Image transmission compression method based on OFDM wireless transmission

Abstract

The application discloses an image transmission compression method based on OFDM wireless transmission and relates to the field of image processing.The method comprises the following steps: collecting image signals and dividing the signals into multiple subcarriers with QAM modulation data through OFDM modulation; adjusting the bandwidth of the subcarriers that do not conform to the target power peak value range; setting new constellation point coordinates in the complex plane to adjust the PAPR value in the QAM modulation block; then sequentially performing QAM demodulation and LDPC decoding on all QAM modulation blocks; and restoring the image data to the initial format through decompression.Compared with the prior art, the application limits and recombines the power peak value and constellation point coordinates of the subcarriers, effectively controls the peak-to-average ratio (PAPR) of the signals, has the dynamic adjustment characteristic, can optimize the signals according to the change of the real-time transmission environment, reduces the peak power of the signals, and has remarkable advantages and beneficial effects in reducing the nonlinear distortion, reducing the high peak-to-average ratio and carrier offset and the like.

Description

Technical Field

[0001] This invention relates to the field of image processing, and more specifically to an image transmission compression method based on OFDM wireless transmission. Background Technology

[0002] In modern communication systems, latency in image transmission and compression has always been a significant concern. With the increasing demand for high-quality image transmission, especially in wireless communication environments, innovative solutions are needed to reduce latency and improve real-time performance. Image compression and transmission require numerous encoding and decoding steps and involve complex computational processes, and the encoding and decoding processes can impact the overall transmission or compression latency. In existing technologies, JPEG compression or Web compression are primarily used to maintain low latency in image transmission and compression. However, while maintaining image quality without loss, JPEG and Web compression typically do not provide spectral efficiency, failing to fully utilize available spectrum resources. This leads to insufficient utilization of image spectrum resources during complex encoding and decoding processes, and can easily result in high image transmission and compression latency. OFDM, a multi-carrier modulation technique, is characterized by dividing the signal into multiple subcarriers to improve data transmission efficiency. It has been successfully used in many wireless communication standards, such as Wi-Fi, LTE, and 5G. The parallel transmission nature of OFDM helps reduce transmission latency. Currently, OFDM technology is rarely used to reduce image transmission and compression latency. The main reason is that OFDM wireless signals have a high peak-to-average power ratio (PAPR) and are prone to carrier offset, which leads to higher requirements for the system's power dynamic range, linearity, and offset correction. In practical use, it is difficult to limit the PAPR channel carrier, resulting in high operating costs for existing OFDM image transmission and compression methods. Summary of the Invention

[0003] This invention provides an image transmission compression method based on OFDM wireless transmission, which solves the problems of peak-to-average power ratio and carrier offset that easily occur in existing OFDM image transmission compression technology when utilizing the image spectrum to reduce latency.

[0004] This invention is achieved through the following technical solution: An image transmission compression method based on OFDM wireless transmission, the method comprising: Step S1: Acquire image signals and perform preprocessing and compression. Encode the initial image data using LDPC and divide the encoded data into multiple LDPC coding blocks. Perform QAM modulation on each LDPC coding block to generate a QAM modulation block. Calculate the current PAPR value in each QAM modulation block by the ratio of total peak power to sequence average power. Step S2: Based on the current PAPR value of each QAM modulation block, evaluate the expected PAPR value range that each QAM modulation block needs to adjust, and set the target power peak range and constellation point group coordinate range according to the PAPR expected value range requirements. Then, divide each QAM modulation block into multiple subcarriers with QAM modulation data through OFDM modulation. Step S3: Filter and compare the initial power peak of all current subcarriers according to the target power peak range. Adjust the bandwidth of all subcarriers whose filtering and comparison results do not conform to the target power peak range to correct the initial power peak. Use the adjusted value as the corrected power peak and control it within the target power peak range. Step S4: Establish a complex plane coordinate system to display the constellation diagram. After initializing all constellation points, set the coordinates of new constellation points according to the range of constellation point group coordinates. Update the PAPR value in the QAM modulation block by updating the position and spacing of the new constellation point coordinates, and mark the position of the new constellation points on the complex plane. Step S5: Check the corrected power peak value of the subcarrier and the coordinates of the new constellation point. If the corrected power peak value and the coordinates of the new constellation point both meet the target peak value range and the constellation point group coordinate range respectively, then proceed to the next step after mapping and recombining into a QAM modulation block. If either the corrected power peak value or the coordinates of the new constellation point does not meet the requirements, then re-enter step S3. Step S6: Perform frame detection and frequency offset correction on each corrected QAM modulation block to record the start and end positions of the corrected data and the frequency offset of the correction signal. Then, perform QAM demodulation and LDPC decoding on all QAM modulation blocks in sequence, and decompress to restore the image data to its initial format.

