Digital domain phase regeneration self-coherent receiving system and method for chaotic optical communication

Abstract

This invention belongs to the field of optical communication technology, specifically referring to a digital domain phase regeneration self-coherent receiving system and method for chaotic optical communication. The system includes a chaotic decryption module that receives a chaotic encrypted optical signal and a common chaotic optical signal, injects the common chaotic optical signal into a semiconductor laser to generate a synchronous chaotic copy, and uses the synchronous chaotic copy to decrypt the chaotic encrypted optical signal, outputting a decrypted optical signal. An auxiliary carrier laser generates a continuous wave laser. An optical coupler couples the decrypted optical signal and the continuous wave laser together to obtain a combined optical signal. A photodetector detects the intensity of the combined optical signal and outputs an analog electrical signal proportional to the instantaneous power of the combined optical signal based on the square-law detection principle. An analog-to-digital converter converts the analog electrical signal into a digital time-domain intensity sequence. A digital signal processing module reconstructs the complex phase information of the decrypted optical signal to recover the complex optical field signal, and performs post-processing and demodulation on the complex optical field signal to obtain the original binary information data.

Description

Technical Field

[0001] This invention relates to the field of optical communication technology, and in particular to a digital domain phase regeneration autocoherent receiving system and method for chaotic optical communication. Background Technology

[0002] With the rapid development of information technology, optical communication systems are evolving towards ultra-high speed and ultra-large capacity. Faced with increasingly complex signal processing requirements, physical layer security has become a focal point in the field of optical communication. Chaotic optical communication, utilizing the broadband noise-like characteristics of chaotic carriers and their extreme sensitivity to initial conditions, can achieve efficient, covert signal transmission at the physical layer without complex upper-layer encryption protocols, making it a key technological path for solving fiber optic communication security problems. However, the nonlinearity and complexity of chaotic signals also pose significant technical challenges to high-fidelity signal recovery at the receiving end.

[0003] Currently, chaotic optical communication systems generally employ traditional coherent reception techniques based on opto-mixers and balanced detectors to achieve signal phase recovery and demodulation. While this structure can effectively recover signals, it relies on complex optical front-end hardware (such as 90-degree optical mixers and balanced photodetector arrays). This complex optical front-end not only significantly increases the system's hardware cost and package size but also introduces a substantial power consumption burden. Especially in cost- and power-sensitive applications such as access networks or data center interconnects, traditional coherent reception schemes struggle to meet the deployment requirements of high integration and low power consumption, thus limiting the large-scale application of chaotic optical communication technology.

[0004] In summary, the coherent optical receiving structure in existing chaotic optical communication systems relies on complex optical front-ends, resulting in large size and high cost of chaotic optical communication systems, making it difficult to meet the needs of high integration and low power consumption application scenarios. Summary of the Invention

[0005] Therefore, the technical problem to be solved by the present invention is to overcome the fact that the coherent optical receiving structure in the existing chaotic optical communication system requires a complex optical front end, which leads to the large size and high cost of the chaotic optical communication system, making it difficult to meet the needs of high integration and low power consumption application scenarios.

[0006] To address the aforementioned technical problems, this invention provides a digital domain phase regeneration self-coherent receiving system for chaotic optical communication, comprising:

[0007] The chaotic decryption module is used to receive the chaotic encrypted optical signal and the common chaotic optical signal after the transmission link is demultiplexed, and inject the common chaotic optical signal into the semiconductor laser to induce a synchronization mechanism to generate a synchronous chaotic copy. The synchronous chaotic copy is used to decrypt the chaotic encrypted optical signal and output the decrypted optical signal.

[0008] An auxiliary carrier laser is used to generate a continuous wave laser. The center frequency of the continuous wave laser maintains a set frequency shift with the decryption optical signal and satisfies the minimum phase condition corresponding to the carrier signal power ratio.

[0009] An optical coupler, connected to the chaotic decryption module and the auxiliary carrier laser, is used to combine the decryption optical signal with the continuous wave laser to obtain a combined optical signal. When the combined optical signal satisfies the carrier signal power ratio and frequency shift, it is equivalent to a minimum-phase single-sideband signal.

[0010] A photodetector, connected to an optical coupler, is used to detect the intensity of the combined optical signal. Based on the square-law detection principle, it outputs an analog electrical signal that is proportional to the instantaneous power of the combined optical signal.

[0011] An analog-to-digital converter, connected to a photodetector, is used to convert analog electrical signals into digital time-domain intensity sequences;

[0012] The digital signal processing module, connected to the analog-to-digital converter, is used to reconstruct the complex phase information of the decrypted optical signal from the digital time-domain intensity sequence, thereby recovering the complex optical field signal. The complex optical field signal is then post-processed and demodulated to obtain the original binary information data.

[0013] Preferably, the digital signal processing module utilizes an improved DC-Value phase recovery module based on Anderson acceleration to reconstruct the complex phase information of the decrypted optical signal from the digital time-domain intensity sequence, including:

[0014] When the number of iterations is less than the set historical depth parameter of Anderson acceleration, the complex signal estimate at the k-th iteration is used as the input signal to perform DC-Value operation, and the DC component amplitude of the complex signal at the k-th iteration output by DC-Value operation is used as the complex signal estimate at the (k+1)-th iteration.

[0015] When the number of iterations is greater than or equal to the set historical depth parameter of Anderson acceleration, the complex signal estimate at the k-th iteration is used as the input signal to perform DC-Value operation; based on the complex signal estimate at the historical iteration and the residual vector generated by the DC-Value operation, the optimal weight coefficient vector that minimizes the linear combination norm of the historical iteration residuals is obtained; the DC component amplitude of the complex signal at the k-th iteration is corrected using the optimal weight coefficient vector, and the accelerated complex signal estimate obtained after correction is used as the complex signal estimate for the (k+1)-th iteration;

[0016] The accelerated complex signal estimate obtained in the last iteration is used as the recovered complex optical field signal.

[0017] Preferably, the complex phase information of the decrypted optical signal is reconstructed from the digital time-domain intensity sequence using an improved DC-Value phase recovery module based on Anderson acceleration, including:

[0018] S11: Set the maximum allowed number of iterations, the target convergence error threshold, and the historical depth parameters of Anderson acceleration. ; Construct initial complex signal estimates based on digital time-domain intensity sequences; Initialize historical estimate matrices and historical residual matrices for storing historical data, and initialize k=0;

[0019] S12: Take the estimated value of the complex signal at the k-th iteration as the input signal and perform the DC-Value operation. Output the amplitude of the DC component of the complex signal at the k-th iteration. Obtain the residual vector generated in the k-th iteration based on the difference between the input signal and the output signal.

