CN113376445B - Deep learning enhanced Reedberg atom multi-frequency microwave receiver and detection method - Google Patents
Deep learning enhanced Reedberg atom multi-frequency microwave receiver and detection method Download PDFInfo
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Abstract
The invention provides a deep learning enhanced multi-frequency microwave receiver for a Reedberg atom and a detection method, wherein the deep learning enhanced multi-frequency microwave receiver comprises a laser, a polarization beam splitter divides incident light of the laser into two parallel beams, one beam is detection light, and the other beam is reference light; the polarization directions of the two beams of light are mutually orthogonal; two beams of light are transmitted into the rubidium bubble through a dichroic mirror; the detection light and the reference light are finally received by the differential photoelectric detector; the coupling light sequentially passes through a second dichroic mirror, a rubidium bubble and a first dichroic mirror on a light path, enters the rubidium bubble after being reflected by the second dichroic mirror, is reversely coincided with the detection light, and is reflected by the second dichroic mirror; the outer side of the rubidium bubble is provided with a loudspeaker for loading a multi-frequency microwave signal to atoms of the rubidium bubble. The number of the signals of the frequency division multiplexing can reach 20, thereby improving the information transmission rate, quickly responding to the input signals and detecting the frequency spectrums of multiple frequencies at one time.
Description
Technical Field
The invention belongs to the technical field of microwave electric field sensors, and particularly relates to a deep learning enhanced Reedberg atom multi-frequency microwave receiver and a detection method.
Background
With the development of microwave technology (radar, 5G, WiFi, etc.), the requirements for microwave receivers are becoming higher and higher, and the size of conventional receiver antennas increases with increasing microwave wavelengths. The size of the recently proposed rydberg atom receiver is not limited by the wavelength of the microwave, and the rydberg atoms are very sensitive to the microwave signal due to their large dipole moment, and can be used as a high-precision and high-sensitive microwave receiver, and in addition, the atoms have many different states of rydberg, and can be used for receiving microwave signals of different frequencies.
The detection of the relative phase signal of two microwave signals based on rydberg atoms is reported in the literature [ appl.phys.lett.114,114101(2019) ]. However, the document can only detect the relative phase between two microwave signals, cannot detect the phase between a plurality of microwave signals, and has no expandability. The utilization rate of the signal frequency band is not high.
The document IEEE Antennas and Wireless Transmission Letters, vol.18, No.9, pp.1853-1857, Sept.2019 proposes to use the phase between two microwave signals to transmit information for digital communication. The literature describes experiments in which phase shift keying signals and quadrature amplitude modulation signals are received using rydberg atoms. However, the receiving apparatus is complicated, and when different frequency division multiplexing signals are separated, a band pass filter is used. The document shows only two frequency division multiplexed signals for demonstration purposes, if multiple frequency division multiplexed signals are used, multiple band pass filters are required.
Disclosure of Invention
The present invention proposes to decode frequency division multiplexing phase shift keying signals (FDM-2PSK) using the rydberg atoms and a deep learning model. The invention consists of a Reidberg atom and a deep learning model. The multi-frequency microwave signals are loaded on the rydberg atoms through a loudspeaker to influence the transmission spectrum of the detection light, then the transmission spectrum is input into a trained deep learning model, the model outputs the relative phase between the microwave signals, and the original signals are demodulated.
The specific technical scheme is as follows:
the deep learning enhanced multi-frequency microwave receiver for the rydberg atoms comprises a laser, wherein a light path of the laser sequentially comprises a half-wave plate, a polarization beam splitter, a first dichroic mirror, a rubidium bubble, a second dichroic mirror and a differential photoelectric detector;
the polarization beam splitter divides the incident light of the laser into two parallel beams, one beam is probe light, and the other beam is reference light; the polarization directions of the two beams of light are mutually orthogonal; two beams of light are transmitted into the rubidium bubble through a dichroic mirror; the detection light and the reference light are finally received by the differential photoelectric detector;
the coupling light sequentially passes through a second dichroic mirror, a rubidium bubble and a first dichroic mirror on a light path, enters the rubidium bubble after being reflected by the second dichroic mirror, is reversely coincided with the detection light, and is reflected by the second dichroic mirror;
the outer side of the rubidium bubble is provided with a loudspeaker for loading a multi-frequency microwave signal to atoms of the rubidium bubble.
The half-wave plate is used for changing the polarization direction of light and is combined with the polarization beam splitter to adjust the relative light intensity of the two beams of light split by the polarization beam splitter.
