even enough signals for identification are difficult to obtain, so that modulation format identification based on less data received in a short time is more desirable.
Disclosure of Invention
Aiming at the defects in the prior art, the invention aims to provide a signal modulation format identification method and a signal modulation format identification system, which can identify various high-order modulation formats through a small amount of received signals and can obtain a high identification success rate under the conditions of rapid channel change or limited data quantity.
In order to achieve the above object, in one aspect, a signal modulation format identification method is adopted, including: the receiving end subtracts every two of N symbols obtained by receiving and processing N original receiving signals to obtain a plurality of pseudo signal vectors; obtaining a pseudo signal constellation diagram according to the pseudo signal corresponding to the pseudo signal vector, and generating images with uniform sizes from the pseudo signal constellation diagram; and identifying the modulation format adopted by the original receiving signal corresponding to the image through a deep learning network.
Preferably, the receiving end receives N original received signals, receives and processes the received signals to obtain N symbols, and subtracts the symbols two by two to obtain N (N-1) pseudo signal vectors, where the pseudo signal vectors correspond to vector connecting lines between the N symbols.
Preferably, the receiving end holds a list with N symbols, and N 2 A list of pseudo signal vectors; the list of N symbols is dynamically updatable; when a symbol is newly received, subtracting the N +1 th symbol and the previous N symbols two by two to generate N new pseudo signal vectors; the oldest symbol in the list of N symbols is deleted, as are the N pseudo signal vectors terminated by it; adding newly received symbols to the list of N symbols last, newly generatedN dummy signal vectors are added to the dummy signal set.
Preferably, obtaining a pseudo signal constellation according to the pseudo signal corresponding to the pseudo signal vector, and generating images with uniform size from the pseudo signal constellation, specifically including:
the method comprises the steps of normalizing a pseudo signal, dividing the normalized pseudo signal into different pixel regions according to coordinates, counting the number of symbols in each pixel region, dividing the accumulated value in each pixel region by the maximum value of the number of symbols in all the regions, and multiplying the accumulated value by 256 to obtain a grayscale image with uniform size of M multiplied by M pixels.
Preferably, the deep learning network adopts an artificial neural network.
In another aspect, a signal modulation format recognition system is provided, including:
the vector generating unit is used for receiving and processing N original receiving signals received by the receiving end to obtain N symbols, and subtracting every two N symbols to obtain a plurality of pseudo signal vectors;
the image generating unit is used for generating a pseudo signal constellation diagram according to the plurality of pseudo signal vectors obtained by the vector generating unit and generating images with uniform sizes from the pseudo signal constellation diagram;
and the deep learning network is used for outputting the modulation format adopted by the original receiving signal corresponding to the image by inputting the image.
One of the above technical solutions has the following beneficial effects:
and obtaining a large number of inter-signal pseudo signal vectors capable of identifying modulation formats based on a small number of received signals, constructing an image of a pseudo constellation diagram based on the inter-signal pseudo signal vectors, and judging the image by using a deep learning network to identify various high-order modulation formats. Compared with the traditional method, the method needs an order of magnitude less number of received symbols, can obtain sufficient samples for signal identification, and can obtain high identification success rate under the condition of rapid channel change or limited data quantity.
Drawings
Fig. 1 is a schematic diagram of a process of constructing a pseudo signal vector between constellation points according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of an embodiment of the invention for generating an image based on a pseudo-signal;
fig. 3 is a schematic diagram of a signal modulation format recognition system according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and do not limit the invention. In addition, the technical features involved in the embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.
The invention provides an embodiment of a signal modulation format identification method, which comprises the following steps:
at a receiving end, N original receiving signals are received, N symbols are obtained after a receiving processing flow, N symbols are subtracted from each other to obtain N (N-1) pseudo signal vectors, and the pseudo signal vectors correspond to vector connecting lines among the N receiving symbols. The pseudo signals corresponding to the pseudo signal vectors are marked on the constellation diagram, so that a dense pseudo signal constellation diagram can be obtained, and the pseudo signal constellation diagram is generated into images with uniform size.
Because the pseudo signal constellation diagram contains the modulation format information such as amplitude, phase and the like of the original received signal, the modulation format adopted by the original received signal corresponding to the generated image can be identified through the deep learning network. And since the number of the pseudo signals is larger than that of the original received signals, the recognition success rate is higher through deep learning.
In the embodiment of the present invention, in order to distinguish from the pseudo signal, the original received signal refers to a signal received by a receiving end, and a signal obtained by subtracting two signals after a receiving processing flow is a pseudo signal.
