Wireless optical communication equalization method and system based on base large model

Abstract

The present invention discloses a wireless optical communication equalization method and system based on a large pedestal model, relating to the field of wireless optical communication technology. The method comprises: when a first user terminal sends a preset known signal to a second user terminal via a wireless optical communication system, determining the current characteristics of the wireless optical communication system's channel based on the preset known signal and the signal received by the second user terminal; inputting the current characteristics of the wireless optical communication system's channel into a trained large pedestal model to obtain target equalization parameters; adjusting the current communication indicators of the wireless optical communication system's channel based on the target equalization parameters; and when the first user terminal continues to send a signal to be sent to the second user terminal, sending the signal to be sent via the channel after the state adjustment. The present invention can adapt to the ever-changing channel environment in real time, maximizing the communication stability of the wireless optical communication system.

Description

Technical Field

[0001] The present invention relates to the technical field of wireless optical communications, and in particular to a wireless optical communication balancing method and system based on a large base model. Background Art

[0002] Visible light wireless communication, also known as "light fidelity technology", is called Light Fidelity (Li-Fi) in English. It is a new wireless transmission technology that uses the visible light spectrum (such as the light emitted by a light bulb) for data transmission. The Li-Fi communication system consists of a light source and a receiver. The light source generally uses a light-emitting diode (LED) to convert the electrical signal into a high-frequency flickering light signal that is invisible to the naked eye; the receiver uses a photodiode to convert the received light signal emitted by the light source LED into an electrical signal, thus realizing the transmission of wireless signals. The system composition is as follows Figure 1 shown.

[0003] Wireless optical communication systems use light waves to transmit data. With wireless spectrum resources becoming increasingly scarce, wireless optical communication systems have garnered significant attention due to their potential for high-speed data transmission, several times faster than radio frequency communications, as well as their lack of electromagnetic interference and exceptional security. However, channel distortion caused by ambient light interference, multipath propagation, optical signal fading, and, in particular, the nonlinear characteristics of LEDs significantly impacts the performance of wireless optical communication systems. Equalization in visible light communication systems involves both pre-equalization and post-equalization. Pre-equalization involves adding an equalization module before the LEDs, i.e., at the transmitter, to improve their frequency response and bandwidth. This aims to calibrate the LED signal at the transmitter, allowing it to better adapt to channel characteristics during transmission, thereby enhancing communication system performance. Post-equalization primarily compensates for channel distortion at the receiver, compensating for signal quality requirements. Traditional equalization methods, such as linear equalization and decision feedback equalization, are designed based on certain assumptions about channel conditions and are suitable for environments with relatively stable channel conditions. However, they may not achieve optimal equalization performance in the highly dynamic nature of wireless optical communication channels. Therefore, an upgraded adaptive equalization method that can adapt to high dynamics more quickly is needed. The equalization parameters can be adjusted dynamically and in real time according to the current channel conditions. This can more effectively cope with the dynamic characteristics of the wireless optical communication system channel, thereby improving system performance and reliability. The existing technologies are as follows:

[0004] The invention patent with publication number "CN116455706A" and subject name "A Design Method and System for Channel Estimation and Equalization" discloses that the received data undergoes time synchronization, cyclic prefix removal, and OFDM demodulation at the receiving end, and then is processed by the channel estimation and equalization module, and then undergoes channel decoding, and finally outputs the received bits; the channel estimation and equalization module includes three parts: data merging and real and imaginary part splitting, a channel estimation and equalization deep convolutional neural network, and a masker; when various data are input into the module, the data is first merged, and then the real and imaginary parts of the merged data are split; then it is input into the channel estimation and equalization deep convolutional neural network to obtain the data after channel estimation and channel equalization; then it is input into the masker, and the corresponding mask length is configured according to the QAM order, and finally the processed data is output from the module, achieving the goal of using fewer resources to overcome rapidly changing channels and improving the accuracy of channel estimation and equalization. However, the technical solution in this patent has multiple shortcomings, resulting in the technical solution in this patent being insufficient to cope with complex channel environments. The multiple shortcomings are as follows:

[0005] 1) Complex system design:

[0006] CNN architecture design requires task-specific optimization, including multi-layer convolution, pooling, and activation function selection. This complicates system design, especially in the face of changing channel environments, which may require constant adjustment and optimization of the model structure. Furthermore, for adaptive equalization tasks, designing an effective feature extractor is particularly important. CNNs require manual design and tuning of feature extractors to capture channel state information and received signal characteristics, further increasing design complexity.

[0007] 2) Poor versatility:

[0008] CNNs typically need to be trained on specific tasks and datasets and lack a broad pre-training foundation. Their versatility and transferability are weak across different environments and tasks.

