Method and apparatus for interference management in optical communication systems

Abstract

The application belongs to optical communication system, disclose a kind of interference management method and device in optical communication system, comprising the following steps: S1, the channel state information between each transmitter and receiver in optical communication system is collected in real time, data model is constructed based on channel state information;S2, based on data model, the beam parameter of LED array is dynamically adjusted by adaptive beam forming strategy;S3, interference signal parameter in channel state information is used, and the interference signal of each transmitter is structured alignment processing by interference vector orthogonalization algorithm;S4, set interference suppression evaluation criterion, whether current interference level meets the requirement based on evaluation criterion is judged, if it does not meet the requirement, start dynamic time slot scheduling strategy;S5, the data model of channel state information is updated according to preset period, repeat steps S2-S4, beam parameter, interference orthogonalization parameter and time slot allocation parameter are continuously adjusted by iterative optimization algorithm, realize dynamic adaptive interference management.

Description

Technical Field

[0001] This specification relates to the field of optical communication system technology, and in particular to an interference management method and apparatus in an optical communication system. Background Technology

[0002] With the rapid development of optical communication technology, LED-based optical communication systems such as LiFi have been widely used in indoor high-density communication and industrial data transmission scenarios due to their unique advantages, including abundant spectrum resources, good electromagnetic compatibility, high security, and energy saving. However, in scenarios where multiple transmitters and receivers operate simultaneously, the optical signals emitted by different transmitters can overlap in space, resulting in severe co-channel interference. This leads to difficulties in signal demodulation at the receiving end, reduced transmission rates, and increased bit error rates, becoming a key bottleneck restricting the performance improvement of optical communication systems.

[0003] Existing interference management technologies are mainly divided into two categories: one is the passive interference suppression method based on power control, which reduces interference spillover by reducing the transmitter's transmission power. However, this method leads to a shortened signal transmission distance and a reduced coverage area, sacrificing the coverage performance of the communication system. The other is the scheduling method based on time division / frequency division multiplexing, which avoids signal collisions by allocating independent time or frequency resources to different transmitters. However, this method does not fully consider the dynamic changes in channel conditions, has poor resource allocation flexibility, and is prone to resource waste. Even when the channel quality is good and the interference is low, a fixed resource allocation mode is still used, which reduces transmission efficiency.

[0004] Therefore, there is an urgent need for an interference management scheme that can deeply explore the characteristics of interference signals, dynamically adapt to channel changes, and balance interference suppression accuracy with transmission efficiency. Summary of the Invention

[0005] This invention addresses the problems existing in the prior art by proposing an interference management method and device for optical communication systems. By integrating adaptive beamforming optimization, multi-dimensional interference vector orthogonalization, and dynamic time slot scheduling strategies, a closed-loop optimization system is constructed based on channel state information to achieve active interference suppression and precise improvement of transmission efficiency. This invention deeply mines the characteristics of interference signals and adapts to channel changes through dynamic iterative optimization, enabling optical communication systems to maintain low-interference and highly stable transmission performance in multi-user dynamic scenarios. It is applicable to various high-density optical communication networks such as LiFi.

[0006] To achieve the above objectives, this application provides the following technical solution: In a first aspect, an interference management method for an optical communication system is characterized by comprising the following steps: S1, Real-time acquisition of channel state information between each transmitter and receiver in the optical communication system, and construction of a data model based on the channel state information, wherein the channel state information includes at least channel transmission characteristic parameters and interference signal parameters; S2, based on the data model, the beam parameters of the LED array are dynamically adjusted through an adaptive beamforming strategy to focus the signal energy on the target receiver and reduce energy spillover from non-target channels; S3, using the interference signal parameters in the channel state information, the interference signals of each transmitter are structurally aligned using the interference vector orthogonalization algorithm, so that the interference signals of different transmitters are independent of each other and do not affect the reception of the target signal; S4, set the interference suppression judgment criteria, and judge whether the current interference level meets the requirements based on the judgment criteria. If it does not meet the requirements, start the dynamic time slot scheduling strategy to allocate independent transmission windows to different transmitters to avoid signal collisions. S5. The data model that updates the channel state information according to the preset period repeats steps S2-S4. The beam parameters, interference orthogonality parameters and time slot allocation parameters are continuously adjusted through iterative optimization algorithms to achieve dynamic adaptive interference management.

