A hollow-core optical fiber communication method and system for reducing complexity

Abstract

This invention discloses a hollow-core optical fiber communication method and system for reducing complexity, comprising: acquiring raw bit data; mapping the raw bit data onto an initial constellation diagram using an orthogonal amplitude modulation mapping algorithm; rotating the initial constellation diagram according to a preset rotation angle to obtain a first optimized constellation diagram; adjusting the first optimized constellation diagram using a sparrow search algorithm to obtain a second optimized constellation diagram; converting the second optimized constellation diagram into a transmission signal; transmitting the transmission signal to the receiving end through a hollow-core optical fiber; performing data preprocessing on the received transmission signal to obtain a denoised signal; performing phase recovery on the denoised signal based on a quadratic algorithm to obtain the second optimized constellation diagram; and demapping the second optimized constellation diagram to reconvert it back into raw bit data. This invention reduces the complexity of digital signal processing and significantly improves the accuracy and efficiency of phase recovery, thereby comprehensively improving the performance of coherent optical communication systems based on hollow-core optical fibers.

Description

Technical Field

[0001] This invention belongs to the field of optical fiber communication technology, specifically relating to a hollow-core optical fiber communication method and system for reducing complexity. Background Technology

[0002] With the rapid development of communication technology, data traffic has experienced explosive growth. In long-distance, high-volume data transmission scenarios, the transmission latency of traditional solid optical fibers is insufficient to meet the stringent ultra-low latency standards of current and future high-speed communications, severely restricting the improvement of communication network performance. Against this backdrop, hollow-core optical fiber (HCF), with its unique air transmission medium, can significantly reduce optical signal transmission latency compared to traditional solid optical fibers. This demonstrates the enormous potential of HCF in meeting the urgent need for ultra-low latency in the communications field.

[0003] However, the signal processing architecture of existing hollow fiber systems still follows the traditional coherent detection algorithm framework. The traditional coherent detection algorithm framework relies on a complex signal processing flow, which leads to a waste of hardware resources and excessive computational load of digital signal processing (DSP), thus restricting the full realization of the ultra-low transmission delay and low loss characteristics of hollow fiber. Summary of the Invention

[0004] This invention provides a hollow-core optical fiber communication method and system for reducing complexity, thereby reducing the complexity of digital signal processing and significantly improving the accuracy and efficiency of phase recovery, thus comprehensively enhancing the performance of coherent optical communication systems based on hollow-core optical fibers.

[0005] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0006] The first aspect of this invention provides a method for reducing the complexity of hollow-core optical fiber communication, comprising:

[0007] The raw bit data to be transmitted is obtained and mapped onto the initial constellation diagram using an orthogonal amplitude modulation mapping algorithm.

[0008] The initial constellation diagram is rotated according to a preset rotation angle to obtain the first optimized constellation diagram. The first optimized constellation diagram is then adjusted using a sparrow search algorithm to obtain the second optimized constellation diagram. The second optimized constellation diagram is then converted into a transmission signal, which is transmitted to the receiving end through a hollow optical fiber.

[0009] The received transmitted signal is preprocessed to obtain a denoised signal. The denoised signal is then phase-recovered using a quadratic algorithm to obtain a second constellation optimization map. The second constellation optimization map is then demapped and converted back into the original bit data.

[0010] Furthermore, the original bit data is mapped onto the initial constellation diagram using an orthogonal amplitude modulation mapping algorithm, specifically including:

[0011] Will An initial constellation map is constructed by distributing constellation points in an h-row, h-column grid on the complex plane; the original bit data is divided into bit groups according to the set number of bits, and Gray coding is used to map each bit group to a constellation point on the initial constellation map.

[0012] Furthermore, the initial constellation diagram is rotated according to a preset rotation angle to obtain a first optimized constellation diagram, specifically including:

[0013] The rotation factor is calculated based on the preset rotation angle, expressed by the following formula;

[0014]

[0015] In the formula, For rotation factor, The value is the rotation angle; j represents the imaginary unit.

[0016] The first optimized constellation diagram is obtained by multiplying each constellation point in the initial constellation diagram by the rotation factor.