The propagation direction of the multi-frequency microwave signal loaded by the horn is perpendicular to the detection light.
The laser emitted by the laser is 795 nm; the coupled light is 474nm laser.
Laser emitted by the laser is divided into two parallel beams by a polarization beam splitter and penetrates through the rubidium bubble, one beam is probe light, and the other beam is reference light; the coupling light and the detection light are reversely coincided, and atoms are excited into a Reedberg state through a two-photon process;
the detection light and the reference light are finally received by a differential photoelectric detector, and an output signal of the differential photoelectric detector is a transmission spectrum of the detection light;
the microwave is loaded on atoms through a loudspeaker, and the propagation direction of the microwave is vertical to the probe light, the reference light and the coupling light; and inputting the output signal of the differential photoelectric detector into the trained deep learning model to obtain a prediction result.
The deep learning model builds a neural network by using a bidirectional long-short term memory layer and a one-dimensional convolutional layer;
sequentially adding a batch normalization layer, an activation function layer and a one-dimensional pooling layer in front of a two-way long-short term memory layer behind the one-dimensional convolution layer;
the final fully-connected layer of the model is used to scale the output to the appropriate dimension.
The invention has the technical effects that:
the invention has expandability, and the number of the signals of the frequency division multiplexing can reach 20, thereby improving the information transmission rate.
The device is simple, and because a deep learning model is used, the invention does not need a plurality of band-pass filters to separate frequency division multiplexing signals.
Robustness, due to the fact that the rydberg atoms are very sensitive to an electromagnetic field, measured data often have large noise, and robustness of the rydberg atom receiver to the noise can be improved by means of deep learning.
And fourthly, the method can be used for multi-target detection. Due to the Doppler effect, different speeds correspond to different frequency spectrums, and the frequency spectrums of multiple frequencies can be detected completely by the model provided by the invention at one time.
And the speed is high, and compared with the method of fitting by using a principal equation, the trained model can quickly respond to the input signal.
Drawings
FIG. 1a is an energy level diagram of an embodiment;
FIG. 1b is a schematic structural view of the present invention;
FIG. 1c is a probe light transmission spectrum of the example upon incidence of microwaves of 4 frequencies;
FIG. 2 is an example deep learning model and input-output data dimensions for each layer;
FIG. 3 is a loss curve for the training set and validation set of an embodiment;
FIG. 4 shows the test results of the model of the example;
fig. 5 shows the result of transmitting the two-dimensional code according to the embodiment.
Detailed Description
The specific technical scheme of the invention is described by combining the embodiment.
As shown in fig. 1b, the deep learning enhanced rydberg atom multi-frequency microwave receiver comprises a laser 1, wherein a light path of the laser 1 sequentially comprises a half-wave plate 2, a polarization beam splitter 3, a first dichroic mirror 4, a rubidium bubble 5, a second dichroic mirror 7 and a differential photoelectric detector 8;
the polarization beam splitter 3 splits the incident light of the laser 1 into two parallel beams, one beam is probe light 12, and the other beam is reference light 11; the polarization directions of the two beams of light are mutually orthogonal; the two beams of light are transmitted into a rubidium bubble 5 through a first dichroic mirror 4, and atoms in the rubidium bubble are rubidium 85 atoms; the detection light 12 and the reference light 11 are finally received by the differential photodetector 8;
the coupling light 10 is emitted by a second laser 9, the light path sequentially passes through a second dichroic mirror 7, a rubidium bubble 5 and a first dichroic mirror 4, the coupling light 10 enters the rubidium bubble 5 after being reflected by the second dichroic mirror 7, is parallel to and reversely coincided with the detection light 12, and is then reflected by the second dichroic mirror 7;
the rubidium bubble 5 is provided with a loudspeaker 6 at the outer side for loading multi-frequency microwave signals on atoms of the rubidium bubble 5.
The half-wave plate 2 is used for changing the polarization direction of light and is combined with the polarization beam splitter 3 to adjust the relative light intensity of the two beams of light split by the polarization beam splitter 3.
The propagation direction of the multi-frequency microwave signal loaded by the horn 6 is perpendicular to the probe light 12.
The laser 1 emits 795nm laser; the coupled light 10 is a 474nm laser.
