Unmanned aerial vehicle semantic communication method and device, electronic equipment, storage medium and program product

Abstract

The invention provides an unmanned aerial vehicle semantic communication method and device, electronic equipment, a storage medium and a program product, and the method comprises the steps: determining user position information, and obtaining channel gain data based on the user position information; determining an original sending signal, inputting the original sending signal and the channel gain data into a pre-trained data transmission optimization model, performing optimization processing on the original sending signal based on the channel gain data through the data transmission optimization model, and outputting to obtain an optimized sending signal; determining noise information of a communication channel, and obtaining an original receiving signal according to the noise information, the channel gain data and the optimized sending signal; and inputting the original receiving signal and the channel gain data into the data transmission optimization model, performing optimization processing on the original receiving signal based on the channel gain data through the transmission data optimization model, and outputting to obtain an optimized receiving signal. According to the invention, the acquisition capability of mobile user data in an unmanned aerial vehicle application environment can be improved, and the restoration quality of data information is remarkably enhanced.

Description

Technical Field

[0001] The present disclosure relates to the technical field of wireless communication networks, and in particular to a method, device, electronic device, storage medium and program product for semantic communication between unmanned aerial vehicles. Background Art

[0002] This section is intended to provide a background or context to the embodiments of the disclosure that are recited in the claims. No description herein is admitted to be prior art by inclusion in this section.

[0003] UAV communication is a technology that uses UAVs as aerial platforms to enhance or restore the connectivity of ground communication networks. UAVs can carry communication equipment to provide wireless signal coverage for ground users. Signal processing refers to the general term for signal extraction, transformation, analysis, and synthesis. Its basic contents include transformation, filtering, modulation, demodulation, detection, spectrum analysis, and estimation.

[0004] However, in the related art, mobile user information cannot be collected efficiently in drone scenarios, and the reconstruction quality of the data information is not high. Summary of the invention

[0005] In view of this, the purpose of the present disclosure is to propose a drone semantic communication method, device, electronic device, storage medium and program product, which can at least solve one of the technical problems in the related technology to a certain extent.

[0006] Based on the above purpose, the first aspect of the exemplary embodiment of the present disclosure provides a drone semantic communication method, which is applied to a server, and the method includes:

[0007] Determine user location information, and obtain channel gain data based on the user location information;

[0008] Determine an original transmission signal, input the original transmission signal and the channel gain data into a pre-trained data transmission optimization model, optimize the original transmission signal based on the channel gain data through the data transmission optimization model, and output an optimized transmission signal;

[0009] Determine noise information of the communication channel, and obtain an original received signal based on the noise information, the channel gain data and the optimized transmitted signal;

[0010] The original received signal and the channel gain data are input into the data transmission optimization model, and the original received signal is optimized based on the channel gain data by the transmission data optimization model to output an optimized received signal.

[0011] Based on the same inventive concept, the second aspect of the exemplary embodiment of the present disclosure provides a drone semantic communication device, including:

[0012] A gain data determination module, configured to determine user location information, and obtain channel gain data based on the user location information;

[0013] a transmission signal determination module, configured to determine an original transmission signal, input the original transmission signal and the channel gain data into a pre-trained data transmission optimization model, optimize the original transmission signal based on the channel gain data through the data transmission optimization model, and output an optimized transmission signal;

[0014] An original received signal determination module is configured to determine noise information of a communication channel, and obtain an original received signal based on the noise information, the channel gain data and the optimized transmission signal;

[0015] The optimized received signal determination module is configured to input the original received signal and the channel gain data into the data transmission optimization model, optimize the original received signal based on the channel gain data through the transmission data optimization model, and output an optimized received signal.

[0016] Based on the same inventive concept, a third aspect of the exemplary embodiment of the present disclosure provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the method described in the first aspect is implemented.

[0017] Based on the same inventive concept, a fourth aspect of the exemplary embodiments of the present disclosure provides a non-transitory computer-readable storage medium, wherein the non-transitory computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a computer to execute the method described in the first aspect.

[0018] Based on the same inventive concept, a fifth aspect of the exemplary embodiments of the present disclosure provides a computer program product, including computer program instructions. When the computer program instructions are executed on a computer, the computer executes the method described in the first aspect.

[0019] As can be seen from the above, the embodiments of the present disclosure provide a method, device, electronic device, storage medium, and program product for semantic communication between drones. The method includes:

[0020] Determine the user location information, and obtain the channel gain data based on the user location information; determine the original transmission signal, input the original transmission signal and the channel gain data into a pre-trained data transmission optimization model, optimize the original transmission signal based on the channel gain data through the data transmission optimization model, and output the optimized transmission signal; determine the noise information of the communication channel, and obtain the original reception signal based on the noise information, the channel gain data and the optimized transmission signal; input the original reception signal and the channel gain data into the data transmission optimization model, optimize the original reception signal based on the channel gain data through the transmission data optimization model, and output the optimized reception signal. The present disclosure can improve the ability to collect mobile user data in the application environment of drones, and significantly enhance the restoration quality of data information. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the technical solutions in the present disclosure or related technologies, the drawings required for use in the embodiments or related technical descriptions are briefly introduced below. Obviously, the drawings described below are only embodiments of the present disclosure. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0022] Figure 1 A schematic diagram of an application scenario of the UAV semantic communication method provided by the exemplary embodiment of the present disclosure;

[0023] Figure 2 A flowchart of a UAV semantic communication method provided by an exemplary embodiment of the present disclosure;

[0024] Figure 3 A schematic diagram of a structure of a UAV semantic communication device provided by an exemplary embodiment of the present disclosure;

[0025] Figure 4 A schematic diagram of the hardware structure of an electronic device provided for an exemplary embodiment of the present disclosure. DETAILED DESCRIPTION

[0026] It is understandable that before using the technical solutions disclosed in the embodiments of this application, the type, scope of use, usage scenarios, etc. of the personal information involved in this application should be informed to the user and the user's authorization should be obtained in an appropriate manner in accordance with relevant laws and regulations.