[0017] Furthermore, the first constellation optimization map is adjusted using the sparrow search algorithm to obtain the second constellation optimization map, specifically including:

[0018] Each constellation point in the first constellation optimization map is considered a sparrow individual, and the sparrow individuals are divided into discoverers, followers, and vigilants; the sparrow population is composed of discoverers, followers, and vigilants.

[0019] A fitness function is constructed based on the Euclidean distance between constellation points. The fitness values ​​are obtained by substituting the Euclidean distance between constellation points into the fitness function. The sparrow population is then updated with the goal of maximizing the fitness value under the following constraints:

[0020] like hour, ;

[0021] In the formula, The modulation phase of the k-th constellation point, This refers to the set of points in the outermost constellation of the current constellation chart. Pi; This is the phase offset.

[0022] Adjust the constellation points according to the sparrow population; repeat the process of adjusting the first constellation optimization map using the sparrow search algorithm until the fitness value converges or the maximum number of iterations is reached, and then output the second constellation optimization map.

[0023] Furthermore, the sparrow population is updated with the goal of maximizing fitness values, specifically including:

[0024] According to the modulation phase Calculate the exploration vector Randomly generate the first unit vector Q, and use the first unit vector Q and the exploration vector The formula for updating the discoverer's location is:

[0025] ;

[0026] In the formula, Let the position of the discoverer be the k-th constellation point in the t-th iteration. The position of the discoverer in the (t-1)th iteration of the k-th constellation point; This is the step size coefficient; I represents the set of outermost constellation points in the current constellation diagram; I represents the set of innermost constellation points in the current constellation diagram.

[0027] Substituting the positions of the discoverer, follower, and watcher in the (t-1)th iteration into the fitness function yields the optimal sparrow position corresponding to the maximum fitness value. Based on the optimal sparrow individual position The formula for updating follower positions is:

[0028]

[0029] In the formula, Let the position of the follower of the k-th constellation point in the t-th iteration be _t_. Let be the position of the follower of the k-th constellation point in the (t-1)-th iteration; The learning coefficient;

[0030] Randomly generate the second unit vector According to the second unit vector The formula for updating the location of the vigilant is as follows:

[0031]

[0032] In the formula, Let the position of the vigilant be the k-th constellation point in the t-th iteration. The position of the watchman in the (t-1)th iteration of the kth constellation point; This is the warning level.

[0033] Furthermore, by substituting the positions of the discoverer, follower, and watcher in the (t-1)th iteration into the fitness function, we obtain the optimal sparrow individual position corresponding to the maximum fitness value. , specifically including:

[0034]

[0035]

[0036]

[0037]

[0038] In the formula, For the locations of discoverers, followers, and watchdogs, This is the constellation diagram output during the (t-1)th iteration. , , and This represents the constellation point coordinate vector corresponding to the position of an individual sparrow. , , and Number the constellations; Output the set of the outermost constellation points in the constellation diagram for the (t-1)th iteration. Output the set of inner constellation points in the constellation graph for the (t-1)th iteration; For fitness value, and To set weights; The minimum Euclidean distance between the four outermost constellation points in the current constellation diagram; This represents the minimum Euclidean distance between the inner constellation points in the current constellation diagram.

[0039] Furthermore, the received transmitted signal undergoes data preprocessing to obtain a denoised signal, specifically including:

[0040] The received transmitted signal is input into a low-pass filter to suppress additive white Gaussian noise: the formula is as follows:

[0041]

[0042]

[0043]

[0044] In the formula, This represents the k-th transmitted signal sent by the transmitter. This represents the sign amplitude corresponding to the k-th constellation point in the second constellation optimization graph. This represents the sign phase corresponding to the k-th constellation point in the second constellation optimization diagram; The additive white Gaussian noise of the kth transmitted signal; The phase noise of the k-th transmitted signal; This is the k-th transmitted signal after noise reduction; This is the kth transmitted signal received;

[0045] A phase-locked loop is used to recover the clock and frequency of the transmitted signal to obtain a denoised signal.

[0046] Furthermore, a second constellation optimization map is obtained by performing phase retrieval on the denoised signal based on a quadratic algorithm, specifically including:

[0047] Phase noise is extracted from the denoised signal based on the quadratic algorithm, expressed as follows:

[0048]

[0049] In the formula, This is the k-th denoised signal; This represents the sign amplitude corresponding to the k-th constellation point in the second constellation optimization diagram; Pi; Let be the phase noise of the k-th denoised signal; This is the phase offset.

