A semantic communication method based on deep learning and energy optimization

Abstract

The application discloses a kind of based on deep learning and energy consumption optimization semantic communication method, comprising: step S1: construct the deep neural network semantic communication system based on super-prior structure;Step S2: energy consumption modeling is carried out to deep neural network semantic communication system, including source coding and decoding energy consumption modeling and communication transmission energy consumption modeling;Step S3: model inference complexity, calculation frequency and wireless transmission power are used as adjustable decision variables, a joint optimization problem is constructed, and communication strategy is obtained by solving.This application constructs unified computing energy consumption and communication energy consumption model, under the premise of meeting end-to-end delay constraint, jointly optimizes semantic source coding model complexity, processor operating frequency and wireless transmission power and other key parameters, so as to realize the minimization of system total energy consumption.

Description

Technical Field

[0001] This invention relates to the field of semantic communication, and in particular to a semantic communication method based on deep learning and energy optimization. Background Technology

[0002] As mobile communication systems evolve towards sixth generation (6G), these systems not only need to support higher data rates and lower latency, but also require energy-efficient operation on energy-constrained terminals and edge devices. Traditional communication systems primarily focus on spectral efficiency, optimizing transmission rates using Shannon's theory, but neglect the computational energy consumption during source processing and communication.

[0003] In recent years, semantic communication has significantly reduced the number of transmitted bits while maintaining perception or task performance by utilizing deep neural networks to extract task-related semantic features from raw data. However, most existing semantic communication research focuses only on transmission performance or perception accuracy, typically assuming that the energy consumption of source coding and channel transmission is negligible, or only considering communication energy consumption while ignoring the computational energy consumption of deep neural network inference. This modeling approach fails to reflect the energy efficiency characteristics of the deep coupling between computation and communication in real-world systems.

[0004] Therefore, there is an urgent need for an energy efficiency modeling and optimization method that can simultaneously characterize the semantic coding complexity of deep neural networks, wireless transmission resource configuration, and end-to-end latency constraints, so as to achieve energy-efficient semantic communication for practical systems. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of the prior art and provide a semantic communication method based on deep learning and energy consumption optimization. It constructs a unified computational energy consumption and communication energy consumption model, and under the premise of satisfying end-to-end latency constraints, jointly optimizes key parameters such as semantic source coding model complexity, processor operating frequency and wireless transmission power, thereby minimizing the total system energy consumption.

[0006] The objective of this invention is achieved through the following technical solution: a semantic communication method based on deep learning and energy consumption optimization, comprising:

[0007] Step S1: Construct a deep neural network semantic communication system based on a super-prior structure, including a sender and a receiver;

[0008] The transmitting end includes a deep source encoder and a channel encoder. In the deep source encoder, a deep neural network is used to extract and compress semantic features of the source data, and after digital channel encoding by the channel encoder, it is transmitted using a wireless channel.

[0009] The receiving end includes a deep source decoder and a channel decoder. After the channel decoder performs channel decoding on the received signal, the deep source decoder recovers the corresponding source data.

[0010] Step S2: Perform energy consumption modeling for the deep neural network semantic communication system, including source encoding / decoding energy consumption modeling and communication transmission energy consumption modeling;

[0011] Step S3: Using model inference complexity, computation frequency, and wireless transmission power as adjustable decision variables, construct a joint optimization problem and solve it to obtain the communication strategy.

[0012] The beneficial effects of this invention are as follows: This invention combines semantic feature extraction and compression technology based on deep neural networks with digital channel coding and wireless transmission technology. Considering the actual scenario where computing and communication energy consumption coexist in end-to-end semantic communication systems, it constructs a unified computing energy consumption and communication energy consumption model. Under the premise of meeting end-to-end latency constraints, it jointly optimizes key parameters such as semantic source coding model complexity, processor operating frequency, and wireless transmission power, thereby minimizing the total system energy consumption. Attached Figure Description

[0013] Figure 1 This is a flowchart of the method of the present invention;

[0014] Figure 2 This is a schematic diagram of a semantic communication system based on deep neural networks.

[0015] Figure 3 This is a schematic diagram of a deep source encoder based on a priori structure.

[0016] Figure 4 This is a schematic diagram of a deep source decoder based on a super-prior structure.

