Electronic device and method for wireless communication, and computer-readable storage medium

Abstract

An electronic device and method for wireless communication, and a computer-readable storage medium. The electronic device for wireless communication comprises at least one processor and at least one memory, wherein the at least one memory comprises a computer program code, and the at least one memory and the computer program code are configured to cause, by means of the at least one processor, the electronic device to determine, on the basis of a semantic communication error rate related to a user equipment for semantic communication, whether to update a semantic library for semantic communication of the user equipment and / or an inference model related to the semantic library.

Description

Electronic device and method for wireless communication, and computer-readable storage medium This application claims priority to the Chinese patent application filed with the China Patent Office on February 5, 2024, with application number 202410172862.6 and invention name “Electronic device and method for wireless communication, computer-readable storage medium”, the entire contents of which are incorporated by reference into this application. Technical Field

[0001] The present disclosure relates to the field of wireless communication technology, and more particularly to electronic devices and methods for wireless communication, and more particularly to electronic devices and methods for wireless communication that can improve the continuity and reliability of semantic communication. Background Art

[0002] With the continuous development of the Internet, semantic communication has become a highly anticipated communication method. For example, the Internet of Vehicles (IoV) is a scenario involving voice communication. For example, communication between end-user devices is also a scenario involving voice communication.

[0003] Taking the Internet of Vehicles (IoV) as an example, vehicle communications encompass not only traditional mobile communications but also semantic communication, a unique communication model. As an advanced communication method, semantic communication not only expands the diversity of communications but also profoundly transforms vehicle communications and related fields within urban networks. It has become a key pillar of modern urban networks and plays a vital role in building safer, more efficient, and more intelligent urban transportation systems.

[0004] First, semantic communication improves communication efficiency and reliability. By supporting different types of communication, such as voice, data, and sensor information, semantic communication enables vehicles to select the most appropriate communication method based on their specific needs. This helps improve the efficiency and reliability of information transmission, ensuring that vehicles can accurately and promptly transmit and receive critical information. Second, semantic communication is the foundation for realizing intelligent transportation systems. It enables vehicles to interact in real time with their surroundings, other vehicles, and transportation infrastructure. This real-time interaction helps optimize traffic flow, improve traffic safety, reduce traffic congestion, and enhance fuel efficiency. Therefore, semantic communication provides key support for the construction and development of intelligent transportation systems.

[0005] In urban networks, vehicles often need to traverse multiple semantic communication service areas, which differ significantly from traditional cellular network cells. This raises a key question: how to enable vehicle handover between different semantic communication cells, especially since these cells cannot simply rely on traditional signal power measurement methods. Summary of the Invention

[0006] A brief overview of the present invention is provided below to provide a basic understanding of certain aspects of the present invention. It should be understood that this overview is not an exhaustive overview of the present invention. It is not intended to identify key or important aspects of the present invention, nor is it intended to limit the scope of the present invention. Its purpose is simply to present certain concepts in a simplified form as a prelude to the more detailed description discussed later.

[0007] According to one aspect of the present disclosure, an electronic device for wireless communication is provided, comprising: at least one processor; and at least one memory including computer program code, wherein the at least one memory and the computer program code are configured to enable the electronic device to execute, through the at least one processor: based on a semantic communication error rate associated with a user device for semantic communication, determining whether to update a semantic library for semantic communication of the user device and / or an inference model associated with the semantic library.

[0008] According to one aspect of the present disclosure, an electronic device for wireless communication is provided, comprising: at least one processor; and at least one memory including computer program code, wherein the at least one memory and the computer program code are configured to enable the electronic device to execute, through the at least one processor: calculating a semantic communication error rate related to semantic communication, so that a network-side device can determine whether to update a semantic library for semantic communication of the electronic device and / or an inference model related to the semantic library.

[0009] According to one aspect of the present disclosure, an electronic device for wireless communication is provided, comprising: at least one processor; and at least one memory including computer program code, wherein the at least one memory and the computer program code are configured to, through the at least one processor, enable the electronic device to execute: transmitting a semantic communication error rate received from other electronic devices to a network-side device, so that the network-side device determines whether to update a semantic library used for semantic communication of the other electronic devices and / or an inference model related to the semantic library.

[0010] According to one aspect of the present disclosure, a method for wireless communication is provided, comprising: determining whether to update a semantic library for semantic communication of a user device and / or an inference model related to the semantic library based on a semantic communication error rate related to the user device for semantic communication.

[0011] According to one aspect of the present disclosure, a method for wireless communication is provided, including: calculating a semantic communication error rate related to semantic communication, so that a network-side device can determine whether to update a semantic library used for semantic communication of the electronic device and / or an inference model related to the semantic library.

[0012] According to one aspect of the present disclosure, a method for wireless communication is provided, comprising: transmitting a semantic communication error rate received from other electronic devices to a network-side device, so that the network-side device can determine whether to update a semantic library used for semantic communication of the other electronic devices and / or an inference model related to the semantic library.

[0013] According to other aspects of the present invention, there are also provided computer program codes and computer program products for implementing the above methods, as well as computer-readable storage media having the computer program codes for implementing the above methods recorded thereon. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to further illustrate the above and other advantages and features of the present invention, the following is a further detailed description of specific embodiments of the present invention in conjunction with the accompanying drawings. The accompanying drawings, together with the detailed description below, are included in this specification and form a part of this specification. Elements with the same function and structure are represented by the same reference numerals. It should be understood that these drawings only depict typical examples of the present invention and should not be regarded as limiting the scope of the present invention. In the drawings:

[0015] FIG1 shows a functional module block diagram of an electronic device for wireless communication according to an embodiment of the present disclosure;

[0016] FIG2 is a schematic diagram illustrating an example of a semantic library according to an embodiment of the present disclosure;

[0017] FIG3 is a schematic diagram showing an example of semantic information;

[0018] FIG4A shows an example of a semantic communication cell handover scenario according to an embodiment of the present disclosure;

[0019] FIG4B is another example showing a semantic communication cell handover scenario according to an embodiment of the present disclosure;

[0020] FIG5A shows an example of simulation results of changes in semantic communication accuracy according to an embodiment of the present disclosure;

[0021] FIG5B shows another example of simulation results of changes in semantic communication accuracy according to an embodiment of the present disclosure;

[0022] FIG6A is a first example showing a process of semantic communication cell switching according to an embodiment of the present disclosure;

[0023] FIG6B is a diagram showing a second example of a process of semantic communication cell switching according to an embodiment of the present disclosure;

[0024] FIG7A is a diagram showing a third example of a process of semantic communication cell switching according to an embodiment of the present disclosure;

[0025] FIG7B is a fourth example showing a process of semantic communication cell switching according to an embodiment of the present disclosure;

[0026] FIG8A is a fifth example showing a process of semantic communication cell switching according to an embodiment of the present disclosure;

[0027] FIG8B is a sixth example showing a process of semantic communication cell switching according to an embodiment of the present disclosure;

[0028] FIG9 shows a functional module block diagram of an electronic device for wireless communication according to another embodiment of the present disclosure;

[0029] FIG10 shows a functional module block diagram of an electronic device for wireless communication according to yet another embodiment of the present disclosure;

[0030] FIG11 shows a flowchart of a method for wireless communication according to one embodiment of the present disclosure;

[0031] FIG12 shows a flowchart of a method for wireless communication according to another embodiment of the present disclosure;

[0032] FIG13 shows a flowchart of a method for wireless communication according to yet another embodiment of the present disclosure;

[0033] FIG14 is a block diagram showing a first example of a schematic configuration of an eNB or gNB to which the technology of the present disclosure may be applied;

[0034] FIG15 is a block diagram illustrating a second example of a schematic configuration of an eNB or gNB to which the technology of the present disclosure may be applied;

[0035] FIG16 is a block diagram showing an example of a schematic configuration of a smartphone to which the technology of the present disclosure can be applied;

[0036] FIG17 is a block diagram showing an example of a schematic configuration of a car navigation device to which the technology of the present disclosure can be applied; and

[0037] 18 is a block diagram of an exemplary structure of a general-purpose personal computer in which methods and / or apparatuses and / or systems according to embodiments of the present invention may be implemented. DETAILED DESCRIPTION

[0038] Exemplary embodiments of the present invention are described below with reference to the accompanying drawings. For the sake of clarity and conciseness, not all features of an actual implementation are described in this specification. However, it should be understood that in the process of developing any such actual implementation, many implementation-specific decisions must be made in order to achieve the developer's specific goals, such as meeting system and business-related constraints, which may vary from implementation to implementation. Furthermore, it should be understood that while development work may be complex and time-consuming, it will be a routine task for those skilled in the art who benefit from this disclosure.