[0027] For example, in response to receiving an active request from a user, a prompt message is sent to the user to clearly prompt the user that the operation requested to be performed will require obtaining and using the user's personal information. Thus, the user can autonomously choose whether to provide personal information to software or hardware such as an electronic device, application, server, or storage medium that performs the operation of the technical solution of the present application according to the prompt message.

[0028] As an optional but non-limiting implementation, in response to receiving an active request from the user, the prompt information may be sent to the user in the form of a pop-up window, in which the prompt information may be presented in text form. In addition, the pop-up window may also carry a selection control for the user to choose "agree" or "disagree" to provide personal information to the electronic device.

[0029] It is understandable that the above notification and the process of obtaining user authorization are merely illustrative and do not constitute a limitation on the implementation method of the present application. Other methods that meet relevant laws and regulations may also be applied to the implementation method of the present application.

[0030] It is understandable that the data involved in this technical solution (including but not limited to the data itself, the acquisition or use of the data) shall comply with the requirements of relevant laws, regulations and relevant provisions.

[0031] In order to make the purpose, technical solutions and advantages of the present disclosure more clear, the principles and spirit of the present disclosure will be described with reference to several exemplary embodiments. It should be understood that these embodiments are provided only to enable those skilled in the art to better understand and implement the present disclosure, and are not intended to limit the scope of the present disclosure in any way. On the contrary, these embodiments are provided to make the present disclosure more thorough and complete, and to fully convey the scope of the present disclosure to those skilled in the art.

[0032] It should be understood herein that any number of elements in the drawings is for illustration rather than limitation, and any naming is only for distinction rather than having any limiting meaning.

[0033] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the embodiments of the present disclosure should be understood by people with ordinary skills in the field to which the present disclosure belongs. The "first", "second" and similar words used in the embodiments of the present disclosure do not represent any order, quantity or importance, but are only used to distinguish different components. "Including" or "comprising" and similar words mean that the elements or objects appearing in front of the word cover the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connecting" or "connected" and similar words are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly. The article "one" or "a" before an element does not exclude the existence of multiple such elements.

[0034] The principle and spirit of the present disclosure are explained in detail below with reference to several representative embodiments of the present disclosure.

[0035] As described in the background technology, in the related art, mobile user information cannot be collected efficiently in the drone scene, and the reconstruction quality of the data information is not high. Specifically, in the related art, drones are used to collect visual information in the real world, and then deep learning technology is used to analyze the collected data, extract core semantic information, and construct a scene graph; in order to efficiently distribute these semantically rich data to virtual service providers (VSPs) in need, this technical solution adopts a combinatorial auction method, allowing VSPs to bid based on the importance and value of the data to their services; once the bid is successful, the drone will send the corresponding semantic information to the winning VSP, and the VSP will then use this information to update the various services it provides in the metaverse, such as virtual tourism, online conferences and other services; the entire process includes automated data collection, extraction of semantic information, auction process and update of metaverse services, ensuring the synchronization and consistency of virtual environment and real-world data.

[0036] However, although this technical solution collects data through drones and uses deep learning models to extract semantic information to support metaverse services, it focuses on the semantic processing of data and the design of auction mechanisms; the comparative document only regards drones as fixed edge devices in the scenario and trains them under an additive white Gaussian noise (AWGN) channel with a specific signal-to-noise ratio (SNR), lacking the use of its dynamics and optimization research on corresponding problems.

[0037] In the related technologies, the importance of personalization for specific tasks was explored in depth, and an innovative semantic communication architecture was developed; this architecture can customize the weight allocation of image data based on the user's personal preferences and specific task requirements, so as to improve the level of personalization and efficiency in the communication process; in the drone network environment, this technical solution has been verified by simulation experiments; the experiment demonstrated how the relative position between the drone and the user significantly affects the performance of semantic communication by changing the distance between the drone and the user.

[0038] However, this technical solution only demonstrates the effect of the relative position between the drone and the user on the image transmission quality through a one-by-one trial method. This solution does not take into account the dynamic adjustment of the drone's position; when the user's geographical location changes, this technical solution cannot effectively adapt to these changes, thereby failing to ensure the stability and reliability of communication quality; therefore, this technical solution fails to achieve flexible optimization of the drone's position to meet the communication challenges brought about by user movement.

[0039] Among the related technologies, the focus is on the transmission of image data when drones perform classification tasks; by installing a lightweight model on the drone and using deep reinforcement learning technology, this solution can select the semantic information blocks that contribute most to the classification task for transmission, and send the collected image information to the back-end mobile edge computing platform for in-depth analysis; considering the limitations of drones in storage and processing capabilities, how to efficiently transmit data that is critical to artificial intelligence algorithms has become a key issue; therefore, deep reinforcement learning technology is used to accurately identify and transmit the semantic information blocks that are most critical to the classification algorithm, and at the same time, under different channel conditions, the balance between transmission delay and classification accuracy is optimized.