[0017] Figure 5 This is a schematic diagram illustrating the relationship between total energy consumption and reasoning complexity.

[0018] Figure 6 This is a diagram illustrating the relationship between total latency and inference complexity. Detailed Implementation

[0019] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings, but the scope of protection of the present invention is not limited to the following description.

[0020] like Figure 1 As shown, a semantic communication method based on deep learning and energy consumption optimization includes:

[0021] Step S1: Construct a deep neural network semantic communication system based on a super-prior structure, including a sender and a receiver;

[0022] like Figure 2 The diagram illustrates the architecture of an energy efficiency-aware semantic communication system that integrates deep learning and digital communication technologies. At the transmitting end, the system utilizes a deep neural network to extract and compress semantic features from the source data, and transmits semantic information through digital channel coding and wireless channels. At the receiving end, based on different user needs, the system performs channel decoding on the received signal and recovers the corresponding semantic information or source data.

[0023] like Figure 3 As shown, in this invention, the source coding at the transmitting end adopts a deep neural network image compression model based on a super-prior structure to extract and compress semantic features from the original source data (in this application, semantic data, such as image data).

[0024] A1. Let the source encoder be a deep neural network source encoder based on a priori structure, including a feature extraction network, a quantization module, an entropy encoder, a splicing module, and a priori model; the feature extraction network is a deep neural network;

[0025] The input raw source samples are represented as vectors. ,in , Indicates the dimension of the original source data. express 3D real space, Follows statistical distribution ;

[0026] A2. Original source data First, the input is fed into the feature extraction network, and then processed by the encoding function composed of the feature extraction network. Mapped to a continuous latent variable vector The relationship is expressed as:

[0027] ;

[0028] in Represents a continuous latent variable vector. Represents the dimension of the latent variable, and satisfies , This represents the set of parameters for the feature extraction network.

[0029] A3. Continuous latent variables The discrete latent variable vector is then obtained after processing by the quantization module. ,in , express 3D integer space;

[0030] The quantization module performs analysis on continuous latent variables. Each element in the vector is rounded to obtain the discrete latent variable vector. ;

[0031] A4. Introduce a prior model to model the distribution of latent variables:

[0032] Discrete latent variables The input is given to the entropy encoder, and the prior model outputs the corresponding distribution parameters based on the statistical properties of the latent variables. and ,in This represents the mean vector of each dimension of the latent variable. This represents the standard deviation vector of each dimension of the latent variable;

[0033] The super-prior model includes a super-prior encoder, a super-prior quantization module, and a super-prior decoder; the super-prior encoder and super-prior decoder are both implemented using deep neural networks.

[0034] First, Input the hyper-prior encoder to obtain hyper-prior latent variables :

[0035] ;

[0036] in Indicates a priori encoder, This represents the network parameters of the hyper-prior encoder;

[0037] The prior quantization module uses rounding to... Process each element in the process, and Quantized into discrete variables ;

[0038] Will The latent variable's distribution parameters, including the mean, are obtained by inputting the super-prior decoder. and variance :

[0039] ;

[0040] in Indicates a priori decoder, This represents the network parameters of the hyper-prior decoder;

[0041] Entropy encoders are based on distributed parameter pairs Perform probabilistic modeling and generate bit sequences Simultaneously, the advanced prior model generates an auxiliary bit sequence to describe the information on the distribution side of the latent variables. ;

[0042] The advanced prior model generates the auxiliary bit sequence in the following way: assuming Following a standard Gaussian distribution, directly... As input, the entropy encoder is called to obtain the bit sequence. ;

[0043] A5. The transmitting end uses a bit sequence splicing module to... and The bits are spliced ​​together to form the complete bit stream output by the source encoder. After digital channel encoding via a channel encoder, it is transmitted using a wireless channel.

[0044] like Figure 4 As shown, at the receiving end, the source decoding module is used to recover the latent variable representation from the received bit stream and reconstruct the source data.

[0045] B1. Suppose that the source decoder adopts a deep neural network source decoder based on a priori structure, including a segmentation module, an entropy decoder, a priori model, and a feature recovery network (also called an image recovery network when the source data is an image).