[0005] Currently, OFDM technology is rarely used in existing technologies to reduce image transmission and compression latency. This is primarily because OFDM wireless signals have a high peak-to-average power ratio (PAPR) and are prone to carrier offset, leading to higher requirements for system power dynamic range, linearity, and offset correction. Furthermore, it is difficult to limit the PAPR and channel carrier in practical applications, resulting in high operating costs for existing OFDM image transmission compression methods. This invention provides an image transmission compression method based on OFDM wireless transmission, addressing the problems of PAPR and carrier offset that arise in existing OFDM image transmission compression technologies when utilizing the image spectrum to reduce latency.

[0006] Furthermore, the target power peak range includes a first peak range based on each subcarrier and a second peak range based on each QAM modulation block. If the measurement result of the first peak range does not meet the PAPR expected value range requirement, and the measurement result of the second peak range meets the PAPR expected value range requirement, then the initial power peak adjustment of the subcarrier is not performed; if the measurement result of the second peak range does not meet the PAPR expected value range requirement, then the initial power peak adjustment of the subcarrier is performed.

[0007] Furthermore, the subcarrier bandwidth adjustment process includes: when the subcarrier power is too high, spreading the bandwidth of the subcarrier; when the subcarrier power is too low, narrowing the bandwidth of the subcarrier; and iterating the adjusted subcarrier bandwidth multiple times to gradually approach the reference value of the target power peak range.

[0008] Furthermore, a bat swarm optimization algorithm is set up to set the coordinates of the new constellation points. This method includes: Step S401: Initialize a swarm of bats and construct a solution space. Let each bat represent a new constellation point. The position, speed and pulse emission probability of each bat represent the target coordinate position, movement speed and target coordinate selection rate of the potential solution of a new constellation point, respectively. The frequency of each bat represents the attraction force of the target coordinate selected by the new constellation point. Step S402: Randomly generate a swarm of bats in the constellation point space. Each bat searches for and selects an initial target coordinate within the constellation point group coordinate range based on its own constellation point characteristics, and calculates the PAPR value of the initial target coordinate as the fitness value. Then, use the bat swarm optimization rule to update the position and speed of each bat and guide all bats to move closer to the global optimum. Step S403: Recalculate the fitness value of the current position for each bat after the position is updated. This is used to determine whether the final target coordinates updated by the current bat swarm meet the range of constellation point group coordinates. If they do, all final target coordinates are set as new constellation point coordinates and have met the expected PAPR value range of the current QAM modulation block. If they do not meet, proceed to step S402 again.

[0009] Furthermore, the restricted region of the constellation point group coordinate range includes the complex plane range and the coordinate point range, wherein the coordinate point range is contained within the complex plane range; the complex plane range is used for preliminary screening of potential solutions for new constellation points during bat swarm initialization, and the coordinate point range is used for advanced screening of potential solutions for new constellation points when updating bat positions.

[0010] Furthermore, the method for optimizing bat swarm rules includes a position update method and a velocity update method, wherein, The location update method is obtained through the following calculation: X i(t+1)=X i (t)+V i (t)+A i ⋅R i (t), The speed update method is obtained through the following calculation: V i (t+1)=β⋅V i (t)+A i ⋅(X g (t)−X i (t)), Where t represents the number of update iterations, and i represents the bat ordinal number; X i (t) represents the target coordinates after the current iteration, where X... i (t+1) represents the target coordinates after the next iteration update, where V... i (t) represents the velocity after the current iteration, where A is... i and R i (t) represents the disturbance term and the random term; the V i (t+1) represents the velocity after the next iteration update, β is the adjustment weight value, and X... g (t) represents the final target coordinates of the current bat.

