A semantic communication method, apparatus, device and storage medium

Abstract

This disclosure provides a semantic communication method, apparatus, device, and storage medium, relating to the field of communication technology. The specific implementation scheme is as follows: The transmitting end performs fusion and overlay processing on source data and channel data at the application layer to obtain a multi-user semantic vector, wherein the channel data is obtained through a data feedback link connecting the application layer and the physical layer; the multi-user semantic vector is encrypted, and the encrypted multi-user semantic vector is sent to the receiving end, wherein the receiving end matches the multi-user semantic vector; the receiving end performs comprehensive decoding on the encrypted multi-user semantic vector to obtain the source data. This disclosure significantly reduces the load on the communication system while effectively improving the data transmission efficiency of the communication system, promoting the development and application of semantic communication technology in communication systems.

Description

Technical Field

[0001] This disclosure relates to the field of communication technology, and in particular to a semantic communication method, apparatus, device, and storage medium. Background Technology

[0002] With the development of the intelligent era, the demand for data transmission bandwidth from various intelligent applications is growing rapidly, and the capacity of existing communication systems is gradually approaching the Shannon limit. Semantic communication technology can significantly reduce data transmission bandwidth by filtering redundant information and extracting the meaning of effective information. However, when semantic communication technology is applied to existing communication systems, data transmission efficiency is reduced. Summary of the Invention

[0003] This disclosure provides a semantic communication method, apparatus, device, and storage medium.

[0004] According to a first aspect of this disclosure, a method for semantic communication is provided, comprising:

[0005] The transmitting end performs fusion and overlay processing on the source data and channel data at the application layer to obtain a multi-user semantic vector. The channel data is obtained through a data feedback link connecting the application layer and the physical layer.

[0006] The multi-user semantic vector is encrypted, and the encrypted multi-user semantic vector is sent to the receiving end, wherein the receiving end matches the multi-user semantic vector.

[0007] The receiving end performs comprehensive decoding on the encrypted multi-user semantic vector to obtain the source data.

[0008] According to a second aspect of this disclosure, a semantic communication apparatus is provided, comprising:

[0009] The fusion and overlay processing module is used to perform fusion and overlay processing on source data and channel data at the application layer at the transmitting end to obtain a multi-user semantic vector. The channel data is obtained through a data feedback link connecting the application layer and the physical layer.

[0010] An encrypted transmission module is used to encrypt the multi-user semantic vector and send the encrypted multi-user semantic vector to a receiving end, wherein the receiving end matches the multi-user semantic vector.

[0011] The integrated decoding module is used to perform integrated decoding on the encrypted multi-user semantic vector at the receiving end to obtain the source data.

[0012] According to a third aspect of this disclosure, an electronic device is provided, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the semantic communication method described in the first aspect of this disclosure.

[0013] According to a fourth aspect of this disclosure, a non-transitory computer-readable storage medium is provided storing computer instructions, wherein the computer instructions are used to cause the computer to perform the semantic communication method according to a first aspect of this disclosure.

[0014] According to a fifth aspect of this disclosure, a computer program product is provided, comprising a computer program that, when executed by a processor, implements the steps of the semantic communication method according to a first aspect of this disclosure.

[0015] The technology disclosed herein solves the technical problem of reduced data transmission efficiency when semantic communication technology is applied to existing communication systems. It significantly reduces the load on the communication system while effectively improving the data transmission efficiency of the communication system, thus promoting the development and application of semantic communication technology in communication systems.

[0016] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of this disclosure, nor is it intended to limit the scope of this disclosure. Other features of this disclosure will become readily apparent from the following description. Attached Figure Description

[0017] The accompanying drawings are provided to better understand this solution and do not constitute a limitation of this disclosure. Wherein:

[0018] Figure 1 This is a schematic flowchart of a semantic communication method provided in the first embodiment of this disclosure;

[0019] Figure 2 This is a schematic flowchart of a semantic communication method provided in the second embodiment of this disclosure;

[0020] Figure 3 This is a schematic flowchart of a semantic communication method provided in the third embodiment of this disclosure;

[0021] Figure 4 This is a schematic flowchart of a semantic communication method provided in the fourth embodiment of this disclosure;

[0022] Figure 5 This is a structural block diagram of a semantic communication device provided in the fifth embodiment of this disclosure;

[0023] Figure 6This is a schematic diagram of the process protocol interface provided in the fifth embodiment of this disclosure;

[0024] Figure 7 This is a block diagram of an electronic device used to implement the semantic communication method of Embodiment Six of this disclosure. Detailed Implementation

[0025] The exemplary embodiments of this disclosure are described below with reference to the accompanying drawings, including various details of the embodiments to aid understanding, and should be considered merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of this disclosure. Similarly, for clarity and brevity, descriptions of well-known functions and structures are omitted in the following description.