[0007] Optionally, the data model for channel state information includes a channel gain matrix and an interference power matrix. The channel transmission characteristic parameters are the channel gain matrix, and the interference signal parameters are the interference power matrix. The data model also includes a receiver location coordinate set, forming a three-dimensional data model.

[0008] Optionally, the adaptive beamforming strategy generates an optimal beam weight vector through a beam weight calculation model, adjusting the beamwidth adjustment parameters and propagation direction vector of the LED array. The specific formula for the beam weight calculation model is shown below: (1) in, s is the optimal beam weight vector; s is the target signal vector; Here, H is the regularization coefficient; H is the channel gain matrix. Let be the beam weight vector to be optimized; This is the operation of squaring the 2-norm of a vector.

[0009] Optionally, a mapping relationship is established between the regularization coefficient and the channel signal-to-noise ratio (SNR). The SNR value of the current channel is obtained in real time through the SNR detection module. The regularization coefficient is dynamically adjusted based on the preset mapping rules so that the regularization coefficient changes monotonically decreasing as the SNR increases and increasing monotonically as the SNR decreases, in order to balance the signal fitting accuracy and the stability of the weight vector.

[0010] Optionally, interference vector orthogonalization algorithms include: Multi-dimensional feature extraction is performed using a joint time-domain and frequency-domain analysis method to extract amplitude distribution features, phase change features, time-domain attenuation features, and frequency-domain spectral peak features from the interference vector of each transmitter. Interference vector decomposition is based on the eigenvalue decomposition (EVD) algorithm to split the interference vector after feature extraction, select the main feature components, and remove noise and redundant components. Orthogonal subspace construction: Based on the Gram-Schmidt orthogonalization process, an interference projection subspace orthogonal to the target signal subspace is constructed. Dynamic projection transformation projects the denoised interference vector onto the orthogonal subspace. An adaptive adjustment factor is introduced during the projection process to ensure that the projected interference vector satisfies the orthogonality constraint. Amplitude normalization and verification: A normalization algorithm is used to calibrate the amplitude of the projected interference vector, while correlation verification is used to ensure that the correlation between the interference vector and the target signal vector is lower than the set requirements.

[0011] Optional, orthogonal constraints, the specific formula is shown below: (2) Where is the interference vector of the i-th transmitter; is the interference vector of the j-th transmitter; and H is the conjugate transpose operation.

[0012] Optionally, the interference suppression criterion is implemented through an interference evaluation function, the specific formula of which is shown below: (3) Where J represents the percentage of interference intensity; The trace operation is performed on a matrix; This is the interference power matrix; Interference power matrix The conjugate transpose of ; H is the channel gain matrix; Let H be the conjugate transpose of the channel gain matrix H.

[0013] Optionally, the dynamic time slot scheduling strategy allocates independent parameter windows based on the time slot allocation optimization function, the specific formula of which is shown below: (4) in, This represents the optimal time slot allocation vector. The operation of the independent variable to find the maximum value; M is the total number of transmitters; K is the transmitter number; The time slot percentage for the Kth transmitter; Let K be the transmission rate of the Kth transmitter; For summation operations.

[0014] In a second aspect, the present invention provides an interference management device in an optical communication system for implementing the interference management method in the optical communication system of the first aspect, comprising: The CSI acquisition and modeling module is used to acquire channel state information in real time, process the acquired raw data to build a data model, and output channel transmission characteristic parameters and interference signal parameters. An adaptive beamforming module includes an independently adjustable LED array and a beamforming unit. The beamforming unit adjusts the beam parameters of the LED array based on the data model output by the CSI acquisition and modeling module through an adaptive beamforming strategy. The interference orthogonalization processing module receives interference signal parameters output by the CSI acquisition and modeling module, performs structured alignment processing of the interference signal through the interference vector orthogonalization algorithm, and outputs the interference level evaluation result. The dynamic scheduling and iterative optimization module is used to receive the evaluation results from the interference orthogonalization processing module, start the dynamic time slot scheduling strategy to allocate the transmission window, and continuously optimize the parameters of each module through iterative optimization algorithm. The central control module communicates with the above modules, synchronizes data transmission, coordinates the working sequence of the modules, and outputs global optimization instructions.