Atoms undergo a two-photon process |5S1/2,F＝2>→|5P1/2,F'＝3>→|51D3/2>Excited into a rydberg state |51D3/2>. The energy level diagram is shown in FIG. 1a, and the probe light 12 excites rubidium 85 atom transition |5S1/2,F＝2>→|5P1/2,F'＝3>Coupled light 10 excites atomic transition |5P1/2,F'＝3>→|51D3/2>Multifrequency microwave driven atomic transition |51D3/2>→|50F5/2>. The detuning of the probe light 12, the coupling light 10 and the microwave is respectively deltap,Δc,ΔsAnd its ratio frequency omegap,Ωc,Ωs. The detection light 12 and the reference light 11 are finally received by the differential photodetector 8, and the output signal of the differential photodetector 8 is the transmission spectrum of the atom.
FIG. 1c shows the transmission spectrum of the detected light obtained from the differential photodetector 8, in this case Δp＝0,Δc＝0,ΔsI.e. the laser resonates with an atom. The microwave field is the superposition of 4 microwaves with different frequencies, and the frequencies of the microwaves are respectively f1＝17.62GHz-3kHz f2＝17.62GHz-1kHz f317.62GHz +1kHz, and f417.62GHz +3kHz, the frequency spacing Δ f is 2 kHz.
In the experiment, multi-frequency microwave signals
Wherein
The phase of the single-frequency microwave signal takes 0 or pi, omega1,2,3Is the angular frequency of a single frequency microwave signal. The single-frequency signals are loaded on atoms through a horn 6 after being superposed, and the propagation direction of the microwave is vertical to the detection light 12, the reference light 11 and the coupling light 10. The output signal of the differential photoelectric detector 8 is input into a trained deep learning model to obtain a prediction result
One of the multi-frequency microwave signals is a reference signal whose amplitude is greater than the other signals in order to eliminate non-linearities, i.e., the phenomenon of corresponding identical waveforms with different phases. The modulation signal of the multi-frequency microwave signal is the relative phase of the other microwave signals relative to the reference signal.
Since data is time series and requires a model to have long-term memory capability, the present embodiment uses a long short-term memory layer (LSTM layer) in Keras to build a neural network. In order to further improve the memory of the network, the present embodiment uses a Bi-directional LSTM layer and a one-dimensional convolution layer (1-d convolution layer). A batch normalization layer (batch normalization layer), an activation function layer (ReLU layer), and a one-dimensional pooling layer (1-d max-firing layer) are sequentially added after the one-dimensional convolution layer in order to make the model converge faster during training [ series Ioffe, Christian Szegedy Proceedings of the 32nd International Conference on Machine Learning, PMLR 37: 448-. The last fully connected layer (dense layer) of the model is used to adjust the output to the appropriate dimension.
Fig. 2 shows the neural network structure used, and the input-output data dimension of each layer. The dimensionality of the model input data is (sample number, transmission spectrum data point number and characteristic number), the sample number is 64, the transmission spectrum duration is 1ms, the time interval is 1us, namely the data point number is 1000, and the characteristic number is 1. The dimension of the input data is thus (64, 1000, 1).
Relative phase of microwave signal with respect to reference signal
(as labels) and the corresponding probe light transmission spectra as data sets, the data sets being randomly scrambled and divided into a training set, a validation set and a test set, for training, validating and testing the deep learning model, respectively. The verification set is used for judging whether the model is over-fitted in the training process. The probe light transmission spectrum and the corresponding phase are input into the model for model learning during training. During learning, namely training, the 'distance' between the predicted value and the true value of the model, namely the loss function, is calculated, and the derivative of the loss function to the weight of each layer is reversely propagated to update the weight of each layer. After the training is completed, i.e. after the Loss curve (Loss) of the model converges, the embodiment can use the model to predict, and perform the test by using the test set. During the test, only the probe light transmission spectrum is provided, and no corresponding phase is provided, and the performance index of the model on the test set is obtained by comparing the predicted value of the model with the actual value.
Fig. 3 shows that the loss of the training set and the verification set is converged and close after 130 generations in the training and verification process of the model, which indicates that the model is trained well and has no over-fitting and under-fitting phenomena. The training set size is 524 and the validation set size is 172. Wherein the model traverses the data of the training set all over one generation. The loss function is the mean square error between the predicted value and the true value
m is the number of samples, yiIs the true value, f (x)i) And (4) predicting the value of the model. Finally, the frequency division multiplexing signals of arbitrary different phase combinations are combined by a loudspeaker (
0 or pi) to the rydberg atom,through the prediction of the neural network, the original relative phase can be obtained
The number of frequency division multiplexing signals used for training the neural network is the same as that used for prediction.
The model test results are shown in fig. 4, and a confusion matrix is drawn by using the prediction results of the model in the test set. The test set size was 160% and the accuracy of the model was 99.375%.
