A method, device, equipment and storage medium for optimizing constellation point modulation in an AJSCC system

Abstract

The present invention provides a method, apparatus, device, and storage medium for optimizing constellation point modulation in an AJSCC system. The method first generates two random sequences that obey a Gaussian distribution as source signals, performs simulated joint coding on the source signals using S-K mapping, and generates a coded output sequence. The coded output sequence is then mapped to irregularly distributed constellation points, and the irregular mapping region is divided to generate a modulated signal. The modulated signal is then transmitted through a Rayleigh channel to generate each channel output symbol. The distance between each channel output symbol and each constellation point is calculated to generate a corresponding demodulated symbol. Finally, the demodulated symbols are decoded using a maximum likelihood probability algorithm to obtain a reconstructed source signal. This method achieves adaptive adjustment of the constellation point distribution and mapping region division, thereby reducing transmission cost and complexity while achieving high-performance communication.

Description

Technical Field

[0001] The present invention relates to the field of communications, and in particular to an optimization method, device, equipment and storage medium for constellation point modulation in an AJSCC system. Background Art

[0002] In existing technologies, most communication systems design digital communications based on Shannon's separation theory, meaning that source coding and channel coding are designed and optimized independently. This approach theoretically achieves optimal performance, but at the cost of requiring infinite code lengths, extremely high latency, and complex system implementation. In recent years, analog joint source-channel coding (AJSCC) has garnered widespread attention as an alternative. By directly mapping the analog signal from the source into the channel space, AJSCC simultaneously achieves source compression and channel error control, achieving near-theoretical optimal communication performance with extremely low complexity and negligible latency.

[0003] In AJSCC systems, encoders typically implement signal mapping using specific coding functions. A representative example is the Archimedean double helix-based coding function (SK mapping) proposed by PA Floor et al. in "Shannon-Kotel'nikov mappings for analog point-to-point communications" [IEEE Trans. Information Theory, vol. 70, no. 4, pp. 2491-2517, April 2024]. SK mapping improves system performance by fully utilizing the source space and preserving information details. However, the output of an AJSCC encoder is a continuous analog value with a complex and dense distribution. Directly transmitting these signals would place extremely high demands on the RF circuitry and transmit antennas, increasing system transmission cost and complexity. Therefore, modulation of the coded output is necessary to achieve low-cost, high-performance communication on existing communication equipment.

[0004] In traditional digital communications, quadrature amplitude modulation (QAM) is commonly used for signal modulation. While QAM's constellation points and mapping areas are regularly distributed, due to the uneven distribution of the signal output from AJSCC coding, QAM is not the optimal choice for quantization. In recent years, novel neural network-based modulation methods have been proposed for semantic communication systems, introducing learnable parameters to achieve irregular mapping. However, these methods are complex and unsuitable for low-power, low-latency devices.

[0005] In view of this, this application is filed. Summary of the Invention

[0006] The present invention discloses an optimization method, apparatus, device and storage medium for constellation point modulation in an AJSCC system, aiming to adaptively adjust constellation point distribution and mapping area division to achieve high-performance communication while reducing transmission cost and complexity.

[0007] A first embodiment of the present invention provides a method for optimizing constellation point modulation in an AJSCC system, including:

[0008] Generate two random sequences obeying Gaussian distribution as source signals, and simulate joint encoding of the source signals using SK mapping to generate a coded output sequence;

[0009] Mapping the coded output sequence into irregularly distributed constellation points and dividing the irregular mapping area to generate a modulated signal, wherein the constellation points are generated based on the distribution of signal points, and the constellation points adaptively adjust the coordinates of the constellation points and the mapping area as the coding parameters change;

[0010] Transmitting the modulated signal through a Rayleigh channel to generate each channel output symbol, calculating the distance between each channel output symbol and each constellation point, and generating a corresponding demodulation symbol, wherein each demodulation symbol is the constellation point with the smallest distance between the channel output symbol and the constellation point;

[0011] The demodulated symbols are decoded using a maximum likelihood probability algorithm to obtain a reconstructed source signal.

[0012] Preferably, the two random sequences obeying the Gaussian distribution are generated as source signals, and the source signals are simulated and jointly encoded using SK mapping to generate a coded output sequence, specifically:

[0013] Generate two paths with mean 0 and variance Normally distributed random sequence ;

[0014] The two-way information source is geometrically encoded, and the Archimedean double helix curve is used as the encoding geometric structure. The encoding function consists of a mapping function and a matching function. The minimum square norm principle is used to convert the normal distribution random sequence into Projected as the closest point on the spiral , and then the two-dimensional information is converted into one-dimensional coding information through the matching function to achieve 2:1 compression. The specific process is shown in the following formula:

[0015]

[0016]

[0017]

[0018] in, is the angle between the origin and the spiral point, is the distance between the two spirals, is an approximate constant , is the angle of the point on the spiral, are coordinates, is the mapping function, is the matching function, is the encoded output sequence, is a constant for adjusting the spiral density.