[0050] According to phase noise Phase compensation is performed on the k-th denoised signal to obtain the second constellation optimization diagram.

[0051] A second aspect of the present invention provides a hollow-core optical fiber communication system for reducing complexity, comprising:

[0052] The transmitting module is used to acquire the raw bit data to be transmitted and map the raw bit data onto the initial constellation diagram through an orthogonal amplitude modulation mapping algorithm;

[0053] The optimization module is used to rotate the initial constellation diagram according to a preset rotation angle to obtain a first optimized constellation diagram, and to adjust the first optimized constellation diagram using a sparrow search algorithm to obtain a second optimized constellation diagram.

[0054] The transmission module is used to convert the second constellation optimization diagram into a transmission signal and transmit the transmission signal to the receiving end through a hollow optical fiber;

[0055] The receiving module is used to preprocess the received transmission signal to obtain a denoised signal, perform phase recovery on the denoised signal based on the quadratic algorithm to obtain a second constellation optimization map, and demap the second constellation optimization map to convert it back into the original bit data.

[0056] A third aspect of the present invention provides an electronic terminal, including a processor and a storage medium; the storage medium is used to store instructions; the processor is used to operate according to the instructions to execute the steps of the hollow optical fiber communication method of the first aspect.

[0057] Compared with the prior art, the present invention has the following beneficial effects:

[0058] This invention rotates an initial constellation map by a preset rotation angle to obtain a first optimized constellation map. This rotation alters the distribution of constellation points. Then, the Sparrow Search Algorithm (SSA) is used to further adjust the first optimized constellation map, resulting in a second optimized constellation map. The SSA algorithm simulates the foraging behavior of sparrows to perform global and local optimization of the constellation point positions, further improving the constellation map's performance. The optimized constellation map not only increases the minimum Euclidean distance between constellation points, but also improves the system's anti-interference capability and error rate performance.

[0059] This invention preprocesses the received transmitted signal to remove noise interference and obtain a denoised signal. Phase recovery is then performed on the denoised signal using a quadratic algorithm to obtain the optimized second constellation diagram. The quadratic algorithm simplifies the phase recovery process, reduces the computational complexity at the receiver, and improves the accuracy of phase estimation.

[0060] This invention significantly improves the spectral efficiency, anti-interference capability, and data transmission reliability of communication systems by combining constellation diagram optimization, signal processing, and efficient modulation and demodulation techniques, while reducing system complexity and power consumption, providing an efficient solution for hollow-core optical fiber communication systems. Attached Figure Description

[0061] Figure 1 This is a flowchart of hollow optical fiber communication provided in Embodiment 1 of the present invention;

[0062] Figure 2 This is a flowchart of the sparrow search algorithm for optimizing constellation diagrams provided in Embodiment 1 of the present invention;

[0063] Figure 3 This is an optimized structural diagram of the constellation diagram provided in Embodiment 1 of the present invention. Detailed Implementation

[0064] The present invention will be further described below in conjunction with the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solutions of the present invention and are not intended to limit the scope of protection of the present invention.

[0065] Example 1

[0066] like Figure 1 As shown, this embodiment provides a hollow-core optical fiber communication method for reducing complexity, including:

[0067] The process involves acquiring the raw bit data to be transmitted and mapping it onto the initial constellation diagram using an orthogonal amplitude modulation mapping algorithm. Specifically, this includes:

[0068] In this embodiment, the orthogonal amplitude modulation mapping algorithm is selected as the 16QAM mapping algorithm; 16 constellation points are distributed in a 4x4 grid on the complex plane to construct an initial constellation diagram; the original bit data is divided into bit groups according to the set number of bits, and Gray coding is used to map each bit group to a constellation point on the initial constellation diagram; the orthogonal amplitude modulation enables a single symbol to carry more bits of information, which effectively improves spectral efficiency and transmission capacity.