[0020] S13: If k < m, store the complex signal estimate and residual vector at the k-th iteration into the historical estimate matrix and historical residual matrix respectively, take the DC component amplitude of the complex signal at the k-th iteration as the complex signal estimate at the (k+1)-th iteration, update k = k+1, and return to execute step S12.

[0021] S14: If k≥m, construct the historical estimate difference matrix and the historical residual difference matrix using the complex signal estimate and residual vector from the most recent m+1 iterations, respectively. With the objective of minimizing the difference norm between the residual vector of the kth iteration and the product of the historical residual difference matrix and the weight coefficient vector, construct the objective function and solve it to obtain the optimal weight coefficient vector.

[0022] S15: Calculate the difference between the magnitude of the DC component of the complex signal at the k-th iteration and the product of the difference matrix of the historical estimate and the optimal weight coefficient vector to obtain the accelerated complex signal estimate; remove the earliest complex signal estimate and residual vector stored in the current historical data from the historical estimate matrix and the historical residual matrix, and store the complex signal estimate and residual vector at the k-th iteration into the historical estimate matrix and the historical residual matrix, respectively;

[0023] S16: Use the accelerated complex signal estimate as the complex signal estimate at the (k+1)th iteration, update k=k+1 and return to execute step S12 until the norm of the residual vector generated at the current iteration is less than the target convergence error threshold, or the number of iterations reaches the maximum allowed number of iterations, and use the accelerated complex signal estimate obtained at the current iteration as the recovered complex optical field signal.

[0024] Preferably, the amplitude of the DC component of the complex signal at the k-th iteration Represented as:

[0025] ,

[0026] in, and These represent the Fast Fourier Transform and the Inverse Fast Fourier Transform, respectively. This indicates a time-domain amplitude projection operation, which means keeping the signal phase unchanged and replacing the signal amplitude with the measured amplitude; This indicates a single-sideband filtering operation in the frequency domain, which preserves the zero-frequency and positive-frequency components of the signal while setting the negative-frequency components to zero. This represents the estimated value of the complex signal at the k-th iteration;

[0027] The residual vector generated in the kth iteration Represented as:

[0028] .

[0029] Preferably, the difference matrix of historical estimates is constructed using the complex signal estimates from the most recent m+1 iterations. Represented as:

[0030] ,

[0031] in, This represents the estimated value of the complex signal at the (k-m+1)th iteration;

[0032] The historical residual difference matrix is ​​constructed using the residual vectors from the most recent m+1 iterations. Represented as:

[0033] ,

[0034] in, This represents the residual vector generated in the (k-m+1)th iteration.

[0035] Preferably, the objective function Represented as:

[0036] ,

[0037] in, Represents the weight coefficient vector; This represents the square of the L2 norm.

[0038] Preferably, the optimal weight coefficient vector Represented as:

[0039] ,

[0040] in, express The conjugate transpose of;

[0041] Accelerated complex signal estimation value Represented as:

[0042] .

[0043] Preferably, the initial complex signal estimate Represented as:

[0044] ,

[0045] ,

[0046] in, Represents a digital time-domain intensity sequence; Represents an amplitude sequence; Indicates either all-zero phase or random phase; It represents the imaginary unit.

[0047] Preferably, the complex optical field signal is post-processed and demodulated to obtain the original binary information data, including:

[0048] The complex optical field signal is sequentially subjected to dispersion compensation, clock recovery, channel equalization, and carrier phase estimation to obtain the complex signal constellation diagram;

[0049] Hard decision is performed on the complex signal constellation diagram to map the complex signal constellation diagram into signed values;

[0050] Demap the symbolic numerical values ​​into raw binary information data.

[0051] This invention also provides a digital domain phase regeneration self-coherent reception method for chaotic optical communication, the method being applied to the aforementioned digital domain phase regeneration self-coherent reception system for chaotic optical communication, comprising:

[0052] The chaotic decryption module receives the chaotic encrypted optical signal and the common chaotic optical signal after the transmission link is demultiplexed. The common chaotic optical signal is injected into the semiconductor laser to induce a synchronization mechanism to generate a synchronous chaotic copy. The synchronous chaotic copy is used to decrypt the chaotic encrypted optical signal and output the decrypted optical signal.

[0053] A continuous wave laser is generated using an auxiliary carrier laser. The center frequency of the continuous wave laser maintains a set frequency shift with the decryption optical signal and satisfies the carrier signal power ratio corresponding to the minimum phase condition.

[0054] The decryption optical signal is coupled with a continuous wave laser using an optical coupler to obtain a combined optical signal. When the combined optical signal satisfies the carrier signal power ratio and frequency shift, it is equivalent to a minimum phase single-sideband signal.

[0055] The intensity of the combined optical signal is detected by a photodetector, and based on the square-law detection principle, an analog electrical signal that is proportional to the instantaneous power of the combined optical signal is output.

[0056] Analog-to-digital converters are used to convert analog electrical signals into digital time-domain intensity sequences.

[0057] The complex phase information of the decrypted optical signal is reconstructed from the digital time-domain intensity sequence using a digital signal processing module, thereby recovering the complex optical field signal. The complex optical field signal is then post-processed and demodulated to obtain the original binary information data.

[0058] The digital domain phase regeneration self-coherent receiving system for chaotic optical communication provided in this application has the following advantages:

[0059] 1. This application eliminates the expensive local oscillator laser, complex 90-degree optical mixer, and multi-channel balanced photodetector array required in traditional coherent receiver structures. Signal reception can be achieved with only an auxiliary carrier light source, a passive optical coupler, and a single-ended photodetector. Furthermore, despite the significantly simplified hardware structure, this application retains the ability to recover complex signals in its device principle without sacrificing signal quality. The principle is as follows: by introducing a high-power auxiliary carrier in the optical domain, a single-sideband signal that satisfies the minimum phase condition is artificially constructed. According to signal processing theory, for a minimum phase signal, there is a unique and definite hope-law relationship between its amplitude spectrum and phase spectrum. Based on the Elbert transform relationship, even if a single-ended detector only physically detects light intensity (i.e., signal power) information, phase information is not lost but is implicit in the amplitude evolution characteristics. Through this physical structure, this application successfully transfers the I / Q separation work performed by traditional receivers in the optical analog domain to the digital domain and completes it through algorithm inversion, thereby achieving equivalent high-fidelity reception function with extremely simple hardware. The system cost is further reduced in terms of hardware selection, circuit design, and resource consumption. Compared with the receiver architecture of the prior art, the cost advantage of this application is particularly significant, and it is more suitable for cost-sensitive application scenarios such as access networks and data center interconnection.