[0019] Preferably, the coded output sequence is mapped to irregularly distributed constellation points, and the irregular mapping area is divided to generate a modulated signal, specifically:

[0020] S21, scaling the coded output sequence in a power normalization form to generate a full-resolution signal point set of the coded output sequence;

[0021] S22, randomly specifying m initial finite constellation points from the full-resolution signal point set to generate a finite point set;

[0022] S23, calculating the distance between each signal point and each constellation point in the finite point set, and dividing the signal points with the smallest distance to the same constellation point into a mapping area set;

[0023] S24, calculating the sum of the distances from all signal points in each mapping area to the corresponding constellation points, taking the average of the signal points in each mapping area set, and obtaining a new constellation point set, wherein the sum of the squares of the distances is the quantization distortion;

[0024] S25, repeat steps (S23) to (S24) until the quantization distortion is minimized, save the optimized mapping area, and map each signal point in the encoded output sequence to a constellation point corresponding to the area to which it belongs according to the optimized mapping area to generate a modulated signal.

[0025] Preferably, the expression for generating the full-resolution signal point set of the encoded output sequence is:

[0026] ;

[0027] ;

[0028] in, is the average power of the coded output sequence, is the probability density function, is the full-resolution signal point set of the encoded output sequence.

[0029] Preferably, the expression for calculating the distance from each signal point to each constellation point in the finite point set is: ;

[0030] in, is the full-resolution signal point of the encoded output sequence, is a constellation point in a finite set of constellation points, Signal point To the constellation point distance;

[0031] The expression for dividing the signal points with the smallest distance from the same constellation point into a mapping area set is:

[0032]

[0033]

[0034] in, To minimize the distance The index of the obtained constellation point, Represents constellation points The set of signal point indices with the smallest distance, for The index of .

[0035] Preferably, the expression of the quantization distortion is:

[0036]

[0037]

[0038]

[0039] in, It is a constellation point The distribution probability of is the total number of signals in the full-resolution signal point set, Is the mapping area The center of mass of m is the total number of constellation points, It is quantization distortion.

[0040] A second embodiment of the present invention provides an apparatus for optimizing constellation point modulation in an AJSCC system, including:

[0041] A coding output sequence generating unit is used to generate two random sequences obeying Gaussian distribution as source signals, and perform simulated joint coding on the source signals using SK mapping to generate a coding output sequence;

[0042] a modulation signal generating unit, configured to map the coded output sequence into irregularly distributed constellation points and generate a modulation signal after dividing the irregular mapping area, wherein the constellation points are generated based on the distribution of signal points, and the constellation points adaptively adjust the constellation point coordinates and mapping area as the coding parameters change;

[0043] a demodulation symbol generating unit, configured to transmit the modulated signal through a Rayleigh channel to generate each channel output symbol, calculate the distance between each channel output symbol and each constellation point, and generate a corresponding demodulation symbol, wherein each demodulation symbol is the constellation point with the smallest distance between the channel output symbol and the constellation point;

[0044] The reconstructed information source generating unit is used to decode the demodulated symbols using a maximum likelihood probability algorithm to obtain a reconstructed information source signal.

[0045] The third embodiment of the present invention provides an optimization device for constellation point modulation in an AJSCC system, characterized in that it includes a memory and a processor, wherein a computer program is stored in the memory, and the computer program can be executed by the processor to implement a downhill assist method for a pure electric vehicle as described in any one of the above items.

[0046] A fourth embodiment of the present invention provides a computer-readable storage medium, characterized in that a computer program is stored therein, and the computer program can be executed by a processor of the device where the computer-readable storage medium is located to implement a method for optimizing constellation point modulation in an AJSCC system as described in any one of the above items.

