Multi-mode intelligent modulation data transmission method based on channel state perception

Abstract

The invention relates to the technical field of data transmission, and discloses a multi-mode intelligent modulation data transmission method based on channel state perception, and the method comprises the steps: obtaining channel state data, and determining a channel state parameter and a fluctuation parameter of a channel according to the channel state data; determining a channel state comprehensive parameter according to the channel state parameter and the fluctuation parameter, and determining a modulation mode of transmission data according to the channel state comprehensive parameter; transmitting the transmission data according to the modulation mode, monitoring a signal transmission error rate and a signal transmission delay of the transmission data, and determining a coding parameter of the transmission data according to the signal transmission error rate and the signal transmission delay; and carrying out Huffman coding on the transmission data according to the coding parameters, and carrying out compression transmission on the coded transmission data. According to the invention, the optimal modulation mode can be selected for the channel state to modulate the transmission data, and the transmission data is compressed and transmitted through the signal quality, so that the data transmission efficiency is effectively improved.

Description

Technical Field

[0001] This application relates to the field of data transmission technology, and more specifically, to a multi-mode intelligent modulation data transmission method based on channel state awareness. Background Technology

[0002] With the development of science and technology, the explosive growth in data volume and the demand for high-speed and low-latency communication in recent years have brought enormous challenges to communication systems, which are struggling to meet these needs. To achieve higher transmission rates, Automatic Modulation Classification (AMC) technology is employed in wireless communication systems to achieve modulated data transmission.

[0003] In existing technologies, data transmission using automatic modulation and identification technology suffers from low transmission efficiency and poor transmission quality. Summary of the Invention

[0004] This invention provides a channel state-aware multi-mode intelligent modulation data transmission method to solve the problems of low transmission efficiency and poor transmission quality in existing technologies, including: The process involves acquiring channel state data, determining channel state parameters and fluctuation parameters based on the data, determining comprehensive channel state parameters based on these parameters, determining the modulation scheme for the transmitted data based on these parameters, transmitting the data according to the modulation scheme, monitoring the bit error rate and delay of the transmitted data, determining the encoding parameters for the transmitted data based on the bit error rate and delay, performing Huffman coding on the transmitted data according to the encoding parameters, and compressing the encoded data for transmission.

[0005] Further, determining the channel state parameters based on channel state data includes: acquiring channel state data; constructing a channel state data sequence corresponding to each channel state data based on the channel state data; taking any channel state data sequence as a target channel state data sequence; calculating the correlation coefficient between the target channel state data sequence and the remaining channel state data sequences; establishing a correlation coefficient set based on the correlation coefficients between all channel state data sequences and the remaining channel state data sequences; calculating the sum of the correlation coefficients between the target channel state data sequence and the remaining channel state data sequences; calculating the ratio of the sum of the correlation coefficients between the target channel state data sequence and the remaining channel state data sequences to the sum of all correlation coefficients in the correlation coefficient set; and obtaining the channel state parameters of the target channel state data sequence.

[0006] Further, determining the channel fluctuation parameters based on channel state data includes: plotting the state change curves of each channel state data sequence within a preset time period, obtaining a preset time segment, dividing the state change curves according to the preset time segment to obtain several state change curve segments; calculating the average value of each state change curve segment, plotting the state average value change curve based on the average value of each state change curve segment; statistically analyzing the absolute slope values ​​of two adjacent average values ​​in the state average value change curve, and determining the fluctuation parameters of the channel state data sequence based on all the absolute slope values ​​in the state average value change curve.

[0007] Further, determining the channel state composite parameter based on the channel state parameters and fluctuation parameters includes: determining the channel state composite parameter according to the channel state composite parameter calculation formula, wherein the channel state composite parameter calculation formula is specifically as follows:

[0008] in, These are the channel state synthesis parameters. The total number of channel state data. For the first Channel state parameters of channel state data, For the first Fluctuation parameters of channel state data.

[0009] Furthermore, determining the modulation scheme of the transmitted data based on the channel state comprehensive parameters includes: acquiring training data and establishing an initial model structure for a neural network model based on the training data; acquiring the channel state comprehensive parameters and the corresponding optimal signal modulation scheme of the training data, training the initial model structure based on the channel state comprehensive parameters and the corresponding optimal signal modulation scheme to obtain a trained modulation scheme recognition model; inputting the current channel state comprehensive parameters into the trained modulation scheme recognition model, outputting the optimal signal modulation scheme of the current channel, and determining the modulation scheme of the transmitted data based on the optimal signal modulation scheme of the current channel.