[0009] 3) Limited model size:

[0010] Due to computational resource and storage constraints, CNN models typically have a limited number of parameters and layers. This can lead to poor performance in characterizing complex channel characteristics and capturing nonlinear relationships, making them difficult to handle intricate and dynamically changing channel environments. CNNs excel at capturing local features and expanding the receptive field by stacking multiple convolutional layers. However, they are limited in capturing global information and long-range dependencies, making them disadvantageous for handling complex channel environments. Summary of the Invention

[0011] The technical problem to be solved by the present invention is to address the deficiencies of the existing technology and specifically provide a wireless optical communication equalization method and system based on a large base model, as follows:

[0012] 1) In the first aspect, the present invention provides a wireless optical communication equalization method based on a large base model, and the specific technical solution is as follows:

[0013] When the first user terminal sends a preset known signal to the second user terminal through the wireless optical communication system, determining the current characteristics of the channel of the wireless optical communication system according to the preset known signal and the signal received by the second user terminal;

[0014] Input the current characteristics of the channel of the wireless optical communication system into the trained base model to obtain the target equalization parameters;

[0015] According to the target equalization parameter, the current communication index of the channel of the wireless optical communication system is adjusted by the programmable driving circuit of the wireless optical communication system;

[0016] When the first user terminal continues to send the signal to be sent to the second user terminal, the signal to be sent is sent through the channel with the adjusted state.

[0017] The beneficial effects of the wireless optical communication equalization method based on the base large model provided by the present invention are as follows:

[0018] The large base model has better versatility and migration capabilities. During the training process, it can effectively capture the characteristics of the channel, and can well capture global information and long-distance dependencies, improve the accuracy of the output equalization parameters (i.e., target equalization parameters), and does not require manual design and adjustment, reducing complexity. Moreover, when facing a changing channel environment, it reduces the frequency of adjusting and optimizing the large base model, and can adapt to the ever-changing channel environment in real time, thereby maximizing the communication stability of the wireless optical communication system.

[0019] On the basis of the above solution, the wireless optical communication equalization method based on the base large model of the present invention can also be improved as follows.

[0020] Furthermore, the process of obtaining the trained large base model includes:

[0021] Establishing a sample set including a plurality of samples, wherein a set of characteristics of a channel of the wireless optical communication system and equalization parameters corresponding to the set of characteristics constitute one sample;

[0022] Based on the sample set, the base large model is pre-trained to obtain a pre-trained base large model;

[0023] Fine-tune the pre-trained base model to obtain a trained base model.

[0024] Furthermore, it also includes:

[0025] Multiple sets of channel features in the sample set are obtained from historical transmitted signals, historical received signals, historical channel model data, and historical equalized signals.

[0026] Furthermore, the base large model is BERT, GPT, RoBERTa, T5 or Sora.

[0027] 2) In a second aspect, the present invention further provides a wireless optical communication equalization system based on a large base model, the specific technical solution of which is as follows:

[0028] It includes a channel feature determination module, a target equalization parameter acquisition module, a channel index adjustment module and a communication module;

[0029] The channel characteristic determination module is used to: when the first user terminal sends a preset known signal to the second user terminal through the wireless optical communication system, determine the current characteristics of the channel of the wireless optical communication system according to the preset known signal and the signal received by the second user terminal;

[0030] The target equalization parameter acquisition module is used to: input the current characteristics of the channel of the wireless optical communication system into the trained base large model to obtain the target equalization parameters;

[0031] The channel index adjustment module is used to adjust the current communication index of the channel of the wireless optical communication system through the programmable driving circuit of the wireless optical communication system according to the target equalization parameter;

[0032] The communication module is used for: when the first user terminal continues to send the signal to be sent to the second user terminal, sending the signal to be sent through the channel with the adjusted state.

[0033] On the basis of the above solution, the wireless optical communication equalization system based on the base large model of the present invention can also be improved as follows.

[0034] Furthermore, a model determination module is included, which is used to:

[0035] Establishing a sample set including a plurality of samples, wherein a set of characteristics of a channel of the wireless optical communication system and equalization parameters corresponding to the set of characteristics constitute one sample;

[0036] Based on the sample set, the base large model is pre-trained to obtain a pre-trained base large model;

[0037] Fine-tune the pre-trained base model to obtain a trained base model.

[0038] Furthermore, it also includes a data acquisition module, which is used to obtain multiple sets of channel features in the sample set from historical transmitted signals, historical received signals, historical channel model data and historical equalized signals.

[0039] Furthermore, the base large model is BERT, GPT, RoBERTa, T5 or Sora.

[0040] 3) In a third aspect, the present invention further provides an adaptive equalization wireless optical communication system, including a wireless optical communication system, wherein the adaptive equalizer of the wireless optical communication system is used to implement any of the above-mentioned wireless optical communication equalization methods based on the base large model.

[0041] 4) In a fourth aspect, the present invention also provides an electronic device, which includes a processor, the processor is coupled to a memory, and at least one computer program is stored in the memory. The at least one computer program is loaded and executed by the processor to enable the electronic device to implement any of the above-mentioned wireless optical communication equalization methods based on the base large model.

[0042] 5) In a fifth aspect, the present invention also provides a computer-readable storage medium, in which at least one computer program is stored, and at least one computer program is loaded and executed by a processor so that the computer implements any of the above-mentioned wireless optical communication equalization methods based on the base large model.

