A secure communication method and system based on reserve pool chaos prediction and interleaving coding

Abstract

The present invention discloses a secure communication method and system based on chaotic prediction and interleaving coding in a reserve pool. The chaotic encryption module includes a light source output by a first continuous wave laser (CW), which is modulated by a parallel quadrature modulator (IQM) to convert the signal output by an arbitrary waveform generator (AWG) for transmission. The signal is pseudo-randomly interleaved and encoded. The transmitted signal is loaded onto a chaotic carrier through a first optical coupler (FC) to achieve chaotic hiding. The chaotic optical carrier is then transmitted through an information transmission module. The chaotic optical carrier is predicted in advance by the reserve pool in the chaotic decryption module and modulated onto the continuous optical carrier output by a second continuous wave laser (CW) through a Mach-Zehnder modulator (MZM). The signal is then decrypted by a 90° mixer to initially restore the original transmitted signal. Finally, the resulting signal is processed by a local oscillator (LO) and a coherent receiver, and digital information processing (DSP) is performed to compensate, recover, and decrypt the digital signal, thereby realizing chaotic secure coherent optical communication.

Description

Technical Field

[0001] The present invention belongs to the technical field of chaotic secure communication in the field of optical fiber communication, and in particular relates to a secure communication method and system based on reserve pool chaos prediction and interleaving coding. Background Art

[0002] Secure communications based on chaotic synchronization were first proposed in the early 1990s. Since then, due to the potential application of chaotic optical communication in high-speed secure communications, many new schemes have been proposed. Among them, semiconductor laser-based chaotic secure communication schemes generate optical chaotic signals using a laser-based nonlinear dynamic system. However, due to performance limitations and technical bottlenecks in various electro-optical components, the capacity and speed of chaotic optical secure communication systems are severely affected. Therefore, the bandwidth and latency of chaotic sources for high-speed, long-distance secure communications need to be further improved.

[0003] For semiconductor laser-based chaotic optical secure communication systems, one of their core technologies is achieving stable, long-lasting, high-quality chaotic synchronization between the transmitter and receiver. In recent years, reservoir computing (RC), a machine learning method for predicting the evolutionary behavior of nonlinear dynamical systems, has attracted the interest of many researchers. For example, existing patent CN112822003A discloses a laser chaotic synchronization secure communication method and system based on reservoir computing. The basic principle of chaos prediction and chaos synchronization is to train the target nonlinear dynamical system to learn the system's behavior. By feedback regression to the reservoir, the trained reservoir approximates the target nonlinear dynamical system and can perform further predictions, ultimately achieving chaotic synchronization. However, the commonly used reservoir system has limited nonlinear expression, and the reservoir computing method suffers from low prediction accuracy. Its prediction accuracy is difficult to achieve good synchronization for dynamical systems over long distances, resulting in inevitable losses and errors after information decryption. Furthermore, high-speed, long-distance signal transmission between the transmitter and receiver results in significant signal attenuation and distortion, leading to information distortion after transmission. Therefore, how to solve the signal processing technology involved in the process of high-speed, long-distance and confidential signal transmission and the efficient chaotic synchronization between the transmitter and receiver are issues that need to be urgently addressed in the current field of chaotic confidential communication.

[0004] With the recent maturation of coherent optical communication technology, digital signal processing techniques have been used to address distortion and loss during signal transmission, significantly increasing the transmission distance of information and enabling high-speed, long-distance chaotic secure communication. Furthermore, the continuous development of digital encryption algorithms further safeguards the secure transmission of information. By combining digital encryption algorithms with chaotic secure communication technology, dual encryption of information transmission can be achieved, further enhancing the security of data transmission. Based on this, the present invention proposes a secure communication method based on reserve pool chaotic prediction and interleaved coding to address some of the existing issues, with the goal of promoting the application and development of chaotic secure communication technology in related fields. Summary of the Invention

[0005] The purpose of the present invention is to solve the problems of signal processing technology involved in the process of high-speed long-distance confidential transmission of signals and efficient chaotic synchronization of transmitting and receiving ends.

[0006] To achieve the above-mentioned object, the present invention provides a secure communication system based on reserve pool chaos prediction and interleaving coding, comprising a chaotic encryption module, a reserve pool calculation neural network prediction module, a chaotic decryption module, an information transmission module and an information receiving module;

[0007] The chaotic encryption module includes a plane reflector M, the output end of which is sequentially connected to a driving laser SL, a phase modulator PM, a first dispersion compensation optical fiber DCF, and a second optical coupler FC; the output end of the second optical coupler FC is connected to the first optical coupler FC and a photodetector PD;

[0008] The input end of the first optical coupler FC is connected to the parallel quadrature modulator IQM, and the parallel quadrature modulator IQM is connected to the first continuous wave laser CW and the arbitrary waveform generator AWG; the output end of the first optical coupler FC is connected to the input end of the erbium-doped fiber amplifier EDFA;