[0010] Further, determining the encoding parameters of the transmitted data based on the signal transmission error rate and signal transmission delay includes: clustering each transmitted data according to the signal transmission error rate and signal transmission delay of the modulated transmitted data; determining the signal quality parameters of the transmitted data within each cluster based on the clustering results; counting the total number of transmitted data in the cluster to which the transmitted data belongs; determining the signal distribution parameters based on the total number of transmitted data in the cluster to which the transmitted data belongs; obtaining a preset first weight and a preset second weight; and performing a weighted summation of the signal quality parameters and the signal distribution parameters based on the preset first weight and the preset second weight to obtain the encoding parameters of the transmitted data.

[0011] Further, the step of clustering each transmitted data according to the signal transmission error rate and signal transmission delay of the modulated transmitted data includes: establishing a sample dataset based on the signal transmission error rate and signal transmission delay of each transmitted data, and randomly selecting k initial cluster centers from the sample dataset; calculating the Euclidean distance from the sample data in the sample dataset to the initial cluster centers, and dividing each transmitted data into corresponding cluster partitions based on the Euclidean distance from the sample data in the sample dataset to the initial cluster centers; calculating the mean of the sample data in each cluster partition, and recalculating the cluster centers based on the mean of the sample data in each cluster partition; repeating the above steps iteratively until the cluster centers no longer change or the number of iterations reaches the preset maximum number of iterations, thereby obtaining the clustering result of the transmitted data.

[0012] Furthermore, determining the signal quality parameters of the transmitted data within each cluster partition based on the clustering results includes: determining the cluster center of the cluster partition to which the transmitted data belongs based on the clustering results, and calculating the signal transmission bit error rate and signal transmission delay corresponding to the cluster center; multiplying the signal transmission bit error rate and signal transmission delay corresponding to the cluster center to obtain the signal quality parameters of the corresponding transmitted data.

[0013] Further, determining the signal distribution parameters based on the total number of transmitted data in the cluster partition to which the transmitted data belongs includes: obtaining the total number of transmitted data, calculating the ratio of the total number of transmitted data in the cluster partition to which the transmitted data belongs to the total number of transmitted data, and obtaining the signal distribution parameters.

[0014] Furthermore, the step of performing Huffman coding on the transmitted data according to the coding parameters includes: sorting all the transmitted data in descending order of the coding parameters, constructing a Huffman tree based on the sorting result of the transmitted data, and encoding the transmitted data based on the Huffman tree.

[0015] The beneficial effects of this invention are as follows: By applying the above technical solutions, this invention can select the optimal modulation method to modulate the transmitted data based on the channel state, monitor the signal transmission of the modulated transmitted data, and compress the transmitted data based on the signal quality of the transmitted data, thereby effectively improving the data transmission efficiency. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1This invention illustrates a multi-mode intelligent modulation data transmission method based on channel state awareness, as proposed in an embodiment of the present invention. Detailed Implementation

[0018] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments 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.

[0019] This application provides a channel state-aware multi-mode intelligent modulation data transmission method, such as... Figure 1 As shown, it includes: S101, acquire channel state data, and determine the channel state parameters and fluctuation parameters based on the channel state data; In some embodiments of this application, determining the channel state parameters based on channel state data includes: acquiring channel state data; constructing a channel state data sequence corresponding to each channel state data based on the channel state data; taking any channel state data sequence as a target channel state data sequence; calculating the correlation coefficient between the target channel state data sequence and the remaining channel state data sequences; establishing a correlation coefficient set based on the correlation coefficients between all channel state data sequences and the remaining channel state data sequences; calculating the sum of the correlation coefficients between the target channel state data sequence and the remaining channel state data sequences; calculating the ratio of the sum of the correlation coefficients between the target channel state data sequence and the remaining channel state data sequences to the sum of all correlation coefficients in the correlation coefficient set; and obtaining the channel state parameters of the target channel state data sequence.

[0020] In this embodiment, the channel state data specifically includes the channel's spectral efficiency, bit error rate, and signal stability. A corresponding channel state data sequence is generated by collecting these metrics within a preset time period. The channel state parameters of the channel state data sequence are calculated by dividing the sum of the correlation coefficients of each channel state data sequence with the sum of all other channel state data sequences by the sum of all correlation coefficients in the correlation coefficient set. When a channel state anomaly occurs, it will cause significant anomalies in each channel state data, leading to anomalies in the channel state data sequence and consequently, anomalies in the channel state parameters.