[0060] 2. Addressing the challenge of convergence stagnation caused by residual noise in existing DC-Value phase retrieval algorithms in chaotic optical communication, this application proposes an improved DC-Value phase retrieval algorithm based on Anderson acceleration, grounded in a unique inventive concept. Analysis reveals that residual noise after chaotic decryption leads to numerous local minima traps in the solution space for traditional iterative algorithms. Therefore, leveraging the characteristic of Anderson's mechanism—using historical iteration information for linear combination prediction—it transforms this into an intelligent filter to combat chaotic random oscillations. This mechanism effectively smooths iterative trajectory oscillations caused by chaotic residual noise, providing a clear descent gradient for the algorithm and preventing it from falling into local minima, thus achieving the same recovery accuracy (e.g., ...). Under the premise of ), the number of iterations required is reduced by more than 50% compared with traditional algorithms, significantly reducing computation latency and meeting the real-time requirements of gigabit (Gbps) level high-speed chaotic secure communication;

[0061] 3. The auxiliary carrier injection hardware structure adopted in this application forces the signal to meet the minimum phase condition at the physical level, directly providing an indispensable physical foundation for the DC-Value phase recovery algorithm in the backend. At the same time, in view of the inherent disadvantage of the simplified single-end detection architecture in terms of noise resistance (i.e., it cannot cancel common-mode noise and chaotic residuals through differential as in balanced detection), the Anderson acceleration algorithm matched in this application utilizes its excellent robustness to effectively compensate for the signal-to-noise ratio loss caused by hardware simplification. Finally, by combining the simplified hardware architecture and the improved phase recovery algorithm, this application not only avoids the stringent requirements of the Kramers-Kronig (KK) receiver scheme for extremely high hardware oversampling rate (ADC sampling rate), but also solves the pain point of slow convergence of the existing DC-Value algorithm, and finally achieves high-precision and low-latency signal recovery performance under low-cost and low-bandwidth hardware conditions. Attached Figure Description

[0062] To make the content of this invention easier to understand, the invention will be further described in detail below with reference to specific embodiments and accompanying drawings, wherein:

[0063] Figure 1 This is a schematic diagram of the chaotic optical communication system structure provided in this application;

[0064] Figure 2 A schematic diagram of the structure of the digital domain phase regeneration self-coherent receiving system for chaotic optical communication provided in this application;

[0065] Figure 3 This is a schematic diagram of the receiver structure in a conventional chaotic optical communication system.

[0066] Figure 4The signal processing flowchart of the improved DC-Value phase retrieval algorithm based on Anderson acceleration provided in this application;

[0067] Figure 5 The signal processing flowchart of the DC-Value (DC component) phase recovery algorithm in the prior art is shown.

[0068] Figure 6 The technical performance comparison diagram provided in this application between the improved DC-Value algorithm based on Anderson acceleration and the traditional DC-Value algorithm is shown; among them, Figure 6 (a) in the figure represents the convergence speed and accuracy (normalized mean square error) of the two algorithms. Compare the curves. Figure 6 (b) in the figure is a comparison of the 16-QAM constellation diagrams for the two algorithms in recovering signal quality;

[0069] Explanation of reference numerals in the accompanying drawings: 1. Chaotic light encryption transmitter; 2. Optical fiber transmission link; 3. Chaotic light decryption receiver;

[0070] 10. Chaos decryption module; 11. Auxiliary carrier laser; 12. Optical coupler; 13. Photodetector; 14. Analog-to-digital converter; 15. Digital signal processing module;

[0071] 20. Laser; 21. 90° optical mixer; 22. Balance detector. Detailed Implementation

[0072] The present invention will be further described below with reference to the accompanying drawings and specific embodiments, so that those skilled in the art can better understand and implement the present invention. However, the embodiments described are not intended to limit the present invention.

[0073] The digital domain phase regeneration self-coherent receiving system for chaotic optical communication provided in this application is used in, for example... Figure 1 The chaotic optical communication system shown includes a chaotic optical encryption transmitter 1, an optical fiber transmission link 2, and a chaotic optical decryption receiver 3.

[0074] The chaotic optical encryption transmitter 1 uses an external modulator or direct modulation method to encrypt and load the original signal onto the chaotic carrier, forming a chaotic optical signal with noise-like statistical characteristics.

[0075] Fiber optic transmission link 2 is used to transmit chaotic optical signals and encrypted signals. During transmission, the two signals are affected by various channel impairments such as dispersion, polarization mode dispersion, nonlinear effects and spontaneous emission noise.

[0076] The chaotic light decryption receiver 3 is responsible for receiving chaotic light signals and encrypted signals, decrypting the received encrypted signals, and recovering the amplitude and phase information of the original signal in the digital domain.

[0077] The digital domain phase regeneration self-coherent receiving system for chaotic optical communication provided in this application can serve as a chaotic optical decryption receiver in a chaotic optical communication system, such as... Figure 2 As shown, the digital domain phase regeneration self-coherent receiving system for chaotic optical communication specifically includes: a chaotic decryption module 10, an auxiliary carrier laser 11, an optical coupler 12, a photodetector 13, an analog-to-digital converter 14, and a digital signal processing module 15.

[0078] The chaotic decryption module 10 receives the chaotic encrypted optical signal and the common chaotic optical signal after the transmission link is demultiplexed, injects the common chaotic optical signal into the semiconductor laser to induce a synchronization mechanism to generate a synchronous chaotic copy, uses the synchronous chaotic copy to decrypt the chaotic encrypted optical signal, and outputs a decrypted optical signal. .

[0079] It should be noted that in actual physical communication links, due to the difficulty in achieving absolute consistency in the intrinsic parameters of the semiconductor lasers at the transmitting and receiving ends, and the existence of factors such as channel disturbances, the synchronization mechanism induced by the common chaotic signal is often incomplete. This incomplete chaotic synchronization means that physical-level decryption cannot completely remove all the masking, thus hindering the decryption of the optical signal. The signal inevitably contains residual chaotic synchronization noise, which can disrupt the ideal single-sideband signal constraint conditions and pose a challenge to subsequent digital domain phase recovery algorithms. To address this, this application introduces a unique acceleration algorithm in the digital signal processing module 15.