[0046] B2. The receiver first uses a channel decoder to perform channel decoding on the received complex channel symbol sequence to obtain the bit stream. Then, the segmentation module is used to process the received bit stream. Decompose the data to obtain the corresponding bit sequence. and The channel decoding process involves restoring a complex sequence of channel symbols to a bit stream b.

[0047] B3. The advanced prior model is based on the bit sequence. Recovery of latent variable distribution parameters and ;

[0048] Will As input, the entropy decoder is called directly to obtain... ,Bundle The input is fed into the hyper-prior decoder to obtain and :

[0049] ;

[0050] in Indicates a priori decoder, This represents the network parameters of the hyper-prior decoder;

[0051] B3, Entropy Decoder Based on and Bit sequence Perform lossless entropy decoding to recover the discrete latent variable vector. ;

[0052] B4. Recovered discrete latent variables The data is then input into the feature recovery network to obtain the reconstructed source data. The relationship is expressed as:

[0053] ;

[0054] The feature recovery network is a deep neural network, and its decoding function is: , This represents the recovered source data vector. This represents the parameter set of the feature recovery network. Through the above process, the receiver can achieve high-quality reconstruction of the original source data while ensuring compression efficiency.

[0055] In this invention, the channel coding module is used to compress the bit stream output by the source coding module. This is further mapped to a complex sequence of channel symbols that can be transmitted over a wireless channel, in order to achieve reliable transmission under given bandwidth and channel conditions and characterize the achievable Shannon transmission rate. Specifically, the transmitter obtains the bit stream after completing source coding. ,in , This represents the main bit sequence obtained by entropy encoding. This represents the sequence of bits representing the prior information. and These represent the lengths of the corresponding bit sequences, and the total bit length is... The channel encoder will output the bit stream. Encoded as a length of Complex channel symbol sequences ,in C represents the complex field. This represents the number of channel symbols to be transmitted. For ease of unified modeling, this invention employs a unit average power normalization constraint for the channel symbols, meaning that the average power of each symbol is expected to be 1, expressed as:

[0056] ;

[0057] in Represents the mathematical expectation operator. Representing vectors The norm of 2. Combining this with the subsequent wireless transmission model, let the communication bandwidth be... (Unit: Hz), the communication power used by the transmitting end for wireless transmission is (Unit: W), the equivalent complex channel coefficients from the transmitter to the receiver are: The noise is additive circularly symmetric complex Gaussian noise with a noise power spectral density of... (Unit: W / Hz), then according to Shannon's channel coding theorem, the maximum transmission rate (Shannon rate) that the system can support under the above bandwidth and channel conditions is denoted as: (Unit: bit / s), its expression is:

[0058] ;

[0059] in This represents the logarithmic operation with base 2. Represents complex channel coefficients The modulus; the Shannon rate Used to characterize in a given , , , Under the given conditions, the maximum reliable transmission capability achievable through channel coding and transmission, and the average code rate of the aforementioned source coding, are compared. Together they determine the subsequent transmission delay relationship, thus providing a unified parameter interface for the joint configuration of power and rate in energy efficiency optimization.

[0060] In step S1, the deep source encoder and deep source decoder need to be pre-trained. The training method is as follows:

[0061] (1) Convert the original source sample vector As semantic data to be transmitted, after being transmitted according to the system in step S1, let the source data recovered by the receiving end be... ;

[0062] The distortion in source data recovery is defined as:

[0063] ;

[0064] Indicates the magnitude of the vector;

[0065] The loss function for model training is defined as:

[0066] ;

[0067] in, The set distortion threshold, This represents the average code rate of the source coding;

[0068] Using a loss function, the parameter set of the feature extraction network is... The parameter set of the feature recovery network Network parameters of the priori encoder Parameters of the super-prior decoder A joint update is performed using the gradient descent algorithm.

[0069] (2) For different original source sample vectors Repeat step (1) until the training rounds are reached, and then obtain the final model parameters. , , , And apply it in actual communication processes;

[0070] In the embodiments of this application, during each training iteration, the average code rate of the source coding will change with the model parameters. , , , The model changes with updates; after training, the model parameters are fixed. It also remains unchanged.