[0047] The present invention provides an optimization method, apparatus, device, and storage medium for constellation point modulation in an AJSCC system. Two random sequences obeying a Gaussian distribution are first generated as source signals. The source signals are simulated and jointly encoded using SK mapping to generate a coded output sequence. The coded output sequence is then mapped to irregularly distributed constellation points, and the irregular mapping regions are divided to generate a modulated signal. The constellation points are generated based on the distribution of signal points, and the constellation point coordinates and mapping regions are adaptively adjusted as coding parameters change. Finally, the modulated signal is transmitted through a Rayleigh channel to generate each channel output symbol. The distance between each channel output symbol and each constellation point is calculated to generate a corresponding demodulated symbol. Each demodulated symbol is the constellation point with the smallest distance between the channel output symbol and the constellation point. Finally, the demodulated symbols are decoded using a maximum likelihood algorithm to obtain a reconstructed source signal. This adaptive adjustment of the constellation point distribution and mapping region division is achieved to achieve high-performance communication while reducing transmission cost and complexity. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 1 is a flow chart of a method for optimizing constellation point modulation in an AJSCC system provided by a first embodiment of the present invention;

[0049] Figure 2 It is a diagram of the spatial distribution of information sources and the encoding process using a double helix curve provided by the present invention;

[0050] Figure 3 It is the constellation diagram of the coded signal point using 16QAM modulation;

[0051] Figure 4 The irregular constellation diagram obtained by using the scheme of the present invention for coding signal points;

[0052] Figure 5 This is a performance comparison chart of different combinations of the optimization algorithms proposed in the present invention;

[0053] Figure 6 It is a robust shape verification diagram of the present invention under different encoding parameters.

[0054] Figure 7 This is a module diagram of a device for optimizing constellation point modulation in an AJSCC system provided by a second embodiment of the present invention. DETAILED DESCRIPTION

[0055] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0056] In order to better understand the technical solution of the present invention, the embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0057] It should be understood that the embodiments described are only a portion of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without creative work are within the scope of protection of the present invention.

[0058] The terms used in the embodiments of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The singular forms "a", "an", "the" and "the" used in the embodiments of the present invention and the appended claims are also intended to include plural forms unless the context clearly indicates otherwise.

[0059] It should be understood that the term "and / or" as used herein is merely a description of the relationship between associated objects, indicating that three possible relationships exist. For example, "A and / or B" can represent: A exists alone, A and B exist simultaneously, or B exists alone. Furthermore, the character " / " in this document generally indicates that the associated objects are in an "or" relationship.

[0060] The word "if," as used herein, may be interpreted as "at the time of" or "when" or "in response to determining" or "in response to detecting," depending on the context. Similarly, the phrases "if it is determined" or "if (stated condition or event) is detected" may be interpreted as "when it is determined" or "in response to the determination" or "when detecting (stated condition or event)" or "in response to detecting (stated condition or event)," depending on the context.

[0061] The "first" and "second" mentioned in the embodiments are merely used to distinguish similar objects and do not represent a specific ordering of the objects. It is understood that the specific order or precedence of "first" and "second" can be interchanged where appropriate. It should be understood that the objects distinguished by "first" and "second" can be interchanged where appropriate, so that the embodiments described herein can be implemented in an order other than that illustrated or described herein.

[0062] The specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0063] The present invention discloses a method, apparatus, device, and storage medium for optimizing constellation point modulation in an AJSCC (Analog Joint Source-Channel Coding) system, aiming to adaptively adjust constellation point distribution and mapping area division to achieve high-performance communication while reducing transmission cost and complexity.

[0064] See also Figure 1 A first embodiment of the present invention provides a method for optimizing constellation point modulation in an AJSCC system. The method can be performed by an optimization device for constellation point modulation in a JSCC system (hereinafter referred to as the optimization device), and in particular, by one or more processors in the optimization device to implement at least the following steps:

[0065] S101, generating two random sequences obeying Gaussian distribution as source signals, and performing simulated joint encoding on the source signals using SK mapping to generate a coded output sequence;

[0066] In this embodiment, the optimization device can be a terminal with data processing capabilities such as a server, workstation, desktop computer, laptop computer, etc. The optimization device can be installed with a corresponding operating system and application software, and the functions required by this embodiment can be realized through the combination of the operating system and application software.

[0067] It should be noted that the generation of the source signal is the starting point of the system. Two random sequences that obey the Gaussian distribution are generated as the source signal. Specifically, the two signals have a mean of 0 and a variance of Normally distributed random sequence ;

[0068] In the process of geometric coding of two-way information sources, the Archimedean double helix curve is used as the coding geometric structure, in which the coding function consists of a mapping function and a matching function, and the minimum square norm principle is used to convert the normal distribution random sequence into Projected as the closest point on the spiral , and then the two-dimensional information is converted into one-dimensional coded information through the matching function to achieve 2:1 compression;

[0069] Specifically, the encoding process first uses the least squares principle to project the normally distributed random sequence onto the nearest point on the Archimedean double helix. This projection process can be considered a form of vector quantization, where the points on the helix constitute the encoded codebook. The projection process determines the optimal angle by solving the following optimization problem:

[0070]

[0071]

[0072] in, is the angle between the origin and the spiral point, is the distance between the two spirals, is an approximate constant , is the angle of the point on the spiral, are coordinates, is the mapping function, is the matching function, is the encoded output sequence, is a constant for adjusting the spiral density.