[0080] The auxiliary carrier laser 11 is used to generate a continuous wave laser. The center frequency of the continuous wave laser maintains a set frequency shift with the decryption optical signal and satisfies the carrier signal power ratio corresponding to the minimum phase condition.

[0081] Specifically, unlike the LO in traditional chaotic optical receivers, the auxiliary carrier laser 11 does not require strict frequency or phase locking with the signal light; its main function is to generate a high-power continuous-wave laser beam. This provides a strong DC light background to facilitate the subsequent construction of a single-sideband signal.

[0082] Optical coupler 12 is connected to chaotic decryption module 10 and auxiliary carrier laser 11, and is used to combine the decrypted optical signal with the continuous wave laser to obtain a combined optical signal. When the combined optical signal satisfies the carrier signal power ratio and frequency shift, it is equivalent to a minimum-phase single-sideband signal.

[0083] For example, a 2x1 optical coupler 12 will decrypt the optical signal. With continuous wave laser For beam combining and coupling, the combined optical signal can be represented as: By adjusting the output power of the auxiliary carrier laser 11, the synthesized signal is ensured to meet the minimum phase condition of a single-sideband signal, i.e., the carrier power is high enough so that the signal trajectory on the complex plane does not encircle the origin. This is a prerequisite for subsequent phase recovery using the Kramers-Kronig relation or the DC-Value method.

[0084] The photodetector 13 is connected to the optical coupler 12 and is used to detect the intensity of the combined optical signal. Based on the square-law detection principle, it outputs an analog electrical signal that is proportional to the instantaneous power of the combined optical signal.

[0085] Specifically, the photodetector has 13 pairs of combined optical signals. Direct intensity detection is performed, following the square-law detection principle, to output an analog electrical signal proportional to the instantaneous power of the combined optical signal. :

[0086] ,

[0087] The analog-to-digital converter 14 is connected to the photodetector 13 and is used to convert analog electrical signals into digital time-domain intensity sequences. .

[0088] The digital signal processing module 15 is connected to the analog-to-digital converter 14 and is used to reconstruct the complex phase information of the decrypted optical signal from the digital time-domain intensity sequence, thereby recovering the complex optical field signal. The complex optical field signal is then post-processed and demodulated to obtain the original binary information data.

[0089] Specifically, the complex optical field signal is post-processed and demodulated to obtain the original binary information data, including: performing dispersion compensation, clock recovery, channel equalization, and carrier phase estimation on the complex optical field signal in sequence to obtain a complex signal constellation diagram; performing hard decision on the complex signal constellation diagram to map the complex signal constellation diagram into symbol values; and demapping the symbol values ​​into the original binary information data.

[0090] like Figure 3 The diagram shows a receiver structure in a conventional chaotic optical communication system. The receiver structure specifically includes a laser 20, a 90° optical mixer 21, a balanced detector 22, an analog-to-digital converter 14, and a digital signal processing module 15.

[0091] Laser 20 is used to generate a local optical signal as a phase reference.

[0092] The 90° optical mixer 21 is a complex optical device used to interfere the received chaotic optical signal with the local optical signal and separate the in-phase component I and the quadrature component Q.

[0093] The balanced detector 22 contains two pairs of photodetectors 13, each pair of photodetectors 13 detects the I-channel interference signal and the Q-channel interference signal respectively, so as to suppress common-mode noise and improve sensitivity.

[0094] The analog-to-digital converter 14 is used to convert the probe signal into two digital signals.

[0095] The digital signal processing module 15 uses the KK receiving algorithm to simultaneously acquire two digital signals and perform phase recovery and signal reconstruction.

[0096] The 90° optical mixer 21 in this receiver structure is an extremely precise optical component that needs to accurately and orthogonally separate chaotic light from local light. This places high demands on the alignment and packaging processes of the optical path, resulting in high hardware costs. Meanwhile, the balanced detector 22 needs to include two pairs of photodetectors 13 and requires a high-precision differential amplifier circuit to suppress common-mode noise. This means that the hardware complexity doubles, the number of components increases, and the hardware cost rises directly. In addition, the analog-to-digital converter 14 needs to process two high-speed data streams simultaneously, which requires high bandwidth and dynamic range, thus necessitating an expensive high-speed ADC chip.

[0097] and Figure 3 Compared to the existing structure, the digital domain phase regeneration self-coherent receiving system for chaotic optical communication provided in this application, because the optical coupler 12 couples the decrypted optical signal with the local auxiliary carrier, and controls the carrier power to make the synthesized signal meet the minimum phase condition of the single sideband, forms the optical signal to be detected. Only one photodetector 13 is needed to directly detect its intensity and convert the optical signal into an analog electrical signal, replacing the 90° optical mixer 21 and balanced detector 22 in the prior art, reducing the system size, hardware complexity and cost; the analog-to-digital converter 14 only needs to convert the analog electrical signal into a digital discrete sequence, without having to process the I and Q high-speed data streams simultaneously, and without having to deal with The dual-channel signal conditioning and synchronization circuits not only reduce the hardware procurement cost of the analog-to-digital converter, but also simplify the circuit design and packaging process, reducing the number of components. At the same time, the processing complexity of a single-channel digital discrete sequence is much lower than that of two high-speed data streams, eliminating the need for the digital signal processing module 15 to allocate a large amount of resources for the synchronization, alignment, and parallel operation of the two signals. This further reduces the hardware configuration requirements and power consumption of the digital signal processing module 15, compressing system costs from multiple aspects such as hardware selection, circuit design, and resource usage. Compared with the receiver architecture of existing technologies, the cost advantage of this application is particularly significant, making it more suitable for cost-sensitive application scenarios such as access networks and data center interconnects.

[0098] Furthermore, most existing technologies use the KK algorithm or DC-Value (DC component) phase recovery algorithm for phase recovery.