[0009] The reservoir computing neural network prediction module includes a photodetector PD, the output end of the photodetector PD is sequentially connected to an ADC module and a reservoir neural network RC; the output end of the reservoir neural network RC is connected to a Mach-Zehnder modulator MZM;

[0010] The chaos decryption module includes a Mach-Zehnder modulator MZM, which is connected to a second continuous wave laser CW and a 90° mixer;

[0011] The information transmission module includes N optical fiber link modules, each of which is composed of an erbium-doped fiber amplifier (EDFA), a single-mode fiber (SSFM), and a second dispersion-compensating fiber (DCF). The output end of the erbium-doped fiber amplifier (EDFA) is sequentially connected to the single-mode fiber (SSFM) and the second dispersion-compensating fiber (DCF). The second dispersion-compensating fiber (DCF) is connected to a 90° mixer. The 90° mixer is connected to a coherent receiver.

[0012] The information receiving module includes a local oscillator laser LO and a coherent receiver; the coherent receiver is connected to the output end of the 90° mixer.

[0013] Furthermore, the reservoir neural network RC includes an input layer, a reservoir layer and an output layer; each layer contains a number of neurons.

[0014] Furthermore, the reservoir layer is composed of two reservoirs with the same parameters through linear coupling. The number of neurons in the reservoir can be set according to the complexity of the problem; the reservoir size N = 500, the spectral radius rho = 1.2, and the regularization parameter reg = 1e -6 When the coupling coefficient α=0, the results calculated by the two reservoirs do not interact and are output in sequence; when α=1, the calculations of the two reservoirs are in a one-way coupled output relationship.

[0015] A secure communication method based on reserve pool chaos prediction and interleaving coding comprises the following steps:

[0016] S1: The optical feedback structure formed by the driving laser SL and the plane mirror M is used to generate a chaotic laser. After being optimized by the phase modulator PM and the first dispersion-compensating fiber DCF, the chaotic laser outputs two identical optical signals through the second coupler FC with a splitting ratio of 50:50. One optical signal is used as a driving signal and is converted into a chaotic electrical signal through the photoelectric conversion of the photodetector PD and the ADC module. The signal is then input into the storage pool calculation neural network prediction module; the other optical signal is input into the first optical coupler FC as a driving signal.

[0017] S2: A first continuous wave laser (CW) generates an optical signal. The constellation signal to be transmitted is encoded offline using an arbitrary waveform generator (AWG) and then modulated onto the optical signal generated by the first continuous wave laser (CW) using a parallel quadrature modulator (IQM). The constellation signal is encrypted and modulated onto the optical signal to generate the original information m(t).

[0018] S3, the optical signal input into the first optical coupler FC in step S1 is superimposed with the original information m(t) in step S2 to generate a chaotic encrypted signal c(t)+m(t), and the chaotic encrypted signal is sequentially transmitted to the chaotic decryption module via an information transmission module consisting of an erbium-doped fiber amplifier EDFA, a single-mode fiber SSFM, and a second dispersion-compensating fiber DCF;

[0019] S4: The chaotic electrical signal input to the photodetector PD and the ADC module in S1 is used as the input of the reserve pool calculation neural network prediction module. The weight of the reserve pool neural network RC is determined by optimizing the reserve pool neural network RC training phase, so that the output signal of the output layer of the reserve pool neural network RC is synchronized with the input signal of the input layer of the reserve pool neural network RC, thereby realizing chaotic prediction of the input signal and obtaining a chaotic prediction signal.

[0020] S5: During the reserve pool neural network RC test phase, the output layer outputs a chaotic prediction signal and, through the Mach-Zehnder modulator (MZM), outputs a chaotic optical carrier c'(t) synchronized with the output of the chaotic encryption module under the synchronous control of the second continuous wave laser CW. The chaotic encryption signal c(t)+m(t) output by the information transmission module and the chaotic optical carrier c'(t) are decrypted through a 90° mixer to obtain the original transmission signal m'(t).

[0021] S6: The original transmission signal m'(t) is input to the information receiving module. After coherent reception by the local oscillator laser LO and the coherent receiver, offline digital information processing DSP is performed to optimize and compensate the original transmission signal m'(t).

[0022] Furthermore, the generation of the constellation signal in step S2 is to map the binary bit data to different symbol points of the constellation according to the phase amplitude, thereby generating a QAM signal to be transmitted.