[0069] like Figure 3 As shown, the first optimized constellation diagram is obtained by rotating the initial constellation diagram according to a preset rotation angle, specifically including:

[0070] The rotation factor is calculated based on the preset rotation angle, expressed by the following formula;

[0071]

[0072] In the formula, For rotation factor, The value is the rotation angle; j represents the imaginary unit; in this embodiment... It is 45 degrees;

[0073] The first optimized constellation diagram is obtained by multiplying each constellation point in the initial constellation diagram by the rotation factor.

[0074] like Figure 2 As shown, the second optimized constellation diagram is obtained by adjusting the first constellation optimization diagram using the sparrow search algorithm, specifically including:

[0075] Each constellation point in the first constellation optimization map is considered a sparrow individual, and the sparrow individuals are divided into discoverers, followers, and vigilants; the sparrow population is composed of discoverers, followers, and vigilants.

[0076] A fitness function is constructed based on the Euclidean distance between constellation points. The fitness values ​​are obtained by substituting the Euclidean distance between constellation points into the fitness function. Under constraints, the sparrow population is updated with the goal of maximizing the fitness values. Specifically, this includes:

[0077] According to the modulation phase Calculate the exploration vector Randomly generate the first unit vector Q, and use the first unit vector Q and the exploration vector The formula for updating the discoverer's location is:

[0078]

[0079]

[0080] In the formula, Let the position of the discoverer be the k-th constellation point in the t-th iteration. The position of the discoverer in the (t-1)th iteration of the k-th constellation point; This is the step size coefficient; I represents the set of outermost constellation points in the current constellation diagram; I represents the set of innermost constellation points in the current constellation diagram. The modulation phase of the k-th constellation point, Pi; This is the phase offset; the discoverer increased the minimum Euclidean distance between the four outermost constellation points and decreased the minimum Euclidean distance between the other constellation points.

[0081] Substituting the positions of the discoverer, follower, and watcher in the (t-1)th iteration into the fitness function yields the optimal sparrow position corresponding to the maximum fitness value. The formula is as follows:

[0082]

[0083]

[0084]

[0085]

[0086] In the formula, For the locations of discoverers, followers, and watchdogs, This is the constellation diagram output during the (t-1)th iteration. , , and This represents the constellation point coordinate vector corresponding to the position of an individual sparrow. , , and Number the constellations; Output the set of the outermost constellation points in the constellation diagram for the (t-1)th iteration. Output the set of inner constellation points in the constellation graph for the (t-1)th iteration; For fitness value, and To set weights; The minimum Euclidean distance between the four outermost constellation points in the current constellation diagram; The minimum Euclidean distance between the inner constellation points in the current constellation diagram;

[0087] Based on the optimal sparrow individual position The formula for updating follower positions is:

[0088]

[0089] In the formula, The position of the follower of the k-th constellation point in the t-th iteration; Let be the position of the follower of the k-th constellation point in the (t-1)-th iteration; The learning coefficient;

[0090] Randomly generate the second unit vector According to the second unit vector The formula for updating the location of the vigilant is as follows:

[0091]

[0092] In the formula, Let the position of the vigilant be the k-th constellation point in the t-th iteration. Let the position of the vigilant be the k-th constellation point in the (t-1)-th iteration. This is the warning coefficient;

[0093] When the Sparrow Search algorithm gets stuck in a local optimum and the fitness function value no longer changes significantly in multiple iterations, the watchdog will trigger a danger warning mechanism. The position of the constellation points will be adjusted, allowing the Sparrow Search algorithm to escape the local optimum and continue searching for a better constellation point distribution. This helps to prevent the algorithm from converging too early, thus making it more likely to find a constellation point distribution that satisfies the target.

[0094] Adjust the constellation points according to the sparrow population; repeat the process of adjusting the first constellation optimization map using the sparrow search algorithm until the fitness value converges or the maximum number of iterations is reached, and then output the second constellation optimization map.

[0095] The second constellation optimization map enables signal points to better resist the effects of phase rotation, maintain the distinguishability of the signal, make the outermost constellation points easier to detect and recover, and reduce the computational complexity of the signal recovery process at the receiver.

[0096] The second constellation optimization diagram is converted into a transmission signal, which is then transmitted to the receiving end via a hollow optical fiber.