[0099] The KK algorithm is based on the Hilbert transform relationship between the real and imaginary parts of the analytical signal. Its core processing steps usually include: (1) processing the detected light intensity signal. Taking the square root and the natural logarithm, we get (2) Perform Hilbert transform on the logarithmic signal to extract phase information; (3) Combine amplitude and phase to reconstruct the complex signal. It can be seen that in step (1), in order to ensure the natural logarithm The operation is mathematically continuous and meaningful. It must be ensured that the trajectory of the signal in the complex plane never encircles the origin (i.e., the minimum phase condition is met). This requires the introduction of a very high-power DC carrier component at the transmitting end, which leads to the system requiring a very high CSPR, which significantly reduces the energy efficiency of the system. During the nonlinear logarithmic operation in step (1), the spectrum of the signal will undergo significant nonlinear broadening. In order to prevent aliasing distortion in the digital domain Hilbert transform in step (2), the sampling rate of the ADC must be able to cover the broadened spectrum. This usually requires the oversampling rate at the receiving end to reach 3 or even 4 times the signal baud rate, which greatly increases the hardware cost and DSP processing pressure.

[0100] The DC-Value phase recovery algorithm adopts the Gerchberg-Saxton iterative architecture. Its core loop steps are usually as follows: (1) Perform FFT transformation on the current estimated signal to the frequency domain; (2) Apply "single sideband filtering" and "DC component fixed" constraints in the frequency domain; (3) Perform IFFT transformation back to the time domain; (4) Keep the phase unchanged in the time domain and forcibly replace the amplitude with the measured amplitude. It can be seen that after completing step (4), the result after amplitude replacement is directly used as the input of the next iteration (i.e. This simple linear feedback mechanism lacks the ability to predict or correct error gradients; in chaotic optical communication scenarios, the decrypted signal often retains chaotic synchronization noise, causing the measured amplitude to not perfectly match the characteristics of an ideal single-sideband signal. In this non-ideal environment, the iterative steps of the above direct feedback are prone to getting trapped in local minima, manifesting as the error (…) increasing after dozens or even hundreds of algorithm iterations. The value remains at a high level and cannot decrease, resulting in extremely slow convergence speed and severely limited recovery accuracy.

[0101] Therefore, the KK algorithm used in the receiver in the existing technology has strict limitations on the carrier signal power ratio and sampling rate. The DC-Value phase recovery algorithm has slow convergence speed and limited recovery accuracy under chaotic residual noise interference. These factors will affect the recovery accuracy of the receiver for chaotic optical signals.

[0102] To this end, this application utilizes an improved DC-Value phase recovery module based on Anderson acceleration to reconstruct the complex phase information of the decrypted optical signal from the digital time-domain intensity sequence, including steps 1 to 3:

[0103] Step 1: When the number of iterations is less than the set historical depth parameter of Anderson acceleration, the complex signal estimate at the k-th iteration is used as the input signal to perform the DC-Value operation, and the DC component amplitude of the complex signal at the k-th iteration output by the DC-Value operation is used as the complex signal estimate at the (k+1)-th iteration.

[0104] Step 2: When the number of iterations is greater than or equal to the set historical depth parameter of Anderson acceleration, the complex signal estimate at the k-th iteration is used as the input signal to perform the DC-Value operation; based on the complex signal estimate at the historical iteration and the residual vector generated by the DC-Value operation, the optimal weight coefficient vector that minimizes the linear combination norm of the historical iteration residuals is obtained; the DC component amplitude of the complex signal at the k-th iteration is corrected using the optimal weight coefficient vector, and the accelerated complex signal estimate obtained after correction is used as the complex signal estimate for the (k+1)-th iteration.

[0105] Step 3: Use the accelerated complex signal estimate obtained in the last iteration as the recovered complex optical field signal.

[0106] Furthermore, such as Figure 4 The diagram shown is a signal processing flowchart of the improved DC-Value phase retrieval algorithm based on Anderson acceleration provided in this application. Figure 4 This application provides another method for reconstructing the complex phase information of the decrypted optical signal from the digital time-domain intensity sequence using an improved DC-Value phase recovery module based on Anderson acceleration, to explain in detail steps 1 to 3 above, specifically including S11 to S16:

[0107] S11: Set the maximum allowed number of iterations Target convergence error threshold and historical depth parameters of Anderson acceleration Construct initial complex signal estimates based on digital time-domain intensity sequences; initialize historical estimate matrix to store historical data. and historical residual matrix Initialize k=0.

[0108] Specifically, the initial complex signal estimate Represented as:

[0109] ,

[0110] ,

[0111] in, Represents a digital time-domain intensity sequence; Represents an amplitude sequence; Indicates either all-zero phase or random phase; It represents the imaginary unit.

[0112] For example, the target convergence error threshold It can be Historical depth parameters The value range can be [3, 5].

[0113] S12: Take the estimated value of the complex signal at the k-th iteration as the input signal and perform the DC-Value operation. Output the amplitude of the DC component of the complex signal at the k-th iteration. Obtain the residual vector generated at the k-th iteration based on the difference between the input signal and the output signal.

[0114] Specifically, the amplitude of the DC component of the complex signal at the k-th iteration Represented as:

[0115] ,

[0116] in, and These represent the Fast Fourier Transform and the Inverse Fast Fourier Transform, respectively. This indicates a time-domain amplitude projection operation, which means keeping the signal phase unchanged and replacing the signal amplitude with the measured amplitude; This indicates a single-sideband filtering operation in the frequency domain, which preserves the zero-frequency and positive-frequency components of the signal while setting the negative-frequency components to zero. This represents the estimated value of the complex signal at the k-th iteration;

[0117] The residual vector generated in the kth iteration Represented as:

[0118] .

[0119] S13: If k < m, store the complex signal estimate and residual vector at the k-th iteration into the historical estimate matrix and historical residual matrix, respectively. Use the DC component amplitude of the complex signal at the k-th iteration as the complex signal estimate at the (k+1)-th iteration, update k = k+1, and return to execute step S12.

[0120] Specifically, when the number of iterations is less than the historical depth parameter, it indicates that the historical data accumulation in the historical estimation matrix and the historical residual matrix is ​​insufficient. Therefore, the amplitude of the DC component of the currently output complex signal is directly used as the complex signal estimate for the next iteration.

[0121] S14: If k≥m, construct the historical estimate difference matrix and the historical residual difference matrix using the complex signal estimate and residual vector from the most recent m+1 iterations, respectively. With the objective of minimizing the difference norm between the residual vector of the kth iteration and the product of the historical residual difference matrix and the weight coefficient vector, construct the objective function and solve it to obtain the optimal weight coefficient vector.

[0122] Specifically, when the number of iterations is greater than or equal to the historical depth parameter, it indicates that sufficient historical data is available, and the acceleration operation begins, utilizing the historical information from the most recent m+1 iterations (i.e., and Construct a matrix.