[0040] However, this technical solution mainly focuses on selecting important semantic information blocks and improving transmission efficiency, but when dealing with changes in the positions of drones and users, it only optimizes the channel gain by simplifying it to several fixed discrete values; this solution does not deeply consider the specific impact of the dynamically changing positions between drones and users on communication quality, and lacks research on comprehensive optimization of semantic signal power allocation and drone positions; this may lead to the inability to achieve optimal signal coverage and transmission efficiency in certain dynamic environments, thereby affecting the flexibility of communication strategies and overall communication performance; therefore, this technical solution has limitations in adapting to user mobility and optimizing communication quality.

[0041] In order to solve the above problems, the present disclosure provides a method, device, electronic device, storage medium and program product solution for semantic communication of unmanned aerial vehicles, which specifically include:

[0042] Determine the user location information, and obtain the channel gain data based on the user location information; determine the original transmission signal, input the original transmission signal and the channel gain data into a pre-trained data transmission optimization model, optimize the original transmission signal based on the channel gain data through the data transmission optimization model, and output the optimized transmission signal; determine the noise information of the communication channel, and obtain the original reception signal based on the noise information, the channel gain data, and the optimized transmission signal; input the original reception signal and the channel gain data into the data transmission optimization model, optimize the original reception signal based on the channel gain data through the transmission data optimization model, and output the optimized reception signal. The present disclosure obtains the user location information, calculates the channel gain data, and optimizes the original transmission signal and the reception signal using the pre-trained data transmission optimization model, and finally obtains the optimized transmission signal and the optimized reception signal, thereby improving the efficiency of mobile user information collection and the quality of data reconstruction in the drone scene.

[0043] After introducing the basic principles of the present disclosure, various non-limiting embodiments of the present disclosure are described in detail below.

[0044] refer to Figure 1 , which is a schematic diagram of an application scenario of the drone semantic communication method provided by an exemplary embodiment of the present disclosure.

[0045] This application scenario includes a terminal device 101 and a server 102. The terminal device 101 and the server 102 may be connected via a wired or wireless communication network to achieve data interaction.

[0046] The terminal device 101 may be an electronic device with data transmission and multimedia input / output functions close to the user side, including but not limited to a desktop computer, a mobile phone, a mobile computer, a tablet computer, a media player, a smart wearable device, a personal digital assistant (PDA) or other electronic devices capable of realizing the above functions. The electronic device may include a processor and a display screen with a touch input function, the display screen is used to present a graphical user interface, the graphical user interface can display an application interface, and the processor is used to process application data, generate a graphical user interface, and control the display of the graphical user interface on the display screen.

[0047] Server 102 can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers. It can also be a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN (Content Delivery Network), as well as big data and artificial intelligence platforms.

[0048] In some exemplary embodiments, the drone semantic communication method can be run on the terminal device 101 or the server 102.

[0049] When the drone semantic communication method is run on the server 102 , the server 102 is used to provide drone semantic communication services to the user of the terminal device 101 .

[0050] The server 102 determines the user location information, and the server 102 obtains the channel gain data based on the user location information;

[0051] The server 102 determines an original transmission signal, and inputs the original transmission signal and the channel gain data into a pre-trained data transmission optimization model. The server 102 optimizes the original transmission signal based on the channel gain data through the data transmission optimization model, and outputs an optimized transmission signal;

[0052] The server 102 determines noise information of the communication channel, and the server 102 obtains an original received signal based on the noise information, the channel gain data and the optimized transmitted signal;

[0053] The server 102 inputs the original received signal and the channel gain data into the data transmission optimization model. The server 102 optimizes the original received signal based on the channel gain data through the transmission data optimization model and outputs an optimized received signal.

[0054] It should be noted that the above application scenarios are only shown to facilitate understanding of the spirit and principle of the present disclosure, and the embodiments of the present disclosure are not limited in this respect. On the contrary, the embodiments of the present disclosure can be applied to any applicable scenario.

[0055] refer to Figure 2 ,UAV semantic communication method, applied to a server, the method comprises the following steps:

[0056] Step S210: Determine user location information, and obtain channel gain data based on the user location information.

[0057] In specific implementation, the method of determining the user location information includes but is not limited to at least one of the following:

[0058] The user's location information is obtained through positioning technology. Specifically, the global positioning system (GPS) or other positioning technologies (such as cellular network triangulation, Wi-Fi positioning, Bluetooth positioning, etc.) are used to determine the user's geographic location. The user's location information is obtained through drone sensor data. Specifically, sensors on drones or user devices (such as accelerometers, gyroscopes, magnetometers, etc.) are used to track the user's movement and position changes. The user's location information is obtained using the signal characteristics of the user's device. Specifically, the signal characteristics sent from the user device, such as signal arrival time (TOA), signal arrival angle (AOA) or signal strength (RSSI) are analyzed to estimate the user's location or the user's location information is obtained through a communication network. Specifically, the user's location relative to the network infrastructure is determined through interaction with the communication network, such as feedback information from base stations.

[0059] In the above exemplary embodiment, a method for obtaining user location information is introduced. The following describes a method for obtaining channel gain data:

[0060] In this exemplary embodiment, obtaining channel gain data based on the user location information includes:

[0061] Determine the rated transmission power of user information communication, and obtain the drone base station location based on the user location information and the rated transmission power; perform path loss calculation based on the user location information and the drone base station location to obtain the channel gain data.