[0039] It is also necessary to explain here that, in order to avoid obscuring the present invention due to unnecessary details, the accompanying drawings only show the device structure and / or processing steps that are closely related to the solution according to the present invention, while other details that are not closely related to the present invention are omitted.

[0040] The present disclosure provides a wireless electronic device 100 according to one embodiment of the present disclosure. The electronic device 100 includes at least one processor and at least one memory, wherein the at least one memory includes computer program code, wherein the at least one memory and the computer program code are configured to, through the at least one processor, cause the electronic device 100 to execute: based on a semantic communication error rate associated with a user device for semantic communication, determining whether to update a semantic library for semantic communication of the user device and / or an inference model associated with the semantic library.

[0041] FIG1 shows a functional module block diagram of an electronic device 100 for wireless communication according to an embodiment of the present disclosure.

[0042] As shown in Figure 1, the electronic device 100 includes: a control unit 101, which performs control; a processing unit 103, which can be configured to, under the control of the control unit 101, determine whether to update the semantic library for semantic communication of the user device and / or the reasoning model related to the semantic library based on the semantic communication error rate related to the user device for semantic communication.

[0043] The control unit 101 and the processing unit 103 may be implemented as one or more processing circuits and at least one memory. The processing circuit may be implemented as a processor or chip, for example. The at least one memory may be RAM, ROM, etc., and the at least one memory is used to store computer program code and data required for the processing circuit to perform processing. Furthermore, it should be understood that the various functional units in the electronic device 100 shown in FIG1 are merely logical modules divided according to the specific functions they implement, and are not intended to limit specific implementation methods.

[0044] The electronic device 100 may be provided on the base station side or communicatively connected to the base station, for example. For example, the electronic device 100 may operate as the base station itself and may further include external devices such as a memory and a transceiver (not shown). The memory may be used to store programs and related data information that the electronic device 100 needs to execute to implement various functions. The transceiver may include one or more communication interfaces to support communication with different devices (e.g., UE, base station, etc.), and the implementation form of the transceiver is not specifically limited here.

[0045] As an example, the base station may be, for example, an eNB or a gNB.

[0046] The wireless communication system according to the present disclosure may be a 5G NR (New Radio) communication system, a 5G+ communication system, or a 6G communication system. Furthermore, the wireless communication system according to the present disclosure may include a non-terrestrial network (NTN). Optionally, the wireless communication system according to the present disclosure may also include a terrestrial network (TN). Furthermore, those skilled in the art will appreciate that the wireless communication system according to the present disclosure may also be a 4G or 3G communication system.

[0047] For example, the reasoning model of the user device related to the semantic library may be an encoder or a decoder on the user device side.

[0048] For example, the wireless communication system according to the present disclosure can be applied to the Internet of Vehicles scenario, or to the scenario where ordinary users perform semantic communication. Those skilled in the art can also think of other application scenarios, which will not be repeated here.

[0049] The semantic communication error rate can take into account errors and packet loss in data transmission and is therefore an indicator that accurately reflects the quality of semantic communication. The electronic device 100 according to an embodiment of the present disclosure uses the semantic communication error rate to more intelligently determine whether to update the user device's semantic library for semantic communication and / or the inference model associated with the semantic library, thereby ensuring that the user device maintains continuous and high-quality communication during semantic communication, thereby improving the efficiency and reliability of semantic communication.

[0050] For simplicity, the following description uses the IoV scenario as an example, unless otherwise specified. More specifically, the description uses the example of whether to update the semantic library and / or reasoning model of a user device (also known as semantic communication cell switching) in the IoV scenario. While the cell switching proposed by 3GPP only considers cellular cell switching, this disclosure also considers the switching of semantic communication cells.

[0051] For example, when a vehicle travels in a city network, it will pass through multiple semantic communication service areas.

[0052] In traditional cellular network communications, cell handovers are typically determined by measuring signal power. When a vehicle enters a new cell, the system determines whether to initiate a handover based on the signal power strength. This approach is effective to a certain extent, but it is not suitable for semantic communication, as its characteristics are not constrained by traditional power measurement. Therefore, semantic communication cannot simply rely on power measurements to determine handovers.

[0053] Semantic communication encompasses a variety of communication methods, including voice calls, data transmission, and sensor data. This makes simple power measurements inadequate for fully reflecting communication quality and requirements. Because semantic communication involves high-bandwidth data transmission, real-time voice communication, and large-scale sensor data processing, each type of communication has different requirements for communication quality and reliability. This complicates semantic handover decisions.

[0054] Furthermore, the cell range for semantic communication is dynamic, adjusting over time and location because it is significantly influenced by environmental conditions. This is because semantic communication is affected by urban environmental conditions (such as buildings, road topology, and weather). Dynamic cell range adjustment is necessary to adapt to changing communication needs and environmental conditions. Therefore, traditional fixed cell management methods are no longer applicable, and determining cell range becomes more complex, requiring consideration of more dynamic factors.

[0055] According to the embodiment of the present disclosure, the electronic device 100 determines the need for semantic communication cell switching based on the semantic communication error rate, so as to better deal with the cell management issues in semantic communication. According to the embodiment of the present disclosure, the electronic device 100 can more intelligently determine whether semantic cell switching is needed based on the semantic communication error rate, thereby ensuring that the vehicle maintains continuous and high-quality communication between semantic communication service areas. According to the switching method based on the semantic communication error rate of the embodiment of the present disclosure, it is possible to achieve more intelligent switching of vehicles between different semantic communication cells, thereby improving the reliability and efficiency of semantic communication. A more intelligent and efficient cell management solution is provided for the semantic communication of vehicles in urban networks to meet the growing demand for vehicle communication and ensure the reliability and quality of semantic communication services.

[0056] As an example, the semantic library includes a relationship mapping between data information and semantic information, a machine learning model for generating a relationship mapping between data information and semantic information, a deep learning model, a mapping table, and at least one of matrix compression.

[0057] FIG2 is a schematic diagram illustrating an example of a semantic library according to an embodiment of the present disclosure.

[0058] In the following, the semantic base is sometimes also referred to as a semantic communication database, database, or knowledge base.