[0021] In some embodiments of this application, determining the channel fluctuation parameters based on channel state data includes: plotting the state change curves of each channel state data sequence within a preset time period; obtaining a preset time segment; dividing the state change curves according to the preset time segment to obtain several state change curve segments; calculating the average value of each state change curve segment; plotting the state average value change curve based on the average value of each state change curve segment; calculating the absolute slope values ​​of two adjacent average values ​​in the state average value change curve; and determining the fluctuation parameters of the channel state data sequence based on all the absolute slope values ​​in the state average value change curve.

[0022] In this embodiment, the fluctuation parameter of the channel state data sequence is calculated by analyzing the fluctuation of the average value of each segment of the channel state data state change curve.

[0023] In some embodiments of this application, determining the channel state composite parameter based on the channel state parameters and fluctuation parameters includes: determining the channel state composite parameter according to the channel state composite parameter calculation formula, wherein the channel state composite parameter calculation formula is specifically as follows:

[0024] in, These are the channel state synthesis parameters. The total number of channel state data. For the first Channel state parameters of channel state data, For the first Fluctuation parameters of channel state data.

[0025] S102, determine the channel state comprehensive parameters based on the channel state parameters and fluctuation parameters, and determine the modulation method of the transmitted data based on the channel state comprehensive parameters; In some embodiments of this application, determining the modulation scheme of transmitted data based on channel state comprehensive parameters includes: acquiring training data and establishing an initial model structure of a neural network model based on the training data; acquiring the channel state comprehensive parameters and corresponding optimal signal modulation scheme of the training data, training the initial model structure based on the channel state comprehensive parameters and corresponding optimal signal modulation scheme to obtain a trained modulation scheme recognition model; inputting the current channel state comprehensive parameters into the trained modulation scheme recognition model, outputting the optimal signal modulation scheme of the current channel, and determining the modulation scheme of transmitted data based on the optimal signal modulation scheme of the current channel.

[0026] In this embodiment, the modulation scheme identification model can be, but is not limited to, Support Vector Machine (SVM), Distributed Neural Network (DNN), or other types of classifiers. The model's learning process involves a large amount of performance data of various modulation schemes under different conditions. The purpose is to learn the mapping relationship between the comprehensive parameters of different channel states and the optimal modulation scheme. Taking the current comprehensive parameters of the channel state as the input vector, the trained modulation scheme identification model outputs the corresponding optimal signal modulation scheme to obtain the modulation scheme of the transmitted data.

[0027] S103, transmit data according to the modulation method, monitor the signal transmission error rate and signal transmission delay of the transmitted data, and determine the encoding parameters of the transmitted data based on the signal transmission error rate and signal transmission delay; In some embodiments of this application, determining the encoding parameters of the transmitted data based on the signal transmission error rate and signal transmission delay includes: clustering each transmitted data according to the signal transmission error rate and signal transmission delay of the modulated transmitted data; determining the signal quality parameters of the transmitted data within each cluster based on the clustering results; counting the total number of transmitted data in the cluster to which the transmitted data belongs; determining the signal distribution parameters based on the total number of transmitted data in the cluster to which the transmitted data belongs; obtaining a preset first weight and a preset second weight; and performing a weighted summation of the signal quality parameters and the signal distribution parameters based on the preset first weight and the preset second weight to obtain the encoding parameters of the transmitted data.

[0028] In this embodiment, each transmitted data is clustered by the signal transmission bit error rate and the signal transmission delay. The signal quality parameters and signal distribution parameters are calculated by the cluster partitions corresponding to the transmitted data. The signal quality parameters and signal distribution parameters are dimensionless by the preset first weight and preset second weight, thereby obtaining the encoding parameters of the transmitted data.

[0029] In some embodiments of this application, the step of clustering each transmitted data according to the signal transmission bit error rate and signal transmission delay of the modulated transmitted data includes: establishing a sample dataset based on the signal transmission bit error rate and signal transmission delay of each transmitted data, and randomly selecting k initial cluster centers from the sample dataset; calculating the Euclidean distance from the sample data in the sample dataset to the initial cluster centers, and dividing each transmitted data into corresponding cluster partitions based on the Euclidean distance from the sample data in the sample dataset to the initial cluster centers; calculating the mean of the sample data in each cluster partition, and recalculating the cluster centers based on the mean of the sample data in each cluster partition; repeating the above steps iteratively until the cluster centers no longer change or the number of iterations reaches a preset maximum number of iterations, thereby obtaining the clustering result of the transmitted data.

[0030] In this embodiment, the transmitted data in the sample dataset is clustered based on the k-means clustering algorithm. The k value is set by the total number of transmitted data; the more data there is, the higher the corresponding k value.