[0011] Furthermore, let N be the number of subcarriers in each QAM modulation block, U be the number of transmitted symbols in the entire QAM modulation block, j be the subcarrier ordinal number, f be the constellation point ordinal number, and P be the average power of each transmitted symbol. avg-Q And let the average power of the sequence in the QAM modulation block be P. avg-A The peak power of each subcarrier is P peak The total peak power of each QAM modulation block is P total-peak The calculation methods for the total peak power and sequence average power of each QAM modulation block are expressed as follows: P total-peak =∑ N j=1 P j peak ;P avg-A =(1 / U)∙∑ U f=1 •P avg-Q .

[0012] Furthermore, let the total number of constellation points in each QAM modulation block be z, and let the average power of each constellation point be P. avg-symbol Let the complex form of the f-th constellation point be E. f And the real and imaginary parts of the f-th constellation point are represented by B respectively. f and Cf Then, the calculation methods for the symbol average power and sequence average power of each QAM modulation block are expressed as follows: When the average power of each transmitted symbol is equal, P avg-A =2⋅P avg-symbol =2∙(1 ∕ Z∙∑ z f=1 |E f | 2 ); When the average power of each transmission symbol is not equal, P avg-Q =(1 / 2)∙ (|B f | 2 +|C f | 2 ), Then P avg-A =(1 / U)∙∑ U f=1 ∙ P avg-Q =(1 / U)∙∑ U f=1 ∙(1 / 2)∙ (|B f | 2 +|C f | 2 ).

[0013] Compared with existing technologies, this invention limits and reorganizes the peak power of subcarriers and constellation point coordinates, effectively controlling the peak-to-average power ratio (PAPR) of the signal. It has dynamic adjustment characteristics and can optimize the signal according to changes in the real-time transmission environment, reducing the peak power of the signal. It has significant advantages and beneficial effects in reducing nonlinear distortion, reducing PAPR and carrier offset. At the same time, by dividing the QAM modulation block into multiple subcarriers for transmission through OFDM and by making efficient use of the spectrum, it overcomes the transmission attenuation problem caused by channel fading, and significantly improves the image transmission compression efficiency. Attached Figure Description

[0014] The accompanying drawings, which are included to provide a further understanding of embodiments of the invention and form part of this application, do not constitute a limitation thereof. In the drawings: Figure 1 This is a flowchart of the present invention; Figure 2 This is a schematic diagram of the process structure of the present invention. Detailed Implementation

[0015] To make the objectives, technical solutions, and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the embodiments and accompanying drawings. The illustrative embodiments and descriptions of the present invention are only used to explain the present invention and are not intended to limit the present invention. Example

[0016] like Figure 1 and Figure 2 As shown, the present invention provides an image transmission compression method based on OFDM wireless transmission, the method comprising: Step S1: Acquire image signals and perform preprocessing and compression. Encode the initial image data using LDPC and divide the encoded data into multiple LDPC coding blocks. Perform QAM modulation on each LDPC coding block to generate a QAM modulation block. Calculate the current PAPR value in each QAM modulation block by the ratio of total peak power to sequence average power.

[0017] Step S2: Based on the current PAPR value of each QAM modulation block, evaluate the expected PAPR value range that each QAM modulation block needs to adjust, and set the target power peak range and constellation point group coordinate range according to the PAPR expected value range requirements. Then, divide each QAM modulation block into multiple subcarriers with QAM modulation data through OFDM modulation.

[0018] Step S3: Filter and compare the initial power peak values ​​of all current subcarriers according to the target power peak value range. Adjust the bandwidth of all subcarriers whose filtering and comparison results do not conform to the target power peak value range to correct the initial power peak value. Use the adjusted value as the corrected power peak value to control it within the target power peak value range.

[0019] Step S4: Establish a complex plane coordinate system to display the constellation diagram. After initializing all constellation points, set the coordinates of new constellation points according to the range of constellation point group coordinates. Update the PAPR value in the QAM modulation block by updating the position and spacing of the new constellation point coordinates, and mark the position of the new constellation points on the complex plane.

[0020] Step S5: Verify the corrected peak power of the subcarrier and the coordinates of the new constellation point. If the corrected peak power and the coordinates of the new constellation point both meet the target peak range and the constellation point group coordinate range respectively, then reassemble them into a QAM modulation block through mapping and proceed to the next step. If either the corrected peak power or the coordinates of the new constellation point do not meet the requirements, then re-enter step S3.

[0021] Step S6: Perform frame detection and frequency offset correction on each corrected QAM modulation block to record the start and end positions of the corrected data and the frequency offset of the correction signal. Then, perform QAM demodulation and LDPC decoding on all QAM modulation blocks in sequence, and decompress to restore the image data to its initial format.