[0026] Semantic communication technology is a novel communication technology that integrates semantic information into communication engineering. By selectively extracting, compressing, and transmitting features from the original signal, it first understands the meaning of the signal the sender needs to transmit before transmitting the data, thereby significantly reducing the bandwidth requirements of intelligent applications and lowering the load on the communication system.

[0027] Transmission efficiency is an important indicator for evaluating the performance of a communication system, including the speed and quality of data transmission.

[0028] Applying semantic communication technology to existing communication systems presents challenges due to the need for compatibility and coordination with existing communication protocols, increasing implementation difficulty and complexity. For example, the execution location of current semantic communication systems based on the joint source-channel coding framework within the existing protocol layer architecture is unclear, and source coding and channel coding primarily operate at the application and physical layers respectively, leading to cross-layer protocol conflicts. Existing communication protocols cannot effectively support joint source-channel coding, resulting in reduced data transmission speeds and limiting the development and application of semantic communication technology in communication systems.

[0029] During data transmission, encryption technology must be used to protect the transmitted data from theft or tampering. However, traditional encryption technology itself destroys the semantic characteristics of the source information. Because the encrypted data cannot be correctly understood and parsed, information transmission cannot be effectively achieved, resulting in reduced data transmission quality.

[0030] The semantic communication methods, apparatus, devices, and storage media provided in this disclosure are described below with reference to the accompanying drawings.

[0031] The acquisition, storage, and application of user personal information involved in the technical solution disclosed herein comply with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0032] First Embodiment

[0033] Figure 1 This is a flowchart illustrating a semantic communication method provided in an embodiment of this disclosure. It should be noted that the semantic communication method in this embodiment can be executed by an electronic device. Figure 1 As shown, the semantic communication method may include, but is not limited to, the following steps.

[0034] To address the aforementioned issues, this disclosure provides a semantic communication method. In this embodiment, a multi-user communication system with one sender and multiple receivers is employed. A new data feedback link connecting the application layer and the physical layer is added to the communication protocol architecture.

[0035] In step 101, the transmitting end performs fusion and overlay processing on the source data and channel data at the application layer to obtain a multi-user semantic vector, wherein the channel data is obtained through a data feedback link connecting the application layer and the physical layer.

[0036] The source data is located at the application layer and can be collected from base stations or any other device that can be used to collect source data; no restrictions are imposed here.

[0037] Channel data located at the physical layer is obtained by feeding back from the physical layer to the application layer through the data feedback link.

[0038] Multi-user semantic vectors are vector representations of semantic information from multiple users. They allow for intuitive explanation of the meaning and importance of each vector. As user behavior data continues to increase, multi-user semantic vectors can be continuously updated and optimized to maintain their accuracy.

[0039] In this step, the channel data used for fusion and overlay processing is directly transmitted from the physical layer to the application layer via a data feedback link. The channel state information fed back from the physical layer allows for understanding the channel states between users, thus improving signal detection and error correction in subsequent decryption and decoding steps. Furthermore, by fusing and overlaying source and channel data at the application layer to obtain multi-user semantic vectors, and using the channel data to encode and modulate the source data, redundant information can be reduced, thereby reducing the amount of data transmitted and improving the quality and speed of data transmission.

[0040] In step 102, the multi-user semantic vector is encrypted, and the encrypted multi-user semantic vector is sent to the receiving end, wherein the receiving end matches the multi-user semantic vector.