[0059] In Figure 2, taking the data information as a picture and the semantic information as a "tree" as an example, the semantic communication database contains multiple "picture-tree" relationship mappings, and each "picture-tree" is part of the database. The role of the database is to allow the vehicle to generate the semantic information of the "tree" when reading the "picture". In addition, the semantic communication database may include an AI model that generates a "tree" based on the "picture". The AI ​​model may include the network model shown in Figure 2 (for example, a machine learning model, a deep learning model, etc.), or it may be a mapping table (i.e., table mapping), matrix compression, or other algorithms that can extract picture information. Among them, the machine learning model may include a support vector machine, etc., and the deep learning model may include a CNN (convolutional neural network), an RNN (recurrent neural network), etc.

[0060] The wireless communication system disclosed herein can utilize any data suitable for semantic communication. In connected vehicle scenarios, this can include any data acquired by vehicle sensors, such as images captured by onboard cameras, radar data, and voice information captured by the vehicle. This data can serve as input for AI models. In scenarios where ordinary users engage in semantic communication, this can include any information accessible by mobile phones that can serve as input for AI models.

[0061] Semantic extraction for semantic communication is usually achieved by analyzing and understanding the language or data in a specific domain.

[0062] 1. Text / Speech / Image Input: The raw data can be text, speech, or other forms of language input. This can be human speech, computer-generated text, or language information extracted from speech signals.

[0063] 2. Semantic Analysis: The system performs a structural analysis on the input data, identifying the basic building blocks of a sentence, such as subject, predicate, and object. This helps understand the basic grammatical structure of a sentence. Semantic analysis interprets words and phrases in the text to understand their meaning in a specific context. This may involve analyzing lexical semantics, context, and word relationships.

[0064] 3. Semantic representation: Based on semantic analysis, the system will convert the extracted semantic information into a form that can be understood by the computer, usually in a form of semantic representation, such as concise text information, semantic graph, logical representation, etc.

[0065] For a specific example, refer to the "image-tree" diagram in Figure 2. The image represents the input image information, while the "tree" represents the extracted semantic information. LiDAR point clouds and images collected by image sensors can both serve as input for semantic communication. The corresponding semantic information can then be extracted through a network model such as the one shown in Figure 2.

[0066] In Simultaneous Localization and Mapping (SLAM) technology, for example, a robot starts moving from an unknown location in an unknown environment, localizes itself based on its location and a map, and simultaneously builds an incremental map based on its localization, enabling autonomous positioning and navigation. In SLAM, the data collected by the robot can be used as input for semantic communication, which then extracts the corresponding semantic information for communication.

[0067] As an example, the inference model is used to convert data information into semantic information. As described above, the inference model of the user device related to the semantic library can be an encoder or decoder on the user device side. As an example, the inference model can be an AI model.

[0068] Semantic communication can be performed between the electronic device 100 and a user device. That is, the electronic device 100 and the user device can serve as the sender and receiver of semantic communication, respectively. Semantic communication can also be performed between the user device and other user devices. That is, the user device and other user devices can serve as the sender and receiver of semantic communication, respectively.

[0069] The semantic information transmitted between the sender and receiver of semantic communication is usually exchanged in a structured format or representation to ensure the consistency and interpretability of the information. There are several types of semantic representations of scenes.

[0070] 1. Natural Language: Although the goal of semantic communication is to enable computers to understand semantics, text or natural language remains the primary form of communication in actual delivery. The sender can use natural language to express intent, commands, or queries, while the receiver uses natural language processing technology to parse and understand this information.

[0071] 2. Semantic markup language: To represent semantic information in a more structured manner, semantic markup languages ​​such as XML (Extensible Markup Language) or JSON (JavaScript Object Notation) can be used. These markup languages ​​can be used to organize information in a nested manner, making it easier to parse and process.

[0072] 3. Semantic graph: A semantic graph is a graphical representation in which nodes represent entities or concepts and edges represent the relationships between them. This representation helps to visualize the associations between entities, thereby conveying semantic information more intuitively.

[0073] 4. Logical expressions: Logical expressions can be used to express the logical structure of semantic information. For example, first-order logic or description logic can be used to express logical statements about the relationships between entities.

[0074] 5. Semantic Network: Similar to a semantic graph, a semantic network represents entities and relationships in the form of nodes and edges. This representation helps convey complex semantic structures in communication.

[0075] The specific representation form to be chosen depends on the application requirements, the complexity of communication, and the available technology. Typically, in practical applications, multiple forms of semantic representation may be used in combination to more comprehensively convey rich semantic information.

[0076] In the field of semantic communication, AI models help achieve the goals of natural language understanding, semantic analysis, and interaction. The following are some examples of AI models that can be applied to semantic communication:

[0077] Natural Language Processing (NLP) Models:

[0078] BERT (Bidirectional Encoder Representations from Transformers): BERT is a pre-trained natural language processing model that improves performance in semantic tasks such as text classification and question answering by learning contextual information in deep bidirectional learning.

[0079] GPT (Generative Pre-trained Transformer): The GPT series of models are pre-trained models based on the transformer architecture. They are pre-trained with large-scale language models and are suitable for generative natural language tasks such as dialogue generation and text generation.

[0080] Semantic analysis model:

[0081] Word Embeddings model: Word Embeddings models (such as Word2Vec and GloVe) map words to a high-dimensional vector space, capturing the semantic relationships between words for semantic analysis and similarity matching.

[0082] ELMo (Embeddings from Language Models): ELMo uses contextual information to generate dynamic representations of words, which helps solve polysemy and context-sensitive semantic analysis tasks.

[0083] Entity Recognition Model:

[0084] BERT-based entity recognition model: BERT-based models can be used to identify named entities in text, such as names of people, places, and organizations.

[0085] CRF (Conditional Random Fields): CRF is a sequence labeling model commonly used in entity recognition tasks, which takes into account the dependency between contextual information and labels.

[0086] Sentiment Analysis Model:

[0087] LSTM (Long Short-Term Memory): LSTM is a recurrent neural network (RNN) variant suitable for processing sequence data. It is often used in sentiment analysis tasks and can capture emotional changes in text.

[0088] BERT-based sentiment analysis model: The BERT-based model can better understand the emotional tendency of the text while taking contextual information into account.

[0089] Graph Neural Network (GNN):

[0090] Graph neural networks for semantic graph representation: For semantic graphs in semantic communication, GNNs can be used to learn the relationships between nodes and edges, improving the representation capabilities of entities and concepts in graph structures.

[0091] For example, the model for generating the relationship mapping between data information and semantic information and the reasoning model can be based on the above-mentioned AI model.

[0092] The information used for semantic communication is transmitted from the output layer of the AI ​​model. Semantic communication can be to use the above-mentioned AI model to generate semantic information, and then use this semantic information to interact.

[0093] Semantic communication information (semantic information) can be divided into two categories: 1. Information that can be judged as true or false; 2. Information that cannot be judged as true or false. Using the above information classification, different methods for calculating semantic communication error rates can be used.

[0094] FIG. 3 is a schematic diagram illustrating an example of semantic information.

[0095] In Figure 3, the leading vehicle has been traveling within the cell, while the trailing vehicle has just entered. The leading vehicle can extract four pieces of semantic information based on its own network model: 1. Six trees on the left. 2. One tree on the right. 3. One base station on the right. 4. Obstacle ahead. When the trailing vehicle receives these four pieces of information, pieces 1-3 are correct, while piece 4 is inaccurate.

[0096] As an example, the semantic communication error rate is obtained by calculating the proportion of erroneous information among the semantic information received from other user devices within a predetermined time period by the user device based on its original semantic library and / or original reasoning model (method 1 for calculating the semantic communication error rate).

[0097] In method 1, it is assumed that the user equipment receives N pieces of semantic information from other user equipment within a predetermined time period, and e[n] is used to indicate whether the nth piece of semantic information is correct, where e[n] is 0 if the nth piece of semantic information is correct, otherwise it is 1.