[0015] Optionally, the CSI acquisition and modeling module includes a high-resolution optical sensor, a signal conditioning unit, and a data modeling unit. The optical sensor acquires raw channel and interference data, the signal conditioning unit performs filtering, amplification, and analog-to-digital conversion, and the data modeling unit constructs a three-dimensional data model containing a channel gain matrix and an interference power matrix.

[0016] The beneficial effects of this invention are as follows: 1. By deeply mining the essential features of interference signals through multi-dimensional feature extraction, and combining an integrated process of feature value decomposition denoising, dynamic projection orthogonalization and normalization verification, the interference signals are accurately separated and suppressed. Compared with the traditional simple projection method, the interference suppression accuracy is significantly improved and the correlation coefficient between the target signal and the interference signal is greatly reduced. 2. By constructing a closed-loop optimization system based on real-time CSI data, parameters such as regularization coefficient, iteration step size, and interference projection adjustment factor change dynamically with the channel state. This enables the system to quickly adapt to fluctuations in the channel environment and user mobility scenarios, ensuring stable communication performance even in dynamic environments.

[0017] 3. By adopting a hybrid strategy of "interference orthogonalization as the main method and time slot scheduling as a supplementary method", time slot scheduling is only initiated when the interference level exceeds the threshold, avoiding the waste of resources in traditional time division multiplexing. At the same time, time slot allocation is dynamically adjusted based on channel quality to ensure that high-quality channels obtain more resources and improve overall transmission efficiency. Attached Figure Description

[0018] This specification will be further described by way of exemplary embodiments, which will be described in detail with reference to the accompanying drawings. The same numbers in the drawings denote the same structures or steps.

[0019] Figure 1 This is a schematic diagram of an interference management method in an optical communication system according to Embodiment 1 of this application.

[0020] Figure 2 For the purposes of this application Figure 1 The diagram shows the internal structure of the interference orthogonalization processing module.

[0021] Figure 3 For the purposes of this application Figure 1 The diagram shows the LED array control principle of the beamforming module.

[0022] Figure 4 For the purposes of this application Figure 1 The diagram shows a comparison of the effects of orthogonalization of interference vectors.

[0023] Figure 5 This is a schematic diagram of an interference management device in an optical communication system according to Embodiment 2 of this application. Detailed Implementation

[0024] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description of this application is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely one preferred embodiment of this application and are only used to explain this application. They do not limit the scope of protection of this application. All other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application. Example

[0025] like Figure 1-4 As shown, an interference management method in an optical communication system includes the following steps: S1, Real-time acquisition of channel state information between each transmitter and receiver in the optical communication system, and construction of a data model based on the channel state information, wherein the channel state information includes at least channel transmission characteristic parameters and interference signal parameters; S2, based on the data model, dynamically adjusts the beam parameters of the LED array through an adaptive beamforming strategy to focus signal energy on the target receiver and reduce energy spillover from non-target channels. S3, using the interference signal parameters in the channel state information, the interference signals of each transmitter are structurally aligned using the interference vector orthogonalization algorithm, so that the interference signals of different transmitters are independent of each other and do not affect the reception of the target signal; S4, set the interference suppression judgment criteria, and judge whether the current interference level meets the requirements based on the judgment criteria. If it does not meet the requirements, start the dynamic time slot scheduling strategy to allocate independent transmission windows to different transmitters to avoid signal collisions. S5. The data model that updates the channel state information according to the preset period repeats steps S2-S4. The beam parameters, interference orthogonality parameters and time slot allocation parameters are continuously adjusted through iterative optimization algorithms to achieve dynamic adaptive interference management.

