CN113325375B - Self-adaptive cancellation method based on deep neural network - Google Patents
Self-adaptive cancellation method based on deep neural network Download PDFInfo
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- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
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Abstract
The invention provides a self-adaptive cancellation method based on a deep neural network, 1) defining a signal model received by a receiving antenna, including transmitting signal power P f Nonlinear distortion function G [. For power amplifier]And carrier center frequency f c (ii) a 2) Defining a model of a non-linear power amplifier; 3) Carrying out nonlinear modeling on a target signal, and training a DNN network by using a large amount of data; 4) Inputting a signal generated after an original reference signal passes through a trained network as a new reference signal into an adaptive filter; 5) And comparing signals before and after cancellation by the adaptive filter. The invention utilizes a large amount of training prior information to simulate radar trunkThe non-linear characteristic of the power amplifier of the interference machine solves the interference problem, and the method directly estimates the amplitude of the signal and reduces the algorithm steps by using a large amount of data.
Description
Technical Field
The invention belongs to the field of self-interference cancellation of radar jammers, and particularly relates to a self-adaptive cancellation method based on a deep neural network.
Background
The elimination of self-coupling interference in a radar jammer is always a key technology and a hot topic, and in recent years, a self-adaptive algorithm is widely applied to the elimination of the self-coupling interference. With the increasing complexity of the electromagnetic environment, the self-interference signal is difficult to estimate, and the traditional algorithm is difficult to adapt to the nonlinear signal generated by the radar jammer power amplifier, so that the self-interference signal is effectively eliminated.
With the continuous and deep research of the adaptive algorithm, the application of the adaptive algorithm in the radar jammer is more and more mature. Currently, adaptive filtering algorithms, such as Normalized Least Mean Square Error (NLMS), are mainly used to remove self-interference signals. Mine ("Results and trade-off of self-interference cancellation in a full-duplex radio front-end,"2015 International Workshop on Antenna Technology (iWAT), seoul, pp.249-251, 2015.) demonstrates that as the weights of the adaptive filter change adaptively, the error signal approaches the target value more and more. The method only has a good experimental result for the radio frequency cancellation in the indoor electromagnetic environment, and the nonlinearity of the self-interference signal is not considered. L.Sun, Y.Li, Y.ZHao, L.Huang and Z.Gao ("Optimized adaptive algorithm of digital self-interference based on improved variable step size," 2015 IEEE 9 International Conference on Anti-computing, security, and Identification (ASID), xiaomen, pp.176-179, 2015.) proposes an adaptive digital self-interference cancellation optimization algorithm based on improved variable step size, and a new non-linear relationship is established between the step factor and the error signal by using an iteration threshold, so that the problem of slow change of the error signal approaching zero is solved, and the convergence speed is accelerated. The method does not mention cancellation of the radar jammer non-linear self-interference signal. Dani Korpi, lauri Anttila, and Mikko Valkama ("Nonlinear selection-interference cancellation in MIMO full-duplex transceivers under cross talk." "Eurasip Journal on Wireless Communications & Networking 2017.1 (2017)") proposes a novel digital self-interference canceller for an in-band multiple-input multiple-output (MIMO) full-duplex radio. It details various models of full duplex including analysis of the non-linear component of the self-interference signal, but does not suggest how to cancel the non-linear self-interference signal. In summary, the above cancellation methods are all directed to the electromagnetic environment, which is relatively simple, and have a better cancellation effect when the generated self-interference signal is linear, and when the self-interference signal is non-linear due to the power amplifier of the radar jammer, it is not easy to obtain a better cancellation result by using these methods to achieve the cancellation result.
Because the neural network can well solve the nonlinear problem, recent studies of scholars show that the neural network can be used for channel modeling in full-duplex communication, and therefore reconstructed channel signals are more accurate. This method is based on adaptive filtering, where the weights of the filter are estimated by DNN (Deep Neural Network), and the non-linear performance of the adaptive filter itself is not particularly good, so DNN is introduced to optimize the performance of the adaptive filter.