[0062] In specific implementation, the method of determining the rated transmission power of user information communication includes but is not limited to at least one of the following:

[0063] The rated transmit power of user information communication is obtained by evaluating the capabilities of user equipment. Specifically, the hardware capabilities of the user equipment are evaluated, including battery life, transmit power limit and equipment performance, to determine the maximum transmit power that each user equipment can support. The rated transmit power of user information communication is obtained through communication protocol standards. Specifically, the rated transmit power of the user equipment is determined according to the power level specified in the wireless communication protocols and standards (such as 3GPP, Wi-Fi Alliance, etc.) or the power category and allowed power range of the user equipment are understood through the network configuration information provided by the wireless network operator.

[0064] In a specific implementation, obtaining the location of the drone base station based on the user location information and the rated transmission power means:

[0065] As a specific embodiment, assume a communication scenario where a drone base station collects information of N users on the ground, the user set is represented by N = {1, 2, ..., n, ..., N}, and the position of user n is represented by Among them, the maximum transmission power P of each user is n As weight, the updated position of the drone base station is set as the weighted centroid of the user’s position, and the position of the drone s v It is expressed as:

[0066]

[0067] In a specific implementation, performing path loss calculation based on the user location information and the drone base station location to obtain the channel gain data means:

[0068] Following the above exemplary embodiment, the position information of the user and the drone base station is used to calculate the straight-line distance between the two, and the calculated distance is substituted into the free space path loss model. The path loss L is expressed as:

[0069] L=20log 10 (d)+20log 10 (f)+20log 10 (c) + constant term;

[0070] Where d is the distance, f is the frequency, c is the speed of light, and the constant term includes antenna gain and polarization loss. Considering the impact of environmental factors on signal propagation, such as buildings, terrain, weather conditions, etc., these factors may increase additional path loss. The channel gain G can be expressed as the inverse of the path loss, that is, G = 1 / L, and the channel gain G = [G1,…,G n ], the channel gain G will be fed back to the transmitting user and the receiving drone base station at the same time.

[0071] Step S220, determine the original transmission signal, input the original transmission signal and the channel gain data into a pre-trained data transmission optimization model, optimize the original transmission signal based on the channel gain data through the data transmission optimization model, and output an optimized transmission signal.

[0072] In this exemplary embodiment, determining the original transmitted signal includes:

[0073] Determine the data to be transmitted, perform semantic extraction on the data to be transmitted to obtain semantic feature information; normalize the semantic feature information to obtain the original transmission signal.

[0074] In specific implementation, the data to be transmitted includes but is not limited to at least one of the following:

[0075] Text data, audio data, image data or video data.

[0076] In a specific implementation, performing semantic extraction on the data to be transmitted to obtain semantic feature information means:

[0077] Following the above exemplary embodiment, it is assumed that the information transmitted by the user is an image, and the image data transmitted by them is recorded as M = {M1, ..., M n ,…,M N}, under the AWGN channel condition with fixed user position, the Joint Source-Channel (JSC) codec and φ D The purpose of training is to obtain a set of high-performance codecs under simple channels. In order to focus on the semantic feature extraction of the source, it provides a basis for the next layer of semantic signal processing. The specific description of the process is as follows:

[0078] JSC Encoder From user n to transmit image M n The semantic features X extracted from n It is expressed as:

[0079]

[0080] Among them, C represents the complex field, that is, a set of real and imaginary numbers, K represents the number of subcarriers, I is the number of OFDM-NOMA symbols (refers to the number of independent symbols that the system can transmit within one OFDM symbol period. Each OFDM symbol is composed of modulation symbols on multiple subcarriers. These subcarriers are orthogonal in frequency and can transmit data simultaneously without interfering with each other. In the NOMA system, these OFDM symbols can be used to serve multiple users at the same time, superimposing the user's data at different power levels), and each symbol carries K semantic features that need to be transmitted.

[0081] In a specific implementation, normalizing the semantic feature information to obtain the original transmitted signal means:

[0082] Following the above exemplary embodiment, since the transmission power of each user is constrained by their own hardware devices, it is necessary to convert the semantic feature X of each OFDM-NOMA symbol of the user on all subcarriers n Normalize to get the transmission signal

[0083]

[0084] Where P nrepresents the maximum average transmit power of user n on all subcarriers in each OFDM-NOMA symbol.

[0085] In the above exemplary embodiment, a method of obtaining the original transmission signal is introduced. Next, a method of training the data transmission optimization model is introduced:

[0086] In this exemplary embodiment, the method of training the data transmission optimization model includes:

[0087] Construct a sample set including several samples; wherein the samples include: sample data and label data; the sample data includes an original transmission signal for training and an original reception signal for training; the label data includes an optimized transmission signal for training corresponding to the original transmission signal for training and an optimized reception signal for training corresponding to the original reception signal for training; input the sample data into the data transmission optimization model to obtain predicted data output by the model, wherein the predicted data includes a reconstructed transmission signal and a reconstructed reception signal output by the model; determine a first difference between the predicted data and the label data; based on the first difference, update the model parameters through a loss function until the difference between the predicted data and the label data is minimized, thereby obtaining the pre-trained data transmission optimization model.

[0088] In a specific implementation, a sample set including several samples is constructed; wherein the samples include: sample data and label data; the sample data includes the original transmitted signal for training and the original received signal for training, which means:

[0089] Following the above exemplary embodiment, when the encoder and decoder are used to provide communication services for N users, the users will be divided into Q groups, represented by Q = {1, 2, ..., q, ..., Q}. After channel transmission, the drone base station will receive the superimposed power signal, where the superimposed signal received by the qth group of users is Expressed as

[0090]

[0091] Among them G fix is the path loss between the UAV base station and user n at a fixed location, W q ∈C L×K represents the noise matrix, It not only represents the transmission signal, but also the original transmission signal used for data transmission optimization model training. It not only represents the received signal, but also the original received signal used for data transmission optimization model training.