[0023] Furthermore, the specific process of the encryption algorithm in step S2 is as follows:

[0024] The pseudo-random interleaving encoding method of the signal is to perform pseudo-random interleaving encoding on the QAM binary sequence through the Lorenz three-dimensional chaotic system and the specific random numbers generated by the function to achieve encryption; the Lorenz three-dimensional chaotic equation is as follows:

[0025]

[0026] Where σ, ρ, and β are dimensionless constants, x, y, and z are the state components of the chaotic system, and t is time. After solving the chaotic equation and digitizing the chaotic sequence, the obtained x, y, and z are integerized, as shown in the following formula:

[0027] P x，i =mod(Extract(x i ,m,n,p),M)

[0028] P y，i =mod(Extract(y i ,m,n,p),M)

[0029] P z，i =mod(Extract(z i ,m,n,p),M)

[0030] where x i 、y i 、z i is a chaotic sequence, m, n, p are specific bits of numbers extracted from the decimal part, and the Extract function is implemented to extract specific bits of numbers from the decimal part. The resulting random sequence {P x，i}、{P y，i} and {P z，i}, using {P x，i}Generate a chaotic synchronization sequence and insert it into the front of the QAM signal frame; use the digitized sequence {P y，i} and {P z，i} to construct a scrambling sequence, and the QAM signal and the scrambling sequence can be pseudo-randomly interleaved to achieve signal encryption.

[0031] Furthermore, the decryption in step S5 uses the same chaotic equation, the same initial value and the same number of iterations as those used in the chaotic encryption module to construct the same scrambling sequence and obtain the same digital encryption information, thereby restoring the original transmission signal m'(t) after scrambling to the original constellation diagram.

[0032] Beneficial effects:

[0033] (1) The chaotic light source based on optical feedback is optimized by combining a phase modulator with a dispersion-compensating fiber, which increases the bandwidth of the chaotic source and effectively suppresses the delay characteristics.

[0034] (2) The reservoir computing neural network prediction module acts as the receiver of the chaotic synchronization communication system. Compared with the traditional reservoir computing prediction model, the linear coupled reservoir computing training uses the output of the first reservoir as a new feedback input to the second reservoir. That is, the input of the second reservoir is replaced by the output of the first reservoir. The two reservoirs can store information through the connection of each unit in the loop, where the previous input affects the next response. After training, the reservoir becomes an autonomous dynamic system that imitates the system from the input end. This model solves the problem of insufficient prediction accuracy of traditional methods and achieves the effect of smaller prediction error and longer prediction time.

[0035] (3) Because the computational neural network prediction module of the reserve pool system uses a third-party laser as its input, its synchronization quality is independent of the degree of effective information coverage and is easy to control. Good demodulation results can still be obtained at extremely low coverage, while ensuring security. The third-party driving laser does not need to be synchronized with the main laser or the computational neural network prediction model of the reserve pool, effectively preventing eavesdropping and greatly enhancing the security of the system.

[0036] (4) The transmitted constellation diagram is pre-coded using pseudo-random interleaving during the information encoding process. The binary sequence converted from the QAM signal is scrambled and encoded using a specific random sequence generated by a digital chaotic system and a function to achieve encryption. At the receiving end, the recovered information is processed by a coherent receiver using DSP. This can optimize, compensate, and deinterleave the signal loss of the original signal after high-speed, long-distance transmission. The secondary digital encryption method increases system security and significantly improves system transmission performance. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 It is a schematic diagram of the structure of the device of the present invention;

[0038] Figure 2 It is an internal principle diagram of the reserve pool calculation neural network prediction module of the present invention.

[0039] Figure 1: 1-first continuous wave laser CW, 2-parallel orthogonal modulator IQM, 3-arbitrary waveform generator AWG, 4-first optical coupler FC, 5-plane reflector M, 6-driving laser SL, 7-phase modulator PM, 8-first dispersion compensating fiber DCF, 9-second optical coupler FC, 10-photodetector PD, 11-ADC module, 12-reserve pool neural network RC, 13-erbium-doped fiber amplifier EDFA, 14-single mode fiber SSFM, 15-second dispersion compensating fiber DCF, 16-second continuous wave laser CW, 17-Mach-Zehnder modulator MZM, 18-90° mixer, 19-coherent receiver, 20-local oscillator laser LO, 21-chaos encryption module, 22-reserve pool calculation neural network prediction module, 23-chaos decryption module, 24-information transmission module, 25-information receiving module. DETAILED DESCRIPTION

[0040] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0041] The application principle of the present invention will be further described below with reference to the accompanying drawings and specific embodiments.