[0043] It should be noted that the beneficial effects achieved by the technical solutions of the second to fifth aspects of the present invention and the corresponding possible implementation methods can be found in the above-mentioned technical effects of the first aspect and its corresponding possible implementation methods, and will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Other features, objects and advantages of the present invention will become more apparent upon reading the detailed description of non-limiting embodiments made with reference to the following drawings:

[0045] Figure 1 Schematic diagram of the structure of a visible light communication system;

[0046] Figure 2 A schematic flow chart of a wireless optical communication equalization method based on a large base model according to an embodiment of the present invention;

[0047] Figure 3 This is one of the application schematic diagrams of a wireless optical communication equalization method based on a large base model of the present invention;

[0048] Figure 4 This is a second application diagram of a wireless optical communication equalization method based on a large base model of the present invention;

[0049] Figure 5Schematic diagram of the process of obtaining the trained base large model;

[0050] Figure 6 This is a schematic structural diagram of a wireless optical communication equalization system based on a large base model according to an embodiment of the present invention;

[0051] Figure 7 The figure is a schematic structural diagram of an electronic device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0052] To make the objectives, technical solutions and advantages of the present invention more clear, the embodiments of the present invention will be described in further detail below with reference to the accompanying drawings.

[0053] like Figure 2 As shown, a wireless optical communication equalization method based on a large base model according to an embodiment of the present invention includes the following steps:

[0054] S1. When a first user terminal sends a preset known signal to a second user terminal through the wireless optical communication system, determining a current characteristic of a channel of the wireless optical communication system based on the preset known signal and a signal received by the second user terminal;

[0055] S2. Input the current characteristics of the channel of the wireless optical communication system into the trained base large model to obtain the target equalization parameters;

[0056] A large-scale foundational model generally refers to the infrastructure or framework used to build larger and more complex models in the fields of machine learning and artificial intelligence. These models are based on deep learning techniques and are trained using large amounts of data to perform various tasks. Large-scale foundational models are typically used in fields such as natural language processing, computer vision, and speech recognition, and were originally designed to solve language- and vision-related problems. A typical example of a large-scale foundational model is the GPT (Generative Pre-trained Transformer) model series, developed by OpenAI. These models utilize the Transformer architecture and achieve general language understanding capabilities through large-scale pre-training. These models can be applied to various natural language processing tasks, such as text generation, language understanding, and translation. Specifically, large-scale foundational models can be BERT, GPT, RoBERTa, T5, or Sora, and can be set according to actual circumstances.

[0057] S3. adjusting a current communication indicator of a channel of the wireless optical communication system through a programmable driving circuit of the wireless optical communication system according to the target equalization parameter;

[0058] S4. When the first user end continues to send the signal to be sent to the second user end, the signal to be sent is sent through the channel with the adjusted state.

[0059] Optionally, sending a preset known signal and sending a signal to be sent can be performed alternately, or after sending a preset known signal once (i.e. obtaining the target equalization parameter and adjusting the communication index once), the signal to be sent can be sent multiple times, which can be set according to actual conditions.

[0060] The wireless optical communication system specifically includes a transmitting system, a receiving system, and an adaptive equalizer. The transmitter uses an LED light to send optical signals, and the receiver uses a photodetector (PD) to receive the optical signals after passing through the channel. The adaptive equalizer outputs the equalization parameters, namely the target equalization parameters, through the trained base model. The specific explanation is as follows:

[0061] 1) Launch system:

[0062] The transmission system includes a light source, a programmable driver circuit, a modulation circuit, and a signal source. The modulation circuit modulates the input data (i.e., a preset known signal and the signal to be transmitted) onto the optical signal. The programmable driver circuit dynamically controls communication parameters, such as the optical signal's amplitude and communication parameters.

[0063] 2) Receiving system:

[0064] The receiving circuit includes a photodetector and a signal processing circuit (such as a filter, attenuator, and transimpedance amplifier). The photodetector converts the received optical signal into an electrical signal. The signal processed by the signal processing circuit serves as the received signal (referring to the signal received by the second user end) and is fed back to the adaptive equalizer. The adaptive equalizer determines the current characteristics of the wireless optical communication system channel based on the preset known signal and the signal received by the second user end. The current characteristics of the wireless optical communication system channel are input into the trained base model to obtain the target equalization parameters, that is, the equalization parameters are adjusted. Then, based on the target equalization parameters, the current communication indicators of the wireless optical communication system channel are adjusted.

[0065] 3) Adaptive equalizer:

[0066] The trained base model is input into an adaptive equalizer. When a first user terminal sends a preset known signal to a second user terminal via a wireless optical communication system, the current characteristics of the wireless optical communication system's channel are determined based on the preset known signal and the signal received by the second user terminal. The current characteristics of the wireless optical communication system's channel are input into the trained base model to obtain target equalization parameters. Based on the target equalization parameters, the current communication indicators of the wireless optical communication system's channel are adjusted via a programmable drive circuit. Specifically, the current communication parameters and modulation signal amplitude of the wireless optical communication system's channel are adjusted to achieve intelligent optimization of the LED modulation bandwidth, thereby optimizing the communication rate of the wireless optical communication system. The trained base model effectively improves the performance of unstable systems through efficient computation.