[0092] In a specific implementation, the sample data is input into the data transmission optimization model to obtain the prediction data output by the model, wherein the prediction data includes the reconstructed transmission signal and the reconstructed reception signal output by the model, which means:

[0093] Following the above exemplary embodiment, the JSC decoder φ deployed at the drone base station D The received superimposed signal Decode and get the reconstructed image It is expressed as:

[0094]

[0095] In a specific implementation, determining a first difference between the predicted data and the label data; and updating the model parameters by a loss function based on the first difference until the difference between the predicted data and the label data is minimized, and obtaining the pre-trained data transmission optimization model means:

[0096] in During the training process of the semantic feature extraction layer, the loss function of minimizing the error between the reconstructed image and the original image is:

[0097]

[0098] Obtaining a data transfer optimization model Including N max encoders and a single decoder.

[0099] In the above exemplary embodiments, a method of training a data transmission optimization model is introduced. Next, a method of obtaining an optimized transmission signal is introduced:

[0100] In a specific implementation, the original transmission signal and the channel gain data are input into a pre-trained data transmission optimization model, and the original transmission signal is optimized based on the channel gain data by the data transmission optimization model, and the optimized transmission signal is outputted, which means:

[0101] Following the above exemplary embodiment, the original transmission signal passes through the transmitting end semantic signal processor θ in the data transmission optimization model. s Get the optimized sending signal T opt , the process is expressed as:

[0102] T opt =θ s (T fix ,G);

[0103] Among them, θ s The semantic signal processor representing the originating end of the data transmission optimization model in the case where a drone follows a user.

[0104] Step S230: determine the noise information of the communication channel, and obtain the original received signal based on the noise information, the channel gain data and the optimized transmitted signal.

[0105] In a specific implementation, determining the noise information of the communication channel means:

[0106] In a simulation environment, noise information is usually randomly generated based on the statistical characteristics of the channel, such as noise power spectrum density and noise distribution (usually Gaussian distribution) to simulate interference in actual communication processes. In actual communication systems, noise information can be measured by the signal processing hardware at the receiving end.

[0107] In the above exemplary embodiment, a method for obtaining noise information of a communication channel is introduced. Next, a method for obtaining an original received signal is introduced:

[0108] In this exemplary embodiment, obtaining an original received signal based on the noise information, the channel gain data and the optimized transmitted signal includes:

[0109] The noise information, the channel gain data and the optimized transmission signal of at least one user are determined, and the noise information of the at least one user is added to the sum of the products of the channel gain data and the optimized transmission signal to obtain the original received signal.

[0110] In a specific implementation, determining the noise information, the channel gain data, and the optimized transmission signal of at least one user, adding the noise information of the at least one user to the sum of the product of the channel gain data and the optimized transmission signal, and obtaining the original received signal means:

[0111] Following the above exemplary embodiment, under the changing drone-user position and channel conditions, the received signal of the qth group of non-orthogonal users at the base station is It is expressed as:

[0112]

[0113] in With W q Consistently represents the noise matrix, With W q Both represent noise matrices, but they represent the noise characteristics at different processing stages; W q It is usually the additive noise matrix under the initial assumption that the channel gain is fixed and the transmitted signal is not optimized. It reflects the random noise inherent in the received signal, which may come from thermal noise, intermodulation interference or other interference factors in the process of wireless signal propagation; It is the new noise matrix after optimization at the receiving end. It may take into account the changes in the noise model during the optimization process, such as the impact of the signal processing algorithm on the noise, the introduction of channel estimation errors, or the changes in noise characteristics due to changes in the transmitted signal and channel conditions.

[0114] Step S240: input the original received signal and the channel gain data into the data transmission optimization model, optimize the original received signal based on the channel gain data through the transmission data optimization model, and output an optimized received signal.

[0115] In a specific implementation, the original received signal and the channel gain data are input into the data transmission optimization model, and the original received signal is optimized based on the channel gain data by the transmission data optimization model, and the optimized received signal is outputted, which means:

[0116] At the drone base station, the receiving semantic signal processor φ in the data transmission optimization model s For the original received signal Y rec Processing is performed to obtain the optimized received signal Y opt It is expressed as:

[0117] Y opt =φ s (Y rec ,G).

[0118] Based on the above exemplary embodiments, the optimized received signal Y can be obtained. opt , below, we introduce the received signal Y based on the optimization opt The method for updating the loss function in the data transfer optimization model is as follows:

[0119] Determine a second difference between the optimized received signal and the original received signal for training; and update the loss function of the data transmission optimization model based on the first difference and the second difference.

[0120] In a specific implementation, determining the second difference between the optimized received signal and the original received signal for training refers to:

[0121] Following the above exemplary embodiment, in the end-to-end training process, due to the data transmission optimization model Γ * The parameters of are frozen. In addition to the image dataset M, the training set can also be the original transmitted signal T fix ; In order to achieve the optimized received signal Y opt Adaptor decoder In the present exemplary embodiment, the semantic signal error function is defined as:

[0122]

[0123] In a specific implementation, based on the first difference and the second difference, updating the loss function of the data transmission optimization model means:

[0124] Combining image distortion and semantic signal error, a hybrid loss function is proposed to train the semantic signal processor at the transceiver end, which is expressed as

[0125]

[0126] Where w1 and w2 are the weights of the two parts of distortion, which are used to balance the numerical differences caused by different data meanings; since the semantic signal error d(Y fix ,Y opt ) The gradient of the network parameters does not pass through the JSC encoder, so the gradient disappearance phenomenon will not occur, which can better guide the training process and enable the network to gradually converge; while the first part is distorted After the second part of the distortion guidance converges, it can also play an auxiliary role in enhancing the image reconstruction quality. In summary, end-to-end training of the semantic signal processor at the transceiver end enables the transmitted signal to adapt to changes in the user's position, improves the image reconstruction quality at the receiving end, and meets the high concurrency requirements of mobile users.