[0097] The received transmitted signal is preprocessed to obtain a denoised signal, specifically including:

[0098] The received transmitted signal is input into a low-pass filter to suppress additive white Gaussian noise: the formula is as follows:

[0099]

[0100]

[0101]

[0102] In the formula, This represents the k-th transmitted signal sent by the transmitter. This represents the sign amplitude corresponding to the k-th constellation point in the second constellation optimization graph. This represents the sign phase corresponding to the k-th constellation point in the second constellation optimization diagram; The additive white Gaussian noise of the kth transmitted signal; The phase noise of the k-th transmitted signal; This is the k-th transmitted signal after noise reduction; This is the kth transmitted signal received;

[0103] A phase-locked loop is used to recover the clock and frequency of the transmitted signal to obtain a denoised signal.

[0104] In the traditional 16QAM modulation format, M is 4. After the M-th power operation, the received signal can be expressed as:

[0105]

[0106] When selecting the outermost constellation points for phase noise estimation, the modulation phase of these constellation points satisfies:

[0107]

[0108] Therefore, the second constellation optimization map can be obtained simply by using the quadratic algorithm to perform phase recovery on the denoised signal, specifically including:

[0109] Phase noise is extracted from the denoised signal based on the quadratic algorithm, expressed as follows:

[0110]

[0111] In the formula, This is the k-th denoised signal; This represents the sign amplitude corresponding to the k-th constellation point in the second constellation optimization diagram; Pi; Let be the phase noise of the k-th denoised signal; This is the phase offset.

[0112] According to phase noise Phase compensation is performed on the k-th denoised signal to obtain the second constellation optimization diagram.

[0113] The optimized second constellation diagram is demapped and converted back into the original bit data. This embodiment reduces the fourth power operation to a second power operation, thereby reducing the computational load in the signal detection and phase recovery process and lowering the hardware processing requirements of the optical fiber communication system. On the other hand, it effectively improves the system's anti-interference capability and reduces the bit error rate, ensuring the stability and reliability of signal transmission in long-distance, high-speed hollow-core optical fiber communication scenarios.

[0114] Example 2

[0115] This embodiment provides a hollow-core optical fiber communication system for reducing complexity. The hollow-core optical fiber communication system is used to execute the hollow-core optical fiber communication method described in Embodiment 1. The hollow-core optical fiber communication system includes:

[0116] The transmitting module is used to acquire the raw bit data to be transmitted and map the raw bit data onto the initial constellation diagram through an orthogonal amplitude modulation mapping algorithm;

[0117] The optimization module is used to rotate the initial constellation diagram according to a preset rotation angle to obtain a first optimized constellation diagram, and to adjust the first optimized constellation diagram using a sparrow search algorithm to obtain a second optimized constellation diagram.

[0118] The transmission module is used to convert the second constellation optimization diagram into a transmission signal and transmit the transmission signal to the receiving end through a hollow optical fiber;

[0119] The receiving module is used to preprocess the received transmission signal to obtain a denoised signal, perform phase recovery on the denoised signal based on the quadratic algorithm to obtain a second constellation optimization map, and demap the second constellation optimization map to convert it back into the original bit data.

[0120] The optimization module uses the Sparrow Search algorithm to adjust the first constellation optimization map to obtain the second constellation optimization map, specifically including:

[0121] Each constellation point in the first constellation optimization map is considered a sparrow individual, and the sparrow individuals are divided into discoverers, followers, and vigilants; the sparrow population is composed of discoverers, followers, and vigilants.

[0122] A fitness function is constructed based on the Euclidean distance between constellation points. The fitness values ​​are obtained by substituting the Euclidean distance between constellation points into the fitness function. The sparrow population is then updated with the goal of maximizing the fitness value under the following constraints:

[0123] like hour, ;

[0124] In the formula, The modulation phase of the k-th constellation point, This refers to the set of points in the outermost constellation of the current constellation chart. Pi; This is the phase offset.

[0125] Adjust the constellation points according to the sparrow population; repeat the process of adjusting the first constellation optimization map using the sparrow search algorithm until the fitness value converges or the maximum number of iterations is reached, and then output the second constellation optimization map.