[0071] Step S2: Perform energy consumption modeling for the deep neural network semantic communication system, including source encoding / decoding energy consumption modeling and communication transmission energy consumption modeling;

[0072] In this invention, the depth source encoder and depth source decoder are implemented in the same type of processor; the processor processes individual source samples. One deep source coding and one deep source decoding operation is denoted as one inference computation. The number of floating-point operations required for one inference computation is: ,in, Used to characterize the inference computational complexity of floating-point operations; assuming the processor's computational frequency is... ,in, The computational latency for a single inference operation on a single source sample is used to characterize the number of floating-point operations performed by the processor per second. Represented as:

[0073] ;

[0074] in Indicates computation delay;

[0075] Using a processor power consumption model, the computational power of the inference process is... Represented as:

[0076] ;

[0077] in Indicates calculated power. This represents a constant related to the hardware architecture. Indicates the calculated frequency;

[0078] Therefore, the computational energy consumption of inference from a single source sample is... Represented as:

[0079] ;

[0080] The average code rate of the source coding is denoted as . , with reasoning complexity They satisfy a power-law relationship:

[0081] ;

[0082] in and Represents the power-law fitting parameters. The asymptotic lower bound of the compression ratio is represented by a uniform variable. Simultaneously characterize the "average code rate of the source coding" "and "calculate energy consumption" The coupling relationship between "".

[0083] The processing steps for depth source encoders and depth source decoders, By adjusting the number of hidden layer channels in the network To achieve continuous change, where This represents the number of channels in the hidden layers of the encoding and decoding networks; at this time Represented as:

[0084] ;

[0085] in This indicates the number of floating-point operations performed by modules other than the hidden layer. This represents the floating-point operation coefficients for a single-input, single-output channel convolutional layer. This item is used to indicate when both the number of input and output channels are 0. The number of connections and computational cost exhibit a quadratic growth characteristic.

[0086] Through the above modeling, this invention directly uses inference complexity without introducing block coding assumptions. With calculation frequency To quantify the energy consumption of source coding and the average code rate of the aforementioned source coding Maintaining consistent notation provides a resolvable energy consumption expression for subsequent joint "computation-communication" energy efficiency optimization.

[0087] In this invention, communication transmission energy consumption refers to the energy consumed when transmitting a source-coded and compressed bit stream in a wireless channel. Average transmission delay. (Unit: s) is determined by the number of compressed bits and the channel transmission rate, specifically:

[0088] ;

[0089] Based on this, the energy consumption during the communication phase is determined by the transmission power. and communication circuit power It was jointly decided that a common power consumption model would be adopted for communication energy consumption. (Unit: J) is expressed as:

[0090] ;

[0091] in This indicates the power consumption of the communication circuit, mainly generated by digital signal processing modules, mixers, low-noise amplifiers, and synchronizers; for systems with fixed bandwidth and data rates, The variation is small and can be considered a constant. Through the above modeling, this invention expresses the communication transmission energy consumption as the average code rate of the source coding, while maintaining symbolic consistency with the energy consumption modeling of source coding. Communication power and Shannon rate The function provides a clear parameter interface and energy consumption expression for subsequent joint optimization of computation and communication energy efficiency.

[0092] Step S3: Using model inference complexity, computation frequency, and wireless transmission power as adjustable decision variables, construct a joint optimization problem and solve it to obtain the communication strategy.

[0093] To leverage the aforementioned scaling law to achieve high energy efficiency in semantic communication systems, this invention uses model inference complexity, computation frequency, and wireless transmission power as adjustable decision variables to construct a joint optimization problem. Total energy consumption End-to-end delay They are represented as follows:

[0094] ;

[0095] ;

[0096] Given maximum allowable end-to-end delay The optimization objective of this invention is to minimize total energy consumption while satisfying time delay constraints. The optimization problem can be expressed as:

[0097] ;

[0098] This problem involves finding decision variables. , and The joint configuration makes the objective function Taking the minimum value, the constraint means that the end-to-end delay does not exceed a given threshold. In this problem, Control source encoding output bit count and computational delay ; Determines the latency and energy consumption of inference calculations; Simultaneously affects Shannon's rate and communication energy consumption ; in constraints Given the application scenario, this represents the user experience or system real-time requirements. Through this modeling, guided by the scaling law, this invention couples the configuration of computing and communication resources into a unified energy-latency optimization framework.