[0098] The semantic communication error rate in method 1 can be expressed as:

[0099]

[0100] Those skilled in the art may predetermine the predetermined time period based on experience or application scenarios.

[0101] A user device (e.g., a vehicle) continuously receives semantic information from other vehicles. In Method 1, the vehicle uses its own original semantic library and / or original reasoning model to determine the correctness of all received semantic information (including both information that the vehicle can determine is correct and information that it cannot determine is correct). For example, the vehicle may determine that some information is incorrect. The vehicle dynamically calculates the proportion of incorrect information among the N messages it recently received as the semantic communication error rate.

[0102] For example, referring to Figure 3, in different environments, base stations may be shaped like utility poles or trees, and the following vehicle may not have a database containing this information. Therefore, if the following vehicle, based on its own network model, interprets the third piece of information as "0 base stations on the right," then this information is incorrect. Furthermore, the semantic error rate calculated in Method 1 is:

[0103]

[0104] As an example, the semantic communication error rate is obtained by calculating the proportion of erroneous information among the semantic information received from other user devices within a predetermined time period by the user device based on its original semantic library and / or original reasoning model, the correctness of which the user device can judge (method 2 for calculating the semantic communication error rate).

[0105] In method 2, it is assumed that the user equipment receives N pieces of semantic information from other user equipment within a predetermined time period, and the user equipment can determine the correctness of the semantic information. judge [n] is used to indicate whether the nth semantic information that can be used to determine its correctness is correct. If the nth semantic information is correct, then e judge [n] is 0 otherwise 1.

[0106] The semantic communication error rate in method 2 can be expressed as:

[0107]

[0108] Those skilled in the art may predetermine the predetermined time period based on experience or application scenarios.

[0109] In Method 2, the vehicle uses its own original semantic library and / or original reasoning model to determine whether the received information it can judge as correct is correct. For example, the vehicle dynamically calculates the proportion of incorrect information among N pieces of information it can judge as correct recently received, and uses this as the semantic communication error rate.

[0110] For example, referring to Figure 3, if the following vehicle, based on its own network model, determines that the third piece of information is "0 base stations on the right," then this information is incorrect. Because the following vehicle cannot determine whether the fourth piece of information is correct, it is not included in the error rate calculation. Furthermore, the semantic error rate calculated in Method 2 is:

[0111]

[0112] As an example, the semantic communication error rate is obtained by calculating the proportion of erroneous information among a predetermined number of landmark information obtained by the user device based on its original semantic library and / or original reasoning model, wherein the landmark information is information whose correct semantics can be obtained in advance by the user device (method 3 for calculating the semantic communication error rate).

[0113] For example, a semantic communication switching sign area is set up in each area of ​​the city, which is used by vehicles to determine whether the information extracted from the sign area (which is an example of sign information) is correct, where, for example, the sign area can be composed of multiple "input information-correct semantic output" similar to the "picture-tree" shown in Figure 2.

[0114] Alternatively, after a vehicle enters an area, a base station in the area may transmit test information (which is another example of sign information) to the vehicle.

[0115] In method 3, assuming that the predetermined number is N, e[n] is used to indicate whether the nth piece of flag information is correct, wherein if the nth piece of flag information is correct, e[n] is 0, otherwise it is 1.

[0116] The semantic communication error rate in method 3 can be expressed as:

[0117]

[0118] Those skilled in the art may predetermine the predetermined number based on experience or application scenarios.

[0119] For example, the sign area can be an area where predetermined sign images (Chinese sign, English sign, safety information, etc.) are placed at each intersection. When a vehicle passes through the sign area, the semantic communication error rate can be calculated without interacting with surrounding vehicles. If the sign area is set with 100 sign information, the vehicle can obtain the sign information through camera sensors, etc. The vehicle then uses its own network model to judge the 100 "input information (sign information)", and then compares the judgment result with the "correct semantic output". In this way, the semantic communication error rate can be calculated. Among them, the vehicle can directly extract the correct semantic output from the sign area for comparison with the above judgment result. As an example, in the sign area, the vehicle can obtain the correct semantic output from the roadside unit. And the vehicle can use radar, camera, etc. to obtain surrounding information.

[0120] As an example, processing unit 103 may be configured to determine whether to update the semantic library and / or inference model of the user device based on a comparison of the semantic communication error rate with the error tolerance or error rate threshold of the semantic communication. The error rate threshold increases with increasing error tolerance, and decreases with decreasing error tolerance. For example, those skilled in the art may pre-set the error rate threshold based on experience or application scenarios.

[0121] As an example, the error tolerance of semantic communication is determined based on at least one of road congestion level, vehicle driving speed, road type and scenario, weather conditions, intersections and complex intersections, driver familiarity, application scenarios and purposes, traffic incidents and construction areas, network connection quality, vehicle type and driving mode, traffic laws and local regulations.

[0122] In semantic communication, examples of factors affecting error rate tolerance under different road conditions are as follows.

[0123] Road congestion: In highly congested urban traffic, semantic communication may have a lower error tolerance. For example, when traffic is congested, information delivery in emergency situations may require higher accuracy to ensure accurate traffic navigation or traffic control.

[0124] Vehicle speed: At high speeds, such as on highways, semantic communication may have a relatively low error tolerance due to shorter reaction times. In these situations, navigation instructions, traffic information, and other information must be more accurate to ensure the driver can react quickly and safely. At lower speeds, such as on roads with low speed limits, semantic communication may have a relatively high error tolerance.

[0125] Road type and scenario: On highways, semantic communication may have a lower tolerance for errors due to the higher risk involved. In contrast, in low-speed scenarios such as low-speed roads or parking lots, semantic communication may be more tolerant of errors.

[0126] Weather conditions: Severe weather conditions (such as rain, snow, and fog) may increase the error rate of semantic communication. In such cases, the driver's need for navigation instructions and traffic information may be more urgent, so the error tolerance of semantic communication may be reduced.

[0127] Intersections and complex intersections: At complex intersections or roundabouts, semantic communication may have a lower error tolerance. Accurate navigation and traffic information are crucial for safely navigating intersections.

[0128] Driver familiarity: For drivers familiar with the road, the error tolerance of semantic communication may be high. However, for unfamiliar roads or novice drivers, accurate semantic information may be more important, so the error tolerance of semantic communication may be lower.

[0129] Application Scenarios and Purpose: In emergency rescue scenarios, semantic communication may have a lower tolerance for errors, as incorrect information can lead to serious consequences. In contrast, in entertainment and navigation scenarios, the tolerance for errors may be higher.

[0130] Traffic incidents and construction zones: When encountering traffic accidents, road construction, or other emergencies, the error tolerance of semantic communication may be reduced. Accurate information becomes crucial for avoiding danger or choosing appropriate detours.

[0131] Network connection quality: In areas with unstable network connections, such as in remote areas or tunnels, the error tolerance of semantic communication may be affected. In such cases, the system may require more robust error correction mechanisms or caching strategies to cope with packet loss or delay.

[0132] Vehicle type and driving mode: Different types of vehicles (e.g., autonomous vehicles, traditional cars, motorcycles) may have different expectations for error tolerance in semantic communication. For example, autonomous vehicles may have higher requirements for accurate maps and navigation information.

[0133] Traffic laws and local regulations: In some regions, traffic laws may place higher demands on the accuracy of semantic communication and may reduce the error tolerance of semantic communication. For example, in some countries or cities, regulations may require navigation systems to provide specific types of information or warnings.