[0042] Example 1:

[0043] according to Figure 1 As shown, this embodiment proposes a secure communication method based on reserve pool chaos prediction and interleaving coding, and the specific steps are as follows:

[0044] S1: A driving laser SL6 and a plane mirror M5 form an optical feedback structure to generate a chaotic laser. The generated chaotic laser is further optimized by a phase modulator PM7 and a first dispersion-compensating fiber DCF8. The nonlinear effects of the phase modulator PM7 and the first dispersion-compensating fiber DCF8 enable the driving laser SL6 to output a chaotic carrier signal with high bandwidth and suppressed delay characteristics to enhance system security. The laser chaotic carrier then passes through a second optical coupler FC9 with a 50:50 splitting ratio and outputs two identical optical signals. One optical signal, serving as a driving signal, is converted into a chaotic electrical signal through the photoelectric conversion of the photodetector PD10 and ADC11 modules. The chaotic electrical signal is then input offline into the reserve pool computation neural network prediction module 22 for chaos prediction. The other optical signal (i.e., the chaotic optical signal c(t)) is input into the first optical coupler FC4 as a driving signal.

[0045] S2, the first continuous-wave laser CW1 generates an optical signal. The constellation signal to be transmitted is offline encoded using an arbitrary waveform generator AWG3. The constellation signal is generated by mapping binary bit data to different symbol points of the constellation according to phase and amplitude, thereby generating the QAM signal (constellation signal) to be transmitted. The constellation signal is modulated onto the optical signal generated by the first continuous-wave laser CW1 using a parallel quadrature modulator IQM2. The constellation signal is then modulated onto the optical signal using an encryption algorithm based on a digital chaotic system. Specifically, the constellation signal is encrypted through "secondary modulation" to generate the pre-processed original information m(t) to be transmitted.

[0046] S3, the chaotic optical signal c(t) input to the first optical coupler FC4 in step S1 is superimposed with the original information m(t) in step S2 to generate a chaotic encrypted signal c(t)+m(t), and the chaotic encrypted signal is sequentially transmitted to the chaotic decryption module 23 via the erbium-doped fiber amplifier EDFA13, the single-mode fiber SSFM14, and the second dispersion-compensating fiber DCF15 to form an information transmission module 24;

[0047] S4: The chaotic electrical signal input to the photodetector PD10 and the ADC module 11 in S1 is used as the input of the reserve pool calculation neural network prediction module 22. The weight of the reserve pool neural network RC12 is determined by optimizing the training phase of the reserve pool neural network RC12, so that the output signal of the output layer of the reserve pool neural network RC12 is synchronized with the input signal of the input layer of the reserve pool neural network RC12, that is, the chaotic prediction of the input signal is realized, which is a chaotic prediction signal.

[0048] The steps of the training phase of the reserve pool neural network RC12 are as follows: the output of the driving laser SL6 passes through the photodetector PD10 and is used as the input of the reserve pool calculation neural network prediction module 22 for iteratively generating the internal node states of the reserve pool;

[0049] During the training phase, the chaotic electrical signal of the input layer is transmitted to the reservoir layer in a back-to-back manner and used as the target output data calculated by the reservoir layer to calculate the connection weight between the reservoir layer and the output layer.

[0050] S5: During the test phase of the reserve pool neural network RC12, the output layer outputs a chaotic prediction signal and, under the synchronous control of the second continuous wave laser CW16, outputs a chaotic optical carrier c'(t) synchronized with the output of the chaotic encryption module 21 via the Mach-Zehnder modulator MZM17. The chaotic encryption signal c(t)+m(t) output by the information transmission module 24 and the synchronized chaotic optical carrier c'(t) are decrypted by a 90° mixer 18 to obtain the useful original transmission signal m'(t). The constellation diagram digital signal decryption method (i.e., decryption of the original transmission signal m'(t)) reuses the same chaotic equations, initial values, and number of iterations used by the transmitter to construct the same scrambling sequence, obtain the same digital encryption information, and then restore the scrambled and encoded original transmission signal m'(t) to the original constellation diagram.

[0051] S6: The original transmission information m'(t) output by the chaotic decryption module 23 will have losses and errors after long-distance transmission. It is input into the information receiving module 25, and after coherent reception by the local oscillator laser LO20 and the coherent receiver CoherentReceiver19, offline digital information processing DSP is performed to optimize and compensate for the loss of the original transmission signal m'(t) after high-speed, long-distance transmission.

[0052] The specific process of the encryption algorithm in step S2 is as follows: the signal is encrypted through pseudo-random interleaving coding, specifically by using the Lorenz three-dimensional chaotic system and a specific random number generated by the function to perform pseudo-random interleaving coding on the binary sequence converted from the QAM signal to achieve encryption. The Lorenz three-dimensional chaotic equation is:

[0053]

[0054] Where σ, ρ, and β are dimensionless constants, x, y, and z are the state components of the chaotic system, and t is time. After solving the chaotic equation and digitizing the chaotic sequence using computing software, the obtained x, y, and z are integerized, as shown in the following formula:

[0055] P x，i =mod(Extract(x i ,m,n,p),M) (4)

[0056] P y,i =mod(Extract(y i ,m,n,p),M) ( 5)

[0057] P z，i =mod(Extract(z i,m,n,p),M) (6)

[0058] where x i 、y i 、z i is a chaotic sequence, m, n, and p are specific bit digits extracted from the decimal part, and here they are 2, 4, and 6 respectively. M is the decimal number of bits. Since this system adopts 16QAM constellation diagram to transmit signals, its value is 256.