[0041] Before sending multi-user semantic vectors, the sending end performs semantic encryption on the multi-user semantic vectors at the application layer or middleware layer. This ensures compatibility with different communication protocols and system platforms, facilitating data transmission and processing. Furthermore, compared to traditional encryption methods, semantic encryption at the application layer or middleware layer allows for efficient encryption and decryption operations using modern computing resources, resulting in lower computational overhead and improved data transmission efficiency. The encrypted multi-user semantic vectors are then sent to matching receiving ends. Before decoding the multi-user semantic vectors, different receiving ends classify and filter the data. During subsequent data processing and decryption, each receiving end focuses only on data relevant to itself, enabling faster identification and parsing of each user's data portion. This avoids unnecessary full data parsing, reducing computational complexity and resource consumption, and shortening data processing time. Through a matching mechanism, if the multi-user semantic vector received by the receiving end does not match the expected semantic vector, the receiving end can quickly detect and correct the error, thereby improving the accuracy of data transmission.

[0042] In step 103, the receiving end performs comprehensive decoding on the encrypted multi-user semantic vector to obtain the source data.

[0043] In this step, the encrypted multi-user semantic vector is decrypted through comprehensive decoding to restore the original semantic information. Then, the restored semantic information is decoded so that the receiving user can obtain the corresponding source data.

[0044] The semantic communication method provided by this disclosure solves the technical problem of reduced data transmission efficiency when semantic communication technology is applied to existing communication systems. It significantly reduces the load on the communication system while effectively improving the data transmission efficiency of the communication system, promoting the development and application of semantic communication technology in communication systems, and driving the development of the 6G era.

[0045] Second Embodiment

[0046] In another embodiment proposed in this disclosure, such as Figure 2 As shown, the semantic communication method may include, but is not limited to, the following steps.

[0047] In step 201, the source data and the channel data are fused to obtain multiple semantic vectors.

[0048] Source data and channel data are both important sources of information. Fusing them to generate semantic vectors can provide a comprehensive perspective, enabling the system to better understand the meaning and context of the data, improve the accuracy of data transmission, filter out irrelevant information, and increase transmission speed.

[0049] Optionally, in this step, joint source-channel coding can be performed on the source data and channel data that match the semantic coding model based on a preset semantic coding model to obtain multiple semantic vectors, wherein the semantic coding models are independent of each other in the semantic space.

[0050] Joint source-channel coding can fully utilize the inherent relationship between source data and channel data, reduce redundant information through cooperative coding, compress data more efficiently, and improve the robustness and adaptability of data by adaptively coding different source and channel characteristics.

[0051] Semantic coding models that are independent in semantic space can more effectively capture and represent independent semantic information in source data and channel data, and generate richer and more accurate semantic vector representations.

[0052] Optionally, the joint source channel coding includes, but is not limited to, at least one of the following: link coding, convolutional coding, and channel coding.

[0053] This embodiment performs joint source-channel coding on source data and channel data at the application layer. Compared to existing communication systems where source data and channel data are coded separately at the application and physical layers, this eliminates cross-layer protocol conflicts and is therefore not limited by the type of joint source-channel coding. Depending on the specific scenario requirements, one or more of link coding, convolutional coding, and channel coding can be selected, but not limited to. For example, link coding is mainly used in low-speed communication systems, while convolutional coding is widely used in high-speed digital communication systems. Channel coding can resist channel noise by adding redundancy, and is not limited here.

[0054] In step 202, the multiple semantic vectors are superimposed to obtain the multi-user semantic vector.

[0055] The semantic vectors are superimposed based on the modular division multiple access (MDMA) mode to generate the multi-user semantic vector.

[0056] By superimposing multiple semantic vectors based on the modular division multiple access (MDD) mode, semantic information from different users can be effectively integrated to generate multi-user semantic vectors with rich content, providing a foundation for further multi-user analysis and processing. Simultaneously, it allows for more efficient use of limited transmission resources, enabling parallel transmission of semantic information from multiple users, allowing multi-user semantic vectors to reach the receiving end in a shorter time, thereby improving transmission efficiency.

[0057] In step 203, the multi-user semantic vector is encrypted, and the encrypted multi-user semantic vector is sent to the receiving end, wherein the receiving end matches the multi-user semantic vector.

[0058] Optionally, step 203 can be implemented in any of the various embodiments of this disclosure. The embodiments provided in this disclosure do not limit this, nor will they be described in detail.

[0059] In step 204, the receiving end performs comprehensive decoding on the encrypted multi-user semantic vector to obtain the source data.

[0060] Optionally, step 204 can be implemented in any of the embodiments of this disclosure. The embodiments provided in this disclosure do not limit this and will not be described in detail.