[0073] By selecting the spiral point closest to the source signal, the system achieves signal compression and approximate representation. This geometric projection-based approach preserves the analog characteristics of the signal and avoids the large quantization error caused by digital quantization. The application of the least squares norm principle ensures the minimization of the projection error, thereby preserving the original signal information to the greatest extent possible. After the projection is completed, the system further converts the two-dimensional information into one-dimensional encoded information through a matching function, thereby achieving a compression ratio of 2:1. The mathematical expression of this process is:

[0074] Encoded output sequence By matching function Determine, among them, Represents the angle of a point on the spiral, while the geometric parameters of the spiral, such as the distance between two spirals Determines the sparseness of the spiral, directly affects the accuracy and compression effect of the encoding, and adjusts the constant of the spiral density By adjusting the spiral density, the encoding process can flexibly adapt to the statistical characteristics of different source signals. Its parameterized design not only improves the system's adaptability but also makes it possible to find the optimal balance between compression ratio and signal fidelity. Notably, because the matching function fully exploits the geometric properties of the spiral, the mapping from two dimensions to one dimension is reversible, minimizing compression losses.

[0075] S102, please combine Figure 2 , mapping the coded output sequence into irregularly distributed constellation points, and dividing the irregular mapping area to generate a modulated signal, wherein the constellation points are generated based on the distribution of signal points, and the constellation points will adaptively adjust the constellation point coordinates and mapping area as the coding parameters change;

[0076] S21, scaling the coded output sequence in a power normalization form to generate a full-resolution signal point set of the coded output sequence;

[0077] It should be noted that scaling the coded output sequence in the form of power normalization can significantly improve system performance. When the adjustment occurs, the distribution of the coded output sequence will change significantly, which is manifested in that the power of the coded output changes with Decrease and increase, Increase and increase, coding sequence The distribution of shows a trend of continuously extending along the horizontal and vertical axes on the probability density diagram. This phenomenon will lead to increased system power consumption and increased modulation quantization error.

[0078] By introducing a power normalization scaling mechanism, the system can maintain stable performance under different coding parameters. The core of this mechanism is to use the average power of the coded output sequence As a benchmark, this value can be obtained by the probability density function Calculate the integral to obtain: ;

[0079] On this basis, the full-resolution signal point set The expression is: .in, is the average power of the coded output sequence, is the probability density function, The advantage of this normalization process is that no matter how the coding parameters change, the processed signal point set has uniform energy characteristics, effectively eliminating the performance fluctuations caused by parameter changes.

[0080] S22, randomly specifying m initial finite constellation points from the full-resolution signal point set to generate a finite point set;

[0081] Specifically, in this embodiment, the full-resolution signal point set composed of the encoder output sequence is Randomly select m initial finite constellation points to form a finite point set It's important to note that the random selection of constellation points offers significant adaptability. When channel conditions change, the system can generate a new, finite set of points by simply updating the randomly selected seed, without having to redesign the entire coding architecture. This adaptability is particularly important in dynamic communication environments, such as mobile communications or in scenarios subject to severe interference.

[0082] S23, calculating the distance between each signal point and each constellation point in the finite point set, and dividing the signal points with the smallest distance to the same constellation point into a mapping area set;

[0083] For each signal point in the full-resolution signal point set, the system calculates the Euclidean distance between it and each constellation point in the finite set and assigns it to the mapping region corresponding to the closest constellation point. This proximity-based partitioning method forms a complete set of mapping regions, ensuring seamless coverage of the signal space and an optimal decision boundary.

[0084] In the specific implementation process, the system first calculates the signal point set Each point in to a finite constellation point set The distance between each constellation point in . The calculation formula is ,in represents the full-resolution signal point of the encoded output sequence, is a constellation point in the finite constellation point set, Indicates signal point To the constellation point For each signal point , the system uses index ; Find the constellation point with the smallest distance, and then add the index j of the signal point to the mapping area set of the corresponding constellation point: In this way, all the signal point indices closest to the same constellation point constitute the mapping area This mapping mechanism can adaptively construct the optimal decision region without pre-setting fixed geometric boundaries. Traditional constellation mapping methods typically use regularly shaped decision regions, such as squares or hexagons, which often lead to suboptimal performance in high-dimensional signal spaces. However, the partitioning method of this embodiment can automatically form the optimal boundary based on the actual distribution of constellation points, fully adapting to the characteristics of different signal spaces.