[0067] like Figure 3 As shown, the present invention is described:

[0068] 1) User terminal A is used as the first user terminal, and user terminal B is used as the second user terminal. The wireless optical communication system is specifically a wireless optical communication system. The trained base large model is deployed at the transmitting end (user terminal A and user terminal B are both transmitting ends). Then:

[0069] When user terminal A sends a preset known signal to user terminal B through the first VLC transceiver-in-one communication device, user terminal B receives the preset known signal through the second VLC transceiver-in-one communication device, and determines the current characteristics of the channel based on the preset known signal and the signal received by user terminal B; the current characteristics of the channel of the wireless optical communication system are input into the trained base large model to obtain the target equalization parameters; according to the target equalization parameters, the current communication indicators of the channel of the wireless optical communication system are adjusted; when user terminal A continues to send the signal to be sent to user terminal B, the signal to be sent is sent through the channel after the state adjustment.

[0070] 2) With client B as the first client and client A as the second client, and the wireless optical communication system specifically being a wireless optical communication system, then:

[0071] When user terminal B sends a preset known signal to user terminal A through the second VLC transceiver-in-one communication device, user terminal A receives the preset known signal through the first VLC transceiver-in-one communication device, and determines the current characteristics of the channel based on the preset known signal and the signal received by user terminal A; the current characteristics of the channel of the wireless optical communication system are input into the trained base large model to obtain the target equalization parameters; according to the target equalization parameters, the current communication indicators of the channel of the wireless optical communication system are adjusted; when user terminal B continues to send the signal to be sent to user terminal A, the signal to be sent is sent through the channel after the state adjustment.

[0072] In this way, two-way communication can be achieved between user terminal A and user terminal B. It should be noted that the trained base large model is deployed at the transmitting end, and the transmitting end can be used as the executor of the wireless optical communication equalization method based on the base large model of the present invention.

[0073] like Figure 4 As shown, the trained large base model can also be deployed at the receiving end; the receiving end can be used as the execution subject of the wireless optical communication equalization method based on the large base model of the present invention.

[0074] Optionally, in the above technical solution, if Figure 5 As shown in the figure, the process of obtaining the trained base large model includes:

[0075] S020. Establish a sample set including a plurality of samples, wherein a set of characteristics of a channel of the wireless optical communication system and equalization parameters corresponding to the set of characteristics constitute one sample;

[0076] S021. Pre-train the base large model based on the sample set to obtain a pre-trained base large model;

[0077] S022. Fine-tune the pre-trained base large model to obtain a trained base large model.

[0078] Optionally, in the above technical solution, the following is further included:

[0079] Multiple sets of channel features in the sample set are obtained from historical transmitted signals, historical received signals, historical channel model data, and historical equalized signals.

[0080] The process of obtaining the trained base model is described as follows:

[0081] S101. Establishing a data set: establishing a data set for expressing the input-output relationship of the wireless optical communication system.

[0082] The data set includes input data and output data. The input data includes multiple sets of transmitted signals, received signals, channel model data, and equalized signals. The output data includes multiple sets of equalization parameters. Each set of transmitted signals, received signals, channel model data, and equalized signals corresponds one-to-one to each set of equalization parameters. Among them, the equalization parameters determine the modulation bandwidth and frequency response of the LED, the signal-to-noise ratio of the wireless optical communication system, and other communication performance.

[0083] The transmitted signal, received signal, channel model data, and equalized signal are described as follows:

[0084] 1) Transmitted signal: a signal sent from a transmitting end (the first user end and the second user end can both serve as transmitting ends), that is, an original signal that has not been affected by the channel.

[0085] 2) Received signal: The electrical signal converted from the optical signal received by the receiver (both the first user end and the second user end can serve as receivers) under the influence of the channel model, which contains signal distortion caused by channel conditions.

[0086] 3) Channel model data: refers to the data generated by the mathematical expression used to characterize the channel model. The channel model parameters correspond one-to-one to the received signal after passing through the channel.

[0087] 4) Equalized signal: The equalized signal is the output signal after adaptive equalization processing, which should be as close as possible to the original transmitted signal.

[0088] The process of obtaining channel model data is as follows:

[0089] Through experiments, collect a large amount of data related to the VLC free-space channel (channel model data), including channel characteristics and signal propagation paths under different environments, angles, and illumination intensities. Simultaneously, use simulation software (such as MATLAB) to generate a large amount of simulated channel data as channel model data. Data augmentation techniques (such as rotation, scaling, and noise addition) are used to expand the data set to supplement the deficiencies in experimental data. Alternatively, obtain VLC channel data from open-source communications knowledge to serve as channel model data.