Further, the general receiving process flow includes steps of a/D conversion, serial-to-parallel conversion, IFFT, carrier recovery, channel equalization, etc., and Symbol mapping is not yet performed to obtain a 0/1 sequence, where the N original received signals are sequences of continuous modulation symbols (symbols) intercepted by the receiving end.
In the above steps, N is greater than 20 and may be set according to actual conditions; preferably, 50. Ltoreq. N.ltoreq.200.
As shown in fig. 1, a process diagram of constructing a pseudo signal vector between constellation points by using QPSK signal as an example is shown. The multiple received QPSK signals correspond to the left 4 constellation points in fig. 1, arrows indicate pseudo signal vectors between various possible signal points, 9 types are provided according to one of vector magnitude and direction, and the pseudo signal constellation corresponding to the right side is 9 pseudo constellation points.
As shown in fig. 2, obtaining a pseudo signal constellation according to a pseudo signal corresponding to a pseudo signal vector, and generating images with uniform sizes from the pseudo signal constellation specifically includes:
and normalizing the amplitude of the pseudo signal corresponding to the pseudo signal vector, and putting all the pseudo signals into the images with uniform sizes. If the maximum amplitude among the plurality of spurious signals is L max Pseudo signal S i Normalized to obtain S i ，＝S i /L max . Assuming that a complete constellation diagram of all the pseudo signals generates an image of M multiplied by M pixels, normalizing the pseudo signals S i Dividing the image into different pixel areas according to coordinates, counting the number of symbols in each pixel area, dividing the accumulated value in each pixel area by the maximum value of the number of symbols in all the areas, and multiplying by 256 to obtain a grayscale image of M multiplied by M pixels. Preferably, M =224.
And an image is generated every N original receiving symbols, and for different modulation formats, the value of N should support the DL network to reach the highest recognition rate.
And generating a corresponding image by using the constellation diagram as an input of the deep learning network for recognition and judgment. Preferably, the deep learning network may be an artificial neural network, such as one based on AlexNet, google lenet, or the like.
The invention also provides an embodiment of a signal modulation format identification method, which is substantially the same as the embodiment. In this embodiment, the receiving end stores two lists, one of which is a list with N symbols, and the other is a list with N symbols 2 A column of a pseudo signal vectorTable; the lists may all be dynamically updated.
A list of N symbols, the list always maintaining the length of N symbols, the oldest symbol in the list being deleted each time a symbol is newly received, the N pseudo signal vectors ending with it also being deleted; the newly received symbols are added to the end of the list and the newly generated N pseudo signal vectors are added to the pseudo signal set. For example, when the (N + 1) th symbol is received, it is subtracted from the previous N symbols two by two to generate N new pseudo signal vectors. In this embodiment, the process of obtaining the pseudo signal constellation according to the pseudo signal corresponding to the pseudo signal vector and generating the images with uniform sizes from the pseudo signal constellation is the same as that in the above embodiment, and is not repeated here.
As shown in fig. 3, an embodiment of a signal modulation format recognition system is further provided in this embodiment, and the system specifically includes a vector generation unit, an image generation unit, and a deep learning network.
The vector generating unit is used for receiving and processing N original receiving signals received by the receiving end to obtain N symbols, subtracting every two N symbols to obtain a plurality of pseudo signal vectors, and preferably, N is more than or equal to 50 and less than or equal to 200.
And the image generating unit is used for generating a pseudo signal constellation diagram according to the plurality of pseudo signal vectors obtained by the vector generating unit and generating images with uniform sizes from the pseudo signal constellation diagram. And, an image is generated every N original received symbols.
And the deep learning network is used for outputting the modulation format adopted by the original receiving signal corresponding to the image through the image generated by the input image generating unit.
Preferably, the receiving end receives N original received signals, N symbols are obtained through a DMT reception process, N symbols are subtracted from each other to obtain N (N-1) pseudo signal vectors, and the pseudo signal vectors correspond to vector connecting lines between the N received symbols.
Preferably, the vector generation unit may further include a list having N symbols, and N 2 A list of pseudo signal vectors. When a symbol is newly received, the N +1 th symbol and the first N symbols are subtracted by two to generate N new pseudo signal vectors(ii) a The oldest symbol in the list of N symbols is deleted, as are the N pseudo signal vectors ending with it; the newly received symbols are added to the list of N symbols and finally, the newly generated N pseudo signal vectors are added to the pseudo signal set.