Disclosure of Invention
The invention provides a self-adaptive cancellation method based on a deep neural network, which is used for solving the problem that the cancellation effect is poor when a traditional self-adaptive cancellation algorithm processes self-interference signals formed by a nonlinear power amplifier. The invention utilizes a large amount of training prior information to simulate the nonlinear characteristic of a power amplifier of the radar jammer, solves the interference problem, directly estimates the amplitude of a signal by the method, and reduces algorithm steps by using a large amount of data.
The purpose of the invention is realized as follows:
a self-adaptive cancellation method based on a deep neural network comprises the following steps:
step 1: defining a model of the signal received by the receiving antenna, including the power P of the transmitted signal f Nonlinear distortion function G [. For power amplifier]And carrier center frequency f c ；
The received signals of the receiving antenna comprise target signals, noise signals and self-interference signals; in the normal case, the noise signal is normally white gaussian noise with zero mean, denoted by n (t), and has a power P n (ω), i.e.
Defining the expected target signal r (t) received by the receiving antenna as:
wherein, P f Is the power of the radar transmitted signal, G [ ·]Representing the nonlinear distortion function of the power amplifier, d f (t) represents a modulated baseband waveform, f c Represents the center frequency of the carrier;
the self-interference signal in the radar jammer is a delay of the transmitted signal, so the self-interference signal x (t) can be set as follows:
x(t)＝r(t-τ 1 )+r(t-τ 2 )+······+r(t-τ n ) (3)
wherein, tau 1 、τ 2 And τ n Is to simulate the interference delay in the actual situation;
after the target signal is modulated by baseband, d n (t) is obtained from x (t), x PA (t) is obtained by power amplification; and finally, the transmitting antenna sends out the self-interference signal SI (t) formed by the receiving antenna, wherein the self-interference signal SI (t) is inevitably input by the transmitting signal:
P n power of the jammer transmission signal, f c Represents the center frequency of the carrier, phi represents the initial phase of the carrier, and k represents a known coefficient;
therefore, the signal y (t) actually received by the receiving antenna is modeled as:
y(t)＝r(t)+SI(t)+n(t) (5)
step 2: defining a model of a non-linear power amplifier;
the reason for causing the nonlinear distortion of the power amplifier is mainly AM/AM distortion, wherein the AM/AM distortion refers to the distortion of the amplitude of an output signal caused by the amplitude change of an input signal; the adopted power amplifier is a traveling wave tube amplifier, the nonlinear distortion of the traveling wave tube amplifier can be described by a saliche model, and the AM/AM and AM/PM of the saliche model have the characteristic functions as follows:
where r is the amplitude of the input signal; alpha is alpha a 、β a 、α φ And beta φ Is a model parameter; obtaining a proper fixed model by adjusting the four parameters; the nonlinear characteristics of the salech model comprise amplitude and phase changes of a signal after the signal passes through the salech model;
taking the derivative of equation (6) to obtain the input signal amplitude when the input signal reaches the input saturation amplitude of the amplifier model:
the model obtains the maximum output signal amplitude:
f(A) max ＝α a A sat /2 (9)
the maximum output value of the power amplifier determines the maximum value that the linearization can correct; if the linear output value corresponding to the input amplitude value of the power amplifier is larger than the maximum output amplitude value of the power amplifier, the nonlinear distortion cannot be compensated;
and 3, step 3: carrying out nonlinear modeling on a target signal, and training a DNN network by using a large amount of data;
3a) The training data takes the form of two signals, namely a Linear Frequency Modulation (LFM) signal and a BPSK signal, as target signals respectively; for these two signals, two data sets were made, each containing 10000 samples; each LFM sample is a pulse signal with the pulse width of 3 mu s, each BPSK sample comprises 13 symbols, and the number of sampling points of each symbol at the sampling frequency is 70; for the LFM signal, the LFM signal sampling frequency f is defined s =300MHz, carrier frequency 100MHz, bandwidth 20MHz, signal amplitude 20; defining the sampling frequency of BPSK signals as f s =300MHz, amplitude 20, carrier frequency 50MHz;
3b) The target signal generated in 3a is a label of the training sample obtained by the salich model in step 2 (the saturation output amplitude of the model is 80); wherein the parameters of the salech model are set as follows a ＝2，α φ ＝π/3，β a ＝1.5625e -5 ，β φ =1; generating a target signal as a training signal of the DNN network, and taking a signal passing through a Saichz model as a label of training data; after nonlinear amplification, the data obviously changes in both time domain and frequency domain;