[0061] Third Embodiment

[0062] In another embodiment proposed in this disclosure, such as Figure 3 As shown, the semantic communication method may include, but is not limited to, the following steps.

[0063] In step 301, the transmitting end performs fusion and overlay processing on the source data and channel data at the application layer to obtain a multi-user semantic vector, wherein the channel data is obtained through a data feedback link connecting the application layer and the physical layer.

[0064] Optionally, step 301 can be implemented using any of the implementation methods in the various embodiments of this disclosure. The embodiments provided in this disclosure do not limit this, nor will they be described in detail.

[0065] In step 302, the multi-user semantic vector is encrypted.

[0066] Encryption can be performed at the application layer and / or the middle layer.

[0067] Encrypting multi-user semantic vectors ensures data security and privacy during transmission. Encryption prevents unauthorized access and theft, protecting the confidentiality and integrity of user data. Furthermore, it provides verification and tamper-proof functionality, ensuring secure and reliable data transmission.

[0068] Optionally, the encryption process includes, but is not limited to, at least one of the following: semantic encryption, sequence encryption, and public key encryption.

[0069] In practical applications, encryption methods can be selected according to actual needs. For example, semantic encryption focuses on ensuring the semantic security of encrypted data. Even if the ciphertext is stolen, attackers cannot obtain the true meaning of the plaintext. Serial encryption encrypts information character by character in sequence, encrypting only one character at a time, combining the characteristics of symmetric encryption and block encryption. Public-key encryption uses asymmetric keys for encryption, allowing anyone to use the public key to encrypt, but only the person holding the corresponding private key can decrypt it. It is suitable for applications such as secure communication and digital signatures. Specific encryption methods are not limited here.

[0070] In step 303, the encrypted multi-user semantic vector is sent to the receiving end through the physical layer.

[0071] The physical layer is responsible for handling the transmission of underlying physical signals, including signal modulation, transmission, and reception. By sending the encrypted multi-user semantic vector to the receiving end through the physical layer, the transmission capabilities and technical means of the physical layer can be fully utilized to further improve the security and reliability of data transmission.

[0072] Optionally, the communication protocol used to send the encrypted multi-user semantic vector to the physical layer includes, but is not limited to, at least one of the following:

[0073] HTTP protocol, XML protocol, RDF protocol, SPARQL protocol, Dublin Core protocol.

[0074] Since the compatibility issues between semantic communication and existing communication systems have been eliminated in the previous steps, the communication protocols between protocol layer architectures can be flexibly selected according to actual needs. For example, the HTTP protocol supports client / server mode, is simple, fast, flexible, connectionless, and stateless; the XML protocol is concise, effective, easy to learn and use, and is an open international standard; the RDF protocol is used to describe the data structure of "subject-verb-object" triples, and has simplicity, flexibility, and scalability; the SPARQL protocol allows users to write queries to access RDF data; the Dublin Core protocol defines a simple metadata standard for describing digital resources, and has universality, scalability, and simplicity, which will not be limited here.

[0075] In step 304, the receiving end performs comprehensive decoding on the encrypted multi-user semantic vector to obtain the source data.

[0076] Optionally, step 304 can be implemented using any of the implementation methods in the various embodiments of this disclosure. The embodiments provided in this disclosure do not limit this, nor will they be described in detail.

[0077] Fourth embodiment

[0078] In another embodiment proposed in this disclosure, such as Figure 4 As shown, the semantic communication method may include, but is not limited to, the following steps.

[0079] In step 401, the transmitting end performs fusion and overlay processing on the source data and channel data at the application layer to obtain a multi-user semantic vector, wherein the channel data is obtained through a data feedback link connecting the application layer and the physical layer.

[0080] Optionally, step 401 can be implemented using any of the various embodiments of this disclosure. The embodiments provided in this disclosure do not limit this and will not be described in detail.

[0081] In step 402, the multi-user semantic vector is encrypted, and the encrypted multi-user semantic vector is sent to the receiving end, wherein the receiving end matches the multi-user semantic vector.

[0082] Optionally, step 402 can be implemented in any of the various embodiments of this disclosure. The embodiments provided in this disclosure do not limit this, nor will they be described in detail.

[0083] In step 403, the receiving end sends the encrypted multi-user semantic vector to the application layer for decryption.