[0134] The above examples demonstrate that the error tolerance of semantic communication under different road conditions can be affected by a variety of factors. Therefore, when designing a semantic communication system, it is necessary to consider user needs and safety requirements in different scenarios to determine an appropriate error tolerance value or error rate threshold. For example, electronic device 100 can set the error tolerance value or error rate threshold by understanding the communication conditions in the surrounding area.

[0135] As can be seen from the above description, the semantic communication error rate is calculated at the user equipment end (sending end), and is included in the signaling or uplink information sent by the sending end to the electronic device 100 (receiving end).

[0136] As an example, the signaling for indicating whether the semantic library of the user equipment and / or the reasoning model related to the semantic library is updated is contained in the PDCCH (physical downlink control channel), which consists of a field in the PDCCH and is used to coordinate the scheduling of downlink resources to complete the update of the semantic library and / or the reasoning model.

[0137] In the case that the communication link between the user equipment and the electronic device 100 that has newly entered its communication service range is good, the user equipment can communicate directly with the electronic device 100 .

[0138] As an example, the processing unit 103 may be configured to receive the semantic communication error rate directly from the user equipment.

[0139] As an example, the processing unit 103 may be configured to directly send a signaling indicating whether to perform an update to the user equipment.

[0140] As an example, the processing unit 103 may be configured to send the updated semantic library and / or reasoning model directly to the user device when an update is required.

[0141] FIG4A shows an example of a semantic communication cell handover scenario according to an embodiment of the present disclosure.

[0142] Figure 4A shows vehicles Veh#1 and Veh#2, and base stations BS#1 and BS#2. Assume the following system states: 1. The communication link between Veh#1 and BS#2 is good. 2. The communication link between Veh#2 and BS#2 is good or poor. 3. The communication link between Veh#1 and Veh#2 is good or poor. Base station BS#2 is an example of electronic device 100 in this embodiment, and vehicle Veh#1 is an example of user equipment in this embodiment.

[0143] The semantic communication process in Figure 4A is as follows:

[0144] 1. While driving, Veh#1 engages in semantic communication with nearby vehicles. The scope of the semantic database changes dynamically over time and across regions, so Veh#1 needs to calculate the error rate of semantic communication in real time to determine whether to continue semantic communication.

[0145] 2. When Veh#1 moves from the service range of BS#1 to the service range of BS#2, Veh#1 can receive a large amount of semantic communication information in the new environment. In addition, since Veh#1 can observe the surrounding environment, it can determine whether some of the information is correct.

[0146] 3. Veh#1 dynamically determines whether the semantic information is correct and calculates the semantic communication error rate ε.

[0147] 4. Establish uplink / downlink between Veh#1 and BS#2.

[0148] 5. Veh#1 transmits the semantic communication error rate ε to the nearest BS#2, which determines whether Veh#1 is suitable for semantic communication in the current area (the area that Veh#1 has just entered and is within the service range of BS#2) based on the semantic communication error rate ε.

[0149] 6. If the semantic communication error rate ε is greater than the error rate threshold (also referred to as the threshold), BS#2 decides to update Veh#1's semantic library and / or reasoning model. If the semantic communication error rate ε is less than the threshold, BS#2 decides to allow Veh#1 to continue semantic communication (i.e., without updating Veh#1's semantic library and / or reasoning model).

[0150] 7. BS#2 transmits a signaling SC to Veh#1 for indicating whether to perform the update (the value is yes or no, as described above, which is included in the PDCCH, for example).

[0151] 8. Veh#1 receives the signaling notification. If an update is required, BS#2 transmits the updated semantic library and / or reasoning model (the semantic library and / or reasoning model of the area) to Veh#1 to update Veh#1's semantic library and / or reasoning model.

[0152] 9. Then, the uplink / downlink between Veh#1 and BS#2 is released, thereby saving channel resources.

[0153] In the example of Figure 4A , Veh#1 has a good communication link with BS#2, which has just entered its service range. Therefore, Veh#1 can communicate directly with BS#2. This allows Veh#1 to send its calculated semantic communication error rate directly to BS#2, which in turn transmits a signaling signal (SC) to Veh#1 indicating whether to perform an update. If an update is required, BS#2 can directly transmit the updated semantic repository and / or inference model to Veh#1.

[0154] In the case where the communication link between the user equipment and the electronic device 100 that has newly entered its communication service range is poor, the user equipment cannot communicate directly with the electronic device 100 .

[0155] As an example, the processing unit 103 may be configured to receive a semantic communication error rate of the user equipment forwarded via other user equipments.

[0156] As an example, the processing unit 103 may be configured to send signaling indicating whether to perform an update to other user equipments, so that the other user equipments forward the signaling to the user equipment.

[0157] As an example, the processing unit 103 may be configured to send the updated semantic library and / or reasoning model to other user devices when updating is required, so that the other user devices forward the updated semantic library and / or reasoning model to the user devices.

[0158] FIG4B shows another example of a semantic communication cell handover scenario according to an embodiment of the present disclosure.

[0159] In Figure 4B , assume the following system states: 1. The communication link between Veh#1 and BS#2 is poor (the communication link between Veh#1 and BS#2 is represented by a dashed line). 2. The communication link between Veh#2 and BS#2 is good. 3. The communication link between Veh#1 and Veh#2 is good. Base station BS#2 is an example of electronic device 100 in this embodiment, and vehicle Veh#1 is an example of user equipment in this embodiment.

[0160] The semantic communication process in Figure 4B is as follows:

[0161] 1. While driving, Veh#1 engages in semantic communication with nearby vehicles. The scope of the semantic database changes dynamically over time and across regions, so Veh#1 needs to calculate the error rate of semantic communication in real time to determine whether to continue semantic communication.

[0162] 2. When Veh#1 enters the service range of BS#2 from the service range of BS#1, Veh#1 can receive a lot of semantic communication information in the new environment. Since Veh#1 can observe the surrounding environment, it can determine whether some of the information is correct.

[0163] 3. Veh#1 dynamically determines whether the semantic information is correct and calculates the semantic communication error rate ε.

[0164] 4. Since the communication link between Veh#1 and BS#2 is very poor, they cannot communicate directly. However, since the communication link between Veh#1 and Veh#2 is good, a side link is established between Veh#1 and Veh#2.

[0165] 5. An uplink / downlink is established between Veh#2 and BS#2.

[0166] 6. Veh#1 transmits the semantic communication error rate ε to Veh#2, which then forwards it to the nearest BS#2. BS#2 determines whether Veh#1 is suitable for semantic communication in the current area based on the current semantic communication error rate ε.

[0167] 7. If the semantic communication error rate ε is greater than a threshold, BS#2 decides to update Veh#1's semantic library and / or reasoning model. If the semantic communication error rate ε is less than the threshold, BS#2 decides to allow Veh#1's semantic communication to continue (i.e., without updating Veh#1's semantic library and / or reasoning model).

[0168] 8. BS#2 transmits a signaling SC to Veh#2 for indicating whether to perform the update (the value is yes or no, as described above, which is included in the PDCCH, for example).

[0169] 9. Veh#2 forwards the signaling SC indicating whether to perform the update to Veh#1.

[0170] 10. Veh#1 receives the signaling notification. If an update is required, BS#2 transmits the updated semantic base and / or reasoning model (the semantic base and / or reasoning model for the area) to Veh#2, which then forwards it to Veh#1 via a sidelink. Alternatively, if Veh#2 can perform semantic communication, it can directly forward the updated semantic base and / or reasoning model to Veh#1.

[0171] 11. Release the sidelink between Veh#1 and Veh#2.

[0172] 12. The uplink / downlink between Veh#2 and BS#2 is released, thereby saving channel resources.