[0123] The historical estimate difference matrix is ​​constructed using the complex signal estimates from the most recent m+1 iterations. Represented as:

[0124] ,

[0125] in, This represents the estimated value of the complex signal at the (k-m+1)th iteration;

[0126] The historical residual difference matrix is ​​constructed using the residual vectors from the most recent m+1 iterations. Represented as:

[0127] ,

[0128] in, This represents the residual vector generated in the (k-m+1)th iteration.

[0129] objective function Represented as:

[0130] ,

[0131] in, Represents the weight coefficient vector; This represents the square of the L2 norm.

[0132] It should be noted that the objective function is constructed by making the linear combination of the historical residual differences as close as possible to the residual vector at the current iteration. In order to improve numerical stability and computational efficiency, numerical linear algebra methods such as QR decomposition are usually used to solve the objective function.

[0133] Optimal weight coefficient vector Represented as:

[0134] ,

[0135] in, express The conjugate transpose of;

[0136] Accelerated complex signal estimation value Represented as:

[0137] .

[0138] By finding a new point in the subspace composed of historical estimates such that the residual generated by the operator is minimized, this linear prediction mechanism based on historical information can effectively smooth the iterative trajectory oscillation caused by chaotic residual noise and guide the algorithm to approach the true solution along the optimal path.

[0139] S15: Calculate the difference between the magnitude of the DC component of the complex signal at the k-th iteration and the product of the difference matrix of the historical estimate and the optimal weight coefficient vector to obtain the accelerated complex signal estimate; remove the earliest complex signal estimate and residual vector stored in the current historical data from the historical estimate matrix and the historical residual matrix, and store the complex signal estimate and residual vector at the k-th iteration into the historical estimate matrix and the historical residual matrix, respectively.

[0140] S16: Use the accelerated complex signal estimate as the complex signal estimate at the (k+1)th iteration, update k=k+1 and return to execute step S12 until the norm of the residual vector generated at the current iteration is less than the target convergence error threshold, or the number of iterations reaches the maximum allowed number of iterations, and use the accelerated complex signal estimate obtained at the current iteration as the recovered complex optical field signal.

[0141] Specifically, the Euclidean norm of the residual vector at the k-th iteration is expressed as: .

[0142] like Figure 5 The diagram shows the signal processing flowchart of the DC-Value (DC component) phase recovery algorithm in the prior art. It constructs an initial complex signal, performs an FFT transformation on the current estimated signal to the frequency domain in each iteration, retains the DC component and the single-sideband spectrum containing signal information (such as the positive frequency part), and forces the negative frequency part of the mirror spectrum to zero; performs an IFFT transformation back to the time domain; keeps the phase of the transformed signal unchanged, and forcibly replaces its amplitude with the actual measured amplitude; calculates the error between the current result and the constraint conditions. If the error does not reach the preset threshold, the signal after time domain amplitude replacement is directly used as the input for the next iteration.

[0143] To verify the effectiveness of the phase retrieval algorithm provided in this application, a comparison was made between two phase retrieval algorithms. Figure 6 shows a comparison of the technical effects of the improved DC-Value algorithm based on Anderson acceleration provided in this application and the traditional DC-Value algorithm; where, Figure 6 (a) in the figure represents the convergence speed and accuracy (normalized mean square error) of the two algorithms. Compare the curves. Figure 6 (b) in the figure is a comparison of the 16-QAM constellation diagrams for the two algorithms to recover signal quality.

[0144] from Figure 6 As shown in (a) of the paper, in chaotic optical communication, due to factors such as hardware non-ideality and channel impairment, chaotic decryption is often incomplete, resulting in the introduction of non-negligible chaotic synchronization residual noise into the decrypted signal. This noise causes the signal characteristics to deviate from the ideal single-sideband condition. Under this non-ideal environment, the direct linear feedback mechanism of the existing DC-Value recovery algorithm lacks robustness to noise and is prone to getting trapped in local minima. This manifests as extremely slow iterative convergence speed (often requiring hundreds of iterations to stabilize) and low final convergence accuracy, leading to poor signal recovery quality. Under the same noise environment, after 50 iterations, the error of the existing algorithm still remains at [value missing]. The algorithm exhibited a significant plateau after the initial warm-up iterations, but the algorithm provided in this application, with the integration of the Anderson acceleration mechanism, caused a sharp drop in the error curve, requiring only about 15-20 iterations to achieve the desired error reduction. The algorithm achieves a low level of convergence and a final convergence accuracy far exceeding that of existing algorithms, which means that the algorithm provided in this application has significant effects in improving convergence speed and recovery accuracy.

[0145] from Figure 6As shown in (b), the phase recovery results of existing algorithms are limited by their limited convergence accuracy and insufficient ability to track dynamic phase drift, resulting in poor signal quality. Constellation points are not only severely diverged and blurred due to noise, but also exhibit significant overall phase rotation and arc-shaped tailing. This phase distortion causes constellation points to cross decision boundaries, leading to extremely high bit error rates and communication interruptions. In contrast, the phase recovery results of the algorithm provided in this application benefit from the accurate prediction and correction of error directions by the Anderson acceleration mechanism, resulting in a qualitative leap in the quality of the recovered constellation diagram. Although a very slight residual rotation consistent with physical reality is still retained in the diagram, the overall constellation point clustering is very clear and compact, with each symbol point converging near the ideal grid point and possessing clear decision boundaries. This not only intuitively demonstrates the excellent noise resistance and phase tracking capabilities of the algorithm provided in this application, but also indicates that the algorithm can significantly reduce the system bit error rate and ensure reliable transmission in chaotic optical communication.

[0146] In summary, the system and method provided in this embodiment effectively reduce system cost and power consumption by adopting a simplified self-coherent receiving architecture in hardware; and successfully solve the technical challenges of slow phase recovery convergence and low accuracy in chaotic residual noise environments by introducing an innovative Anderson accelerated DC-Value algorithm in software. The combination of these two approaches achieves a low-cost, high-efficiency, and robust chaotic optical signal receiving scheme, which has significant practical application value.

[0147] It should be noted that although the above embodiments are described in detail using a single-polarization 16-QAM chaotic optical signal and an FFT-based frequency domain processing flow as an example, this does not limit the application scope of the solution provided in this application:

[0148] 1. The digital domain phase regeneration self-coherent receiving system for chaotic optical communication provided in this application is also applicable to polarization multiplexing (PDM) chaotic optical communication systems. In the polarization multiplexing scenario, only a polarization beam splitter (PBS) needs to be added to the front end of the optical path at the receiving end to decompose the received optical signal into two orthogonal polarization components (X polarization and Y polarization). Subsequently, for each polarization component, the "auxiliary carrier coupling + single-end detection + Anderson accelerated DC-Value phase recovery" architecture described in this application can be applied independently. Since the processing of the two polarization states is physically and logically parallel, the core technical solution of this application can be directly extended to dual-polarization systems through parallel replication, thereby doubling the capacity.