The result of transmitting the two-dimensional code is shown in fig. 5, when the training generations are 3 and 4, the information cannot be read from the two-dimensional code recovered from the model, and the corresponding accuracy rates are 51.02% and 76.87%, respectively. And at the 35 th generation, the information can be read from the two-dimensional code recovered from the model, and the accuracy at the moment is 99.32%. The accuracy is determined by dividing the number of correctly predicted waveforms by the total number of waveforms. In the embodiment, the multi-frequency microwave is used for carrying out frequency division multiplexing digital communication, a two-dimensional code is transmitted, and the two-dimensional code is coded by a letter 'USTC'. The two-dimensional code is coded and transmitted in a frequency division multiplexing binary phase shift keying (FDM-2PSK) mode, the two-dimensional code is received through a Reidberg atom and recovered by using deep learning models trained in different generations, and finally the fact that the two-dimensional code reconstructed by the deep learning models after 35 generations of training can decode the original letter 'USTC' is found, and the model accuracy is 99.32%. The hardware device for machine learning is an NVIDIA GeForce GTX 1650 display card.
Claims (5)
1. The deep learning enhanced Reedberg atom multi-frequency microwave receiver is characterized by comprising a first laser (1), wherein a light path of the first laser (1) sequentially comprises a half-wave plate (2), a polarization beam splitter (3), a first dichroic mirror (4), a rubidium bubble (5), a second dichroic mirror (7) and a differential photoelectric detector (8);
the half-wave plate (2) is used for changing the polarization direction of light and is combined with the polarization beam splitter (3) to adjust the relative light intensity of the two beams of light split by the polarization beam splitter (3);
the polarization beam splitter (3) splits the incident light of the laser (1) into two parallel beams, one beam is probe light (12), and the other beam is reference light (11); the polarization directions of the two beams of light are mutually orthogonal; the two beams of light are transmitted into the rubidium bubble (5) through the first dichroic mirror (4); the detection light (12) and the reference light (11) are finally received by the differential photoelectric detector (8); the output signal of the differential photoelectric detector (8) is the transmission spectrum of the detection light; inputting the transmission spectrum into a trained deep learning model, outputting the relative phase between microwave signals by the model, and demodulating the original signal;
the coupling light (10) is emitted by a second laser (9), the light path sequentially passes through a second dichroic mirror (7), a rubidium bubble (5) and a first dichroic mirror (4), the coupling light (10) enters the rubidium bubble (5) after being reflected by the second dichroic mirror (7), is reversely coincided with the detection light (12), and is reflected by the first dichroic mirror (4);
the outside of the rubidium bubble (5) is provided with a loudspeaker (6) for loading a multi-frequency microwave signal on atoms of the rubidium bubble (5).
2. The deep learning enhanced rydburg atom multi-frequency microwave receiver according to claim 1, wherein the horn (6) is loaded with multi-frequency microwave signals propagating in a direction perpendicular to the probe light (12) and the reference light (11).
3. A deep learning enhanced riedberg atom multi-frequency microwave receiver according to claim 1, characterized in that the first laser (1) emits laser light at 795 nm; the laser light emitted by the second laser (9) is 474nm coupled light (10).
4. The method for detecting a deep learning enhanced Reedberg atom multi-frequency microwave receiver according to any one of claims 1 to 3, wherein the laser emitted from the laser (1) is split into two parallel beams by a polarization beam splitter (3) and passes through a rubidium bubble (5), one beam is a detection light (12), and the other beam is a reference light (11); the coupling light (10) and the detection light (12) are reversely coincided, and atoms are excited into a Reedberg state through a two-photon process;
the detection light (12) and the reference light (11) are finally received by a differential photoelectric detector (8), and the output signal of the differential photoelectric detector (8) is the transmission spectrum of the detection light;
a multi-frequency microwave signal is loaded on atoms through a loudspeaker (6), and the propagation direction of the microwave is vertical to the detection light (12), the reference light (11) and the coupling light (10); and inputting the transmission spectrum into a trained deep learning model, outputting the relative phase between microwave signals by the model, and demodulating the original signal.
5. The method of claim 4, wherein the deep learning model builds a neural network using a two-way long-short term memory layer and a one-dimensional convolutional layer;
sequentially adding a batch normalization layer, an activation function layer and a one-dimensional pooling layer in front of a two-way long-short term memory layer behind the one-dimensional convolution layer;
the final fully-connected layer of the model is used to scale the output to the appropriate dimension.
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