[0099] To solve the aforementioned joint optimization problem, this invention first fixes some of the variables, gradually analyzes the coupling relationship between the decision variables, and then uses scaling laws and energy efficiency models to derive a closed-form solution.

[0100] (1) Fixed model complexity and calculate frequency At that time, the problem degenerated into concerns about the transmission power. The univariate optimization aims to minimize communication energy consumption. Under a given time delay constraint, this is equivalent to maximizing energy efficiency. Among them, energy efficiency Defined as:

[0101] ;

[0102] in Modulo h; energy efficiency That is, the number of effective bits that can be transmitted per unit of energy; by... for By taking the derivative and making it zero, the optimal transmission power is obtained;

[0103] (2) When the calculation frequency and transmission power are fixed, the problem degenerates into a problem of reasoning complexity. Optimization, based on scaling laws Total energy consumption and end-to-end delay They are represented as follows:

[0104] ;

[0105] ;

[0106] Taking the derivative of the above expression and setting it to zero, we obtain the optimal reasoning complexity chosen to minimize the total energy consumption, denoted as . Its expression is:

[0107] ;

[0108] (3) With fixed model complexity and transmission power, calculate the frequency. The lower bound is determined by the time delay constraint, which satisfies:

[0109] ;

[0110] When selecting frequency When the lower bound is reached, the computational energy consumption is minimized;

[0111] S303. Select any one of the cases (1) to (3) in step S302 and execute the energy-optimal strategy.

[0112] In the embodiments of this application, the application is verified through simulation experiments:

[0113] A simulation platform was built and its parameters were set. Firstly, on the computing side, an AMD EPYC8534PN CPU, designed for edge inference, was used for deep neural network model inference. This chip is based on the Zen4c microarchitecture and supports the AVX-512 instruction set; its base clock frequency is [missing information]. =2.0GHz. The single-core computing frequency ν (unit: FLOP / s) is determined by the processor's clock frequency and vector instruction parallelism, and the calculation expression is:

[0114] ;

[0115] in This indicates the number of data elements that a single vector instruction can process simultaneously. This indicates the number of parallel units in the processor that support AVX-512 instructions, with a coefficient of 2 introduced due to the use of hybrid multiply-accumulate (FMA) operations. For the AMD EPYC8534PN, the maximum single-core computing frequency is... =128 GFLOPs / s. To simplify the model and facilitate energy consumption calculation, a calculation energy efficiency constant is introduced. This is used to map computation frequency to power consumption; based on processor specifications and existing models. The relationship between source coding rate and inference complexity is parameterized using a power-law model based on scaling laws, and the power-law coefficients are obtained through fitting. =4.63×10^{12}、 =1.77 and the lower bound of source entropy =0.212.

[0116] On the communication side, the simulation uses a large-scale path loss channel model with a path loss exponent. =3.5, communication bandwidth =1MHz, noise power spectral density =−174dBm / Hz. Considering power amplifier efficiency. =0.35, transmit antenna gain =5dBi, receiver antenna gain =0dBi, Noise Figure =10dB, link margin compensation =10dB. The system operates in the 3.5GHz band, and the link signal-to-noise ratio is... The calculation formula is:

[0117] ;

[0118] in The distance between the transmitter and receiver is set in this experiment. =200 meters, =32.44dB is the reference gain factor. The power consumption of the communication circuit is taken as a constant. =2W. To test the energy efficiency of the semantic communication link, color images from the CIFAR-10 dataset were selected on the data side, with 64×64 RGB images used as the source input.

[0119] Simulations were performed based on the above settings to compare the total energy consumption and end-to-end latency of the system under different inference complexity and transmit power configurations, and the results were compared with the prediction results of the scaling law. The experiment first fixed the transmit power and changed the number of floating-point operations in the neural network inference. Calculate total energy consumption and latency The changes. Figure 5 and Figure 6 Simulation results show that total energy consumption and end-to-end delay increase with... The changes show a trend of first decreasing and then increasing: when the inference complexity is low, the source coding rate is high and the transmission latency is large, resulting in high total energy consumption; as... Increasing the source coding rate reduces the transmission delay and decreases the total energy consumption; when As it continues to increase, calculate the energy consumption factor. The rapid increase in relationships leads to a rise in total energy consumption. During this process, an optimal reasoning complexity can be observed. This minimizes the total energy consumption, a value consistent with the aforementioned analytical expression; there is also an optimal reasoning complexity for minimizing end-to-end latency. The lowest point of the time delay curve is reflected in the simulation curve.