[0149] 2. The core of this application lies in recovering the phase of the complex optical field from the intensity information through an improved DC-Value algorithm. This physical recovery process is independent of the specific modulation format carried by the signal. Therefore, this application is not limited to the 16-QAM signal shown in the embodiments, but is also fully applicable to other types of modulation formats, including but not limited to: phase modulation signals such as QPSK and 8-PSK, orthogonal amplitude modulation signals such as 32-QAM, 64-QAM and higher-order QAM signals, and pulse amplitude modulation signals such as PAM-4 and PAM-8. For PAM signals, although they mainly carry intensity information, after long-distance transmission and damage in the complex domain such as dispersion, the complete complex optical field information can be recovered using this application, which can more effectively compensate for the damage in the digital domain, thereby improving the transmission distance and quality of PAM signals.

[0150] 3. The standard DC-Value operator uses a frequency domain implementation based on the Fast Fourier Transform (FFT) (i.e., FFT). Frequency domain single-sideband constraint However, those skilled in the art should understand that this physical constraint process can also be implemented in the time domain. For example, a finite impulse response (FIR) filter (such as a Hilbert transform filter) can be used to directly convolve the signal in the time domain to generate a single-sideband signal and update the phase, thereby replacing the above-mentioned FFT / IFFT process. The core improvement of this application—the Anderson acceleration module—is independent of the specific physical operator implementation. Regardless of the DC-Value iterative physical constraint operator... Whether implemented based on frequency domain transformation or time domain filtering, as long as it constitutes an iterative convergence process, the Anderson acceleration mechanism proposed in this invention can be used to accelerate prediction using historical information. Therefore, any technical solution that uses time-domain iteration or time-frequency hybrid iteration combined with Anderson acceleration for phase recovery is covered within the scope of protection of this invention.

[0151] This application also provides a digital domain phase regeneration self-coherent reception method for chaotic optical communication, applied to the aforementioned digital domain phase regeneration self-coherent reception system for chaotic optical communication, including:

[0152] The chaotic decryption module receives the chaotic encrypted optical signal and the common chaotic optical signal after the transmission link is demultiplexed. The common chaotic optical signal is injected into the semiconductor laser to induce a synchronization mechanism to generate a synchronous chaotic copy. The synchronous chaotic copy is used to decrypt the chaotic encrypted optical signal and output the decrypted optical signal.

[0153] A continuous wave laser is generated using an auxiliary carrier laser. The center frequency of the continuous wave laser maintains a set frequency shift with the decryption optical signal and satisfies the carrier signal power ratio corresponding to the minimum phase condition.

[0154] The decryption optical signal is coupled with a continuous wave laser using an optical coupler to obtain a combined optical signal. When the combined optical signal satisfies the carrier signal power ratio and frequency shift, it is equivalent to a minimum phase single-sideband signal.

[0155] The intensity of the combined optical signal is detected by a photodetector, and based on the square-law detection principle, an analog electrical signal that is proportional to the instantaneous power of the combined optical signal is output.

[0156] Analog-to-digital converters are used to convert analog electrical signals into digital time-domain intensity sequences.

[0157] The complex phase information of the decrypted optical signal is reconstructed from the digital time-domain intensity sequence using a digital signal processing module, thereby recovering the complex optical field signal. The complex optical field signal is then post-processed and demodulated to obtain the original binary information data.

[0158] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0159] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0160] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0161] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0162] Obviously, the above embodiments are merely illustrative examples for clear explanation and are not intended to limit the implementation. Those skilled in the art will recognize that other variations or modifications can be made based on the above description. It is neither necessary nor possible to exhaustively list all possible implementations here. However, obvious variations or modifications derived therefrom are still within the scope of protection of this invention.

Claims

1. A digital domain phase regeneration self-coherent receiving system for chaotic optical communication, characterized in that, include: The chaotic decryption module is used to receive the chaotic encrypted optical signal and the common chaotic optical signal after the transmission link is demultiplexed, and inject the common chaotic optical signal into the semiconductor laser to induce a synchronization mechanism to generate a synchronous chaotic copy. The synchronous chaotic copy is used to decrypt the chaotic encrypted optical signal and output the decrypted optical signal. An auxiliary carrier laser is used to generate a continuous wave laser. The center frequency of the continuous wave laser maintains a set frequency shift with the decryption optical signal and satisfies the minimum phase condition corresponding to the carrier signal power ratio. An optical coupler, connected to the chaotic decryption module and the auxiliary carrier laser, is used to combine the decryption optical signal with the continuous wave laser to obtain a combined optical signal. When the combined optical signal satisfies the carrier signal power ratio and frequency shift, it is equivalent to a minimum-phase single-sideband signal. A photodetector, connected to an optical coupler, is used to detect the intensity of the combined optical signal. Based on the square-law detection principle, it outputs an analog electrical signal that is proportional to the instantaneous power of the combined optical signal. An analog-to-digital converter, connected to a photodetector, is used to convert analog electrical signals into digital time-domain intensity sequences; The digital signal processing module, connected to the analog-to-digital converter, is used to reconstruct the complex phase information of the decrypted optical signal from the digital time-domain intensity sequence, thereby recovering the complex optical field signal. The complex optical field signal is then post-processed and demodulated to obtain the original binary information data. The digital signal processing module utilizes an improved DC-Value phase recovery module based on Anderson acceleration to reconstruct the complex phase information of the decrypted optical signal from the digital time-domain intensity sequence, including: When the number of iterations is less than the set historical depth parameter of Anderson acceleration, the complex signal estimate at the k-th iteration is used as the input signal to perform DC-Value operation, and the DC component amplitude of the complex signal at the k-th iteration output by DC-Value operation is used as the complex signal estimate at the (k+1)-th iteration. When the number of iterations is greater than or equal to the set historical depth parameter of Anderson acceleration, the complex signal estimate at the k-th iteration is used as the input signal to perform DC-Value operation; based on the complex signal estimate at the historical iteration and the residual vector generated by the DC-Value operation, the optimal weight coefficient vector that minimizes the linear combination norm of the historical iteration residuals is obtained; the DC component amplitude of the complex signal at the k-th iteration is corrected using the optimal weight coefficient vector, and the accelerated complex signal estimate obtained after correction is used as the complex signal estimate for the (k+1)-th iteration; The accelerated complex signal estimate obtained in the last iteration is used as the recovered complex optical field signal.