[0084] Decryption is a crucial step in protecting user data security and privacy, preventing unauthorized access and theft, and ensuring data security and reliability. Through application-layer security mechanisms and key management, encrypted multi-user semantic vectors can be effectively decrypted at the application layer to obtain the original semantic vectors.

[0085] Optionally, the communication protocol used to send the encrypted multi-user semantic vector to the application layer includes, but is not limited to, at least one of the following:

[0086] HTTP, XML, RDF, SPARQL, and Dublin Core protocols are all acceptable. The specific protocol selected must correspond to the protocol used in step 303.

[0087] In step 404, based on a preset semantic decoding model, the decrypted multi-user semantic vector is decoded to obtain the decoded semantic vector.

[0088] The preset semantic decoding model matches the preset semantic encoding model in step 202. Based on the preset semantic decoding model, the decrypted multi-user semantic vector is decoded, which can accurately restore the decoded semantic vector, restoring the encrypted and decrypted data into an understandable form and providing accurate semantic information for subsequent application processing.

[0089] In step 405, the decoded semantic vector is subjected to joint source-channel decoding to obtain the source data.

[0090] Joint source-channel decoding of the decoded semantic vector can more accurately recover the original source data. Joint source-channel decoding utilizes the joint coding information of the source and channel, which can better handle data redundancy and errors during the decoding process. This facilitates the extraction of more accurate source data from the decoded semantic vector, improving the performance and reliability of multi-user communication systems.

[0091] Optionally, in this step, the semantic decoder corresponding to the joint source channel coding is first determined by a preset matching method.

[0092] The preset matching method can select the most suitable decoder based on the characteristics and encoding method of the source data, thereby improving the decoding accuracy and efficiency, which is conducive to better recovery of the original source data, reducing decoding errors and data distortion, and improving the performance and reliability of multi-user communication systems.

[0093] Optionally, the matching method includes, but is not limited to, at least one of the following:

[0094] Matching methods include rule-based matching, similarity-based matching, deep learning-based matching, context-based matching, intent-based matching, and pattern recognition-based matching.

[0095] In practical applications, the specific matching method is determined according to actual needs and is not limited here.

[0096] Finally, the decoded semantic vector is subjected to joint source-channel decoding by the semantic decoder.

[0097] A semantic decoder is a tool that restores encoded data to its original semantic information. Common semantic decoders include dynamic decoders, finite-weighted state converters, and raw dynamic decoders.

[0098] The semantic decoder selects the most suitable algorithm based on different encoding methods and data characteristics, thereby better understanding the semantic meaning in the semantic vector, decoding the semantic vector, handling data redundancy and errors, and improving the accuracy and efficiency of decoding. This decoding method helps to better recover the original source data and improve the performance and reliability of multi-user communication systems.

[0099] The following is combined Figure 5 The operation steps of the device provided in this embodiment will be described in detail.

[0100] Figure 5This is a schematic diagram of the process protocol architecture provided in the embodiments of this disclosure, such as... Figure 5 As shown, in this protocol architecture, from bottom to top, are the high layer (including the application layer, middle layer, etc.), the Link Access Control (LAC) sublayer, the Media Access Control (MAC) sublayer, and the physical layer.

[0101] The application layer is primarily used for: acquiring source information from multiple users at the base station; pairing semantic models that do not interfere with each other in semantic space; receiving channel information from the physical layer feedback interface; and performing joint source-channel coding. It also performs superposition processing on semantic signals obtained through different semantic models based on Modular Division Multiple Access (MDMA). Furthermore, it performs packetization and encryption on the superimposed multi-user semantic vectors.

[0102] The link access control sublayer is mainly used for: processing signaling.

[0103] The media access control sublayer is mainly used for: adding CRC check bits to the sub-packet data stream normally, and executing the retransmission mechanism.

[0104] The physical layer is mainly used for: feeding back channel signals to the application layer, transmitting underlying physical signals, and traditional coding, modulation, and layer mapping.

[0105] First, at the application layer, the base station matches multiple different semantic encoding and decoding models for the information sources that multiple users need to transmit.

[0106] Next, the base station uses the matching semantic coding model at the application layer to perform joint source-channel coding on the image source to obtain the semantic vector.

[0107] Next, the base station superimposes the semantic vectors of multiple users together using modular division multiple access technology to obtain a multi-user semantic vector.