[0127] It should be noted that the method of the embodiment of the present disclosure can be performed by a single device, such as a computer or a server. The method of the present embodiment can also be applied in a distributed scenario and completed by multiple devices cooperating with each other. In the case of such a distributed scenario, one of the multiple devices can only perform one or more steps in the method of the embodiment of the present disclosure, and the multiple devices will interact with each other to complete the described method.

[0128] It should be noted that the above describes some embodiments of the present disclosure. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the above embodiments and still achieve the desired results. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0129] Based on the same inventive concept, corresponding to any of the above-mentioned embodiment methods, the present disclosure also provides a drone semantic communication device.

[0130] refer to Figure 3 , the UAV semantic communication device comprises:

[0131] The gain data determination module 310 is configured to determine user location information and obtain channel gain data based on the user location information;

[0132] The transmission signal determination module 320 is configured to determine an original transmission signal, input the original transmission signal and the channel gain data into a pre-trained data transmission optimization model, optimize the original transmission signal based on the channel gain data through the data transmission optimization model, and output an optimized transmission signal;

[0133] The original received signal determination module 330 is configured to determine noise information of the communication channel, and obtain an original received signal based on the noise information, the channel gain data and the optimized transmission signal;

[0134] The optimized received signal determination module 340 is configured to input the original received signal and the channel gain data into the data transmission optimization model, optimize the original received signal based on the channel gain data through the transmission data optimization model, and output an optimized received signal.

[0135] In this exemplary embodiment, the gain data determination module 310 is specifically configured as follows:

[0136] Determine the user location information and the rated power of the user information communication, and obtain the drone base station location based on the user location information and the rated power; perform path loss calculation based on the user location information and the drone base station location to obtain the channel gain data; obtain the channel gain data based on the user location information.

[0137] In this exemplary embodiment, the sending signal determination module 320 is specifically configured as follows:

[0138] Determine the data to be transmitted, perform semantic extraction on the data to be transmitted, and obtain semantic feature information; normalize the semantic feature information to obtain the original transmission signal; input the original transmission signal and the channel gain data into a pre-trained data transmission optimization model, optimize the original transmission signal based on the channel gain data through the data transmission optimization model, and output an optimized transmission signal; a method of training the data transmission optimization model: construct a sample set including a plurality of samples; wherein the sample includes: sample data and label data; the sample data includes an original transmission signal for training and an original reception signal for training; the label data includes an optimized transmission signal for training corresponding to the original transmission signal for training and an optimized reception signal for training corresponding to the original reception signal for training; input the sample data into the data transmission optimization model to obtain prediction data output by the model, wherein the prediction data includes a reconstructed transmission signal and a reconstructed reception signal output by the model; determine a first difference between the prediction data and the label data; based on the first difference, update the model parameters through a loss function until the difference between the prediction data and the label data is minimized, and obtain the pre-trained data transmission optimization model.

[0139] In this exemplary embodiment, the original received signal determination module 330 is specifically configured as follows:

[0140] Determine the noise information of the communication channel, the noise information of at least one user, the channel gain data and the optimized transmission signal, add the noise information of the at least one user to the sum of the product of the channel gain data and the optimized transmission signal, and obtain the original received signal.

[0141] In this exemplary embodiment, the optimized received signal determination module 340 is specifically configured as follows:

[0142] The original received signal and the channel gain data are input into the data transmission optimization model, and the original received signal is optimized based on the channel gain data by the transmission data optimization model, and an optimized received signal is output; after the optimized received signal is output, it also includes: determining a second difference between the optimized received signal and the original received signal for training; based on the first difference and the second difference, updating the loss function of the data transmission optimization model.

[0143] For the convenience of description, the above device is described by dividing it into various modules according to its functions. Of course, when implementing the present disclosure, the functions of each module can be implemented in the same or multiple software and / or hardware.

[0144] The device of the above embodiment is used to implement the corresponding drone semantic communication method in any of the above embodiments, and has the beneficial effects of the corresponding method embodiment, which will not be repeated here.

[0145] Based on the same inventive concept, corresponding to any of the above-mentioned embodiments and methods, the present disclosure also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the drone semantic communication method described in any of the above embodiments is implemented.

[0146] Figure 4 A more specific schematic diagram of the hardware structure of an electronic device provided in this embodiment is shown, and the device may include: a processor 1010, a memory 1020, an input / output interface 1030, a communication interface 1040, and a bus 1050. The processor 1010, the memory 1020, the input / output interface 1030, and the communication interface 1040 are connected to each other through the bus 1050 in the device.

[0147] The processor 1010 can be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this specification.

[0148] The memory 1020 may be implemented in the form of ROM (Read Only Memory), RAM (Random Access Memory), static storage device, dynamic storage device, etc. The memory 1020 may store an operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented by software or firmware, the relevant program codes are stored in the memory 1020 and are called and executed by the processor 1010.