[0085] S24, calculating the sum of the distances from all signal points in each mapping area to the corresponding constellation points, taking the average of the signal points in each mapping area set, and obtaining a new constellation point set, wherein the sum of the squares of the distances is the quantization distortion;

[0086] Specifically, in this embodiment, each constellation point is first calculated The distribution probability of , that is, the ratio of the number of signal points in the mapping area corresponding to the constellation point to the total number of signal points, the expression is ,in, Indicates the mapping area The number of signal points in The total number of signals representing the full-resolution signal point set. Based on the distribution probability, each mapping area is calculated The centroid position of The formula for calculating the center of mass is ,in Represents each signal point in the mapping area. The centroid-based update strategy ensures that the new constellation point position can minimize the average distance of all signal points in the area, achieving the minimization of local quantization distortion from the perspective of information theory.

[0087] Quantization distortion of the entire system By formula Calculated as follows, where m is the total number of constellation points. This quantization distortion metric comprehensively measures the performance of the coding system and provides a clear target for iterative optimization.

[0088] S25, repeat steps (S23) to (S24) until the quantization distortion is minimized, save the optimized mapping area, and map each signal point in the encoded output sequence to a constellation point corresponding to the area to which it belongs according to the optimized mapping area to generate a modulated signal.

[0089] S103: Transmit the modulated signal through a Rayleigh channel to generate each channel output symbol, calculate the distance between each channel output symbol and each constellation point, and generate a corresponding demodulation symbol, wherein each demodulation symbol is the constellation point with the smallest distance between the channel output symbol and the constellation point;

[0090] In this embodiment, the transmission and demodulation of the modulated signal are achieved through rigorous channel modeling and accurate minimum distance judgment. The modulated signal is first represented by the channel model as Rayleigh channel transmission, where is the modulated signal, is the decay factor, The mean is 0 and the variance is In the entire channel model, the modulated signal is subject to a random, time-varying fading factor during transmission, along with the superposition of zero-mean, specific-variance Gaussian noise. This causes the signal received at the receiver to exhibit random variations in both amplitude and phase in the complex plane. It should be noted that this modeling approach accurately reflects the signal fading phenomenon caused by multipath in actual wireless transmission environments. By introducing Gaussian noise, it further accounts for the influence of thermal noise and other interference factors, allowing the channel output symbols to represent the various uncertainties in actual transmission. At the receiver, to effectively restore the received signal, each channel output symbol is compared with the constellation points adaptively optimized during the modulation phase. Specifically, by calculating the Euclidean distance between the received signal and each constellation point, the matching degree between each constellation point and the received signal can be accurately assessed, even after the signal has undergone Rayleigh fading and noise interference. By finding the constellation point that minimizes this Euclidean distance, the system uses that constellation point as the demodulated symbol corresponding to the channel output symbol.

[0091] It should be noted that it naturally has a certain ability to suppress channel fading and random noise. During the transmission process, the fading factor in the channel model can capture the random fluctuations of signal power, while Gaussian noise ensures that the ubiquitous disturbance effects in the environment are simulated. The combination of the two enables the demodulation scheme to maintain high reliability in various complex environments. Through this signal transmission and demodulation design, even under low signal-to-noise ratio conditions, the system can still accurately identify the correct constellation points, thereby recovering high-fidelity original signal data.

[0092] S104: Decode the demodulated symbols using a maximum likelihood algorithm to obtain a reconstructed source signal.

[0093] In this embodiment, after obtaining demodulated symbols through minimum distance judgment at the receiving end, the system uses a maximum likelihood (ML) decoding strategy to process the demodulated symbols to accurately reconstruct the original source signal. Although the demodulated symbols obtained through channel transmission and minimum distance judgment have established a certain correlation with the pre-generated irregular constellation points, due to channel fading, multipath effects, and noise interference, the demodulated symbols often still have some deviation. Therefore, ML decoding is required to further optimize the matching. Specifically, the system compares the obtained demodulated symbols with each point on the Archimedean double helix code curve generated using SK mapping. The maximum likelihood algorithm is used to determine the most likely corresponding point on the code curve for each demodulated symbol. Specifically, the probability of each decoded symbol being generated at all candidate points is calculated, and the candidate with the highest probability is selected as the correct match for that symbol.