3c) The input layer of the DNN network is x (t), the number of nodes of the hidden layer is respectively amplified and reduced, the final number of nodes of the output layer is the same as that of the input layer, and the output signal x after nonlinear amplification is obtained AP (t); in the experiment, the hidden layer uses Relu (reconstructed linear units) activation function, and the hidden layer number of the deep neural network is Num,1024, 2048, \ 8230;, 1024, num; where Num is the number of points per sample, the number of specific layers may beAdjusting; the loss quadratic cost function in a neural network is:
Loss＝mean(square(x-x AP )) (10)
the output of each node of the DNN network is a nonlinear activation function to which its inputs are applied; weights between layers in the neural network are optimized through extensive learning, and expected outputs of training samples containing known inputs are learned;
and 4, step 4: inputting a signal generated after the original reference signal passes through a trained network as a new reference signal into an adaptive filter;
the receiver receives signals including a target signal, a noise signal and a self-interference signal, the sum of the signals is a signal before cancellation, and a self-interference signal SI (t) in formula (5) is a target to be cancelled;
4a) Taking LFM signal as an example, the received signal is denoised and sampled to become y (n), which is the cancellation signal input to the adaptive filter and has a duration of 3 μ s,
4b) Delaying the y (n) signal in the first step by 0.5 mu s, then carrying out nonlinear processing by using the Sa-Rach model in the step 2 to simulate the nonlinear characteristic of the power amplifier, taking the processed signal as a self-interference signal SI (n), and adding the SI (n), the y (n) and Gaussian white noise to obtain a target cancellation signal;
4c) Inputting the y (n) signal delayed by 0.5 mu s in the second step into DNN neural network as training sample, using the signal after nonlinear processing by the Saichh model as label, and estimating the signal x by the deep neural network AP (n) as a reference signal for the adaptive filter;
4d) Inputting the signals generated in the steps 4b and 4c into the adaptive filter of the LMS algorithm to obtain an output signal x (n) after cancellation of the adaptive filter, and achieving the result of cancellation of the LFM signal;
the cancellation result can also be achieved by changing the LFM signal in the above steps into a BPSK signal.
Compared with the prior art, the invention has the beneficial effects that:
the invention provides an interference machine signal interference elimination method based on a deep neural network. On the basis of the existing interference elimination theory, the nonlinear characteristic of a radar interference power amplifier is considered, and nonlinearity is introduced into modeling of an interference signal. The method is obtained through experimental simulation, and if the adopted training data is rich enough and the sampling frequency is not high, the nonlinear component in the self-interference signal generated by the nonlinear characteristic of the power amplifier and received in the jammer can be eliminated.
Drawings
Fig. 1 is a DNN-based self-interference cancellation system model.
Fig. 2 is a nonlinear characteristic of the salich model.
FIG. 3 shows LFM signals before and after non-linear amplification
Fig. 4 is a BPSK signal before and after non-linear amplification.
Fig. 5 is a structural diagram of a DNN network model.
Fig. 6 is the result of LFM signal cancellation based on the DNN method.
Fig. 7 is a BPSK signal cancellation result based on the DNN method.
Fig. 8 is a result of the LFM signal cancellation of the conventional method.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and specific embodiments.
The technical scheme of the invention is a DNN-based adaptive cancellation algorithm, which comprises the following steps:
1) Defining a model of the signal received by the receiving antenna, including the power P of the transmitted signal f Nonlinear distortion function G [. For power amplifier]And carrier center frequency f c 。
2) A model of a non-linear power amplifier is defined.
3) The target signal is non-linearly modeled and the DNN network is trained using a large amount of data.
4) And inputting a signal generated after the original reference signal passes through the trained network into the adaptive filter as a new reference signal.
5) And comparing signals before and after cancellation by the adaptive filter.
Step 1: definition receptionSignal model of antenna reception, including transmitted signal power P f Nonlinear distortion function G [ DEG ] of power amplifier]And carrier center frequency f c 。
The received signal of the receive antenna includes a target signal, a noise signal, and a self-interference signal. In the normal case, the noise signal is normally white gaussian noise with zero mean, denoted by n (t), and has a power P n (ω), i.e.