2. The digital domain phase regeneration self-coherent receiving system for chaotic optical communication according to claim 1, characterized in that, The complex phase information of the decrypted optical signal is reconstructed from the digital time-domain intensity sequence using an improved DC-Value phase retrieval module based on Anderson acceleration, including: S11: Set the maximum allowed number of iterations, the target convergence error threshold, and the historical depth parameters of Anderson acceleration. ; Construct initial complex signal estimates based on digital time-domain intensity sequences; Initialize historical estimate matrices and historical residual matrices for storing historical data, and initialize k=0; S12: Take the estimated value of the complex signal at the k-th iteration as the input signal and perform the DC-Value operation. Output the amplitude of the DC component of the complex signal at the k-th iteration. Obtain the residual vector generated in the k-th iteration based on the difference between the input signal and the output signal. S13: If k < m, store the complex signal estimate and residual vector at the k-th iteration into the historical estimate matrix and historical residual matrix respectively, take the DC component amplitude of the complex signal at the k-th iteration as the complex signal estimate at the (k+1)-th iteration, update k = k+1, and return to execute step S12. S14: If k≥m, construct the historical estimate difference matrix and the historical residual difference matrix using the complex signal estimate and residual vector from the most recent m+1 iterations, respectively. With the objective of minimizing the difference norm between the residual vector of the kth iteration and the product of the historical residual difference matrix and the weight coefficient vector, construct the objective function and solve it to obtain the optimal weight coefficient vector. S15: Calculate the difference between the magnitude of the DC component of the complex signal at the k-th iteration and the product of the difference matrix of the historical estimate and the optimal weight coefficient vector to obtain the accelerated complex signal estimate; remove the earliest complex signal estimate and residual vector stored in the current historical data from the historical estimate matrix and the historical residual matrix, and store the complex signal estimate and residual vector at the k-th iteration into the historical estimate matrix and the historical residual matrix, respectively; S16: Use the accelerated complex signal estimate as the complex signal estimate at the (k+1)th iteration, update k=k+1 and return to execute step S12 until the norm of the residual vector generated at the current iteration is less than the target convergence error threshold, or the number of iterations reaches the maximum allowed number of iterations, and use the accelerated complex signal estimate obtained at the current iteration as the recovered complex optical field signal.

3. The digital domain phase regeneration self-coherent receiving system for chaotic optical communication according to claim 2, characterized in that, The amplitude of the DC component of the complex signal at the kth iteration Represented as: ， in, and These represent the Fast Fourier Transform and the Inverse Fast Fourier Transform, respectively. This indicates a time-domain amplitude projection operation, which means keeping the signal phase unchanged and replacing the signal amplitude with the measured amplitude; This indicates a single-sideband filtering operation in the frequency domain, which preserves the zero-frequency and positive-frequency components of the signal while setting the negative-frequency components to zero. This represents the estimated value of the complex signal at the k-th iteration; The residual vector generated in the kth iteration Represented as: 。 4. The digital domain phase regeneration self-coherent receiving system for chaotic optical communication according to claim 3, characterized in that, The historical estimate difference matrix is ​​constructed using the complex signal estimates from the most recent m+1 iterations. Represented as: ， in, This represents the estimated value of the complex signal at the (k-m+1)th iteration; The historical residual difference matrix is ​​constructed using the residual vectors from the most recent m+1 iterations. Represented as: ， in, This represents the residual vector generated in the (k-m+1)th iteration.

5. The digital domain phase regeneration self-coherent receiving system for chaotic optical communication according to claim 4, characterized in that, objective function Represented as: ， in, Represents the weight coefficient vector; This represents the square of the L2 norm.

6. The digital domain phase regeneration self-coherent receiving system for chaotic optical communication according to claim 5, characterized in that, Optimal weight coefficient vector Represented as: ， in, express The conjugate transpose of; Accelerated complex signal estimation value Represented as: 。 7. The digital domain phase regeneration self-coherent receiving system for chaotic optical communication according to claim 2, characterized in that, Initial complex signal estimate Represented as: ， ， in, Represents a digital time-domain intensity sequence; Represents an amplitude sequence; Indicates either all-zero phase or random phase; It represents the imaginary unit.

8. The digital domain phase regeneration self-coherent receiving system for chaotic optical communication according to claim 1, characterized in that, Post-processing and demodulation of the complex optical field signal yields the original binary information data, including: The complex optical field signal is sequentially subjected to dispersion compensation, clock recovery, channel equalization, and carrier phase estimation to obtain the complex signal constellation diagram; Hard decision is performed on the complex signal constellation diagram to map the complex signal constellation diagram into signed values; Demap the symbolic numerical values ​​into raw binary information data.

9. A digital domain phase regeneration self-coherent reception method for chaotic optical communication, characterized in that, The method is applied to the digital domain phase regeneration self-coherent receiving system for chaotic optical communication as described in any one of claims 1 to 8, comprising: The chaotic decryption module receives the chaotic encrypted optical signal and the common chaotic optical signal after the transmission link is demultiplexed. The common chaotic optical signal is injected into the semiconductor laser to induce a synchronization mechanism to generate a synchronous chaotic copy. The synchronous chaotic copy is used to decrypt the chaotic encrypted optical signal and output the decrypted optical signal. A continuous wave laser is generated using an auxiliary carrier laser. The center frequency of the continuous wave laser maintains a set frequency shift with the decryption optical signal and satisfies the carrier signal power ratio corresponding to the minimum phase condition. The decryption optical signal is coupled with a continuous wave laser using an optical coupler to obtain a combined optical signal. When the combined optical signal satisfies the carrier signal power ratio and frequency shift, it is equivalent to a minimum phase single-sideband signal. The intensity of the combined optical signal is detected by a photodetector, and based on the square-law detection principle, an analog electrical signal that is proportional to the instantaneous power of the combined optical signal is output. Analog-to-digital converters are used to convert analog electrical signals into digital time-domain intensity sequences. The complex phase information of the decrypted optical signal is reconstructed from the digital time-domain intensity sequence using a digital signal processing module, thereby recovering the complex optical field signal. The complex optical field signal is then post-processed and demodulated to obtain the original binary information data.

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