[0094] Mathematically, this can be expressed as: For each demodulated symbol , calculate its value under a given noise model and the value of each point on the coding curve The conditional probability of generating this symbol , and finally get the reconstruction information Here, the conditional probability model is typically constructed based on additive white noise or other noise models, so that the selected matching points maximize the likelihood of the signal in the noise background. This process ensures that the decoding process does not rely solely on the simple minimization of the Euclidean distance, but fully considers the statistical characteristics of the channel noise and the possible distortion effects of the encoded signal after transmission over the channel.

[0095] Therefore, decoding using the maximum likelihood probability algorithm can more robustly recover the true source information while effectively resisting the error accumulation caused by noise and fading. It should be understood that the decoding method of this embodiment combines traditional geometric matching methods with statistical optimality, quantifying the matching probability of each candidate point through a probabilistic model, thereby achieving high-fidelity reconstruction in complex transmission environments. Ultimately, after ML decoding, the reconstructed signal obtained by the system achieves a high degree of consistency with the original source signal in both statistical distribution and detail.

[0096] Please combine Figure 3 and Figure 4 Compared with the 16QAM constellation diagram, the constellation diagram shown in is obviously more consistent with the actual distribution characteristics of the coded output signal points, thereby reducing the system complexity while effectively reducing the quantization error in the mapping process, enabling the AJSCC system to better realize actual deployment. Figure 5The results of comparing the joint optimization algorithm proposed in this embodiment with other different optimization strategy combinations are presented. The joint optimization algorithm is applicable not only to the irregular constellation points constructed by the present invention, but also to traditional QAM schemes. Scaling processing can alleviate the performance degradation caused by coding parameter changes, and can also maintain relatively good system performance under low to medium signal-to-noise ratio conditions. Figure 6 The simulation results further verify that the AJSCC system used in this embodiment has high robustness. Overall, these simulation data fully demonstrate that the constellation point design and optimization algorithm proposed in this invention maintains good signal fidelity while also having low complexity and excellent robustness.

[0097] See also Figure 7 A second embodiment of the present invention provides an optimization device for constellation point modulation in an AJSCC system, including:

[0098] The coded output sequence generating unit 201 is configured to generate two random sequences obeying a Gaussian distribution as source signals, and perform simulated joint coding on the source signals using SK mapping to generate a coded output sequence;

[0099] a modulation signal generating unit 202, configured to map the coded output sequence into irregularly distributed constellation points and generate a modulation signal after dividing the irregular mapping area, wherein the constellation points are generated based on the distribution of signal points, and the constellation point coordinates and mapping area are adaptively adjusted as coding parameters change;

[0100] a demodulation symbol generating unit 203 configured to transmit the modulated signal through a Rayleigh channel to generate each channel output symbol, calculate the distance between each channel output symbol and each constellation point, and generate a corresponding demodulation symbol, wherein each demodulation symbol is the constellation point with the smallest distance between the channel output symbol and the constellation point;

[0101] The reconstructed information source generating unit 204 is configured to decode the demodulated symbols using a maximum likelihood probability algorithm to obtain a reconstructed information source signal.

[0102] The third embodiment of the present invention provides an optimization device for constellation point modulation in an AJSCC system, characterized in that it includes a memory and a processor, wherein a computer program is stored in the memory, and the computer program can be executed by the processor to implement a downhill assist method for a pure electric vehicle as described in any one of the above items.

[0103] A fourth embodiment of the present invention provides a computer-readable storage medium, characterized in that a computer program is stored therein, and the computer program can be executed by a processor of the device where the computer-readable storage medium is located to implement a method for optimizing constellation point modulation in an AJSCC system as described in any one of the above items.

[0104] The present invention provides an optimization method, apparatus, device, and storage medium for constellation point modulation in an AJSCC system. Two random sequences obeying a Gaussian distribution are first generated as source signals. The source signals are simulated and jointly encoded using SK mapping to generate a coded output sequence. The coded output sequence is then mapped to irregularly distributed constellation points, and the irregular mapping regions are divided to generate a modulated signal. The constellation points are generated based on the distribution of signal points, and the constellation point coordinates and mapping regions are adaptively adjusted as coding parameters change. Finally, the modulated signal is transmitted through a Rayleigh channel to generate each channel output symbol. The distance between each channel output symbol and each constellation point is calculated to generate a corresponding demodulated symbol. Each demodulated symbol is the constellation point with the smallest distance between the channel output symbol and the constellation point. Finally, the demodulated symbols are decoded using a maximum likelihood algorithm to obtain a reconstructed source signal. This adaptive adjustment of the constellation point distribution and mapping region division is achieved to achieve high-performance communication while reducing transmission cost and complexity.