[0126] Example 3

[0127] This embodiment provides an electronic terminal, including a processor and a storage medium; the storage medium is used to store instructions; the processor is used to perform operations according to the instructions to execute the steps of the hollow optical fiber communication method described in Embodiment 1.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the technical principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for reducing the complexity of hollow-core optical fiber communication, characterized in that, include: The raw bit data to be transmitted is obtained and mapped onto the initial constellation diagram using an orthogonal amplitude modulation mapping algorithm. The initial constellation diagram is rotated according to a preset rotation angle to obtain the first optimized constellation diagram. The first optimized constellation diagram is then adjusted using a sparrow search algorithm to obtain the second optimized constellation diagram. The second optimized constellation diagram is then converted into a transmission signal, which is transmitted to the receiving end through a hollow optical fiber. The received transmitted signal is preprocessed to obtain a denoised signal, specifically including: The received transmitted signal is input into a low-pass filter to suppress additive white Gaussian noise: the formula is as follows: ； ； ； In the formula, This represents the k-th transmitted signal sent by the transmitter. This represents the sign amplitude corresponding to the k-th constellation point in the second constellation optimization graph. This represents the sign phase corresponding to the k-th constellation point in the second constellation optimization diagram; The additive white Gaussian noise of the kth transmitted signal; The phase noise of the k-th transmitted signal; This is the k-th transmitted signal after noise reduction; This is the kth transmitted signal received; A phase-locked loop is used to recover the clock and frequency of the transmitted signal to obtain a denoised signal; The second constellation optimization map is obtained by phase recovery of the denoised signal based on the quadratic algorithm, specifically including: Phase noise is extracted from the denoised signal based on the quadratic algorithm, expressed as follows: ； In the formula, This is the k-th denoised signal; This represents the sign amplitude corresponding to the k-th constellation point in the second constellation optimization diagram; Pi; Let be the phase noise of the k-th denoised signal; This is the phase offset. According to phase noise Phase compensation is performed on the k-th denoised signal to obtain the second constellation optimization map; the second constellation optimization map is demapped and converted back into the original bit data.

2. The hollow-core optical fiber communication method according to claim 1, characterized in that, The original bit data is mapped onto the initial constellation diagram using an orthogonal amplitude modulation mapping algorithm, specifically including: Will An initial constellation map is constructed by distributing constellation points in an h-row, h-column grid on the complex plane; the original bit data is divided into bit groups according to the set number of bits, and Gray coding is used to map each bit group to a specific constellation point on the initial constellation map.

3. The hollow-core optical fiber communication method according to claim 1, characterized in that, The first optimized constellation diagram is obtained by rotating the initial constellation diagram according to a preset rotation angle, specifically including: The rotation factor is calculated based on the preset rotation angle, expressed by the following formula; ； In the formula, For rotation factor, The value is the rotation angle; j represents the imaginary unit. The first optimized constellation diagram is obtained by multiplying each constellation point in the initial constellation diagram by the rotation factor.

4. The hollow-core optical fiber communication method according to claim 1, characterized in that, The first optimized constellation diagram was adjusted using the Sparrow Search algorithm to obtain the second optimized constellation diagram, specifically including: Each constellation point in the first constellation optimization map is considered a sparrow individual, and the sparrow individuals are divided into discoverers, followers, and vigilants; the sparrow population is composed of discoverers, followers, and vigilants. A fitness function is constructed based on the Euclidean distance between constellation points. The fitness values ​​are obtained by substituting the Euclidean distance between constellation points into the fitness function. The sparrow population is then updated with the goal of maximizing the fitness value under the following constraints: like hour, ; In the formula, The modulation phase of the k-th constellation point, This refers to the set of points in the outermost constellation of the current constellation chart. Pi; This is the phase offset. Adjust the constellation points according to the sparrow population; repeat the process of adjusting the first constellation optimization map using the sparrow search algorithm until the fitness value converges or the maximum number of iterations is reached, and then output the second constellation optimization map.

