CN113269023B - Modulation identification method based on fractional low-order constellation diagram and deep learning - Google Patents
Modulation identification method based on fractional low-order constellation diagram and deep learning Download PDFInfo
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Abstract
A modulation identification method based on a fractional low-order constellation diagram and deep learning mainly comprises the following steps: acquiring signals and performing fractional low-order processing on the signals; calculating the characteristics of the fractional low-order constellation diagram and manufacturing a training set and a testing set; designing and training a lightweight deep neural network; and testing the deep neural network and carrying out signal modulation identification. The method provided by the invention can obviously inhibit the influence of impulse noise on the signal modulation recognition accuracy, and simultaneously, the light-weight network can obviously reduce the calculated amount in the training and using processes.
Description
Technical Field
The invention relates to the technical field of modulation recognition, in particular to a signal modulation recognition method based on a fractional low-order constellation diagram and deep learning. The method is characterized by taking a fractional low-order constellation diagram as a characteristic and taking a deep neural network as a classifier, so that the accurate identification of various signal modulations under the impulse noise condition is realized.
Background
Signal modulation identification is an important premise for signal demodulation and acquisition of its content. In recent years, with the development of communication technology, the transmission environment of signals has become increasingly complex. On the premise of not having sufficient priori knowledge of the signal, it is important to accurately identify the modulation mode of the signal.
Conventional feature-based signal modulation recognition methods typically rely on manual selection of different kinds of features. However, this approach tends to be costly in terms of labor and time due to the variability of personal experience and subjective judgment.
In recent years, with rapid development of artificial intelligence technology, deep learning has been widely used in various aspects of computer vision, image processing, speech recognition, and the like as one branch of machine learning, and has exhibited excellent performance in the field of pattern recognition. Therefore, by means of the deep neural network involved in the deep learning, the dependence of the characteristic selection stage on artificial experience and subjective judgment in the signal modulation recognition process can be greatly reduced, and a plurality of signal modulation recognition methods by means of the deep learning are developed. However, the existing methods and technologies do not solve two main problems commonly involved in such methods at the same time, namely: non-gaussian noise conditions and lightweight deep neural networks.
In view of the above, the invention provides a modulation identification method based on a fractional lower-order constellation diagram and deep learning. The invention firstly provides a concept of a typical non-Gaussian noise fractional low-order constellation diagram capable of suppressing impulse noise, then designs a lightweight deep neural network capable of reducing calculated amount, and finally provides a signal debugging and identifying method based on the fractional low-order constellation diagram and deep learning. From the perspective of pattern recognition, the fractional lower order constellation is a feature and the lightweight neural network is a classifier. The method provided by the invention can obviously inhibit the influence of impulse noise on the signal modulation recognition accuracy, and simultaneously, the light-weight network can obviously reduce the calculated amount in the training and using processes.
Disclosure of Invention
In order to solve the problems of performance degradation and large calculated amount of the existing signal modulation recognition method based on signal characteristics and deep learning under the condition of impulse noise, the invention provides a modulation recognition method based on a fractional low-order constellation diagram and deep learning.
The invention is realized by adopting the following scheme:
a: the signal is acquired and subjected to a fractional lower order processing.
A1: a signal is acquired.
A2: the signal is subjected to a fractional lower order processing.
B: and calculating the characteristic of the fractional low-order constellation diagram and manufacturing a training set and a testing set.
B1: fractional lower order constellation features of the signal are calculated.
B2: and fully mixing the characteristics, and forming a training set, a verification set and a test set by the mixed characteristics according to a certain proportion.
C: and designing and training a lightweight deep neural network.
C1: and designing a lightweight deep neural network.
C2: the deep neural network is trained using a training set.
And C3: the deep neural network is validated using a validation set.
D: and testing the deep neural network and carrying out signal modulation identification.
D1: the deep neural network is tested using a test set.
D2: and carrying out signal modulation identification.
The beneficial effects of the invention are as follows:
the method solves the problem of performance degradation of the existing signal modulation identification method under the typical non-Gaussian noise condition of impulse noise, and reduces the problem of higher calculated amount introduced by a deep neural network. Meanwhile, the signal debugging and identifying method can identify a wide variety of debugging modes.
Drawings
Fig. 1 is a flow chart of a modulation identification method based on a fractional lower order constellation and deep learning according to the present invention.