[0105] For example, the computer programs described in the third and fourth embodiments of the present invention may be divided into one or more modules, which are stored in the memory and executed by the processor to implement the present invention. The one or more modules may be a series of computer program instruction segments capable of performing specific functions, which are used to describe the execution process of the computer program in the device for optimizing constellation point modulation in an AJSCC system. For example, the apparatus described in the second embodiment of the present invention.

[0106] The processor may be a central processing unit (CPU), or other general-purpose processors, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), an off-the-shelf programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc. The processor serves as the control center of the method for optimizing constellation point modulation in an AJSCC system, and utilizes various interfaces and lines to connect various parts of the method for optimizing constellation point modulation in an AJSCC system.

[0107] The memory can be used to store the computer programs and / or modules. The processor implements various functions of a method for optimizing constellation point modulation in an AJSCC system by running or executing the computer programs and / or modules stored in the memory and accessing data stored in the memory. The memory may primarily include a program storage area and a data storage area. The program storage area may store an operating system and at least one application required for a function (such as audio playback or text conversion); the data storage area may store data generated based on the use of the mobile phone (such as audio data and text message data). Furthermore, the memory may include high-speed random access memory (RAM) and non-volatile memory, such as a hard disk, internal memory, a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, at least one disk storage device, a flash memory device, or other volatile solid-state storage device.

[0108] If the implemented module is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention can implement all or part of the process steps in the above-mentioned method embodiments by using a computer program to instruct the relevant hardware. The computer program can be stored in a computer-readable storage medium. When executed by a processor, the computer program can implement the steps of each of the above-mentioned method embodiments. The computer program includes computer program code, which can be in source code form, object code form, executable file, or some intermediate form. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard drive, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal, and software distribution medium. It should be noted that the content of the computer-readable medium can be appropriately increased or decreased based on the requirements of legislation and patent practice in a jurisdiction. For example, in some jurisdictions, based on legislation and patent practice, computer-readable media does not include electric carrier signals and telecommunication signals.

[0109] It should be noted that the device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed across multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the present embodiment. In addition, in the drawings of the device embodiments provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, which may be specifically implemented as one or more communication buses or signal lines. A person of ordinary skill in the art can understand and implement the present invention without inventive effort.

[0110] The above description is merely a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in the present invention should be included in the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection of the claims.

Claims

1. A method for optimizing constellation point modulation in an AJSCC system, characterized in that: include: Generate two random sequences that obey Gaussian distribution as source signals, and use SK mapping to simulate joint encoding of the source signals to generate a coded output sequence, specifically: Generate two paths with mean 0 and variance Normally distributed random sequence ; The two-way information source is geometrically encoded, and the Archimedean double helix curve is used as the encoding geometric structure. The encoding function consists of a mapping function and a matching function. The minimum square norm principle is used to convert the normal distribution random sequence into Projected as the closest point on the spiral , and then the two-dimensional information is converted into one-dimensional coding information through the matching function to achieve 2:1 compression. The specific process is shown in the following formula: in, is the angle between the origin and the spiral point, is the distance between the two spirals, is an approximate constant , is the angle of the point on the spiral, are coordinates, is the mapping function, is the matching function, is the encoded output sequence, is a constant for adjusting the spiral density; The coded output sequence is mapped into irregularly distributed constellation points, and the modulated signal is generated after the irregular mapping area is divided, specifically: S21, scaling the coded output sequence in a power normalization form to generate a full-resolution signal point set of the coded output sequence; S22, randomly specifying m initial finite constellation points from the full-resolution signal point set to generate a finite point set; S23, calculating the distance between each signal point and each constellation point in the finite point set, and dividing the signal points with the smallest distance to the same constellation point into a mapping area set; S24, calculating the sum of the distances from all signal points in each mapping area to the corresponding constellation points, taking the average of the signal points in each mapping area set, and obtaining a new constellation point set, wherein the sum of the squares of the distances is the quantization distortion; S25, repeating steps (S23) to (S24) until the quantization distortion is minimized, saving the optimized mapping area, and mapping each signal point in the coded output sequence to a constellation point corresponding to the area to which it belongs based on the optimized mapping area to generate a modulated signal; wherein the constellation point is generated based on the distribution of the signal point, and the constellation point coordinates and the mapping area are adaptively adjusted as the coding parameters change; Transmitting the modulated signal through a Rayleigh channel to generate each channel output symbol, calculating the distance between each channel output symbol and each constellation point, and generating a corresponding demodulated symbol, wherein each demodulated symbol is the constellation point with the smallest distance between the channel output symbol and the constellation point; The demodulated symbols are decoded using a maximum likelihood probability algorithm to obtain a reconstructed source signal.