[0026] This embodiment, through the above-described settings, integrates adaptive beamforming optimization, multi-dimensional interference vector orthogonalization, and dynamic time slot scheduling strategies to construct a closed-loop optimization system based on channel state information, achieving proactive interference suppression and precise improvement of transmission efficiency. Interference alignment technology reduces mutual interference during multiple transmission processes and lowers the probability of signal collisions by ensuring that interference vectors do not overlap. Furthermore, by arranging the signal vectors of different transmitters in a structured overlapping manner, it ensures that interference does not affect the desired signal and can be effectively suppressed. This invention deeply explores the characteristics of interference signals and adapts to channel changes through dynamic iterative optimization, enabling optical communication systems to maintain low-interference and highly stable transmission performance in multi-user dynamic scenarios, making it suitable for various high-density optical communication networks such as LiFi.

[0027] In one specific embodiment, in step S1, the data model of the channel state information includes a channel gain matrix and an interference power matrix, the channel transmission characteristic parameter is the channel gain matrix, the interference signal parameter is the interference power matrix, and the data model also includes a receiver location coordinate set, forming a three-dimensional data model.

[0028] In one specific embodiment, in step S2, a beam weight calculation model is constructed to solve for the optimal beam weight vector that balances the fitting accuracy of the target signal with the stability of the weights. This weight vector is then converted into adjustable parameters such as the beamwidth and propagation direction of the LED array. The operating state of each LED unit in the LED array is controlled by a driving circuit to precisely focus the signal energy onto the target receiver, thereby reducing energy leakage to non-target channels and lowering the possibility of interference. Specifically, the adaptive beamforming strategy generates the optimal beam weight vector through the beam weight calculation model and adjusts the beamwidth adjustment parameters and propagation direction vector of the LED array. The specific formula for the beam weight calculation model is shown below: (1) in, s is the optimal beam weight vector; s is the target signal vector; Here, H is the regularization coefficient; H is the channel gain matrix. Let be the beam weight vector to be optimized; This is the operation of squaring the 2-norm of a vector.

[0029] Specifically, a mapping relationship is established between the regularization coefficient and the channel signal-to-noise ratio (SNR). The SNR value of the current channel is obtained in real time through the SNR detection module. The regularization coefficient is dynamically adjusted based on the preset mapping rules, so that the regularization coefficient changes monotonically decreasing as the SNR increases and increasing monotonically as the SNR decreases, in order to balance the signal fitting accuracy and the stability of the weight vector.

[0030] In one specific embodiment, the interference vector orthogonalization algorithm in step S3 includes: Multi-dimensional feature extraction employs a joint time-domain and frequency-domain analysis method to extract amplitude distribution features, phase change features, time-domain attenuation features, and frequency-domain spectral peak features from the interference vector of each transmitter. In the time-domain analysis, the peak-to-mean ratio (reflecting the concentration of amplitude distribution) and variance (reflecting the amplitude fluctuation range) of the interference vector are calculated, and an exponential attenuation model is fitted to obtain the attenuation coefficient (characterizing the time-domain attenuation feature). In the frequency-domain analysis, the interference vector is transformed to the frequency domain using Fast Fourier Transform (FFT), and the peak position of the power spectral density (reflecting the main interference frequency) and bandwidth (reflecting the range of interference frequency distribution) are extracted to form a complete interference feature set. Interference vector decomposition (EVD) is used to split the interference vector after feature extraction, based on the eigenvalue decomposition (EVD) algorithm. It filters the main feature components and removes noise and redundant components. First, the autocorrelation matrix of the interference vector is constructed, and the eigenvalues ​​and eigenvectors of the autocorrelation matrix are calculated. The noise variance is statistically analyzed using the 3σ criterion. The eigenvalue corresponding to 3 times the noise variance is used as the filtering threshold. The main feature components with eigenvalues ​​greater than the threshold are retained, and the secondary feature components containing noise are removed, thereby achieving denoising of the interference vector and extraction of effective information. Orthogonal subspace construction: Based on the Gram-Schmidt orthogonalization process, an interference projection subspace orthogonal to the target signal subspace is constructed. First, the target signal vector is used as the first set of basis vectors, and an orthogonal basis vector set is generated through orthogonalization to ensure that the basis vector set satisfies the orthogonality condition with the target signal subspace. The dynamic projection transformation projects the denoised interference vector onto an orthogonal subspace. An adaptive adjustment factor is introduced during the projection process to ensure that the projected interference vector satisfies the orthogonality constraint. The adaptive adjustment factor is a parameter used to dynamically correct the projection direction of the interference vector. Its value is positively correlated with the fluctuation of the channel state and can be adjusted according to the condition number of the channel gain matrix. The larger the condition number, the worse the channel stability, and the greater the correction of the projection direction by the adjustment factor.