[0090] The above channel data includes deterministic and random components. The deterministic component represents the known characteristics of the transmission medium (free space), such as the attenuation and dispersion of the optical signal. The random component represents the random fluctuations in the channel response, such as environmental noise and equipment errors. The channel model is specifically:

[0091] ① Fluctuation of light source intensity: Fluctuations in light intensity are caused by factors such as the working state of the light source, power supply voltage, and different divergence angles.

[0092] ② Receiver sensitivity fluctuations: The receiver sensitivity may fluctuate due to changes in environmental factors such as temperature and humidity.

[0093] ③ Changes in the optical path: Changes in communication distance and the light propagation path may be affected by factors such as air flow and object movement, causing random fluctuations in the channel response.

[0094] ④ Multipath effect: In indoor environments, light may reach the receiver after multiple reflections and refractions. This multipath effect will also cause random fluctuations in the channel response.

[0095] ⑤ Ambient light changes: Changes in ambient light can also affect channel response. When the ambient light intensity increases, the receiver may be affected by stronger background light noise.

[0096] ⑥ Shadow effect: The movement of an object may block the propagation path of light, causing changes in the light intensity received by the receiver.

[0097] S102: Establish a sample set. Specifically:

[0098] S1020: Preprocess the data set to convert it into a format suitable for the large base model. Specifically:

[0099] Read, parse and process the input data in the data set, including cleaning, conversion and formatting, to obtain the first processed data. The formatted data types include: data types include digital data, text data, model data and other formats.

[0100] Extract key parameters and features from the first processed data, including parameter values, experimental results, trend analysis, etc., to obtain the second processed data;

[0101] The data format of the second processed data is converted as needed, such as for the ChatGPT data format conversion module, and the extracted data can be converted into natural language text through a natural language generation algorithm to interact with ChatGPT. In this embodiment, the second processed data is converted into data in a format suitable for the large base model.

[0102] The data in the format used by the large model of the adapted base obtained after conversion can also be verified and adjusted: verify whether the data generated by the conversion is consistent with expectations, and if not, make necessary adjustments.

[0103] S1021. Normalize the data in the format used by the large model of the adaptation base and extract channel features;

[0104] Normalization is performed to ensure consistency in the scale of the data input into the large base model, and representative channel features are extracted. This involves using existing non-blind channel estimation methods or blind channel estimation methods to solve the amplitude, phase, and time-varying characteristics of the channel transfer function, such as path loss, reflection, and scattering. Multiple samples are then established, each of which includes a channel feature and the equalization parameter corresponding to the channel feature.

[0105] The sample set is divided into training set, validation set and test set. The training set is used to train the base model, the validation set is used to adjust the parameters and hyperparameters of the base model, and the test set is used to evaluate the final performance of the base model.

[0106] S103. Select the large base model:

[0107] Based on the task requirements and data characteristics, a suitable base model architecture is selected, and a suitable model from BERT, GPT, RoBERTa, T5, and Sora is selected as the base model for subsequent training. These models (BERT, GPT, RoBERTa, T5, and Sora) have large parameter sizes and extensive pre-training data, providing powerful and flexible tools for adaptive equalization in wireless optical communication systems.

[0108] It should be noted that other models can be selected as the large base model according to actual conditions.

[0109] S104. Pre-train the large base model selected in S103:

[0110] The pre-training process processes large amounts of unlabeled data, aiming to capture the underlying patterns, structure, and semantic knowledge present in the sample set. Pre-training employs the Transformer architecture and is typically an unsupervised learning process. Fine-tuning follows pre-training, further training the pre-trained model on a small amount of labeled data for the new task. This allows the model to learn specific features and patterns relevant to the target task, enabling it to better adapt to the new task.

[0111] The training process is based on the aforementioned training dataset. The model is guided by numerous prompts and output examples to predict optimal equalization parameters. These parameters are automatically adjusted during training, with the goal of ensuring that the signal received by the receiver is as close as possible to the original transmitted signal.

[0112] S105, fine-tuning: fine-tune the pre-trained base large model to obtain a trained base large model.

[0113] The fine-tuning process includes forward propagation, backpropagation, and parameter updates. During the forward propagation phase, the model receives input data—the original transmitted signal after format conversion, the signal received by the photodetector after channel influence, and the channel model parameters. The model then performs calculations across all layers of the large model until it reaches the predicted output, i.e., the predicted equalization parameters. This process begins at the input layer and proceeds layer by layer. The output of each layer becomes the input for the next layer. After the data has propagated through all layers, the model produces a predicted output. The model then calculates a loss function, which measures the difference between the predicted output and the ideal output.

[0114] In this embodiment, the SFT (Soft Prompt Tuning) method is used to fine-tune the pre-trained base model. The SFT method does not directly fine-tune the model parameters, but instead adds a learnable "soft prompt" at the model input end. The purpose of fine-tuning is achieved by optimizing this "soft prompt". The specific implementation process is as follows:

[0115] S1050, initialization: Use the pre-trained base model as the basis and do not directly update the equalization parameter θ.

[0116] S1051. Define soft hints. Introduce a learnable soft hint vector p, where the length of p is usually smaller than the length of the input sequence.