[0059] The function is implemented as Extract function to extract specific bit numbers from the decimal part, and the resulting random sequence {P x，i}、{P y，i} and {P z，i}, using {P x，i Generate a chaotic synchronization sequence and insert it into the QAM signal frame. This synchronization sequence is actually a pilot sequence that can be used to complete signal estimation and symbol synchronization. y，i} and {P z，i} to construct a scrambling sequence. The QAM signal and the scrambling sequence can be pseudo-randomly interleaved to achieve signal encryption. In the information receiving module 25, as long as the same chaotic equation, the same initial value, and the same number of iterations are used to construct the same scrambling sequence, the same digital encrypted information can be obtained, and then the original data sequence can be correctly deinterleaved and restored.

[0060] A secure communication system based on reserve pool chaos prediction and interleaving coding includes a chaos encryption module 21, a reserve pool calculation neural network prediction module 22, a chaos decryption module 23, an information transmission module 24 and an information receiving module 25.

[0061] The chaotic encryption module 21 includes a plane mirror M5, a driving laser SL6, a phase modulator PM7, a first dispersion-compensating fiber DCF8, a second optical coupler FC9, a first optical coupler FC4, a first continuous wave laser CW1, an arbitrary waveform generator AWG3, and a parallel quadrature modulator IQM2. The output of the plane mirror M5 is sequentially connected to the driving laser SL6, the phase modulator PM7, the first dispersion-compensating fiber DCF8, and the second optical coupler FC9; the output of the second optical coupler FC9 is connected to the first optical coupler FC4 and the photodetector PD10. The plane mirror M5 and the driving laser SL6 generate chaotic laser light through optical feedback, which is then optimized after passing through the phase modulator PM7 and the first dispersion-compensating fiber DCF8. The input end of the first optical coupler FC4 is connected to a parallel quadrature modulator IQM2, which is in turn connected to a first continuous wave laser CW1 and an arbitrary waveform generator AWG3. The output end of the first optical coupler FC4 is connected to the input end of an erbium-doped fiber amplifier EDFA13. The chaotic encryption module 21 maps the binary information to be transmitted into a quadrature amplitude modulated (QAM) signal. The timing of the transmitted signal is randomly altered, and the continuous data is rearranged according to a certain pattern to achieve pseudo-random interleaving encoding. The random method here uses a random sequence extracted from a digital chaotic system and a specific function, followed by digital-to-analog conversion. After being modulated by the parallel quadrature modulator IQM2, the signal is modulated onto the optical signal from the first continuous wave laser CW1 and transmitted through an optical fiber. The chaotic optical signal and the modulated information-loaded light source are subjected to chaotic masking by the first optical coupler FC4.

[0062] The reservoir computing neural network prediction module 22 includes a photodetector PD10, an ADC module 11, and a reservoir neural network RC12. The output of the photodetector PD10 is sequentially connected to the ADC module 11 and the reservoir neural network RC12. The output of the reservoir neural network RC12 is connected to a Mach-Zehnder modulator MZM17. The optical signal output by the chaotic encryption module 21 is converted into an electrical signal by the photodetector PD10 and then input into the ADC module 11. The electrical signal is then input into the reservoir neural network RC12 for offline prediction of the chaotic electrical signal. The reservoir neural network RC12 is composed of two reservoirs with identical parameters, linearly coupled.

[0063] The information transmission module 24 includes N fiber link modules, each composed of an erbium-doped fiber amplifier (EDFA) 13, a single-mode fiber (SSFM) 14, and a second dispersion-compensating fiber (DCF) 15. The output of the EDFA 13 is sequentially connected to the single-mode fiber (SSFM) 14 and the second dispersion-compensating fiber (DCF) 15. The second dispersion-compensating fiber (DCF) 15 is connected to a 90° mixer 18, which is in turn connected to a coherent receiver 19. The second dispersion-compensating fiber (DCF) 15 compensates for optical signal dispersion caused by long-distance transmission, while the EDFA 13 compensates for optical signal attenuation.

[0064] The chaos decryption module 23 includes a second continuous wave laser CW16 , a Mach-Zehnder modulator 17 and a 90° mixer 18 . The Mach-Zehnder modulator MZM17 is connected to the second continuous wave laser CW16 and the 90° mixer 18 .

[0065] The information receiving module 25 includes a local oscillator laser LO20 and a coherent receiver 19. The coherent receiver 19 is connected to the output end of the 90° mixer 18.