[0149] The input / output interface 1030 is used to connect the input / output module to realize information input and output. The input / output module can be configured in the device as a component (not shown in the figure), or it can be externally connected to the device to provide corresponding functions. The input device may include a keyboard, a mouse, a touch screen, a microphone, various sensors, etc., and the output device may include a display, a speaker, a vibrator, an indicator light, etc.

[0150] The communication interface 1040 is used to connect a communication module (not shown) to realize communication interaction between the device and other devices. The communication module can realize communication through a wired mode (such as USB, network cable, etc.) or a wireless mode (such as mobile network, WIFI, Bluetooth, etc.).

[0151] The bus 1050 includes a path that transmits information between the various components of the device (eg, the processor 1010, the memory 1020, the input / output interface 1030, and the communication interface 1040).

[0152] It should be noted that, although the above device only shows the processor 1010, the memory 1020, the input / output interface 1030, the communication interface 1040 and the bus 1050, in the specific implementation process, the device may also include other components necessary for normal operation. In addition, it can be understood by those skilled in the art that the above device may also only include the components necessary for implementing the embodiments of the present specification, and does not necessarily include all the components shown in the figure.

[0153] The electronic device of the above embodiment is used to implement the corresponding drone semantic communication method in any of the above embodiments, and has the beneficial effects of the corresponding method embodiment, which will not be repeated here.

[0154] Based on the same inventive concept, corresponding to any of the above-mentioned embodiment methods, the present disclosure also provides a non-transitory computer-readable storage medium, wherein the non-transitory computer-readable storage medium stores computer instructions, and the computer instructions are used to enable the computer to execute the drone semantic communication method described in any of the above embodiments.

[0155] The computer-readable medium of this embodiment includes permanent and non-permanent, removable and non-removable media, and information storage can be implemented by any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, read-only compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device.

[0156] The above-mentioned non-transitory computer-readable storage medium can be any available medium or data storage device that can be accessed by a computer, including but not limited to magnetic storage (such as floppy disks, hard disks, magnetic tapes, magneto-optical disks (MO), etc.), optical storage (such as CD, DVD, BD, HVD, etc.), and semiconductor storage (such as ROM, EPROM, EEPROM, non-volatile memory (NAND FLASH), solid-state drive (SSD)), etc.

[0157] The computer instructions stored in the storage medium of the above embodiment are used to enable the computer to execute the drone semantic communication method described in any embodiment in the above exemplary method part, and have the beneficial effects of the corresponding method embodiment, which will not be repeated here.

[0158] Based on the same inventive concept, corresponding to the drone semantic communication method described in any of the above embodiments, the present disclosure also provides a computer program product, which includes computer program instructions. In some embodiments, the computer program instructions can be executed by one or more processors of a computer so that the computer and / or the processor executes the drone semantic communication method. Corresponding to the execution subject corresponding to each step in each embodiment of the drone semantic communication method, the processor that executes the corresponding step may belong to the corresponding execution subject.

[0159] The computer program product of the above embodiment is used to enable the computer and / or the processor to execute the drone semantic communication method described in any of the above embodiments, and has the beneficial effects of the corresponding method embodiments, which will not be repeated here.

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

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

[0162] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, which carry computer-readable program code. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer-readable signal media may also be any computer-readable medium other than a computer-readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0163] The program code embodied on the computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

[0164] Computer program code for performing the operations of the present disclosure may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0165] It should be understood that each box in the flowchart and / or block diagram and the combination of boxes in the flowchart and / or block diagram can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer or other programmable data processing device to produce a machine, and these computer program instructions are executed by a computer or other programmable data processing device to produce a device that implements the functions / operations specified in the boxes in the flowchart and / or block diagram.

[0166] These computer program instructions may also be stored in a computer-readable medium that enables a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable medium produce a product that includes an instruction device that implements the functions / operations specified in the blocks in the flowchart and / or block diagram.

[0167] Computer program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device so that a series of operational steps are performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby enabling the instructions executed on the computer or other programmable device to provide a process for implementing the functions / operations specified in the blocks in the flowchart and / or block diagram.

[0168] In addition, although the operations of the disclosed method are described in a particular order in the accompanying drawings, this does not require or imply that the operations must be performed in this particular order, or that all the operations shown must be performed to achieve the desired results. On the contrary, the steps depicted in the flow chart can be performed in a different order. Additionally or alternatively, some steps can be omitted, multiple steps can be combined into one step, and / or one step can be decomposed into multiple steps.

[0169] The flowchart and block diagram in the accompanying drawings illustrate the possible architecture, functions and operations of the system, method and computer program product according to various embodiments of the present application. Wherein, each box in the flowchart or block diagram can represent a module, a program segment, or a part of the code, and the above-mentioned module, program segment, or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flowchart, and the combination of boxes in the block diagram or flowchart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0170] It should be noted that, although several modules or units of the equipment for action execution are mentioned in the above detailed description, this division is not mandatory. In fact, according to the embodiments of the present application, the features and functions of two or more modules or units described above can be embodied in one module or unit. On the contrary, the features and functions of one module or unit described above can be further divided into being embodied by multiple modules or units.

[0171] Those skilled in the art should understand that the discussion of any of the above embodiments is merely illustrative and is not intended to imply that the scope of the present application (including the claims) is limited to these examples. In line with the concept of the present application, the technical features in the above embodiments or different embodiments may be combined, the steps may be implemented in any order, and there are many other variations of the different aspects of the embodiments of the present application as described above, which are not provided in detail for the sake of simplicity.