[0120] The foregoing description illustrates and describes a preferred embodiment of the present invention. However, as previously stated, it should be understood that the present invention is not limited to the forms disclosed herein and should not be construed as excluding other embodiments. It can be used in various other combinations, modifications, and environments, and can be altered within the scope of the inventive concept described herein through the foregoing teachings or techniques or knowledge in related fields. Any modifications and variations made by those skilled in the art that do not depart from the spirit and scope of the present invention should be within the protection scope of the appended claims.

Claims

1. A semantic communication method based on deep learning and energy consumption optimization, characterized by: Includes the following steps: Step S1: Construct a deep neural network semantic communication system based on a super-prior structure, including a sender and a receiver; The transmitting end includes a deep source encoder and a channel encoder. In the deep source encoder, a deep neural network is used to extract and compress semantic features of the source data, and after digital channel encoding by the channel encoder, it is transmitted using a wireless channel. The receiving end includes a deep source decoder and a channel decoder. After the channel decoder performs channel decoding on the received signal, the deep source decoder recovers the corresponding source data. Step S2: Perform energy consumption modeling for the deep neural network semantic communication system, including source encoding / decoding energy consumption modeling and communication transmission energy consumption modeling; Step S3: Using model inference complexity, computation frequency, and wireless transmission power as adjustable decision variables, construct a joint optimization problem and solve it to obtain the communication strategy; Step S3 includes: S301. Construct a joint optimization problem by using model inference complexity, computation frequency, and wireless transmission power as adjustable decision variables: Total energy consumption End-to-end delay They are represented as follows: ； ； Given maximum allowable end-to-end delay The optimization objective is to minimize total energy consumption while satisfying latency constraints. The optimization problem is formulated as follows: ； in, To calculate energy consumption; To find decision variables for the average transmission delay; optimization problem. , and The joint configuration makes the objective function The minimum value is taken, and the constraint means that the end-to-end delay does not exceed a given threshold; in the optimization problem, Control source encoding output bit count and computational delay ; Determines the latency and energy consumption of inference calculations; Simultaneously affects Shannon's rate and communication energy consumption ; in constraints This represents user experience or system real-time requirements; S302. Fix some of the variables, analyze the coupling relationship between the decision variables step by step, and then derive the closed-form solution using the scaling law and energy efficiency model: (1) Fixed model complexity and calculate frequency At that time, the problem degenerated into concerns about the transmission power. The univariate optimization aims to minimize communication energy consumption. Under a given time delay constraint, this is equivalent to maximizing energy efficiency. Among them, energy efficiency Defined as: ； in Modulo h; energy efficiency That is, the number of effective bits that can be transmitted per unit of energy; by... for By taking the derivative and making it zero, the optimal transmission power is obtained; (2) When the calculation frequency and transmission power are fixed, the problem degenerates into a problem of reasoning complexity. Optimization, based on scaling laws Total energy consumption and end-to-end delay They are represented as follows: ； ； right Taking the derivative and setting it to zero, we obtain the optimal reasoning complexity chosen to minimize total energy consumption, denoted as . : ； (3) With fixed model complexity and transmission power, calculate the frequency. The lower bound is determined by the time delay constraint, which satisfies: ； When selecting frequency When the lower bound is reached, the energy consumption calculation is optimal; Choose any one of (1) to (3) and execute the energy-optimal strategy.