[0022] Specifically, the preprocessing process includes operations such as denoising, smoothing, and grayscale transformation to improve image quality. The preprocessed image is then compressed using conventional image compression algorithms, such as JPEG and PNG, to reduce data volume and improve transmission efficiency. The compressed image data is then LDPC encoded and divided into blocks. The independence between different LDPC encoded blocks helps reduce the overall PAPR value, meaning that a high-power portion of one block is less likely to appear simultaneously with high-power portions of other blocks, thus reducing overall PAPR fluctuations. LDPC encoding is an error-correcting code used to improve transmission reliability by adding redundant information to correct errors that may occur during transmission. After LDPC encoding, QAM modulation is applied to the encoded blocks, modulating amplitude and phase information in the signal and preparing for subsequent OFDM modulation. In each QAM modulation block, the ratio of total peak power to sequence average power is calculated to obtain the current PAPR value, used to assess the degree of signal amplitude fluctuation. This PAPR value is also reserved for assessing the magnitude of PAPR adjustment needed for the current QAM modulation block, ensuring that the overall image data's peak-to-average power ratio is controlled to avoid excessively high values.

[0023] By performing a Fourier transform on the QAM modulation blocks and finding the current peak power of each block, the allowable PAPR adjustment range for each QAM modulation block is determined based on the system's design and performance requirements. In practical applications, this may be an absolute value or a percentage relative to the current PAPR value. Based on the current PAPR value and the allowable increase / decrease adjustment range, the expected PAPR value range after adjustment is calculated. The constellation point group coordinate range can be set by determining the maximum and minimum values ​​in each dimension to limit the QAM modulation constellation points within a certain range. After QAM modulation of the LDPC-encoded data, the signal can be mapped to the corresponding constellation points in the QAM constellation diagram on the complex plane. The power of each subcarrier is then adjusted by limiting the power of each subcarrier or by redistributing the power to ensure that the peak power of each subcarrier is within the target peak power range. The QAM constellation points on each subcarrier are adjusted by limiting the amplitude and phase of the constellation points to ensure that the constellation points are within the predefined coordinate point range. Finally, the adjusted subcarriers are combined to form a complete OFDM symbol, with each subcarrier carrying QAM-modulated data.

[0024] Furthermore, the target power peak range includes a first peak range based on each subcarrier and a second peak range based on each QAM modulation block. If the measurement result of the first peak range does not meet the PAPR expected value range requirement, and the measurement result of the second peak range meets the PAPR expected value range requirement, then no initial power peak adjustment is performed on the subcarrier; otherwise, if the measurement result of the second peak range does not meet the PAPR expected value range requirement, then initial power peak adjustment is performed on the subcarrier. The measurement result of the first peak range indicates whether the current subcarrier's power peak is within the first peak range, and the measurement result of the second peak range indicates whether the current QAM modulation block's power peak is within the second peak range. The PAPR expected value range requirement means whether the current PAPR value of a single subcarrier or the PAPR value of all subcarriers falls within the PAPR expected value range, and the PAPR value of a subcarrier is positively correlated with its power peak. In image processing, since the QAM modulation block has a more direct impact on image data, the control of the overall PAPR value prioritizes the second peak range. This means that in practical applications, the PAPR value of individual subcarriers may exceed or fall below the expected PAPR range, but the overall calculated PAPR value of the QAM modulation block remains within the expected range. In this case, initial power peak adjustment of the subcarriers is unnecessary. This saves computational resources, reduces unnecessary adjustments, and improves system efficiency. Simultaneously, using different peak range measurements and selectively adjusting based on the results helps avoid over-adjustment of the entire system. This ensures that the system meets PAPR requirements while maintaining the stability of other performance parameters as much as possible, reducing system complexity. In practical implementation, users can also readjust the PAPR value of each subcarrier to make image transmission compression smoother.

[0025] More specifically, the subcarrier bandwidth adjustment process includes: when the subcarrier power is too high, spreading the subcarrier bandwidth; when the subcarrier power is too low, narrowing the subcarrier bandwidth; and iterating the adjusted subcarrier bandwidth multiple times to gradually approach a reference value within the target power peak range. By spreading or narrowing the subcarrier bandwidth, the subcarrier power can be effectively adjusted, thereby reducing the peak-to-average power ratio (PAPR) of the entire signal. This helps reduce nonlinear distortion. Through dynamic adaptive bandwidth adjustment, it is possible to flexibly adjust according to changes in subcarrier power in the real-time transmission environment. This dynamism allows the system to adapt to different channel conditions and transmission scenarios.