[0117] S1052. Calculate the loss function. Concatenate the input sequence and the soft prompt to obtain the vector y = [p; θ], which is fed into the base model to obtain the output. The loss function L(θ, p) is defined as the mean square error (MSE):

[0118]

[0119] Where N is the number of training data, y i is the i-th item in the vector y, representing the true output of the training data, is the predicted value of the training data.

[0120] S1053. Update soft prompts. Use the gradient descent algorithm to update the soft prompt parameters. Since the gradient of the loss function with respect to the model output is the partial derivative of the loss function with respect to each predicted value, the calculation formula is:

[0121]

[0122] Arranged into vector form:

[0123]

[0124] in, and y j (j=1,2,…,N) are the predicted values and true outputs of the training data respectively.

[0125] The update process is carried out in the opposite direction of the gradient, with the goal of minimizing the loss function. The parameter update formula is:

[0126]

[0127] Among them, η is the learning rate, which determines the step size of updating parameters in the gradient direction; It is the gradient of the loss function L(θ,p) with respect to the equilibrium parameter θ, that is, the partial derivative of the above loss function with respect to each predicted value

[0128] S1054: Iterative optimization. Repeat step S1052 until the loss function L(θ, p) converges and a trained large base model is obtained.

[0129] S106. Re-evaluate the trained large base model.

[0130] After training, the performance of the trained large base model is evaluated on the previously split test set, and evaluation indicators such as accuracy, recall, F1 score and AUC are calculated to ensure that it can accurately predict the optimal equalization parameters. If the evaluation indicators of the trained large base model fail to pass the evaluation, S104 and S105 can be repeated by expanding the sample set, etc. until the evaluation indicators of the trained large base model pass the evaluation.

[0131] S107. Deploy the trained base large model in the wireless optical communication system.

[0132] The trained base model can be deployed in the adaptive equalizer in the wireless optical communication system to dynamically adjust the output signal of the LED. The specific process includes:

[0133] The adaptive equalizer infers the current real-time channel conditions through the distorted signal received by the wireless optical communication transceiver under the current channel conditions, continuously monitors and outputs the equalization parameters through the results generated by the base large model operation, so that the signal emitted by the LED is adjusted according to the real-time channel status, improving the signal quality and thus reducing the impact of channel characteristics and noise on the system.

[0134] This paper demonstrates the design and implementation of pre-equalization at the transmitter. This approach can also be applied to post-equalization at the receiver, where the system's receiving end compensates for channel distortion. By training a large base model, it can identify channel conditions from received signals and dynamically adjust equalization parameters based on reasonable assumptions about channel conditions. This effectively addresses the dynamic characteristics of wireless optical communication channels, thereby improving system performance.

[0135] Based on open-source or closed-source large-scale base models (such as GPT, RoBERTa, or Sora), this invention provides an efficient and highly accurate equalization solution for wireless optical communications, which can bring the following beneficial effects:

[0136] 1) Effectively increase system bandwidth, optimize signal-to-noise ratio, and thus improve communication speed. Using traditional equalization, experimental tests show that a single optical path can achieve a communication speed of 700Mbit / s at a communication distance of 1m. In contrast, using an adaptive equalization solution based on a large base model, simulations show that the communication speed can exceed 2Gbit / s.

[0137] 2) The proposed equalization method is a digital equalization method, which can be combined with analog equalization to further improve the system communication rate;

[0138] 3) It can dynamically and adaptively adjust the equalization parameters according to the changing channel, introduce a large model to improve the pre-training efficiency and the migration and generalization capabilities of multi-task learning, making the design of the equalization algorithm more flexible.

[0139] 4) An adaptive equalization scheme is designed using a large base model. Compared with traditional schemes, it does not need to consider LED performance and channel status, and can achieve intelligent optimization of LED modulation bandwidth, thereby achieving the optimal system communication rate.

[0140] In the above embodiments, although the steps are numbered S1, S2, etc., these are only specific embodiments given by the present invention. Those skilled in the art may adjust the execution order of S1, S2, etc. according to actual conditions, which is also within the scope of protection of the present invention. It can be understood that in some embodiments, some or all of the above embodiments may be included.

[0141] like Figure 6 As shown, a wireless optical communication equalization system 200 based on a base large model according to an embodiment of the present invention includes a channel characteristic determination module 201, a target equalization parameter acquisition module 202, a channel index adjustment module 203 and a communication module 204;

[0142] The channel characteristic determination module 201 is configured to: when a first user terminal sends a preset known signal to a second user terminal via the wireless optical communication system, determine the current characteristics of the channel of the wireless optical communication system based on the preset known signal and the signal received by the second user terminal;

[0143] The target equalization parameter acquisition module 202 is used to: input the current characteristics of the channel of the wireless optical communication system into the trained base large model to obtain the target equalization parameters;

[0144] The channel indicator adjustment module 203 is used to adjust the current communication indicator of the channel of the wireless optical communication system according to the target equalization parameter;

[0145] The communication module 204 is configured to: when the first user terminal continues to send the signal to be sent to the second user terminal, send the signal to be sent through the channel after the state is adjusted.