[0173] In addition, those skilled in the art will understand that there is also the following situation: due to the deterioration of semantic communication quality, the semantic communication error rate increases. Even if the user equipment (for example, Veh#1) does not enter the service range of other base stations (for example, the service range of BS#2), the semantic library and / or reasoning model of the user equipment needs to be updated within the original cell range (for example, the service range of BS#1).

[0174] Figure 5A shows an example of simulation results of changes in semantic communication accuracy according to an embodiment of the present disclosure, and Figure 5B shows another example of simulation results of changes in semantic communication accuracy according to an embodiment of the present disclosure. This is described in conjunction with Veh#1, BS#1, and BS#2 in Figure 4A. The ordinates in Figures 5A and 5B represent the semantic communication accuracy (marked as "terminal's own semantic communication accuracy" in the figure). The abscissa in Figure 5A represents the number of semantic communication messages, the semantic communication error rate is calculated based on the above-mentioned calculation method 1, and the semantic communication accuracy is obtained based on the calculated semantic communication error rate. The abscissa in Figure 5B represents the number of semantic communication messages that can be identified as accurate or not, the semantic communication error rate is calculated based on the above-mentioned calculation method 2, and the semantic communication accuracy is obtained based on the calculated semantic communication error rate.

[0175] The parameters in Figure 5A are set as follows: the accuracy of AI Model A for vehicle Veh#1 in BS#1 is 99.05%, the accuracy of AI Model B in BS#2 is 98.88%, and the accuracy of AI Model A in BS#2 is 79.75%, of which 20% of the data cannot be identified as correct (i.e., the semantic information cannot be judged as correct), and the switching threshold is 92%. The parameters in Figure 5B are set as follows: the accuracy of AI Model A for vehicle Veh#1 in BS#1 is 99.05%, the accuracy of AI Model B in BS#2 is 98.88%, and the accuracy of AI Model A in BS#2 is 79.75%, of which 20% of the data cannot be identified as correct, and the switching threshold is 85%.

[0176] As can be seen from Figures 5A and 5B, in the strategy of executing semantic communication cell switching according to the embodiment of the present disclosure (corresponding to the curve labeled "Accuracy change when switching is executed" in the figure), it can be seen that after the semantic communication cell switching, the semantic communication accuracy returns to a normal level. However, in the strategy of not executing semantic communication cell switching in the prior art (corresponding to the curve labeled "Accuracy change when switching is not executed" in the figure), it can be seen that due to not executing the switching, the semantic communication accuracy after entering the new cell BS#2 is relatively low (i.e., the semantic communication error rate is relatively high).

[0177] An example of a semantic communication cell handover process according to an embodiment of the present disclosure is described below in conjunction with Figures 6A, 6B, 7A, 7B, 8A, and 8B. Figures 6A, 6B, 7A, 7B, 8A, and 8B illustrate base stations (BSs) BS#1 to BS#3 and vehicles Veh#1 to Veh#7, wherein vehicle Veh#5 is an example of a user equipment according to an embodiment of the present disclosure, and vehicle Veh#5 enters the cell of BS#3 from the cell of BS#2, and BS#3 is an example of an electronic device 100 according to an embodiment of the present disclosure.

[0178] Figure 6A shows a first example of a process for semantic communication cell handover according to an embodiment of the present disclosure. Figure 6B shows a second example of a process for semantic communication cell handover according to an embodiment of the present disclosure.

[0179] In Figures 6A and 6B, encoders and decoders are deployed on the BS side, and a large number of information acquisition devices, such as radars and cameras, are deployed on the BS side; a small number of information acquisition devices are deployed on the vehicle side, and the vehicle requests a decoder model (inference model) from the BS (the vehicle combines the global information and local information of the BS for semantic communication).

[0180] In FIG. 6A , it is assumed that vehicle Veh# 5 is able to communicate with base station BS# 3 which has newly entered its cell.

[0181] A1 in FIG6A : BS#1 to BS#3 respectively collect the global information of their own cells and broadcast the global information processed by the encoder to all vehicles in the same cell.

[0182] Figure 6A, A2: Vehicle Veh#2 requests a decoder model (which can be visualized as an AI model) from BS#1 in its own cell to decode the received global information, thereby enabling semantic communication. Although not shown for clarity, it is understood that vehicle Veh#5 requests a decoder model from BS#2 in its home cell to decode the received global information, thereby enabling semantic communication.

[0183] A3 of Figure 6A: BS#1 sends the decoder model or the compressed decoder model to vehicle Veh#2. Although not shown for clarity, it is understood that BS#2 sends the decoder model or the compressed decoder model to vehicle Veh#5.

[0184] Figure 6A, A4: A semantic error rate calculation module is deployed on a vehicle. For example, vehicle Veh#4 is shown as being deployed with the semantic error rate calculation module. However, although not shown for clarity, it is understood that vehicle Veh#5 is also deployed with the semantic error rate calculation module. After entering the cell of BS#3, vehicle Veh#5 transmits to BS#3 the semantic communication error rate calculated based on the semantic information received in the cell.

[0185] A5 in Figure 6A: BS#3 compares the received semantic communication error rate with the error rate threshold to determine whether vehicle Veh#5 needs to replace the existing decoder model. If replacement is required, BS#3 sends the new decoder model or the compressed decoder model to vehicle Veh#5.

[0186] In FIG. 6B , it is assumed that vehicle Veh# 5 cannot communicate with base station BS# 3 which has newly entered its cell.

[0187] A1 in FIG6B : BS#1 to BS#3 respectively collect the global information of their own cells and broadcast the global information processed by the encoder to all vehicles in the same cell.

[0188] Figure 6B, A2: Vehicle Veh#2 requests a decoder model from BS#1 in its own cell to decode the received global information, thereby enabling semantic communication. Although not shown for clarity, it is understood that vehicle Veh#5 requests a decoder model from BS#2 in its home cell to decode the received global information, thereby enabling semantic communication.

[0189] A3 of Figure 6B: BS#1 sends the decoder model or the compressed decoder model to vehicle Veh#2. Although not shown for clarity, it is understood that BS#2 sends the decoder model or the compressed decoder model to vehicle Veh#5.

[0190] A4 in Figure 6B: After entering the cell of BS#3, vehicle Veh#5 establishes a sidelink connection with vehicle Veh#6 in the cell of BS#3 and sends the semantic communication error rate calculated based on the semantic information received in the cell of BS#3 to the connected vehicle Veh#6.

[0191] A5 of FIG6B : The connected vehicle Veh#6 reports the semantic communication error rate to the base station BS#3.

[0192] A6 in Figure 6B: BS#3 compares the received semantic communication error rate with the error rate threshold to determine whether vehicle Veh#5 needs to replace the existing decoder model. If replacement is required, BS#3 sends the new decoder model or the compressed decoder model to vehicle Veh#6 to which vehicle Veh#5 is connected.

[0193] A7 in FIG6B : The connected vehicle Veh#6 sends the received response (whether to replace the existing decoder model of vehicle Veh#5) to vehicle Veh#5. If the decoder model needs to be replaced, the connected vehicle Veh#6 also sends the received decoder model or the compressed decoder model to vehicle Veh#5.

[0194] Figure 7A shows a third example of a process for semantic communication cell handover according to an embodiment of the present disclosure. Figure 7B shows a fourth example of a process for semantic communication cell handover according to an embodiment of the present disclosure.

[0195] In Figures 7A and 7B, an encoder and a semantic library are deployed on the BS side, and a large number of information acquisition devices are deployed on the BS side; a decoder is deployed on the vehicle side, and a small number of information acquisition devices are deployed on the vehicle side, and the vehicle requests the semantic library from the BS.