[0026] Furthermore, as a feasible implementation method, a bat swarm optimization algorithm is set to set the coordinates of the new constellation points. This method includes: Step S401: Initialize a swarm of bats and construct a solution space. Let each bat represent a new constellation point. The position, speed and pulse emission probability of each bat represent the target coordinate position, movement speed and target coordinate selection rate of the potential solution of a new constellation point, respectively. The frequency of each bat represents the attraction force of the target coordinate selected by the new constellation point.

[0027] Step S402: Randomly generate a swarm of bats within the constellation point space. Each bat searches for and selects an initial target coordinate within the constellation point group coordinate range based on its own constellation point characteristics, and calculates the PAPR value of the initial target coordinate as the fitness value. Then, use the bat swarm optimization rule to update the position and speed of each bat and guide all bats to move closer to the global optimum.

[0028] Step S403: Recalculate the fitness value of the current position for each bat after the position is updated. This is used to determine whether the final target coordinates updated by the current bat swarm meet the range of constellation point group coordinates. If they do, all final target coordinates are set as new constellation point coordinates and have met the expected PAPR value range of the current QAM modulation block. If they do not meet, proceed to step S402 again.

[0029] Specifically, a group of bats is randomly generated, with each bat representing a new constellation point. The position of each bat represents the target coordinates of a potential solution for the new constellation point. Initially, the positions of each bat are random. A solution space is defined to ensure that the bat positions are within a suitable range; the solution space defines the range of feasible solutions to the problem. Each bat has a speed representing its movement velocity and a frequency representing the attraction force of the target coordinates selected by the new constellation point. These parameters are key control parameters of the algorithm. The attraction force represents the degree to which each bat is attracted to the global optimum, i.e., the target attraction force. It can influence the bat's movement direction in the solution space, making the algorithm more likely to converge to the global optimum. The attraction force may be related to the bat's own search history or fitness value, causing the proximity of other bat positions to change accordingly due to changes in the gravitational equilibrium point. The pulse emission probability is the probability that an individual bat will choose to perform pulse emission behavior. Pulse emission is a search mechanism in the bat optimization algorithm, exploring the search space by emitting pulses. The target coordinate selection rate represents the degree of selection of target coordinates in the search space, which may involve factors such as the distance to the target coordinates and the fitness of the current solution. A higher selection rate means a greater likelihood of choosing a position closer to the target coordinates. When a bat decides to perform a pulse emission, it selects the probability of moving towards the target coordinates based on the target coordinate selection rate. The fitness value of each bat at its corresponding new constellation point is calculated, representing the objective function value at that position; the fitness value is used to evaluate the bat's performance. The pulse emission operation can guide bats towards the global optimum. Whether to perform a pulse emission operation is determined based on each bat's fitness value and the pulse emission probability setting. Then, using bat swarm optimization rules, the position and speed of each bat are updated according to its current position and speed, as well as its fitness value. Through adjustments to the bat swarm rules, bats are guided to move towards directions with better fitness values. Based on the characteristics of the bat swarm algorithm, by updating the position and speed rules, the bat swarm is guided towards the global optimum, which can be achieved by finding better fitness values ​​in the search space. Steps 402 to 403 are repeated until the final target coordinates conform to the constellation point swarm coordinate range.

[0030] Further, as a feasible implementation, the restricted region of the constellation point group coordinate range includes a complex plane range and a coordinate point range, with the coordinate point range contained within the complex plane range. The complex plane range is used for preliminary screening of potential solutions for new constellation points during bat swarm initialization, while the coordinate point range is used for further screening of potential solutions for new constellation points when updating bat positions. In this embodiment, the restricted region refers to the entire region including both the complex plane range and the coordinate point range. This restricted region is used to screen potential solutions for new constellation points. At different stages, different conditions or restrictions may be used to ensure that the generated new constellation points meet specific requirements. The complex plane range refers to a region defined on the complex plane, which may be a rectangle or other shape. During bat swarm initialization, this complex plane range is used for preliminary screening of potential solutions for new constellation points. This preliminary screening may be to ensure that new constellation points are distributed within a suitable region on the complex plane. The coordinate point range is a portion of the complex plane range, which may be a subset. This coordinate point range is used for further screening of potential solutions for new constellation points when updating bat positions. Advanced screening may require new constellation points to not only fall within the initial screening area on the complex plane but also meet more stringent conditions to ensure they meet more specific requirements. By setting the complex plane range and coordinate point range, progressive screening can be performed during the generation of new constellation points to ensure that the final selected constellation points meet the design or optimization requirements. Initial screening may be relatively broad, while advanced screening is more stringent.