Defining the expected target signal r (t) received by the receiving antenna as:
wherein, P f Is the power of the radar transmitted signal, G [ ·]Representing the nonlinear distortion function of the power amplifier, d f (t) denotes a modulated baseband waveform, f c Representing the center frequency of the carrier.
The self-interference signal in the radar jammer is a delay of the transmission signal, so the self-interference signal x (t) can be set as follows:
x(t)＝r(t-τ 1 )+r(t-τ 2 )+······+r(t-τ n ) (3)
wherein, tau 1 、τ 2 And τ n Is to simulate the interference delay in real situations.
After the target signal is modulated by baseband, d n (t) is obtained from x (t), x PA (t) is obtained by power amplification. And finally, the transmitting antenna sends out the self-interference signal SI (t) formed by the receiving antenna, wherein the self-interference signal SI (t) is inevitably input by the transmitting signal:
P n power of the signal transmitted by the jammer, f c Representative carrierRepresents the initial phase of the carrier and k represents the known coefficient.
Therefore, the signal y (t) actually received by the receiving antenna is modeled as:
y(t)＝r(t)+SI(t)+n(t) (5)
step 2: a model of the non-linear power amplifier is defined.
The reason for the nonlinear distortion of the power amplifier is mainly AM/AM distortion, which means that the amplitude change of the input signal causes the distortion of the amplitude of the output signal. The power amplifier adopted by the invention is a traveling wave tube amplifier, the nonlinear distortion of the power amplifier can be described by a Talbot model, and the characteristic functions of AM/AM and AM/PM of the Talbot model are as follows:
where r is the amplitude of the input signal. Alpha is alpha a 、β a 、α φ And beta φ Are the model parameters. The four parameters are adjusted to obtain a proper fixed model. Fig. 2 shows the nonlinear characteristics of the salech model, which includes the amplitude and phase changes of the signal after passing through the salech model.
Taking the derivative of equation (6) can obtain the input signal amplitude when the input signal reaches the input saturation amplitude of the amplifier model as:
the model obtains the maximum output signal amplitude:
f(A) max ＝α a A sat /2 (9)
the maximum output value of the power amplifier determines the maximum value that the linearization can correct. If the linear output value corresponding to the input amplitude value of the power amplifier is larger than the maximum output amplitude value of the power amplifier, the nonlinear distortion cannot be compensated.
And 3, step 3: the target signal is non-linearly modeled and the DNN network is trained using a large amount of data.
3a) The training data is in the form of two signals, a Linear Frequency Modulated (LFM) signal and a BPSK signal, respectively, as target signals. For these two signals, two data sets were made, each containing 10000 samples. Each LFM sample is a pulse signal with a pulse width of 3 μ s, each BPSK sample contains 13 symbols, and the number of samples per symbol at the sampling frequency is 70. For the LFM signal, the LFM signal sampling frequency f is defined s =300MHz, carrier frequency 100MHz, bandwidth 20MHz, signal amplitude 20; defining the sampling frequency of BPSK signals as f s =300MHz, amplitude 20, carrier frequency 50MHz.
3b) The target signal generated in 3a is a label of the training sample obtained by the salich model in step 2 (the saturation output amplitude of the model is 80). Wherein the parameters of the saleach model are set as follows a ＝2，α φ ＝π/3，β a ＝1.5625e -5 ，β φ And =1. The generated target signal serves as a training signal for the DNN network, and a signal passing through the salech model serves as a label for training data. As shown in fig. 3 and 4, after the nonlinear amplification, the data has a significant change in both time domain and frequency domain.
3c) FIG. 5 is a diagram of a DNN network model, where the input layer of the DNN network is x (t), the number of nodes in the hidden layer is respectively increased and decreased, the final number of nodes in the output layer is the same as the number of nodes in the input layer, and a non-linearly amplified output signal x is obtained AP (t) of (d). In the experiment, the hidden layers used Relu (reconstructed linear units) activation functions, and the number of hidden layers of the deep neural network was Num,1024, 2048, \ 8230;, 1024, num, respectively. Where Num is the number of points per sample, the number of specific layers can be adjusted. The loss quadratic cost function in a neural network is:
Loss＝mean(square(x-x AP )) (10)
the output of each node of the DNN network is a nonlinear activation function that applies its inputs. The weights between layers in a neural network are optimized through extensive learning, and the expected output of training samples containing known inputs is learned.