[0196] In FIG7A , it is assumed that vehicle Veh#5 is able to communicate with base station BS#3 which has newly entered its cell.

[0197] A1 in FIG7A : BS#1 to BS#3 respectively collect the global information of their own cell and broadcast the global information processed by the encoder to all vehicles in the cell.

[0198] In Figure 7A , vehicle A2 requests a semantic library (e.g., a mapping table, AI model, dataset, experience, etc.) from BS#1 in its own cell to assist the decoder in decoding the received global information, thereby enabling semantic communication. Although not shown for clarity, it is understood that vehicle Veh#5 also requests a semantic library from BS#2 in its home cell to assist the decoder in decoding the received global information, thereby enabling semantic communication.

[0199] A3 in Figure 7A: BS#1 sends the semantic library or the compressed semantic library to vehicle Veh#2. Although not shown for clarity, it is understood that BS#2 sends the semantic library or the compressed semantic library to vehicle Veh#5.

[0200] Figure 7A, A4, shows a semantic error rate calculation module deployed on a vehicle. For example, vehicle Veh#4 is shown as being deployed with the module. However, although not shown for clarity, it is understood that vehicle Veh#5 is also deployed with the module. After entering the cell of BS#3, vehicle Veh#5 transmits to BS#3 the semantic communication error rate calculated based on the semantic information received in the cell.

[0201] A5 in Figure 7A: BS#3 compares the error rate of received semantic communication with the error rate threshold to determine whether vehicle Veh#5 should replace its existing semantic library. If so, BS#3 sends the new or compressed semantic library to vehicle Veh#5. Vehicle Veh#5 then trains its decoder based on the new or compressed semantic library.

[0202] In FIG. 7B , it is assumed that vehicle Veh# 5 cannot communicate with base station BS# 3 which has newly entered its cell.

[0203] A1 in FIG7B : BS#1 to BS#3 respectively collect the global information of their own cells and broadcast the global information processed by the encoder to all vehicles in the cell.

[0204] In Figure 7B , A2 shows vehicle Veh#2 requesting a semantic library from BS#1 in its own cell to assist the decoder in decoding the received global information, thereby enabling semantic communication. Although not shown for clarity, it is understood that vehicle Veh#5 also requests a semantic library from BS#2 in its home cell to assist the decoder in decoding the received global information, thereby enabling semantic communication.

[0205] A3 in Figure 7B: BS#1 sends the semantic library or the compressed semantic library to vehicle Veh#2. Although not shown in the figure for clarity, it is understood that BS#2 sends the semantic library or the compressed semantic library to vehicle Veh#5.

[0206] A4 in Figure 7B: After entering the cell of BS#3, vehicle Veh#5 establishes a sidelink connection with vehicle Veh#6 in the cell of BS#3 and sends the semantic communication error rate calculated based on the semantic information received in the cell of BS#3 to the connected vehicle Veh#6.

[0207] A5 in FIG7B : The connected vehicle Veh#6 reports the semantic communication error rate to the local cell base station BS#3.

[0208] A6 in Figure 7B: BS#3 compares the received semantic communication error rate with the error rate threshold to determine whether vehicle Veh#5 needs to replace the existing semantic library. If replacement is required, BS#3 sends the new semantic library or compressed semantic library to vehicle Veh#6 connected to vehicle Veh#5.

[0209] A7 in FIG7B : The connected vehicle Veh#6 sends the received response to the vehicle Veh#5. If the semantic library needs to be replaced, the connected vehicle Veh#6 also sends the received semantic library or the compressed semantic library to the vehicle Veh#5.

[0210] Figure 8A shows a fifth example of a process for semantic communication cell handover according to an embodiment of the present disclosure. Figure 8B shows a sixth example of a process for semantic communication cell handover according to an embodiment of the present disclosure.

[0211] In Figures 8A and 8B, a global encoder and a global decoder are deployed on the BS side, and no information acquisition device is deployed on the BS side; a local encoder and an information acquisition device are deployed on the vehicle side, and the vehicle requests a global decoder.

[0212] In FIG. 8A , it is assumed that vehicle Veh# 5 is able to communicate with base station BS# 3 which has newly entered its cell.

[0213] A1 in FIG8A : Vehicles Veh#1 to Veh#7 extract important local information and upload it to the BS of their own cell.

[0214] A2 in FIG8A : BS#1 to BS#3 aggregate the local information of all vehicles in the cell into global information through a global encoder, and broadcast the global information to all vehicles in the cell.

[0215] In Figure 8A , A3 shows vehicle Veh#2 requesting a decoder model from BS#1 in its own cell to decode the received global information, thereby enabling semantic communication. Although not shown for clarity, it is understood that vehicle Veh#5 also requests a decoder model from BS#2 in its home cell to decode the received global information, thereby enabling semantic communication.

[0216] A4 in Figure 8A: BS#1 sends the decoder model or the compressed decoder model to vehicle Veh#2. Although not shown for clarity, it is understood that BS#2 sends the decoder model or the compressed decoder model to vehicle Veh#5.

[0217] A5 in Figure 8A shows a semantic error rate calculation module deployed on a vehicle. For example, vehicle Veh#4 is shown as being deployed with the module. However, although not shown for clarity, it is understood that vehicle Veh#5 is also deployed with the module. After entering the cell of BS#3, vehicle Veh#5 transmits to BS#3 the semantic communication error rate calculated based on the semantic information received in the cell.

[0218] A6 in Figure 8A: BS#3 compares the received semantic communication error rate with the error rate threshold to determine whether vehicle Veh#5 needs to replace the existing decoder model. If replacement is required, BS#3 sends the new decoder model or the compressed decoder model to vehicle Veh#5.

[0219] In FIG. 8B , it is assumed that vehicle Veh# 5 cannot communicate with base station BS# 3 which has newly entered its cell.

[0220] A1 in FIG8B : Vehicles Veh#1 to Veh#7 extract important local information and upload it to the BS of their own cell.

[0221] A2 in FIG8B : BS#1 to BS#3 aggregate the local information of all vehicles in the cell into global information through a global encoder, and broadcast the global information to all vehicles in the cell.

[0222] In Figure 8B , A3 shows vehicle Veh#2 requesting a decoder model from BS#1 in its own cell to decode the received global information, thereby enabling semantic communication. Although not shown for clarity, it is understood that vehicle Veh#5 also requests a decoder model from BS#2 in its home cell to decode the received global information, thereby enabling semantic communication.

[0223] A4 in Figure 8B: BS#1 sends the decoder model or the compressed decoder model to vehicle Veh#2. Although not shown for clarity, it is understood that BS#2 sends the decoder model or the compressed decoder model to vehicle Veh#5.

[0224] A5 in Figure 8B: After entering the cell of BS#3, vehicle Veh#5 establishes a sidelink connection with vehicle Veh#6 in the cell of BS#3 and sends the semantic communication error rate calculated based on the semantic information received in the cell of BS#3 to the connected vehicle Veh#6.

[0225] A6 in FIG8B : The connected vehicle Veh#6 reports the semantic communication error rate to the local cell base station BS#3.

[0226] A7 in Figure 8B: BS#3 compares the received semantic communication error rate with the error rate threshold to determine whether vehicle Veh#5 needs to replace the existing decoder model. If replacement is required, BS#3 sends the new decoder model or the compressed decoder model to vehicle Veh#6 to which vehicle Veh#5 is connected.