[0031] More specifically, as a feasible implementation, the method for optimizing bat swarm rules includes a position update method and a velocity update method, wherein the position update method is obtained through the following calculation formula: X i (t+1)=X i (t)+V i (t)+A i ⋅R i (t); The velocity update method is obtained through the following formula: V i (t+1)=β⋅V i (t)+A i ⋅(X g (t)−X i (t)); where t represents the update iteration number, and i represents the bat ordinal number; X i (t) represents the target coordinates after the current iteration, where X... i (t+1) represents the target coordinates after the next iteration update, where V... i (t) represents the velocity after the current iteration, and A... i and R i (t) represents the disturbance term and the random term; the V i(t+1) represents the velocity after the next iteration update, β is the adjustment weight value, and X... g (t) represents the final target coordinates of the current bat.

[0032] During the calculation, the location X of the bat swarm is... i (t) and velocity V i (t) Perform initial settings, and simultaneously initialize the control weight value β and the disturbance term A. i Random item R i Parameters such as (t) are used, and the number of update iterations t is preset. The target coordinates X of the bat are updated. i (t+1), the algorithm is actually searching for a better constellation point distribution that is more conducive to controlling the peak-to-average power ratio of the signal. Disturbance term A i and random item R i The introduction of (t) increases the randomness of the algorithm, which can avoid getting trapped in local optima during the search process, facilitates global search, and improves the robustness of the optimization algorithm. After the iteration ends, check whether the termination condition is met, that is, the final iteration position X of all bats. i If the final target coordinates corresponding to (t+1) are within the range of constellation point group coordinates, then the iteration number t is reset and the next iteration is performed to find new final target coordinates for the bat swarm.

[0033] In some optimization algorithms or simulations, the random disturbance term A i Uncertainty or random variation in a system is typically simulated by introducing randomness. The specific calculation process depends on the algorithm and model used. As a feasible implementation, in practice, the random disturbance term A can be generated using normally distributed random numbers. i In more complex models, Monte Carlo simulations can be used to obtain random perturbation terms. This involves running the simulation multiple times, each time using different random inputs or parameters, to simulate uncertainties in the system.

[0034] Furthermore, as a feasible implementation, the number of subcarriers in each QAM modulation block is denoted as N, the number of transmitted symbols in the entire QAM modulation block is denoted as U, the subcarrier ordinal number is denoted as j, the constellation point ordinal number is denoted as f, and the average power of each transmitted symbol is P. avg-Q And let the average power of the sequence in the QAM modulation block be P. avg-A The peak power of each subcarrier is P peak The total peak power of each QAM modulation block is P total-peak The calculation methods for the total peak power and sequence average power of each QAM modulation block are expressed as follows: P total-peak =∑ Nj=1 P j peak ;P avg-A =(1 / U)∙∑ U f=1 •P avg-Q .

[0035] The peak-to-average power ratio (PAPR) measures the ratio between the peak power and the average power of a signal, usually expressed in decibels (dB). The final PAPR value is calculated as PAPR(dB) = 10 * log0 10 (P) total-peak / P avg-A Iterate through each subcarrier j and calculate the peak power P of each subcarrier. j peak Then, the peak powers of all subcarriers are summed to obtain the total peak power P. total-peak =∑ N j= 1P j peak .

[0036] Furthermore, let the total number of constellation points in each QAM modulation block be z, and let the average power of each constellation point be P. avg-symbol Let the complex form of the f-th constellation point be E. f And the real and imaginary parts of the f-th constellation point are represented by B respectively. f and C f Then, the calculation methods for the symbol average power and sequence average power of each QAM modulation block are expressed as follows: When the average power of each transmitted symbol is equal, P avg-A =2⋅P avg-symbol =2∙(1 ∕ Z∙∑ z f=1 |E f | 2 ); When the average power of each transmission symbol is not equal, P avg-Q =(1 / 2)∙ (|B f | 2 +|C f | 2 ), Then P avg-A =(1 / U)∙∑ U f=1 ∙ P avg-Q =(1 / U)∙∑ U f=1 ∙(1 / 2)∙ (|B f | 2 +|C f | 2 ).