2. The semantic communication method based on deep learning and energy consumption optimization according to claim 1, characterized in that: In step S1, the transmitting end includes a deep source encoder and a channel encoder. The deep source encoder uses a deep neural network to extract and compress semantic features from the source data, and after digital channel coding by the channel encoder, it is transmitted via a wireless channel. This includes: A1. Let the source encoder be a deep neural network source encoder based on a priori structure, including a feature extraction network, a quantization module, an entropy encoder, a splicing module, and a priori model; the feature extraction network is a deep neural network; The input raw source samples are represented as vectors. ,in , Indicates the dimension of the original source data. express 3D real space, Follows statistical distribution ; A2. Original source data First, the input is fed into the feature extraction network, and then processed by the encoding function composed of the feature extraction network. Mapped to a continuous latent variable vector The relationship is expressed as: ； in Represents a continuous latent variable vector. Represents the dimension of the latent variable, and satisfies , This represents the set of parameters for the feature extraction network. A3. Continuous latent variables The discrete latent variable vector is then obtained after processing by the quantization module. ,in , express 3D integer space; The quantization module performs analysis on continuous latent variables. Each element in the vector is rounded to obtain the discrete latent variable vector. ; A4. Introduce a prior model to model the distribution of latent variables: Discrete latent variables The input is given to the entropy encoder, and the prior model outputs the corresponding distribution parameters based on the statistical properties of the latent variables. and ,in This represents the mean vector of each dimension of the latent variable. This represents the standard deviation vector of each dimension of the latent variable; The super-prior model includes a super-prior encoder, a super-prior quantization module, and a super-prior decoder; the super-prior encoder and super-prior decoder are both implemented using deep neural networks. First, Input the hyper-prior encoder to obtain hyper-prior latent variables : ； in Indicates a priori encoder, This represents the network parameters of the hyper-prior encoder; The prior quantization module uses rounding to... Process each element in the process, and Quantized into discrete variables ; Will The latent variable's distribution parameters, including the mean, are obtained by inputting the super-prior decoder. and variance : ； in Indicates a priori decoder, This represents the network parameters of the hyper-prior decoder; Entropy encoders are based on distributed parameter pairs Perform probabilistic modeling and generate bit sequences Simultaneously, the advanced prior model generates an auxiliary bit sequence to describe the information on the distribution side of the latent variables. ; The advanced prior model generates the auxiliary bit sequence in the following way: assuming Following a standard Gaussian distribution, directly... As input, the entropy encoder is called to obtain the bit sequence. ; A5. The transmitting end uses a bit sequence splicing module to... and The bits are spliced ​​together to form the complete bit stream output by the source encoder. After digital channel encoding via a channel encoder, it is transmitted using a wireless channel.

3. The semantic communication method based on deep learning and energy consumption optimization according to claim 2, characterized in that: The A5 includes: Suppose that the transmitting end obtains a bit stream after completing source coding. ,in , This represents the main bit sequence obtained by entropy encoding. It is a sequence of bits representing the prior information. and They represent and The length of the bit sequence, and the total bit length is ; Using a channel encoder, the compressed bitstream output by the source encoder is... The Shannon transmission rate, mapped as a sequence of complex channel symbols transmitted in a wireless channel, achieves reliable transmission under given bandwidth and channel conditions: The channel encoder will output the bit stream. Encoded as a length of Complex channel symbol sequences ,in C represents the complex field. Indicates the number of channel symbols to be transmitted; The channel symbols are subject to a unit average power normalization constraint, meaning that the average power of each symbol is expected to be 1. The expression for this constraint is: ； in Represents the mathematical expectation operator. Representing vectors The second norm; Let the communication bandwidth be The communication power used for wireless transmission at the transmitting end is The equivalent complex channel coefficients from the transmitter to the receiver are: The noise is additive circularly symmetric complex Gaussian noise with a noise power spectral density of... According to Shannon's channel coding theorem, the maximum transmission rate supported by the system under the conditions of communication bandwidth and channel, i.e., the Shannon rate, is denoted as... Its expression is: ； in This represents the logarithmic operation with base 2. Represents complex channel coefficients The modulus; Shannon rate Used to characterize in a given , , , The maximum reliable transmission capability of channel coding and transmission under certain conditions, and its relationship with the average code rate of source coding. Together they determine the subsequent transmission delay relationship.