[0108] Next, the base station passes the data down from the application layer to the physical layer. During this process, the application layer and the intermediate layer perform encryption operations.

[0109] Next, the base station sends the encrypted semantic information to the corresponding other users at the physical layer.

[0110] Next, each receiving end first receives the signal and performs the decryption operation.

[0111] Finally, each receiver uses a paired semantic decoder to recover the source information.

[0112] Fifth embodiment

[0113] To implement the above embodiments, this disclosure also proposes a semantic communication device 600. Figure 6 This is a structural block diagram of a semantic communication device 600 provided in this disclosure. Figure 6As shown, the semantic communication device 600 may include:

[0114] The fusion and overlay processing module 601 is used to perform fusion and overlay processing on source data and channel data at the application layer at the transmitting end to obtain a multi-user semantic vector, wherein the channel data is obtained through a data feedback link connecting the application layer and the physical layer.

[0115] Optionally, the functions of the fusion and overlay processing module 601 can be implemented in any of the various embodiments of this disclosure. The embodiments provided in this disclosure do not limit this, nor will they be described in detail.

[0116] The encrypted transmission module 602 is used to encrypt the multi-user semantic vector and send the encrypted multi-user semantic vector to the receiving end, wherein the receiving end matches the multi-user semantic vector.

[0117] Optionally, the function of the encrypted transmission module 602 can be implemented in any of the various embodiments of this disclosure. The embodiments provided in this disclosure do not limit this, nor will they be described in detail.

[0118] The integrated decoding module 603 is used to perform integrated decoding on the encrypted multi-user semantic vector at the receiving end to obtain the source data.

[0119] Optionally, the functions of the integrated decoding module 603 can be implemented in any of the various embodiments of this disclosure. The embodiments provided in this disclosure do not limit this, nor will they be described in detail.

[0120] The semantic communication device 600 provided in this disclosure solves the technical problem of reduced data transmission efficiency when semantic communication technology is applied to existing communication systems. It significantly reduces the load on the communication system while effectively improving the data transmission efficiency of the communication system, promoting the development and application of semantic communication technology in communication systems, and driving the development of the 6G era.

[0121] Sixth Embodiment

[0122] According to embodiments of this disclosure, this disclosure also provides an electronic device, a readable storage medium, and a computer program product.

[0123] Figure 7A schematic block diagram of an example electronic device 700 that can be used to implement embodiments of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0124] like Figure 7 As shown, device 700 includes a computing unit 701, which can perform various appropriate actions and processes based on a computer program stored in read-only memory (ROM) 702 or a computer program loaded from storage unit 708 into random access memory (RAM) 703. RAM 703 may also store various programs and data required for the operation of device 700. The computing unit 701, ROM 702, and RAM 703 are interconnected via bus 704. Input / output (I / O) interface 705 is also connected to bus 704.

[0125] Multiple components in device 700 are connected to I / O interface 705, including: input unit 706, such as keyboard, mouse, etc.; output unit 707, such as various types of monitors, speakers, etc.; storage unit 708, such as disk, optical disk, etc.; and communication unit 709, such as network card, modem, wireless transceiver, etc. Communication unit 709 allows device 700 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0126] The computing unit 701 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 701 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 701 performs the various methods and processes described above, such as method XXX. For example, in some embodiments, method XXX may be implemented as a computer software program tangibly contained in a machine-readable medium, such as storage unit 708. In some embodiments, part or all of the computer program may be loaded and / or installed on device 700 via ROM 702 and / or communication unit 709. When the computer program is loaded into RAM 703 and executed by the computing unit 701, one or more steps of method XXX described above may be performed. Alternatively, in other embodiments, the computing unit 701 may be configured to perform method XXX by any other suitable means (e.g., by means of firmware).

[0127] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0128] The program code used to implement the methods of this disclosure may be written in any combination of one or more programming languages. This program code may be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing apparatus, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code may be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0129] In the context of this disclosure, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0130] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor); and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the computer. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0131] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as a data server), or computing systems that include middleware components (e.g., an application server), or computing systems that include frontend components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with embodiments of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., a communication network). Examples of communication networks include local area networks (LANs), wide area networks (WANs), and the Internet.