[0037] A QAM modulation constellation diagram consists of a set of complex coordinates. Each QAM modulation symbol corresponds to a point in the constellation diagram, where each coordinate represents a possible modulation symbol. The number of constellation point coordinates depends on the modulation order of QAM. It should be noted that in practical implementations, when orthogonal modulation is used in QAM, the number of subcarriers N is usually equal to the number of constellation point coordinates Z. This is because in orthogonal modulation, each subcarrier corresponds to one constellation point, and the coordinates of the constellation point represent the complex value of the modulation. Simultaneously, the number of symbols U is also equal to the number of constellation point coordinates Z; each symbol corresponds to one constellation point, and the coordinates of the constellation point determine the value of the symbol. When using non-orthogonal modulation, the number of subcarriers N may not be equal to the number of constellation point coordinates Z. In common M-QAM modulation, the number of subcarriers N and the number of symbols U are equal. However, in some special scenarios, such as some communication systems that may use irregular modulation schemes where the number of constellation points Z for each symbol may be different, leading to a discrepancy between the number of subcarriers N and the number of symbols U. Some systems may also use hybrid modulation, where different subcarriers use different modulation schemes, which can also result in an inconsistency between the number of subcarriers N and the number of symbols U. j The constellation points are represented in complex form, with the square of their modulus representing the power of the constellation point. These calculation steps are used to determine the total peak power and sequence average power of each QAM modulation block, taking into account factors such as the number of subcarriers, constellation points, and average power. These values ​​may be relevant for the optimization and adjustment of constellation point coordinates, especially in scenarios where PAPR values ​​are controlled. The peak power of the subcarriers is calculated to ensure that the peak power of the subcarriers is within the target power range.

[0038] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. An image transmission compression method based on OFDM wireless transmission, characterized in that, The method includes: Step S1: Acquire image signals and perform preprocessing and compression. Encode the initial image data using LDPC and divide the encoded data into multiple LDPC coding blocks. Perform QAM modulation on each LDPC coding block to generate a QAM modulation block. Calculate the current PAPR value in each QAM modulation block by the ratio of total peak power to sequence average power. Step S2: Based on the current PAPR value of each QAM modulation block, evaluate the expected PAPR value range that each QAM modulation block needs to adjust, and set the target power peak range and constellation point group coordinate range according to the PAPR expected value range requirements. Then, divide each QAM modulation block into multiple subcarriers with QAM modulation data through OFDM modulation. Step S3: Filter and compare the initial power peak of all current subcarriers according to the target power peak range. Adjust the bandwidth of all subcarriers whose filtering and comparison results do not conform to the target power peak range to correct the initial power peak. Use the adjusted value as the corrected power peak and control it within the target power peak range. Step S4: Establish a complex plane coordinate system to display the constellation diagram. After initializing all constellation points, set the coordinates of new constellation points according to the range of constellation point group coordinates. Update the PAPR value in the QAM modulation block by updating the position and spacing of the new constellation point coordinates, and mark the position of the new constellation points on the complex plane. Step S5: Check the corrected power peak value of the subcarrier and the coordinates of the new constellation point. If the corrected power peak value and the coordinates of the new constellation point both meet the target peak value range and the constellation point group coordinate range respectively, then proceed to the next step after mapping and recombining into a QAM modulation block. If either the corrected power peak value or the coordinates of the new constellation point does not meet the requirements, then re-enter step S3. Step S6: Perform frame detection and frequency offset correction on each corrected QAM modulation block to record the start and end positions of the corrected data and the frequency offset of the correction signal. Then, perform QAM demodulation and LDPC decoding on all QAM modulation blocks in sequence, and decompress to restore the image data to its initial format.

2. The image transmission compression method based on OFDM wireless transmission according to claim 1, characterized in that, The target power peak range includes a first peak range based on each subcarrier and a second peak range based on each QAM modulation block. If the measurement result of the first peak range does not meet the PAPR expected value range requirement, and the measurement result of the second peak range meets the PAPR expected value range requirement, then the initial power peak adjustment of the subcarrier is not performed; if the measurement result of the second peak range does not meet the PAPR expected value range requirement, then the initial power peak adjustment of the subcarrier is performed.