4. The semantic communication method based on deep learning and energy consumption optimization according to claim 1, characterized in that: In step S1, the receiving end includes a deep source decoder and a channel decoder. After the channel decoder performs channel decoding on the received signal, the deep source decoder recovers the corresponding source data, including: B1. Assume that the source decoder adopts a deep neural network source decoder based on a priori structure, including a segmentation module, an entropy decoder, a priori model, and a feature recovery network; B2. The receiver first uses a channel decoder to perform channel decoding on the received complex channel symbol sequence to obtain the bit stream. Then, the segmentation module is used to process the received bit stream. Decompose the data to obtain the corresponding bit sequence. and The channel decoding process involves restoring a complex sequence of channel symbols to a bit stream b. B3. The advanced prior model is based on the bit sequence. Recovery of latent variable distribution parameters and ; Will As input, the entropy decoder is called directly to obtain... ,Bundle The input is fed into the hyper-prior decoder to obtain and : ； in Indicates a priori decoder, This represents the network parameters of the hyper-prior decoder; B3, Entropy Decoder Based on and Bit sequence Perform lossless entropy decoding to recover the discrete latent variable vector. ; B4. Recovered discrete latent variables The data is then input into the feature recovery network to obtain the reconstructed source data. The relationship is expressed as: ； The feature recovery network is a deep neural network, and its decoding function is: , This represents the recovered source data vector. This represents the set of parameters for the feature recovery network.

5. The semantic communication method based on deep learning and energy consumption optimization according to claim 1, characterized in that: In step S1, the deep source encoder and deep source decoder need to be pre-trained. The training method is as follows: (1) Convert the original source sample vector As semantic data to be transmitted, after being transmitted according to the system in step S1, let the source data recovered by the receiving end be... ; The distortion in source data recovery is defined as: ； Indicates the magnitude of the vector; The loss function for model training is defined as: ； in, The set distortion threshold, This represents the average code rate of the source coding; Using a loss function, the parameter set of the feature extraction network is... The parameter set of the feature recovery network Network parameters of the priori encoder Parameters of the super-prior decoder A joint update is performed using the gradient descent algorithm. (2) For different original source sample vectors Repeat step (1) until the training rounds are reached, and then obtain the final model parameters. , , , And apply it in actual communication processes.

6. The semantic communication method based on deep learning and energy consumption optimization according to claim 1, characterized in that: In step S2, source encoding / decoding energy consumption modeling includes: Assume that the depth source encoder and depth source decoder are implemented in the same type of processor; and that a single source sample is processed in the processor. One deep source coding and one deep source decoding operation is denoted as one inference computation. The number of floating-point operations required for one inference computation is: ,in, Used to characterize the inference computational complexity of floating-point operations; assuming the processor's computational frequency is... ,in, The computational latency for a single inference operation on a single source sample is used to characterize the number of floating-point operations performed by the processor per second. Represented as: ； in Indicates computation delay; Using a processor power consumption model, the computational power of the inference process is... Represented as: ； in Indicates calculated power. This represents a constant related to the hardware architecture. Indicates the calculated frequency; Therefore, the computational energy consumption of inference from a single source sample is... Represented as: ； The average code rate of the source coding is denoted as . , With reasoning complexity They satisfy a power-law relationship: ； in and Represents the power-law fitting parameters. The asymptotic lower bound of the compression ratio is represented by a uniform variable. Simultaneously characterize the "average code rate of the source coding" "and" calculation of energy consumption The coupling relationship between "". The processing steps for depth source encoders and depth source decoders, By adjusting the number of hidden layer channels in the network To achieve continuous change, where This represents the number of channels in the hidden layers of the encoding and decoding networks; at this time Represented as: ； in This indicates the number of floating-point operations performed by modules other than the hidden layer. This represents the floating-point operation coefficients for a single-input, single-output channel convolutional layer. This item is used to indicate when both the number of input and output channels are 0. The number of connections and computational cost exhibit a quadratic growth characteristic.

7. The semantic communication method based on deep learning and energy consumption optimization according to claim 1, characterized in that: In step S2, communication transmission energy consumption modeling includes: Communication transmission energy consumption refers to the energy consumed when transmitting a source-coded and compressed bit stream in a wireless channel, and the average transmission delay. It is determined by the number of compressed bits and the channel transmission rate, specifically: ； Based on this, the energy consumption during the communication phase is determined by the transmission power. and communication circuit power It was jointly decided to adopt a power consumption model for communication energy consumption. Represented as: ； in The power consumption of the communication circuit is considered a constant. By modeling the energy consumption of communication transmission, the energy consumption of communication transmission is expressed as the average code rate of the source coding. Communication power and Shannon rate The function.

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