[0031] In some embodiments of this application, determining the signal quality parameters of transmitted data within each cluster partition based on the clustering results includes: determining the cluster center of the cluster partition to which the transmitted data belongs based on the clustering results, and calculating the signal transmission bit error rate and signal transmission delay corresponding to the cluster center; multiplying the signal transmission bit error rate and signal transmission delay corresponding to the cluster center to obtain the signal quality parameters of the corresponding transmitted data.

[0032] In some embodiments of this application, determining the signal distribution parameters based on the number of all transmitted data in the cluster partition to which the transmitted data belongs includes: obtaining the number of all transmitted data, calculating the ratio of the number of all transmitted data in the cluster partition to which the transmitted data belongs to the total number of transmitted data, and obtaining the signal distribution parameters.

[0033] S104: Perform Huffman coding on the transmitted data according to the coding parameters, and then compress the encoded transmitted data for transmission.

[0034] In some embodiments of this application, the step of performing Huffman coding on the transmitted data according to the coding parameters includes: sorting all the transmitted data in descending order of the coding parameters, constructing a Huffman tree based on the sorting result of the transmitted data, and encoding the transmitted data based on the Huffman tree.

[0035] In this embodiment, the two transmitted data with the smallest encoding parameters are used to form a binary tree. The sum of these two smallest encoding parameters is then used as a new encoding parameter. This process of reordering the data based on the new encoding parameter and the encoding parameters of the remaining transmitted data is repeated until all encoding parameters are placed in the binary tree, thus completing the construction of the Huffman tree. Paths on the right side of the Huffman tree are encoded as 1, and paths on the left side are encoded as 0. By using the encoding parameters of each transmitted data, the Huffman code length of more important transmitted data is kept shorter, thereby avoiding data loss due to excessively long encoding lengths of important transmitted data.

[0036] By applying the above technical solutions, this invention acquires channel state data, determines channel state parameters and fluctuation parameters based on the channel state data, determines comprehensive channel state parameters based on the channel state parameters and fluctuation parameters, determines the modulation scheme for transmitted data based on the comprehensive channel state parameters, transmits data according to the modulation scheme, monitors the signal transmission error rate and signal transmission delay of the transmitted data, determines the encoding parameters for the transmitted data based on the signal transmission error rate and signal transmission delay, performs Huffman coding on the transmitted data according to the encoding parameters, and compresses the encoded transmitted data for transmission. This invention can select the optimal modulation scheme for transmitting data based on the channel state, and simultaneously compresses the transmitted data for transmission based on signal quality, effectively improving data transmission efficiency.

[0037] Through the above description of the embodiments, those skilled in the art can clearly understand that the present invention can be implemented in hardware or by means of software plus necessary general-purpose hardware platforms. Based on this understanding, the technical solution of the present invention can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, external hard drive, etc.) and includes several instructions to cause a computer device (such as a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments of the present invention.

[0038] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A multi-mode intelligent modulation data transmission method based on channel state awareness, characterized in that, The method includes: Acquire channel state data, and determine the channel state parameters and fluctuation parameters based on the channel state data; The channel state parameters are determined based on the channel state parameters and fluctuation parameters, and the modulation scheme of the transmitted data is determined based on the channel state parameters. Data is transmitted according to the modulation method, the signal transmission error rate and signal transmission delay of the transmitted data are monitored, and the encoding parameters of the transmitted data are determined based on the signal transmission error rate and signal transmission delay. The transmitted data is Huffman encoded according to the encoding parameters, and the encoded transmitted data is then compressed for transmission.

2. The multi-mode intelligent modulation data transmission method based on channel state awareness according to claim 1, characterized in that, The process of determining the channel state parameters based on channel state data includes: Acquire channel state data and construct a channel state data sequence corresponding to each channel state data based on the channel state data; Take any channel state data sequence as the target channel state data sequence, calculate the correlation coefficient between the target channel state data sequence and the other channel state data sequences, and establish a correlation coefficient set based on the correlation coefficients between all channel state data sequences and the other channel state data sequences; The channel state parameters of the target channel state data sequence are obtained by calculating the sum of the correlation coefficients between the target channel state data sequence and the other channel state data sequences, and then calculating the ratio of the sum of the correlation coefficients between the target channel state data sequence and the other channel state data sequences to the sum of all correlation coefficients in the correlation coefficient set.