Fig. 2 is a graph of a fractional lower order mapping function in accordance with the present invention. Wherein each curve corresponds to a different parameter p.
Fig. 3 is a fractional lower order constellation of signals in accordance with the present invention. Wherein, the signals take 2PSK, 4PSK, 8PSK, 16QAM, 32QAM and 64QAM as examples; the values of the parameter p related to the fractional lower-order mapping function are 1.2, 1.5 and 1.8 respectively.
Fig. 4 is a block diagram of a lightweight deep neural network according to the present invention.
FIG. 5 is a graph of recognition accuracy for different methods under different noise conditions in accordance with the present invention. The values of the parameter p related to the fractional lower-order constellation diagram are 1.1, 1.3 and 1.5 respectively, and the comparison method adopts the traditional constellation diagram as the characteristic.
Detailed Description
For better understanding, the following description of the technical solution in the implementation process of the present invention is made clearly and completely with reference to the accompanying drawings.
A modulation identification method based on a fractional lower-order constellation diagram and deep learning comprises the following steps:
a: the signal is acquired and subjected to a fractional lower order processing.
The step A specifically comprises the following steps:
a1: a signal is acquired. The simulation signals can be generated by a computer according to the principles of different modulation modes, the simulation impulse noise is added to the simulation signals, and the real signals under the impulse noise condition can be obtained by a receiver and an antenna. Wherein, the intensity of impulse noise is measured by generalized signal-to-noise ratio (GSNR), and the generalized signal-to-noise ratio is defined as:
wherein P is s Representing the power of the signal, P n Representing the generalized power of the noise. When non-Gaussian noise is characterized by Alpha stable distribution, P n =γ, γ represents the scale parameter of the Alpha stabilizing moiety.
A2: the signal is subjected to a fractional lower order processing. And carrying out fractional lower-order processing on the signal by using a fractional lower-order mapping function, wherein the specific formula is as follows:
y FLO (n)＝(y(n)) <p-1>
＝a FLO (n)+jb FLO (n)
wherein n represents a discrete time variable corresponding to a sampling time, y (n) represents a sampled signal sequence, y FLO (n) represents a signal sequence after fractional lower-order (FLO) processing, a FLO Representing the real part of the signal, b FLO Representing the imaginary part of the signal,<·>representing a fractional lower order operator that satisfies the following relationship:
wherein z represents a complex domainThe superscript x indicates the conjugate operator.
A graph of the fractional lower order mapping function according to the present invention is shown in fig. 2. Wherein each curve corresponds to a different parameter p.
B: and calculating the characteristic of the fractional low-order constellation diagram and manufacturing a training set and a testing set.
The step B specifically comprises the following steps:
b1: fractional lower order constellation features of the signal are calculated. By the real part a of the signal FLO As abscissa, with the imaginary part b of the signal FLO And (3) taking the signal as an ordinate, and calculating the fractional lower-order constellation diagram characteristics corresponding to the signal one by one according to the sampling time n.
B2: and fully mixing the characteristics, and forming a training set, a verification set and a test set by the mixed characteristics according to a certain proportion. Fully mixing the fractional lower-order constellation diagram characteristics obtained in the step B1, and then according to m 1 :m 2 :m 3 The proportions of (a) constitute a training set, a validation set and a test set, respectively. In general, the ratio can be set to m 1 :m 2 :m 3 ＝6:2:2。
The fractional lower order constellation of the signal according to the present invention is shown in fig. 3. Wherein, the signals take 2PSK, 4PSK, 8PSK, 16QAM, 32QAM and 64QAM as examples; the values of the parameter p related to the fractional lower-order mapping function are 1.2, 1.5 and 1.8 respectively.
C: and designing and training a lightweight deep neural network.
The step C specifically comprises the following steps:
c1: and designing a lightweight deep neural network. To reduce the amount of computation in training, validation and testing, less convolutional layers are used to design a lightweight deep neural network.
C2: the deep neural network is trained using a training set. The features in the training set are used as the input of the deep neural network in C1, and the deep neural network is trained. In the training process, RMSprop is adopted as an optimization method of network parameters, cross entropy (cross entropy) is adopted as a loss function, and the learning rate is set to be 0.01.
And C3: the deep neural network is validated using a validation set. The features in the verification set are used as the input of the deep neural network in C1, and are verified.