[0031] Amplitude normalization and verification are performed by calibrating the amplitude of the projected interference vector using a normalization algorithm, while correlation verification ensures that the correlation between the interference vector and the target signal vector is below a set requirement. A min-max normalization algorithm is used to map the amplitude of the projected interference vector to a preset proportional range of the target signal amplitude, preventing excessive interference signal amplitude from overflowing and affecting the demodulation of the target signal. Simultaneously, the correlation coefficient between the normalized interference vector and the target signal vector is calculated. If the coefficient exceeds a set threshold, the projection parameters are readjusted until the correlation coefficient meets the requirements, ensuring that the interference signal does not substantially affect the target signal.

[0032] Orthogonal constraints, the specific formulas are shown below: (2) in, Let i be the interference vector of the i-th transmitter; Let H be the interference vector of the j-th transmitter; H is the conjugate transpose operation, where the complex vector is transposed and the conjugate of each element is taken to ensure the validity of the inner product operation of the complex signal. In practical engineering, due to factors such as channel noise, it is difficult to completely achieve the ideal orthogonal state of "=0", and a correlation threshold is usually set. When the absolute value of the inner product of the interference vectors is lower than the threshold, it is determined that the orthogonal constraint requirement is met.

[0033] In one specific embodiment, in step S4, an interference suppression evaluation criterion is set, and the current interference intensity ratio is calculated using an interference evaluation function and compared with a preset threshold. If the interference level meets the requirements, the current transmission parameters are maintained; otherwise, a dynamic time slot scheduling strategy is initiated. First, a transmission rate model for each transmitter is constructed. Based on the transmission rate and channel characteristic parameters, an optimal welfare transmission window is allocated to each transmitter using a time-domain allocation optimization function, under the premise of satisfying constraints, to avoid signal collisions and further suppress interference. Specifically, the interference suppression evaluation criterion is implemented through an interference evaluation function. An interference suppression threshold is defined. The interference intensity percentage is calculated using the interference evaluation function, as shown in the following formula: (3) Where J represents the percentage of interference intensity; The trace operation is performed on a matrix; This is the interference power matrix; Interference power matrix The conjugate transpose of ; H is the channel gain matrix; Let H be the conjugate transpose of the channel gain matrix H.

[0034] Specifically, if J > interference suppression threshold When the time slot allocation optimization function is activated, the dynamic time slot scheduling strategy is initiated. The dynamic time slot scheduling strategy allocates independent parameter windows based on the time slot allocation optimization function, and the specific formula is shown below: (4) in, This represents the optimal time slot allocation vector. The operation of the independent variable to find the maximum value; M is the total number of transmitters; K is the transmitter number; The time slot percentage for the Kth transmitter; Let K be the transmission rate of the Kth transmitter; For summation operations.

[0035] In one specific embodiment, in step S5, the central control module triggers CSI data model updates according to a preset cycle, repeating the above-mentioned beamforming optimization, interference orthogonalization processing, and dynamic time slot scheduling steps. Simultaneously, iterative optimization algorithms such as gradient descent are used to continuously adjust the beam weight vector, interference orthogonalization parameters, and time slot proportions until the parameter changes meet the termination condition, achieving coordinated iterative optimization of all parameters and ensuring the system is always in its optimal operating state. Specifically, the iterative optimization algorithm is the gradient descent algorithm, used to continuously optimize the beam weight vector, with the specific formula shown below: (5) in, This is the beam weight vector after the (t+1)th iteration; Let be the beam weight vector after the t-th iteration; t is the iteration number. This is the iteration step size; This is for gradient calculation.