3. The multi-mode intelligent modulation data transmission method based on channel state awareness according to claim 2, characterized in that, The process of determining the channel fluctuation parameters based on channel state data includes: Based on the channel state data sequence, plot the state change curve of each channel state data within a preset time period, obtain the preset time segment, and divide the state change curve according to the preset time segment to obtain several state change curve segments. Calculate the average value of each state change curve segment, and plot the state average value change curve based on the average value of each state change curve segment. The absolute slope of two adjacent average values ​​in the statistical state average change curve is used to determine the fluctuation parameters of the channel state data sequence based on all the absolute slope values ​​in the state average change curve.

4. The multi-mode intelligent modulation data transmission method based on channel state awareness according to claim 3, characterized in that, The determination of the comprehensive channel state parameters based on channel state parameters and fluctuation parameters includes: The channel state synthesis parameters are determined according to the channel state synthesis parameter calculation formula, which is as follows: in, These are the channel state synthesis parameters. The total number of channel state data. For the first Channel state parameters of channel state data, For the first Fluctuation parameters of channel state data.

5. The multi-mode intelligent modulation data transmission method based on channel state awareness according to claim 1, characterized in that, The step of determining the modulation scheme of the transmitted data based on the channel state synthesis parameters includes: Acquire training data and build the initial model structure of the neural network model based on the training data; Obtain the channel state comprehensive parameters and the corresponding optimal signal modulation scheme of the training data. Train the initial model structure based on the channel state comprehensive parameters and the corresponding optimal signal modulation scheme to obtain the trained modulation scheme recognition model. The current channel state parameters are input into the trained modulation scheme recognition model, which outputs the optimal signal modulation scheme for the current channel. Based on the optimal signal modulation scheme for the current channel, the modulation scheme for the transmitted data is determined.

6. The multi-mode intelligent modulation data transmission method based on channel state awareness according to claim 1, characterized in that, The step of determining the encoding parameters of the transmitted data based on the signal transmission error rate and the signal transmission delay includes: Based on the signal transmission bit error rate and signal transmission delay of the modulated transmitted data, each transmitted data is clustered, and the signal quality parameters of the transmitted data in each cluster partition are determined based on the clustering results. The number of all transmitted data in the cluster partition to which the transmitted data belongs is counted, and the signal distribution parameters are determined based on the number of all transmitted data in the cluster partition to which the transmitted data belongs; Obtain the preset first weight and preset second weight, and then perform a weighted summation of the signal quality parameters and signal distribution parameters based on the preset first weight and preset second weight to obtain the encoding parameters of the transmitted data.

7. The multi-mode intelligent modulation data transmission method based on channel state awareness according to claim 6, characterized in that, The clustering of each transmitted data based on the signal transmission bit error rate and signal transmission delay of the modulated transmitted data includes: A sample dataset is established based on the signal transmission error rate and signal transmission delay of each transmitted data, and k initial cluster centers of the sample dataset are randomly selected. Calculate the Euclidean distance from the sample data in the sample dataset to the initial cluster center, and divide each transmitted data into the corresponding cluster partition based on the Euclidean distance from the sample data in the sample dataset to the initial cluster center; Calculate the mean of the sample data within each cluster partition, and recalculate the cluster centers based on the mean of the sample data within each cluster partition; Repeat the above steps until the cluster centers no longer change or the number of iterations reaches the preset maximum number of iterations, and obtain the clustering results of the transmitted data.

8. The multi-mode intelligent modulation data transmission method based on channel state awareness according to claim 6, characterized in that, The step of determining the signal quality parameters of transmitted data within each cluster partition based on the clustering results includes: Based on the clustering results, determine the cluster center of the clustering partition to which the transmitted data belongs, and calculate the signal transmission bit error rate and signal transmission delay corresponding to the cluster center; Multiply the signal transmission error rate and signal transmission delay corresponding to the cluster center to obtain the signal quality parameters of the corresponding transmitted data.

9. The multi-mode intelligent modulation data transmission method based on channel state awareness according to claim 6, characterized in that, The step of determining the signal distribution parameters based on the total number of transmitted data in the cluster partition to which the transmitted data belongs includes: Obtain the total number of transmitted data, calculate the ratio of the total number of transmitted data in the cluster partition to the total number of transmitted data, and obtain the signal distribution parameters.

10. The multi-mode intelligent modulation data transmission method based on channel state awareness according to claim 1, characterized in that, The step of performing Huffman coding on the transmitted data according to the coding parameters includes: All transmitted data are sorted in descending order of encoding parameters. A Huffman tree is constructed based on the sorting result of the transmitted data, and the transmitted data is encoded based on the Huffman tree.

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