[0146] Optionally, the above technical solution further includes a model determination module, which is used to:

[0147] Establishing a sample set including a plurality of samples, wherein a set of characteristics of a channel of the wireless optical communication system and equalization parameters corresponding to the set of characteristics constitute one sample;

[0148] Based on the sample set, the base large model is pre-trained to obtain a pre-trained base large model;

[0149] Fine-tune the pre-trained base model to obtain a trained base model.

[0150] Optionally, the above technical solution further includes a data acquisition module, which is used to obtain multiple sets of characteristics of the channels in the sample set from historical transmitted signals, historical received signals, historical channel model data and historical equalized signals.

[0151] Optionally, in the above technical solution, the base large model is BERT, GPT, RoBERTa, T5 or Sora.

[0152] It should be noted that the beneficial effects of the wireless optical communication equalization system 200 based on the base large model provided in the above embodiment are the same as the beneficial effects of the wireless optical communication equalization method based on the base large model, and will not be repeated here. In addition, when implementing its functions, the system provided in the above embodiment is only illustrated by the division of the above functional modules. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the system can be divided into different functional modules according to actual conditions to complete all or part of the functions described above. In addition, the system and method embodiments provided in the above embodiment belong to the same concept. The specific implementation process is detailed in the method embodiment and will not be repeated here.

[0153] like Figure 7 As shown, an electronic device 300 according to an embodiment of the present invention includes a processor 320, the processor 320 is coupled to a memory 310, and the memory 310 stores at least one computer program 330. The at least one computer program 330 is loaded and executed by the processor 320, so that the electronic device 300 implements any of the above-mentioned wireless optical communication equalization methods based on the base large model, specifically:

[0154] The electronic device 300 may vary significantly due to different configurations or performances, and may include one or more processors 320 (Central Processing Units, CPUs) and one or more memories 310, wherein the one or more memories 310 store at least one computer program 330, which is loaded and executed by the one or more processors 320 to enable the electronic device 300 to implement any of the wireless optical communication equalization methods based on the base large model provided in the above embodiments. Of course, the electronic device 300 may also have components such as a wired or wireless network interface, a keyboard, and an input and output interface for input and output. The electronic device 300 may also include other components for implementing device functions, which will not be described in detail here. The electronic device may specifically be a computer, etc.

[0155] An adaptive equalization wireless optical communication system according to an embodiment of the present invention includes a wireless optical communication system, wherein an adaptive equalizer of the wireless optical communication system is used to implement any of the above-mentioned wireless optical communication equalization methods based on a large base model.

[0156] A computer-readable storage medium according to an embodiment of the present invention stores at least one computer program, and the at least one computer program is loaded and executed by a processor so that the computer implements any of the above-mentioned wireless optical communication equalization methods based on the base large model.

[0157] Alternatively, the computer-readable storage medium may be a read-only memory (ROM), a random access memory (RAM), a compact disc (CD-ROM), a magnetic tape, a floppy disk, an optical data storage device, or the like.

[0158] In an exemplary embodiment, a computer program product or computer program is also provided, comprising computer instructions stored in a computer-readable storage medium. A processor of an electronic device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the electronic device to perform any of the aforementioned methods for wireless optical communication equalization based on a large base model.

[0159] It should be noted that the terms "first," "second," and the like in the specification and claims of this application are used to distinguish similar objects and to define a specific order or precedence. Where appropriate, the order used for similar objects may be interchanged, such that the embodiments of the present application described herein can be implemented in an order other than the order shown or described.

[0160] Those skilled in the art will appreciate that the present invention may be implemented as a system, method, or computer program product. Therefore, the present invention may be implemented in the following forms: entirely in hardware, entirely in software (including firmware, resident software, microcode, etc.), or in a combination of hardware and software, generally referred to herein as a "circuit," "module," or "system." Furthermore, in some embodiments, the present invention may be implemented in the form of a computer program product embodied in one or more computer-readable media containing computer-readable program code.

[0161] Any combination of one or more computer-readable media can be used. The computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or component, or any combination thereof. More specific examples of computer-readable storage media (a non-exhaustive list) include: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present invention, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, device or device.

[0162] Although the embodiments of the present invention have been shown and described above, it will be understood that the above embodiments are illustrative and are not to be construed as limitations on the present invention. A person skilled in the art may change, modify, replace and modify the above embodiments within the scope of the present invention.