5. The hollow-core optical fiber communication method according to claim 4, characterized in that, The sparrow population is updated under constraints with the goal of maximizing fitness values. Specifically, this includes: According to the modulation phase Calculate the exploration vector Randomly generate the first unit vector Q, and use the first unit vector Q and the exploration vector The formula for updating the discoverer's location is: ； In the formula, Let the position of the discoverer be the k-th constellation point in the t-th iteration. The position of the discoverer in the (t-1)th iteration of the k-th constellation point; This is the step size coefficient; I represents the set of outermost constellation points in the current constellation diagram; I represents the set of innermost constellation points in the current constellation diagram. Substituting the positions of the discoverer, follower, and watcher in the (t-1)th iteration into the fitness function yields the optimal sparrow position corresponding to the maximum fitness value. Based on the optimal sparrow individual position The formula for updating follower positions is: ； In the formula, Let the position of the follower of the k-th constellation point in the t-th iteration be _t_. Let be the position of the follower of the k-th constellation point in the (t-1)-th iteration; The learning coefficient; Randomly generate the second unit vector According to the second unit vector The formula for updating the location of the vigilant is as follows: ； In the formula, Let the position of the vigilant be the k-th constellation point in the t-th iteration. The position of the watchman in the (t-1)th iteration of the kth constellation point; This is the warning level.

6. The hollow-core optical fiber communication method according to claim 5, characterized in that, Substituting the positions of the discoverer, follower, and watcher in the (t-1)th iteration into the fitness function yields the optimal sparrow position corresponding to the maximum fitness value. , specifically including: ； ； ； ； In the formula, For the locations of discoverers, followers, and watchdogs, This is the constellation diagram output during the (t-1)th iteration. , , and This represents the constellation point coordinate vector corresponding to the position of an individual sparrow. , , and Number the constellations; Output the set of the outermost constellation points in the constellation diagram for the (t-1)th iteration. Output the set of inner constellation points in the constellation graph for the (t-1)th iteration; For fitness value, and To set weights; The minimum Euclidean distance between the four outermost constellation points in the current constellation diagram; This represents the minimum Euclidean distance between the inner constellation points in the current constellation diagram.

7. A hollow-core optical fiber communication system for reducing complexity, characterized in that, include: The transmitting module is used to acquire the raw bit data to be transmitted and map the raw bit data onto the initial constellation diagram through an orthogonal amplitude modulation mapping algorithm; The optimization module is used to rotate the initial constellation diagram according to a preset rotation angle to obtain a first optimized constellation diagram, and to adjust the first optimized constellation diagram using a sparrow search algorithm to obtain a second optimized constellation diagram. The transmission module is used to convert the second constellation optimization diagram into a transmission signal and transmit the transmission signal to the receiving end through hollow optical fiber; The receiving module is used to preprocess the received transmitted signal to obtain a denoised signal, perform phase recovery on the denoised signal based on the quadratic algorithm to obtain a second constellation optimization map, and demap the second constellation optimization map to convert it back into the original bit data. The receiving module performs data preprocessing on the received transmitted signal to obtain a denoised signal, specifically including: The received transmitted signal is input into a low-pass filter to suppress additive white Gaussian noise: the formula is as follows: ； ； ； In the formula, This represents the k-th transmitted signal sent by the transmitter. This represents the sign amplitude corresponding to the k-th constellation point in the second constellation optimization graph. This represents the sign phase corresponding to the k-th constellation point in the second constellation optimization diagram; The additive white Gaussian noise of the kth transmitted signal; The phase noise of the k-th transmitted signal; This is the k-th transmitted signal after noise reduction; This is the kth transmitted signal received; A phase-locked loop is used to recover the clock and frequency of the transmitted signal to obtain a denoised signal; The receiving module performs phase recovery on the denoised signal based on a quadratic algorithm to obtain a second constellation optimized map, specifically including: Phase noise is extracted from the denoised signal based on the quadratic algorithm, expressed as follows: ； In the formula, This is the k-th denoised signal; This represents the sign amplitude corresponding to the k-th constellation point in the second constellation optimization diagram; Pi; Let be the phase noise of the k-th denoised signal; This is the phase offset. According to phase noise Phase compensation is performed on the k-th denoised signal to obtain the second constellation optimization map; the second constellation optimization map is demapped and converted back into the original bit data.

8. An electronic terminal, characterized in that, It includes a processor and a storage medium; the storage medium is used to store instructions; the processor is used to operate according to the instructions to execute the steps of the hollow optical fiber communication method according to any one of claims 1 to 6.

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