[0036] Example 2: like Figure 5 As shown, an interference management device in an optical communication system includes: The CSI acquisition and modeling module is used to acquire channel state information in real time, process the acquired raw data to build a data model, and output channel transmission characteristic parameters and interference signal parameters. An adaptive beamforming module includes an independently adjustable LED array and a beamforming unit. The beamforming unit adjusts the beam parameters of the LED array based on the data model output by the CSI acquisition and modeling module through an adaptive beamforming strategy. The interference orthogonalization processing module receives interference signal parameters output by the CSI acquisition and modeling module, performs structured alignment processing of the interference signal through the interference vector orthogonalization algorithm, and outputs the interference level evaluation result. The dynamic scheduling and iterative optimization module is used to receive the evaluation results from the interference orthogonalization processing module, start the dynamic time slot scheduling strategy to allocate the transmission window, and continuously optimize the parameters of each module through iterative optimization algorithm. The central control module communicates with the above modules, synchronizes data transmission, coordinates the working sequence of the modules, and outputs global optimization instructions.

[0037] Specifically, the CSI acquisition and modeling module includes a high-resolution optical sensor, a signal conditioning unit, and a data modeling unit. The optical sensor acquires raw channel and interference data, the signal conditioning unit performs filtering, amplification, and analog-to-digital conversion, and the data modeling unit constructs a three-dimensional data model containing the channel gain matrix and the interference power matrix.

[0038] Specifically, the LED array consists of multiple independently controllable LED unit arrays. Each LED unit is equipped with an independent driving circuit and angle adjustment mechanism. The beam parameters are adjusted by controlling the number of LED units lit, the luminous intensity, and the pointing angle. The adjustment accuracy is ensured to meet system requirements through closed-loop feedback control.

[0039] Specifically, the interference orthogonalization processing module uses an FPGA chip as the core processing unit. It implements parallel operations of algorithms such as feature extraction, vector decomposition, and projection transformation through hardware logic circuits. The threshold for interference level assessment can be flexibly configured according to the application scenario.

[0040] Specifically, the dynamic scheduling and iterative optimization module supports parallel scheduling of multiple transmitters and adapts to optical communication systems of different scales through a combination of hardware interface expansion and software parameter configuration; the iteration optimization cycle can be configured through the central control module to adapt to the dynamic changes in different channel environments.

[0041] The above-described specific embodiments are preferred embodiments of an interference management method and apparatus in an optical communication system according to this application, and are not intended to limit the specific scope of this application. The scope of this application includes but is not limited to the specific embodiments described herein. All equivalent changes made in accordance with the shape and structure of this application are within the protection scope of this application.

Claims

1. A method of interference management in an optical communication system, characterized by, Includes the following steps: S1, Real-time acquisition of channel state information between each transmitter and receiver in the optical communication system, and construction of a data model based on the channel state information, wherein the channel state information includes at least channel transmission characteristic parameters and interference signal parameters; S2, based on the data model, the beam parameters of the LED array are dynamically adjusted through an adaptive beamforming strategy to focus the signal energy on the target receiver and reduce energy spillover from non-target channels; S3, using the interference signal parameters in the channel state information, the interference signals of each transmitter are structurally aligned using the interference vector orthogonalization algorithm, so that the interference signals of different transmitters are independent of each other and do not affect the reception of the target signal; S4, set the interference suppression judgment criteria, and judge whether the current interference level meets the requirements based on the judgment criteria. If it does not meet the requirements, start the dynamic time slot scheduling strategy to allocate independent transmission windows to different transmitters to avoid signal collisions. S5. The data model that updates the channel state information according to the preset period repeats steps S2-S4. The beam parameters, interference orthogonality parameters and time slot allocation parameters are continuously adjusted through iterative optimization algorithms to achieve dynamic adaptive interference management.

2. The method of interference management in an optical communication system of claim 1, wherein, The data model of the channel state information includes a channel gain matrix and an interference power matrix. The channel transmission characteristic parameters are the channel gain matrix and the interference signal parameters are the interference power matrix. The data model also includes a receiver location coordinate set, forming a three-dimensional data model.

3. The method of interference management in an optical communication system of claim 1, wherein, The adaptive beamforming strategy generates an optimal beam weight vector through a beam weight calculation model, adjusting the beamwidth adjustment parameters and propagation direction vector of the LED array. The specific formula for the beam weight calculation model is shown below: （1） in, s is the optimal beam weight vector; s is the target signal vector; Regularization coefficients; H is the channel gain matrix; Let be the beam weight vector to be optimized; This is the operation of squaring the 2-norm of a vector.