Claims

1. A wireless optical communication equalization method based on a large base model, characterized in that: include: When the first user terminal sends a preset known signal to the second user terminal through the wireless optical communication system, determining the current characteristics of the channel of the wireless optical communication system according to the preset known signal and the signal received by the second user terminal; Inputting the current characteristics of the channel of the wireless optical communication system into the trained base large model to obtain target equalization parameters; According to the target equalization parameter, adjusting a current communication indicator of a channel of the wireless optical communication system through a programmable driving circuit of the wireless optical communication system; When the first user terminal continues to send the signal to be sent to the second user terminal, sending the signal to be sent through the channel with the adjusted state; The wireless optical communication system specifically includes a transmitting system, a receiving system and an adaptive equalizer. The transmitter uses an LED lamp to send an optical signal, and the receiver uses a photodetector PD to receive the optical signal after passing through the channel. The adaptive equalizer outputs the equalization parameters, namely the target equalization parameters, through a trained base large model. Among them, the transmitting system includes a light source, a programmable drive circuit, a modulation circuit and a signal source. The modulation circuit is used to modulate a preset known signal and a signal to be sent onto the optical signal, and the programmable drive circuit is used to dynamically control the amplitude and communication parameters of the optical signal; the receiving system includes a photodetector and a signal processing circuit. The photodetector converts the received optical signal into an electrical signal. The signal processed by the signal processing circuit is used as the signal received by the second user end and is fed back to the adaptive equalizer at the same time. The adaptive equalizer determines the current characteristics of the channel of the wireless optical communication system based on the preset known signal and the signal received by the second user end.

2. The wireless optical communication equalization method based on a large base model according to claim 1, characterized in that: The process of obtaining the trained large base model includes: Establishing a sample set including a plurality of samples, wherein a set of characteristics of a channel of the wireless optical communication system and equalization parameters corresponding to the set of characteristics constitute one sample; Pre-training the base large model based on the sample set to obtain a pre-trained base large model; The pre-trained base large model is fine-tuned to obtain the trained base large model.

3. The wireless optical communication equalization method based on a large base model according to claim 2, characterized in that: Before creating a sample set that includes multiple samples, also include: Multiple sets of features of the channels in the sample set are obtained from historical transmitted signals, historical received signals, historical channel model data, and historical equalized signals.

4. A wireless optical communication equalization method based on a base large model according to any one of claims 1 to 3, characterized in that: The base large model is BERT, GPT, RoBERTa, T5, or Sora.

5. A wireless optical communication equalization system based on a large base model, characterized in that: It includes a channel feature determination module, a target equalization parameter acquisition module, a channel index adjustment module and a communication module; The channel characteristic determination module is configured to: when a first user terminal sends a preset known signal to a second user terminal via the wireless optical communication system, determine the current characteristics of the channel of the wireless optical communication system based on the preset known signal and a signal received by the second user terminal; The target equalization parameter acquisition module is used to: input the current characteristics of the channel of the wireless optical communication system into the trained base large model to obtain the target equalization parameters; The channel indicator adjustment module is configured to adjust a current communication indicator of a channel of the wireless optical communication system through a programmable driving circuit of the wireless optical communication system according to the target equalization parameter; The communication module is configured to: when the first user terminal continues to send a signal to be sent to the second user terminal, send the signal to be sent through the channel after the state is adjusted; The wireless optical communication system specifically includes a transmitting system, a receiving system and an adaptive equalizer. The transmitter uses an LED lamp to send an optical signal, and the receiver uses a photodetector PD to receive the optical signal after passing through the channel. The adaptive equalizer outputs the equalization parameters, namely the target equalization parameters, through a trained base large model. Among them, the transmitting system includes a light source, a programmable drive circuit, a modulation circuit and a signal source. The modulation circuit is used to modulate a preset known signal and a signal to be sent onto the optical signal, and the programmable drive circuit is used to dynamically control the amplitude and communication parameters of the optical signal; the receiving system includes a photodetector and a signal processing circuit. The photodetector converts the received optical signal into an electrical signal. The signal processed by the signal processing circuit is used as the signal received by the second user end and is fed back to the adaptive equalizer at the same time. The adaptive equalizer determines the current characteristics of the channel of the wireless optical communication system based on the preset known signal and the signal received by the second user end.

6. The wireless optical communication equalization system based on a large base model according to claim 5, characterized in that: The invention also includes a model determination module, wherein the model determination module is used to: Establishing a sample set including a plurality of samples, wherein a set of characteristics of a channel of the wireless optical communication system and equalization parameters corresponding to the set of characteristics constitute one sample; Pre-training the base large model based on the sample set to obtain a pre-trained base large model; The pre-trained base large model is fine-tuned to obtain the trained base large model.

7. The wireless optical communication equalization system based on a large base model according to claim 6, characterized in that: It also includes a data acquisition module, which is used to obtain multiple groups of channel features in the sample set from historical transmitted signals, historical received signals, historical channel model data and historical equalized signals before establishing a sample set including multiple samples.

8. A wireless optical communication equalization system based on a base large model according to any one of claims 5 to 7, characterized in that: The base large model is BERT, GPT, RoBERTa, T5, or Sora.

9. An adaptive equalization wireless optical communication system, characterized in that: It comprises a wireless optical communication system, wherein the adaptive equalizer of the wireless optical communication system is used to implement a wireless optical communication equalization method based on a base large model as claimed in any one of claims 1 to 4.

10. An electronic device, characterized in that: The electronic device includes a processor, which is coupled to a memory. The memory stores at least one computer program, and the at least one computer program is loaded and executed by the processor so that the electronic device implements a wireless optical communication equalization method based on a base large model as described in any one of claims 1 to 4.

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