[0132] Computer systems can include clients and servers. Clients and servers are generally located far apart and typically interact via communication networks. Client-server relationships are created by computer programs running on the respective computers and having a client-server relationship with each other. Servers can be cloud servers, servers in distributed systems, or servers incorporating blockchain technology.

[0133] It should be understood that the various forms of processes shown above can be used to rearrange, add, or delete steps. For example, the steps described in this disclosure can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution disclosed in this disclosure can be achieved, and this is not limited herein.

[0134] The specific embodiments described above do not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure should be included within the scope of protection of this disclosure.

Claims

1. A semantic communication method, comprising: The transmitting end performs fusion and overlay processing on the source data and channel data at the application layer to obtain a multi-user semantic vector. The channel data is obtained through a data feedback link connecting the application layer and the physical layer. The multi-user semantic vector is encrypted, and the encrypted multi-user semantic vector is sent to the receiving end, wherein the receiving end matches the multi-user semantic vector. The receiving end performs comprehensive decoding on the encrypted multi-user semantic vector to obtain the source data; The process of fusing and overlaying source data and channel data to obtain multi-user semantic vectors specifically includes: Based on a preset semantic coding model, joint source-channel coding is performed on the source data and channel data that match the semantic coding model to obtain multiple semantic vectors, wherein the semantic coding models are independent of each other in semantic space; The semantic vectors are superimposed based on the modular division multiple access (MDMA) mode to generate the multi-user semantic vector.

2. The method of claim 1, wherein, The joint source-channel coding includes, but is not limited to, at least one of the following: link coding, convolutional coding, and channel coding.

3. The method of claim 1, wherein, The encryption process includes, but is not limited to, at least one of the following: semantic encryption, sequence encryption, and public key encryption.

4. The method of claim 1, wherein, The step of sending the encrypted multi-user semantic vector to the receiving end, wherein the receiving end matches the multi-user semantic vector, includes: The encrypted multi-user semantic vector is sent to the receiving end through the physical layer.

5. The method of claim 1, wherein, The receiving end performs comprehensive decoding on the encrypted multi-user semantic vector to obtain the source data, including: The receiving end sends the encrypted multi-user semantic vector to the application layer for decryption. Based on a pre-defined semantic decoding model, the decrypted multi-user semantic vector is decoded to obtain the decoded semantic vector. The decoded semantic vector is subjected to joint source-channel decoding to obtain the source data.

6. The method of claim 4 or 5, wherein, The communication protocol used to send the encrypted multi-user semantic vector to the physical layer, or to send the encrypted multi-user semantic vector to the application layer, includes, but is not limited to, at least one of the following: HTTP protocol, XML protocol, RDF protocol, SPARQL protocol, Dublin Core protocol.

7. The method of claim 5, wherein, The joint source-channel decoding of the decoded semantic vector includes: The semantic decoder corresponding to the joint source channel coding is determined by a preset matching method; The semantic decoder performs joint source-channel decoding on the decoded semantic vector.

8. The method of claim 7, wherein, The matching method includes, but is not limited to, at least one of the following: Matching methods include rule-based matching, similarity-based matching, deep learning-based matching, context-based matching, intent-based matching, and pattern recognition-based matching.

9. A semantic communication device, comprising: The fusion and overlay processing module is used to perform fusion and overlay processing on source data and channel data at the application layer at the transmitting end to obtain a multi-user semantic vector. The channel data is obtained through a data feedback link connecting the application layer and the physical layer. Specifically, the fusion and overlay processing module is used to: perform joint source-channel coding on the source data and channel data matching the preset semantic coding model to obtain multiple semantic vectors, wherein the semantic coding models are independent of each other in semantic space; and overlay the multiple semantic vectors based on a modular division multiple access (MDD) mode to generate the multi-user semantic vector. An encrypted transmission module is used to encrypt the multi-user semantic vector and send the encrypted multi-user semantic vector to a receiving end, wherein the receiving end matches the multi-user semantic vector. The integrated decoding module is used to perform integrated decoding on the encrypted multi-user semantic vector at the receiving end to obtain the source data.

10. An electronic device, comprising: At least one processor; as well as A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-8.

11. A non-transitory computer readable storage medium having stored thereon computer instructions, wherein, The computer instructions are used to cause the computer to perform the method according to any one of claims 1-8.

12. A computer program product comprising a computer program that, when executed by a processor, implements the method according to any one of claims 1-8.

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