[0066] The reservoir layer calculation of the reservoir calculation neural network prediction module 22 of the present invention is divided into two stages: a training stage and a testing stage.

[0067] The calculation principle of the reservoir layer is as follows: During the training phase, the weight matrices Win1 and Win2 between the input layer and the reservoir, as well as the internal weight matrices W1 and W2 of the nodes within the reservoir, are randomly generated and immutable. Only the connection weight Wout between the reservoir and the output layer needs to be determined during the training process. The training process first iteratively generates the state vector X(t) of the internal nodes of the reservoir based on the input data u(t). Wout is then calculated based on the target output data Y during the training phase and the reservoir node state vector X(t).

[0068] During the testing phase, the reservoir system needs to be driven by input data to update the system state. After the state update, the reservoir no longer relies on new external input signals, but instead recursively updates its future state through its own internal state r(t). At each time step tt, the output of the reservoir system is the output connection weight Wout multiplied by the current state vector r(t) and the input data u(t). Once the reservoir system enters the prediction phase, the model enters self-driving mode, meaning that the next prediction depends on the previous prediction.

[0069] The chaotic encryption module 21 uses a driving laser SL6 with optical feedback to generate a chaotic light source, which can output a high-quality chaotic light source with a wide bandwidth and suppressed delay characteristics through a phase modulator PM7 and a first dispersion compensation optical fiber DCF8. This chaotic light source enters the reserve pool neural network RC12 prediction module through the photodetector PD10 and the ADC module 11. After training, it can accurately predict the dynamic trajectory of the chaotic encryption system and achieve complete chaos synchronization in the information receiving module 25; the light source output by the first continuous wave laser CW1 is modulated by the parallel orthogonal modulator IQM2 to modulate the signal output by the arbitrary waveform generator AWG3 from the electrical domain to the optical domain for transmission. The signal is encoded in the optical domain. The code is implemented by randomly changing the timing of the transmitted signal, rearranging the continuous data according to a certain pattern to achieve pseudo-random interleaving. The transmitted signal is then loaded onto a chaotic carrier through the first optical coupler FC4 to achieve chaos hiding. After transmission through an erbium-doped fiber amplifier EDFA 13, a standard single-mode fiber SSFM 14, and a second dispersion-compensating fiber DCF 15, the chaotic decryption module 23 uses the chaotic prediction signal to modulate the signal onto a continuous optical carrier output by a second continuous-wave laser CW 16 via a Mach-Zehnder modulator MZM 17. The chaotic encrypted signal output by the information transmission module 24 is then decrypted by a 90° mixer 18 to initially recover the original transmitted signal. Finally, the resulting original transmitted signal is processed by the information receiving module 25, consisting of a local oscillator laser LO 20 and a coherent receiver 19, for digital signal processing (DSP) to compensate, recover, and decrypt the digital signal, thus achieving chaotic secure coherent optical communication.

[0070] The chaotic encryption module 21 optimizes the chaotic source and outputs it to the reserve pool calculation neural network prediction module 22 and the information transmission module 24. The chaotic encryption module 21 performs chaotic modulation on the signal modulated by the parallel orthogonal modulator IQM2 and the chaotic encryption signal output by the chaotic encryption module 21, and transmits it over a long distance to the chaotic decryption module 23. The chaotic decryption module 23 then decrypts the required information, and the signal is compensated and optimized by the information receiving module.

[0071] Example 2:

[0072] according to Figure 2 As shown, the present invention adopts Figure 2 The structure of the reservoir neural network (RC) in the reservoir computation neural network prediction module 22 is shown as follows: it comprises an input layer, a reservoir layer consisting of two reservoirs with identical parameters, and an output layer. Each layer contains a number of neurons, and the number of neurons in the reservoir layer can be set based on the complexity of the problem. The present invention employs one input and one output layer, with each layer containing N neurons, and the reservoir layer containing M neurons. The reservoir computation dynamics equation can be expressed as follows.

[0073]

[0074] W out =YX T (XX T +λη)(8)

[0075]

[0076] x(t+1)=αy 2,1 (t)+(1-α)y 1,2 (t)(10)