The deep neural network structure diagram according to the present invention is shown in fig. 4, and specific network structure parameter settings are labeled in fig. 4.
D: and testing the deep neural network and carrying out signal modulation identification.
The step D specifically comprises the following steps:
d1: the deep neural network is tested using a test set. The features in the test set are used as input of the deep neural network in C1, and are tested.
D2: and carrying out signal modulation identification. And taking the fractional low-order constellation diagram characteristic of the signal of the unknown modulation mode as the input of the deep neural network in the C1 to obtain the recognition result of the signal modulation mode.
The graph of recognition accuracy of different methods under different noise conditions according to the present invention is shown in fig. 5. The values of the parameter p related to the fractional lower-order constellation diagram are 1.1, 1.3 and 1.5 respectively, and the comparison method adopts the traditional constellation diagram as the characteristic.
Claims (1)
1. The modulation identification method based on the fractional lower-order constellation diagram and the deep learning is characterized by comprising the following steps:
a: acquiring signals and performing fractional low-order processing on the signals;
the step A specifically comprises the following steps:
a1: acquiring a signal; the simulation signals can be generated by a computer according to the principles of different modulation modes, the simulation impulse noise is added to the simulation signals, and the real signals under the impulse noise condition can be obtained by a receiver and an antenna; wherein, the intensity of impulse noise is measured by generalized signal-to-noise ratio (GSNR), and the generalized signal-to-noise ratio is defined as:
wherein P is s Representing the power of the signal, P n Generalized power representing noise; when non-Gaussian noise is characterized by Alpha stable distribution, P n =γ, γ represents the scale parameter of the Alpha stabilizing moiety;
a2: fractional lower order processing is carried out on the signals; and carrying out fractional lower-order processing on the signal by using a fractional lower-order mapping function, wherein the specific formula is as follows:
y FLO (n)＝(y(n)) <p-1>
＝a FLO (n)+jb FLO (n)
wherein n represents a discrete time variable corresponding to a sampling time, y (n) represents a sampled signal sequence, y FLO (n) represents a signal sequence after fractional lower-order (FLO) processing, a FLO Representing the real part of the signal, b FLO Representing the imaginary part of the signal,<·>representing a fractional lower order operator that satisfies the following relationship:
wherein z represents a complex domainThe superscript x represents the conjugate operator;
b: calculating the characteristics of the fractional low-order constellation diagram and manufacturing a training set and a testing set;
the step B specifically comprises the following steps:
b1: calculating the fractional lower-order constellation characteristic of the signal; by the real part a of the signal FLO As abscissa, with the imaginary part b of the signal FLO As the ordinate, calculating the fractional low-order constellation diagram characteristics corresponding to the signals one by one according to the sampling time n;
b2: fully mixing the characteristics, and forming a training set, a verification set and a test set by the mixed characteristics according to a certain proportion; fully mixing the fractional lower-order constellation diagram characteristics obtained in the step B1, and then according to m 1 :m 2 :m 3 Respectively forming a training set, a verification set and a test set according to the proportion of the number of the training sets; setting the ratio to m 1 :m 2 :m 3 ＝6:2:2；
C: designing and training a lightweight deep neural network;
the step C specifically comprises the following steps:
c1: designing a lightweight deep neural network; in order to reduce the calculated amount in the training, verification and test processes, a light-weight deep neural network is designed by adopting fewer convolution layers;
c2: training the deep neural network using a training set; taking the characteristics in the training set as the input of the deep neural network in C1, and training the deep neural network; in the training process, adopting RMSprop as an optimization method of network parameters, adopting cross entropy (cross entropy) as a loss function, and setting the learning rate to be 0.01;
and C3: validating the deep neural network using a validation set; taking the characteristics in the verification set as the input of the deep neural network in C1, and verifying the deep neural network;
d: testing the deep neural network and carrying out signal modulation identification;
the step D specifically comprises the following steps:
d1: testing the deep neural network by using a test set; taking the characteristics in the test set as the input of the deep neural network in C1, and testing the deep neural network;
d2: carrying out signal modulation identification; and taking the fractional low-order constellation diagram characteristic of the signal of the unknown modulation mode as the input of the deep neural network in the C1 to obtain the recognition result of the signal modulation mode.
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