The image generating unit normalizes the pseudo signals, divides the normalized pseudo signals into different pixel regions according to coordinates, counts the number of symbols in each pixel region, divides the accumulated value in each pixel region by the maximum value of the number of symbols in all the regions, and multiplies the accumulated value by 256 to obtain a grayscale image with uniform size of M multiplied by M pixels.
The deep learning network is constructed based on various mainstream deep learning networks, a grayscale image with M multiplied by M pixels is input, and the output is one of possible modulation formats. The deep learning network needs to firstly train a large number of images with various modulation formats, divide the images generated by data collected in the same channel environment into two groups, mark a known modulation type on one group for training, then judge the other group of images by using a trained artificial neural network, and calculate the accuracy by contrasting the actual modulation type, so that the deep learning network is used for judging the modulation format of the subsequent images after being stabilized.
The image generation unit and the deep learning network can also be selected based on other technologies.
The number of the received symbols required by the above embodiment of the present invention is one order of magnitude less than that of the conventional method, but sufficient samples can be obtained by the vector obtained by subtracting two symbols from each other for signal identification, and a high identification success rate can be obtained under the condition that the channel changes rapidly or the data amount is limited.
The present invention is not limited to the above-described embodiments, and it will be apparent to those skilled in the art that various modifications and improvements can be made without departing from the principle of the present invention, and such modifications and improvements are also considered to be within the scope of the present invention.
Claims (4)
1. A method for identifying a modulation format of a signal, comprising:
the receiving end subtracts every two of N symbols obtained by receiving and processing N original receiving signals to obtain a plurality of pseudo signal vectors; obtaining a pseudo signal constellation diagram according to the pseudo signal corresponding to the pseudo signal vector, and generating images with uniform sizes from the pseudo signal constellation diagram;
identifying a modulation format adopted by an original receiving signal corresponding to the image through a deep learning network;
the receiving end receives N original receiving signals, N symbols are obtained after receiving processing, N (N-1) pseudo signal vectors are obtained after subtraction of every two pseudo signal vectors, and the pseudo signal vectors correspond to vector connecting lines among the N symbols;
the receiving end maintains a list of N symbols, and N 2 A list of pseudo signal vectors; the list of N symbols may be dynamically updated;
when a symbol is newly received, subtracting the N +1 th symbol and the first N symbols pairwise to generate N new pseudo signal vectors;
the oldest symbol in the list of N symbols is deleted, as are the N pseudo signal vectors ending with it; newly received symbols are added to the list of N symbols and finally, newly generated N pseudo signal vectors are added to the pseudo signal set.
2. The method according to claim 1, wherein a pseudo signal constellation is obtained according to the pseudo signal corresponding to the pseudo signal vector, and the pseudo signal constellation is used to generate images with uniform size, specifically comprising:
the method comprises the steps of normalizing a pseudo signal, dividing the normalized pseudo signal into different pixel regions according to coordinates, counting the number of symbols in each pixel region, dividing the accumulated value in each pixel region by the maximum value of the number of symbols in all the regions, and multiplying the accumulated value by 256 to obtain a grayscale image with uniform size of M multiplied by M pixels.
3. A signal modulation format recognition method as claimed in claim 1, wherein: the deep learning network adopts an artificial neural network.
4. A signal modulation format identification system, comprising:
the vector generating unit is used for receiving and processing N original receiving signals received by the receiving end to obtain N symbols, and subtracting every two N symbols to obtain a plurality of pseudo signal vectors;
the image generating unit is used for generating a pseudo signal constellation diagram according to the plurality of pseudo signal vectors obtained by the vector generating unit and generating images with uniform sizes from the pseudo signal constellation diagram;
the deep learning network is used for outputting a modulation format adopted by an original receiving signal corresponding to the image by inputting the image;
the receiving end receives N original receiving signals, N symbols are obtained after receiving processing, N (N-1) pseudo signal vectors are obtained after subtraction of every two pseudo signal vectors, and the pseudo signal vectors correspond to vector connecting lines among the N symbols;
the receiving end maintains a list of N symbols, and N 2 A list of pseudo signal vectors; the list of N symbols may be dynamically updated;
when a symbol is newly received, subtracting the N +1 th symbol and the previous N symbols two by two to generate N new pseudo signal vectors;
the oldest symbol in the list of N symbols is deleted, as are the N pseudo signal vectors ending with it; newly received symbols are added to the list of N symbols and finally, newly generated N pseudo signal vectors are added to the pseudo signal set.
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