[0077] In the above formula: x is input data, t is time, Δt is change time, W * is the internal weight matrix, W in is the input weight matrix, W out is the connection matrix, u(t) is the input data, Y is the output matrix, X is the state matrix, and λ is the ridge regression parameter, which is used to avoid overfitting. η is the unit matrix. a is the leakage rate, and its value range is between (0, 1). tanh is the activation function, which is used to introduce nonlinear factors into the neuron so that the reserve pool can arbitrarily approximate any nonlinear function, so that the reserve pool can be applied to many nonlinear models. According to formula (10), in order to enable the reserve pool calculation to obtain better prediction results, inspired by the diffusion coupling of the oscillator, the two reserve pool calculations are made to achieve an interactive effect. The present invention adopts formula (10) to couple the two reserve pool calculations, where y1 and y2 are the outputs calculated by reserve pool 1 and reserve pool 2 respectively. By coupling the outputs of the two reserve pool calculations, that is, mixing their outputs as new inputs, the two reserve pool calculations are linked together to achieve interaction between the two reserve pool calculations. The reserve pool size N is 500, the spectral radius rho is 1.2, and the regularization parameter reg is 1e -6 α is the coupling coefficient between the two reservoir calculations. When α = 0, the results of the two reservoir calculations do not interact and are output sequentially. When α = 1, the two reservoir calculations are unidirectionally coupled. This also shows that α describes the coupling method and coupling strength between the two reservoir calculations.

[0078] The reserve pool calculation principle of the present invention can be briefly described as follows: in the training stage, the chaotic laser output by the driving laser SL6 is output through the photodetector PD10 and the ADC module 11 as the input u(t) of the reserve pool calculation neural network prediction module 22. Through the randomly determined connection weights Win1 and Win2 between the input layer and the two reserve pools and the connection weights W1 and W2 between the neurons inside the reserve pool, the reserve pool iteratively generates a state vector X(t). At the same time, the chaotic carrier data Y from the chaotic encryption module 21 is used as the target output to be combined with X(t) to calculate the connection weight Wout between the reserve pool and the output layer. After the training is completed, it will no longer change and will be used in the testing stage.

[0079] During the testing phase, the driving laser SL6 continues to output chaotic laser, which passes through the photodetector PD10 and the ADC module 11 and then outputs chaotic data as the input u(t) of the reserve pool calculation neural network prediction module 22. Through the weight matrices Win1, Win2 and the internal weight matrices W1, W2, the reserve pool iteratively generates the state vector X(t). The state vector X(t) quickly generates the output Y(t) = X(t) * Wout through the Wout linear regression action. This output is the predicted chaotic carrier. When the prediction accuracy reaches the set range, the chaotic synchronization of the reserve pool calculation neural network prediction module 22 and the driving laser SL6 can be achieved.

[0080] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A secure communication system based on reserve pool chaos prediction and interleaving coding, characterized in that: It includes a chaos encryption module, a reserve pool calculation neural network prediction module, a chaos decryption module, an information transmission module and an information receiving module; The chaotic encryption module includes a plane reflector M, the output end of which is sequentially connected to a driving laser SL, a phase modulator PM, a first dispersion compensation optical fiber DCF, and a second optical coupler FC; the output end of the second optical coupler FC is connected to the first optical coupler FC and a photodetector PD; The input end of the first optical coupler FC is connected to the parallel quadrature modulator IQM, and the parallel quadrature modulator IQM is connected to the first continuous wave laser CW and the arbitrary waveform generator AWG; the output end of the first optical coupler FC is connected to the input end of the erbium-doped fiber amplifier EDFA; The reservoir computing neural network prediction module includes a photodetector PD, the output end of the photodetector PD is sequentially connected to an ADC module and a reservoir neural network RC; the output end of the reservoir neural network RC is connected to a Mach-Zehnder modulator MZM; The chaos decryption module includes a Mach-Zehnder modulator MZM, which is connected to a second continuous wave laser CW and a 90° mixer; The information transmission module includes N optical fiber link modules, each of which is composed of an erbium-doped fiber amplifier (EDFA), a single-mode fiber (SSFM), and a second dispersion-compensating fiber (DCF). The output end of the erbium-doped fiber amplifier (EDFA) is sequentially connected to the single-mode fiber (SSFM) and the second dispersion-compensating fiber (DCF). The second dispersion-compensating fiber (DCF) is connected to a 90° mixer. The 90° mixer is connected to a coherent receiver. The information receiving module includes a local oscillator laser LO and a coherent receiver; the coherent receiver is connected to the output end of the 90° mixer.

2. The secure communication system based on reserve pool chaos prediction and interleaving coding according to claim 1, characterized in that: The reservoir neural network RC includes an input layer, a reservoir layer and an output layer; each layer contains several neurons.

3. The secure communication system based on reserve pool chaos prediction and interleaving coding according to claim 2, characterized in that: The reservoir layer consists of two reservoirs with the same parameters through linear coupling. The number of neurons in the reservoir is set according to the complexity of the problem; the reservoir size N = 500, the spectral radius rho = 1.2, and the regularization parameter reg = 1e -6 ; When the coupling coefficient α = 0, the results of the two reservoir calculations do not interact and are output sequentially; when α = 1, the calculations of the two reservoirs are in a one-way coupled output relationship.