3. The image transmission compression method based on OFDM wireless transmission according to claim 1, characterized in that, The subcarrier bandwidth adjustment process includes: when the subcarrier power is too high, spreading the bandwidth of the subcarrier; when the subcarrier power is too low, narrowing the bandwidth of the subcarrier; and iterating the adjusted subcarrier bandwidth multiple times to gradually approach the reference value of the target power peak range.

4. The image transmission compression method based on OFDM wireless transmission according to claim 1, characterized in that, The bat swarm optimization algorithm is used to set the coordinates of the new constellation points. This method includes: Step S401: Initialize a swarm of bats and construct a solution space. Let each bat represent a new constellation point. The position, speed and pulse emission probability of each bat represent the target coordinate position, movement speed and target coordinate selection rate of the potential solution of a new constellation point, respectively. The frequency of each bat represents the attraction force of the target coordinate selected by the new constellation point. Step S402: Randomly generate a swarm of bats in the constellation point space. Each bat searches for and selects an initial target coordinate within the constellation point group coordinate range based on its own constellation point characteristics, and calculates the PAPR value of the initial target coordinate as the fitness value. Then, use the bat swarm optimization rule to update the position and speed of each bat and guide all bats to move closer to the global optimum. Step S403: Recalculate the fitness value of the current position for each bat after the position is updated. This is used to determine whether the final target coordinates updated by the current bat swarm meet the range of constellation point group coordinates. If they do, all final target coordinates are set as new constellation point coordinates and have met the expected PAPR value range of the current QAM modulation block. If they do not meet, proceed to step S402 again.

5. The image transmission compression method based on OFDM wireless transmission according to claim 4, characterized in that, The restricted region of the constellation point group coordinate range includes the complex plane range and the coordinate point range, wherein the coordinate point range is contained within the complex plane range; the complex plane range is used for preliminary screening of potential solutions for new constellation points during bat swarm initialization, and the coordinate point range is used for advanced screening of potential solutions for new constellation points when updating bat positions.

6. The image transmission compression method based on OFDM wireless transmission according to claim 4, characterized in that, The method for optimizing bat swarm rules includes a position update method and a velocity update method, wherein... The location update method is obtained through the following calculation: X i (t+1)=X i (t)+V i (t)+A i ⋅R i (t), The speed update method is obtained through the following calculation: V i (t+1)=β⋅V i (t)+A i ⋅(X g (t)−X i (t)), Where t represents the number of update iterations, and i represents the bat ordinal number; X i (t) represents the target coordinates after the current iteration, where X... i (t+1) represents the target coordinates after the next iteration update, where V... i (t) represents the velocity after the current iteration, where A is... i and R i (t) represents the disturbance term and the random term; the V i (t+1) represents the velocity after the next iteration update, β is the adjustment weight value, and X... g (t) represents the final target coordinates of the current bat.

7. The image transmission compression method based on OFDM wireless transmission according to claim 1, characterized in that, Let N be the number of subcarriers in each QAM modulation block, U be the number of transmitted symbols in the entire QAM modulation block, j be the subcarrier ordinal number, f be the constellation point ordinal number, and P be the average power of each transmitted symbol. avg-Q And let the average power of the sequence in the QAM modulation block be P. avg-A The peak power of each subcarrier is P peak The total peak power of each QAM modulation block is P total-peak The calculation methods for the total peak power and sequence average power of each QAM modulation block are expressed as follows: ； 。 8. The image transmission compression method based on OFDM wireless transmission according to claim 7, characterized in that, Let z be the total number of constellation points in each QAM modulation block, and let P be the average power of each constellation point. avg-symbol Let the complex form of the f-th constellation point be E. f And the real and imaginary parts of the f-th constellation point are represented by B respectively. f and C f Then, the calculation methods for the symbol average power and sequence average power of each QAM modulation block are expressed as follows: When the average power of each transmitted symbol is equal, P avg-A =2⋅P avg-symbol =2∙(1 ∕ Z∙∑ z f=1 |E f | 2 ); When the average power of each transmission symbol is not equal, P avg-Q =(1 / 2)∙(|B f | 2 +|C f | 2 ), but .

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