4. The interference management method in an optical communication system according to claim 3, characterized in that, The regularization coefficient is mapped to the channel signal-to-noise ratio (SNR). The SNR value of the current channel is obtained in real time through the SNR detection module. The regularization coefficient is dynamically adjusted based on the preset mapping rules so that the regularization coefficient changes monotonically decreasing as the SNR increases and increasing monotonically as the SNR decreases, in order to balance the signal fitting accuracy and the stability of the weight vector.

5. The interference management method in an optical communication system according to claim 1, characterized in that, The interference vector orthogonalization algorithm includes: Multi-dimensional feature extraction is performed using a joint time-domain and frequency-domain analysis method to extract amplitude distribution features, phase change features, time-domain attenuation features, and frequency-domain spectral peak features from the interference vector of each transmitter. Interference vector decomposition is based on the eigenvalue decomposition (EVD) algorithm to split the interference vector after feature extraction, select the main feature components, and remove noise and redundant components. Orthogonal subspace construction: Based on the Gram-Schmidt orthogonalization process, an interference projection subspace orthogonal to the target signal subspace is constructed. Dynamic projection transformation projects the denoised interference vector onto the orthogonal subspace. An adaptive adjustment factor is introduced during the projection process to ensure that the projected interference vector satisfies the orthogonality constraint. Amplitude normalization and verification: A normalization algorithm is used to calibrate the amplitude of the projected interference vector, while correlation verification is used to ensure that the correlation between the interference vector and the target signal vector is lower than the set requirements.

6. The interference management method in an optical communication system according to claim 5, characterized in that, The orthogonal constraint is specifically formulated as follows: （2） Where is the interference vector of the i-th transmitter; is the interference vector of the j-th transmitter; and H is the conjugate transpose operation.

7. The interference management method in an optical communication system according to claim 1, characterized in that, The interference suppression evaluation criterion is implemented through an interference evaluation function, the specific formula of which is shown below: （3） Where J represents the percentage of interference intensity; The trace operation is performed on a matrix; This is the interference power matrix; Interference power matrix The conjugate transpose of ; H is the channel gain matrix; Let H be the conjugate transpose of the channel gain matrix H.

8. The interference management method in an optical communication system according to claim 7, characterized in that, The dynamic time slot scheduling strategy allocates independent parameter windows based on a time slot allocation optimization function, the specific formula of which is shown below: （4） in, This represents the optimal time slot allocation vector. The operation of the independent variable to find the maximum value; M is the total number of transmitters; K is the transmitter number; The time slot percentage for the Kth transmitter; Let K be the transmission rate of the Kth transmitter; For summation operations.

9. An interference management device in an optical communication system, characterized in that, An interference management method for implementing the optical communication system according to any one of claims 1-8, comprising: The CSI acquisition and modeling module is used to acquire channel state information in real time, process the acquired raw data to build a data model, and output channel transmission characteristic parameters and interference signal parameters. An adaptive beamforming module includes an independently adjustable LED array and a beamforming unit. The beamforming unit adjusts the beam parameters of the LED array based on the data model output by the CSI acquisition and modeling module through an adaptive beamforming strategy. The interference orthogonalization processing module receives interference signal parameters output by the CSI acquisition and modeling module, performs structured alignment processing of the interference signal through the interference vector orthogonalization algorithm, and outputs the interference level evaluation result. The dynamic scheduling and iterative optimization module is used to receive the evaluation results from the interference orthogonalization processing module, start the dynamic time slot scheduling strategy to allocate the transmission window, and continuously optimize the parameters of each module through iterative optimization algorithm. The central control module communicates with the above modules, synchronizes data transmission, coordinates the working sequence of the modules, and outputs global optimization instructions.

10. The interference management device in the optical communication system according to claim 9, characterized in that, The CSI acquisition and modeling module includes a high-resolution optical sensor, a signal conditioning unit, and a data modeling unit. The optical sensor acquires raw channel and interference data, the signal conditioning unit performs filtering, amplification, and analog-to-digital conversion, and the data modeling unit constructs a three-dimensional data model containing a channel gain matrix and an interference power matrix.

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