4. A secure communication method based on reserve pool chaos prediction and interleaving coding, characterized in that: The following steps are involved: S1: The optical feedback structure formed by the driving laser SL and the plane mirror M is used to generate a chaotic laser. After being optimized by the phase modulator PM and the first dispersion-compensating fiber DCF, the chaotic laser outputs two identical optical signals through the second coupler FC with a splitting ratio of 50:

50. One optical signal is used as a driving signal and is converted into a chaotic electrical signal through the photoelectric conversion of the photodetector PD and the ADC module. The signal is then input into the storage pool calculation neural network prediction module; the other optical signal is input into the first optical coupler FC as a driving signal. S2: A first continuous wave laser (CW) generates an optical signal. The constellation signal to be transmitted is encoded offline using an arbitrary waveform generator (AWG) and then modulated onto the optical signal generated by the first continuous wave laser (CW) using a parallel quadrature modulator (IQM). The constellation signal is encrypted and modulated onto the optical signal to generate the original information m(t). S3, the optical signal input into the first optical coupler FC in step S1 is superimposed with the original information m(t) in step S2 to generate a chaotic encrypted signal c(t)+m(t), and the chaotic encrypted signal is sequentially transmitted to the chaotic decryption module via an information transmission module consisting of an erbium-doped fiber amplifier EDFA, a single-mode fiber SSFM, and a second dispersion-compensating fiber DCF; S4: The chaotic electrical signal in S1 is used as the input of the reserve pool calculation neural network prediction module. The weight of the reserve pool neural network RC is determined by optimizing the reserve pool neural network RC training phase, so that the output signal of the output layer of the reserve pool neural network RC is synchronized with the input signal of the input layer of the reserve pool neural network RC, thereby realizing chaotic prediction of the input signal, which is a chaotic prediction signal; S5: During the reserve pool neural network RC test phase, the output layer outputs a chaotic prediction signal and, through the Mach-Zehnder modulator (MZM), outputs a chaotic optical carrier c'(t) synchronized with the output of the chaotic encryption module under the synchronous control of the second continuous wave laser CW. The chaotic encryption signal c(t)+m(t) output by the information transmission module and the chaotic optical carrier c'(t) are decrypted through a 90° mixer to obtain the original transmission signal m'(t). S6: The original transmission signal m'(t) is input to the information receiving module. After coherent reception by the local oscillator laser LO and the coherent receiver, offline digital information processing DSP is performed to optimize and compensate the original transmission signal m'(t).

5. The secure communication method based on reserve pool chaos prediction and interleaving coding according to claim 4, characterized in that: The generation of the constellation signal in step S2 is to map the binary bit data to different symbol points of the constellation according to the phase amplitude, thereby generating a QAM signal to be transmitted.

6. The secure communication method based on reserve pool chaos prediction and interleaving coding according to claim 4, characterized in that: The specific process of the encryption algorithm in step S2: The Lorenz three-dimensional chaos equation is as follows: Where σ, ρ, and β are dimensionless constants, x, y, and z are the state components of the chaotic system, and t is time. After solving the chaotic equation and digitizing the chaotic sequence, the obtained x, y, and z are integerized, as shown in the following formula: P x,i =mod(Extract(x i ,m,n,p),M) P y,i =mod(Extract(y i ,m,n,p),M) P z,i =mod(Extract(z i ,m,n,p),M) where x i 、y i 、z i is a chaotic sequence, m, n, p are specific bit numbers extracted from the decimal part, M is the decimal number of the bit, and the Extract function is implemented to extract specific bit numbers from the decimal part to obtain the random sequence {P x,i }、{P y,i } and {P z,i }, using {P x,i }Generate a chaotic synchronization sequence and insert it into the front of the QAM signal frame; use the digitized sequence {P y,i } and {P z,i } to construct a scrambling sequence, and perform pseudo-random interleaving encoding on the QAM signal and the scrambling sequence to achieve signal encryption.

7. The secure communication method based on reserve pool chaos prediction and interleaving coding according to claim 4, characterized in that: The training phase in step S4 is specifically as follows: The output of the driving laser SL passes through the photodetector PD as the input of the reserve pool calculation neural network prediction module, which is used to iteratively generate the internal node state of the reserve pool; During the training phase, the chaotic electrical signal of the input layer is transmitted to the reservoir layer in a back-to-back manner and used as the target output data calculated by the reservoir layer to calculate the connection weight between the reservoir layer and the output layer.

8. The secure communication method based on reserve pool chaos prediction and interleaving coding according to claim 4, characterized in that: The decryption in step S5 uses the same chaotic equation, the same initial value and the same number of iterations as those used in the chaotic encryption module to construct the same scrambling sequence and obtain the same digital encryption information, thereby restoring the original transmission signal m'(t) after scrambling to the original constellation diagram.

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