[0172] In addition, to simplify the description and discussion, and in order not to make the embodiments of the present application difficult to understand, the known power supply / ground connection with the integrated circuit (IC) chip and other components may or may not be shown in the provided drawings. In addition, the device can be shown in the form of a block diagram to avoid making the embodiments of the present application difficult to understand, and this also takes into account the fact that the details of the implementation of these block diagram devices are highly dependent on the platform to be implemented in the embodiments of the present application (that is, these details should be fully within the scope of understanding of those skilled in the art). In the case of elaborating specific details (e.g., circuits) to describe exemplary embodiments of the present application, it is obvious to those skilled in the art that the embodiments of the present application can be implemented without these specific details or when these specific details are changed. Therefore, these descriptions should be considered to be illustrative rather than restrictive.

[0173] Although the present application has been described in conjunction with specific embodiments of the present application, many replacements, modifications and variations of these embodiments will be apparent to those skilled in the art from the foregoing description. For example, other memory architectures (e.g., dynamic RAM (DRAM)) may use the embodiments discussed.

[0174] The embodiments of the present application are intended to cover all such substitutions, modifications and variations that fall within the broad scope of the appended claims. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the embodiments of the present application should be included in the scope of protection of the present application.

[0175] Although the spirit and principle of the present disclosure have been described with reference to several specific embodiments, it should be understood that the present disclosure is not limited to the disclosed specific embodiments, and the division of various aspects does not mean that the features in these aspects cannot be combined to benefit, and such division is only for the convenience of expression. The present disclosure is intended to cover various modifications and equivalent arrangements included in the spirit and scope of the attached claims. The scope of the attached claims conforms to the broadest interpretation, thereby including all such modifications and equivalent structures and functions.

Claims

1. A UAV semantic communication method, characterized in that: include: Determine user location information, and obtain channel gain data based on the user location information; Determine an original transmission signal, input the original transmission signal and the channel gain data into a pre-trained data transmission optimization model, optimize the original transmission signal based on the channel gain data through the data transmission optimization model, and output an optimized transmission signal; Determine noise information of the communication channel, and obtain an original received signal based on the noise information, the channel gain data and the optimized transmitted signal; The original received signal and the channel gain data are input into the data transmission optimization model, and the original received signal is optimized based on the channel gain data by the transmission data optimization model to output an optimized received signal.

2. The method according to claim 1, characterized in that The obtaining channel gain data based on the user location information includes: Determine a rated transmission power of user information communication, and obtain a drone base station location based on the user location information and the rated transmission power; The path loss is calculated based on the user location information and the drone base station location to obtain the channel gain data.

3. The method according to claim 1, characterized in that The determining of the original transmitted signal comprises: Determine the data to be transmitted, perform semantic extraction on the data to be transmitted, and obtain semantic feature information; The semantic feature information is normalized to obtain the original transmitted signal.

4. The method according to claim 1, characterized in that: The method further comprises training the data transmission optimization model by: Constructing a sample set including a plurality of samples; wherein the samples include: sample data and label data; the sample data includes an original transmission signal for training and an original reception signal for training; the label data includes an optimized transmission signal for training corresponding to the original transmission signal for training and an optimized reception signal for training corresponding to the original reception signal for training; Inputting the sample data into the data transmission optimization model to obtain prediction data output by the model, wherein the prediction data includes a reconstructed transmission signal and a reconstructed reception signal output by the model; Determining a first difference between the predicted data and the label data; Based on the first difference, the model parameters are updated through a loss function until the difference between the predicted data and the label data is minimized, thereby obtaining the pre-trained data transmission optimization model.

5. The method according to claim 1, characterized in that The obtaining of the original received signal based on the noise information, the channel gain data and the optimized transmitted signal comprises: The noise information, the channel gain data and the optimized transmission signal of at least one user are determined, and the noise information of the at least one user is added to the sum of the products of the channel gain data and the optimized transmission signal to obtain the original received signal.

6. The method according to claim 4, characterized in that The method further comprises: inputting the original received signal and the channel gain data into the data transmission optimization model, optimizing the original received signal based on the channel gain data by the transmission data optimization model, and outputting the optimized received signal. Determining a second difference between the optimized received signal and the original received signal for training; The loss function of the data transmission optimization model is updated based on the first difference and the second difference.

7. A UAV semantic communication device, characterized in that: include: A gain data determination module, configured to determine user location information, and obtain channel gain data based on the user location information; a transmission signal determination module, configured to determine an original transmission signal, input the original transmission signal and the channel gain data into a pre-trained data transmission optimization model, optimize the original transmission signal based on the channel gain data through the data transmission optimization model, and output an optimized transmission signal; An original received signal determination module is configured to determine noise information of a communication channel, and obtain an original received signal based on the noise information, the channel gain data and the optimized transmission signal; The optimized received signal determination module is configured to input the original received signal and the channel gain data into the data transmission optimization model, optimize the original received signal based on the channel gain data through the transmission data optimization model, and output an optimized received signal.

8. An electronic device, characterized in that: The method comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the method according to any one of claims 1 to 6 is implemented.

9. A non-transitory computer-readable storage medium, characterized in that: The non-transitory computer-readable storage medium stores computer instructions, and the computer instructions are used to cause a computer to execute the method according to any one of claims 1 to 6.

10. A computer program product, characterized in that The method comprises computer program instructions, which, when executed on a computer, cause the computer to execute the method according to any one of claims 1 to 6.

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