2. The method for optimizing constellation point modulation in an AJSCC system according to claim 1, wherein: The expression for generating the full-resolution signal point set of the coded output sequence is: ； ； in, is the average power of the coded output sequence, is the probability density function, is the full-resolution signal point set of the encoded output sequence.

3. The method for optimizing constellation point modulation in an AJSCC system according to claim 1, wherein: The expression for calculating the distance from each signal point to each constellation point in the finite point set is: ; in, is the full-resolution signal point of the encoded output sequence, is a constellation point in a finite set of constellation points, Signal point To the constellation point distance; The expression for dividing the signal points with the smallest distance from the same constellation point into a mapping area set is: in, To minimize the distance The obtained constellation point index, Represents constellation points The set of signal point indices with the smallest distance, for The index of .

4. The method for optimizing constellation point modulation in an AJSCC system according to claim 1, wherein: The expression of the quantization distortion is: in, It is a constellation point The distribution probability of is the total number of signals in the full-resolution signal point set, Is the mapping area The center of mass, is the total number of constellation points, It is quantization distortion.

5. An optimization device for constellation point modulation in an AJSCC system, characterized in that: include: The coding output sequence generation unit is used to generate two random sequences that obey the Gaussian distribution as source signals, and use SK mapping to simulate joint coding of the source signals to generate a coding output sequence, specifically for: Generate two paths with mean 0 and variance Normally distributed random sequence ; The two-way information source is geometrically encoded, and the Archimedean double helix curve is used as the encoding geometric structure. The encoding function consists of a mapping function and a matching function. The minimum square norm principle is used to convert the normal distribution random sequence into Projected as the closest point on the spiral , and then the two-dimensional information is converted into one-dimensional coding information through the matching function to achieve 2:1 compression. The specific process is shown in the following formula: in, is the angle between the origin and the spiral point, is the distance between the two spirals, is an approximate constant , is the angle of the point on the spiral, are coordinates, is the mapping function, is the matching function, is the encoded output sequence, is a constant for adjusting the spiral density; The modulation signal generating unit is used to map the coded output sequence into irregularly distributed constellation points and generate a modulation signal after dividing the irregular mapping area, specifically for: S21, scaling the coded output sequence in a power normalization form to generate a full-resolution signal point set of the coded output sequence; S22, randomly specifying m initial finite constellation points from the full-resolution signal point set to generate a finite point set; S23, calculating the distance between each signal point and each constellation point in the finite point set, and dividing the signal points with the smallest distance to the same constellation point into a mapping area set; S24, calculating the sum of the distances from all signal points in each mapping area to the corresponding constellation points, taking the average of the signal points in each mapping area set, and obtaining a new constellation point set, wherein the sum of the squares of the distances is the quantization distortion; S25, repeating steps (S23) to (S24) until the quantization distortion is minimized, saving the optimized mapping area, and mapping each signal point in the coded output sequence to a constellation point corresponding to the area to which it belongs based on the optimized mapping area to generate a modulated signal; wherein the constellation point is generated based on the distribution of the signal point, and the constellation point coordinates and the mapping area are adaptively adjusted as the coding parameters change; a demodulation symbol generating unit, configured to transmit the modulated signal through a Rayleigh channel to generate each channel output symbol, calculate the distance between each channel output symbol and each constellation point, and generate a corresponding demodulation symbol, wherein each demodulation symbol is the constellation point with the smallest distance between the channel output symbol and the constellation point; The reconstructed information source generating unit is used to decode the demodulated symbols using a maximum likelihood probability algorithm to obtain a reconstructed information source signal.

6. An optimization device for constellation point modulation in an AJSCC system, characterized in that: The system comprises a memory and a processor, wherein a computer program is stored in the memory and can be executed by the processor to implement the method for optimizing constellation point modulation in an AJSCC system according to any one of claims 1 to 4.

7. A computer-readable storage medium, characterized in that A computer program is stored, and the computer program can be executed by a processor of the device where the computer-readable storage medium is located to implement the optimization method for constellation point modulation in an AJSCC system as described in any one of claims 1 to 4.

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