And 4, step 4: and inputting a signal generated after the original reference signal passes through the trained network into the adaptive filter as a new reference signal.
Fig. 1 is a block diagram of an implementation of an adaptive filter system, where a receiver receiving signal includes a target signal, a noise signal, and a self-interference signal, and the sum of these signals is a signal before we cancel, as shown in equation (5), where the self-interference signal SI (t) is the target to be cancelled.
4a) Taking LFM signal as an example, the received signal is denoised and sampled to become y (n), which is a cancellation signal input to the adaptive filter and has a duration of 3 μ s as shown in the figure,
4b) The y (n) signal in the first step is delayed by 0.5 μ s, and then nonlinear processing is performed by the salech model in step 2 to simulate the nonlinear characteristics of the power amplifier, and the processed signal is used as a self-interference signal SI (n), and SI (n) is added with y (n) and white gaussian noise to be used as a target cancellation signal.
4c) Inputting the y (n) signal delayed by 0.5 mu s in the second step into a DNN neural network as a training sample, taking the signal subjected to nonlinear processing by the Saiher model as a label, and estimating a signal x by a deep neural network AP And (n) as a reference signal of the adaptive filter.
4d) The signals generated in steps 4b and 4c are input into the adaptive filter of the LMS algorithm to obtain an output signal x (n) after cancellation by the adaptive filter, and the result of the cancellation of the LFM signal is shown in fig. 6.
Similarly, the LFM signal in the above steps is changed into a BPSK signal, and the cancellation result is shown in fig. 7.
And 5: compared with the traditional self-adaptive method.
Fig. 6 is a cancellation result of the LFM signal using the new method, fig. 8 is a cancellation result of the existing LMS algorithm, and it is obvious from comparison that the DNN-based method has a better cancellation effect on the self-interference signal. Compared with the traditional self-adaptive algorithm, the new method is adopted to eliminate the self-interference signal, and the BPSK signal and the LFM signal have similar cancellation results.
Claims (1)
1. A self-adaptive cancellation method based on a deep neural network is characterized by comprising the following steps:
step 1: defining a model of the signal received by the receiving antenna, including the power P of the transmitted signal f Nonlinear distortion function G [ DEG ] of power amplifier]And carrier center frequency f c ；
The received signals of the receiving antenna comprise target signals, noise signals and self-interference signals; in the normal case, the noise signal is usually white Gaussian noise with zero mean, denoted by n (t), and has a power P n (ω), i.e.
Defining the expected target signal r (t) received by the receiving antenna as:
wherein, P f Is the power of the radar transmitted signal, G [. Cndot.)]Representing the nonlinear distortion function of the power amplifier, d f (t) denotes a modulated baseband waveform, f c Represents a center frequency of the carrier;
the self-interference signal in the radar jammer is a delay of the transmitted signal, so the self-interference signal x (t) can be set as follows:
x(t)＝r(t-τ 1 )+r(t-τ 2 )+······+r(t-τ n ) (3)
wherein, tau 1 、τ 2 And τ n Is to simulate the interference delay in the actual situation;
after the target signal is modulated by baseband, d n (t) is obtained from x (t), x PA (t) is obtained by power amplification; finally by transmitting antennasSending out, the transmission signal will inevitably input the self-interference signal SI (t) formed by the receiving antenna:
P n power of the signal transmitted by the jammer, f c Represents the center frequency of the carrier, phi represents the initial phase of the carrier, and k represents a known coefficient;
therefore, the signal y (t) actually received by the receiving antenna is modeled as:
y(t)＝r(t)+SI(t)+n(t) (5)
step 2: defining a model of the non-linear power amplifier;
the reason for causing the nonlinear distortion of the power amplifier is mainly AM/AM distortion, wherein the AM/AM distortion refers to the distortion of the amplitude of an output signal caused by the amplitude change of an input signal; the adopted power amplifier is a traveling wave tube amplifier, the nonlinear distortion of the traveling wave tube amplifier can be described by a Talbot model, and the characteristic functions of AM/AM and AM/PM of the Talbot model are as follows:
where r is the amplitude of the input signal; alpha is alpha a 、β a 、α φ And beta φ Is a model parameter; obtaining a proper fixed model by adjusting the four parameters; the nonlinear characteristics of the salech model comprise amplitude and phase changes of a signal after the signal passes through the salech model;
taking the derivative of equation (6) to obtain the input signal amplitude when the input signal reaches the input saturation amplitude of the amplifier model:
the model obtains the maximum output signal amplitude:
f(A) max ＝α a A sat /2 (9)
the maximum output value of the power amplifier determines the maximum value that the linearization can correct; if the linear output value corresponding to the input amplitude value of the power amplifier is larger than the maximum output amplitude value of the power amplifier, the nonlinear distortion cannot be compensated;
and 3, step 3: carrying out nonlinear modeling on a target signal, and training a DNN network by using a large amount of data;
3a) The training data takes the form of two signals, namely a Linear Frequency Modulation (LFM) signal and a BPSK signal, as target signals respectively; for these two signals, two data sets were made, each containing 10000 samples; each LFM sample is a pulse signal with the pulse width of 3 mu s, each BPSK sample comprises 13 symbols, and the number of sampling points of each symbol at the sampling frequency is 70; for the LFM signal, the LFM signal sampling frequency f is defined s =300MHz, carrier frequency 100MHz, bandwidth 20MHz, signal amplitude 20; defining the sampling frequency of BPSK signals as f s =300MHz, amplitude 20, carrier frequency 50MHz;
3b) The target signal generated in 3a is a label of the training sample obtained by the salich model in step 2 (the saturation output amplitude of the model is 80); wherein the parameters of the saleach model are set as follows a ＝2，α φ ＝π/3，β a ＝1.5625e -5 ，β φ =1; generating a target signal as a training signal of the DNN network, and taking a signal passing through a Saichh model as a label of training data; after nonlinear amplification, the data are obviously changed in both time domain and frequency domain;
3c) The input layer of the DNN network is x (t), the number of nodes of the hidden layer is respectively amplified and reduced, the final number of nodes of the output layer is the same as that of the input layer, and the output signal x after nonlinear amplification is obtained AP (t); in the experiment, relu (recovered linear velocities) was used as the concealing layerThe hidden layer number of the deep neural network is Num,1024, 2048, \ 8230;, 1024, num; num is the number of points of each sample, and the specific layer number can be adjusted; the loss quadratic cost function in a neural network is:
Loss＝mean(square(x-x AP )) (10)
the output of each node of the DNN network is a nonlinear activation function to which its inputs are applied; weights between layers in the neural network are optimized through extensive learning, and expected outputs of training samples containing known inputs are learned;
and 4, step 4: inputting a signal generated after the original reference signal passes through a trained network as a new reference signal into an adaptive filter;
the receiver receives signals including a target signal, a noise signal and a self-interference signal, the sum of the signals is a signal before cancellation, and a self-interference signal SI (t) in formula (5) is a target to be cancelled;
4a) Taking LFM signal as an example, the received signal is denoised and sampled to become y (n), which is the cancellation signal input to the adaptive filter and has a duration of 3 μ s,
4b) Delaying the y (n) signal in the step 4a by 0.5 mu s, then carrying out nonlinear processing by using the Sa-Rach model in the step 2 to simulate the nonlinear characteristic of the power amplifier, taking the processed signal as a self-interference signal SI (n), and adding the SI (n), the y (n) and Gaussian white noise to obtain a target cancellation signal;
4c) Inputting the y (n) signal delayed by 0.5 mu s in the step 4b into a DNN neural network as a training sample, taking the signal subjected to nonlinear processing by the Saichh model as a label, and estimating a signal x by a deep neural network AP (n) as a reference signal for the adaptive filter;
4d) Inputting the signals generated in the steps 4b and 4c into the adaptive filter of the LMS algorithm to obtain an output signal x (n) after cancellation of the adaptive filter, and achieving the result of cancellation of the LFM signal;
the cancellation result can also be achieved by changing the LFM signal in the above steps into a BPSK signal.
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