[0227] A8 in FIG8B : The connected vehicle Veh#6 sends the received response to the vehicle Veh#5. If the decoder model needs to be replaced, the connected vehicle Veh#6 also sends the received decoder model or the compressed decoder model to the vehicle Veh#5.

[0228] Hereinafter, the electronic device 100 according to an embodiment of the present disclosure will be described in a configuration form of another form of unit modules.

[0229] The electronic device 100 may include: an information acquisition unit, which is used to obtain a scheduling request, information data, and pilot signaling (for example, including receiving semantic communication error rate signaling and signaling for indicating whether a knowledge base update is to be performed) of a user equipment (UE) (for example, a vehicle) during vehicle semantic communication; a channel measurement unit, which is used to measure the channel state in real time and generate a generalized channel model; and an information sending unit, which is used to send scheduling signaling to the vehicle for resource allocation (for example, including sending semantic communication error rate signaling and signaling for indicating whether a knowledge base update is to be performed). Thus, the channel can be measured in real time according to the received signaling, and then resources can be configured according to the received scheduling policy.

[0230] As an example, the information acquisition unit is deployed at the electronic device 100 to receive scheduling signaling. The unit is used to receive a scheduling request for establishing a downlink, a sidelink, etc.

[0231] For example, the channel measurement unit obtains pilot information from the information acquisition unit and models the channel as a generalized channel model based on the pilot information. After modeling, the model is sent to the information transmission unit. This unit estimates the channel model and can be implemented using deep learning, reinforcement learning, or other methods.

[0232] As an example, the information sending unit receives the status from the information acquisition unit and the channel measurement unit, and sends the scheduling policy decision to the vehicle user. This unit informs the vehicle user how the channel resources are configured and whether to perform data transmission.

[0233] The method of determining whether an update is needed based on the semantic communication error rate according to the embodiment of the present disclosure provides strong support for the development of future smart city transportation and communication systems, and is expected to improve the efficiency and reliability of communication and promote sustainable development in this field.

[0234] The present disclosure also provides a wireless electronic device 9000 according to another embodiment of the present disclosure. The electronic device 9000 includes at least one processor and at least one memory, wherein the at least one memory includes computer program code, wherein the at least one memory and the computer program code are configured to, through the at least one processor, cause the electronic device 9000 to execute: calculating a semantic communication error rate related to semantic communication, so that a network-side device can determine whether to update a semantic library used for semantic communication of the electronic device 9000 and / or an inference model related to the semantic library.

[0235] FIG9 shows a functional module block diagram of an electronic device 9000 for wireless communication according to another embodiment of the present disclosure.

[0236] As shown in Figure 9, the electronic device 9000 includes: a control unit 9001, which performs control; a processing unit 9003, which, under the control of the control unit 9001, calculates the semantic communication error rate related to semantic communication, so that the network side device can determine whether to update the semantic library used for semantic communication of the electronic device 9000 and / or the reasoning model related to the semantic library.

[0237] The control unit 9001 and the processing unit 9003 can be implemented as one or more processing circuits and at least one memory. The processing circuit can be implemented as a processor or chip, for example. The at least one memory can be RAM, ROM, etc., and the at least one memory is used to store computer program code and data required for the processing circuit to perform processing. It should be understood that the various functional units in the electronic device 9000 shown in FIG9 are merely logical modules divided according to the specific functions they implement, and are not intended to limit specific implementation methods.

[0238] For example, the electronic device 9000 may operate as a user device itself and may further include external devices such as a memory and a transceiver (not shown). The memory may be used to store programs and related data information required for the electronic device 9000 to implement various functions. The transceiver may include one or more communication interfaces to support communication with different devices (e.g., UE, base station, etc.), and the implementation form of the transceiver is not specifically limited here.

[0239] As an example, the network side device in the embodiment of the electronic device 9000 may be the electronic device 100 mentioned above. As an example, the electronic device 9000 may be the user equipment involved in the embodiment of the electronic device 100 mentioned above.

[0240] For example, the reasoning model of the electronic device 9000 related to the semantic library can be implemented by an encoder or a decoder on the electronic device 9000.

[0241] According to the embodiment of the present disclosure, the electronic device 9000 calculates the semantic communication error rate so that the network side device can more intelligently judge whether to update the semantic library and / or reasoning model of the electronic device 9000 based on the semantic communication error rate, thereby ensuring that the electronic device 9000 maintains continuous and high-quality communication in semantic communication, that is, it can improve the efficiency and reliability of semantic communication.

[0242] In the Internet of Vehicles scenario, the electronic device 9000 according to the embodiment of the present disclosure calculates the semantic communication error rate so that the network-side device can judge the need for semantic communication cell switching based on the semantic communication error rate, so as to better deal with the cell management problem in semantic communication. The network-side device can more intelligently judge whether semantic cell switching is required based on the semantic communication error rate, thereby ensuring that the electronic device 9000 maintains continuous and high-quality communication between semantic communication service areas. The switching configuration based on the semantic communication error rate according to the embodiment of the present disclosure can achieve more intelligent switching of vehicles between different semantic communication cells, thereby improving the reliability and efficiency of semantic communication. A more intelligent and efficient cell management solution is provided for the semantic communication of vehicles in urban networks to meet the growing demand for vehicle communication and ensure the reliability and quality of semantic communication services.

[0243] As an example, the semantic library includes a relationship mapping between data information and semantic information, a machine learning model for generating a relationship mapping between data information and semantic information, a deep learning model, a mapping table, and at least one of matrix compression.

[0244] For examples of the semantic library, please refer to the corresponding part described in conjunction with Figure 2 in the embodiment of the electronic device 100, which will not be repeated here.

[0245] As an example, the inference model is used to transform data information into semantic information.

[0246] As an example, processing unit 9003 can be configured to calculate, based on the original semantic library and / or original reasoning model of electronic device 9000, the proportion of erroneous information among the semantic information received from other electronic devices within a predetermined time period as the semantic communication error rate. For a description of this method for calculating the semantic communication error rate, please refer to the corresponding part of the description of Expression 1 in the embodiment of electronic device 100, which will not be repeated here.

[0247] As an example, the processing unit 9003 can be configured to calculate, based on the original semantic library and / or original reasoning model of the electronic device 9000, the proportion of erroneous information among the semantic information received from other electronic devices within a predetermined time period, the correctness of which can be determined by the electronic device, as the semantic communication error rate. For a description of this method for calculating the semantic communication error rate, please refer to the corresponding portion of the description of Expression 2 in the embodiment of the electronic device 100, which will not be repeated here.

[0248] As an example, the processing unit 9003 can be configured to calculate the proportion of error information among the predetermined amount of acquired flag information based on the original semantic library and / or original reasoning model of the electronic device 9000, as the semantic communication error rate, wherein the flag information is information for which the electronic device can obtain the correct semantics in advance. For a description of this method for calculating the semantic communication error rate, please refer to the corresponding portion of the description of Expression 3 in the embodiment of the electronic device 100, which will not be repeated here.

[0249] As an example, the processing unit 9003 can be configured to directly send the semantic communication error rate to the network-side device. As an example, the processing unit 9003 can be configured to directly receive signaling from the network-side device to indicate whether to update. As an example, the processing unit 9003 can be configured to directly receive the updated semantic library and / or reasoning model from the network-side device when an update is required. Please refer to the corresponding part described in conjunction with Figure 4A in the embodiment of the electronic device 100, and no further details will be given here.

[0250] As an example, the processing unit 9003 can be configured to send the semantic communication error rate to other electronic devices so that the other electronic devices forward the semantic communication error rate to the network side device. As an example, the processing unit 9003 can be configured to receive a signaling indicating whether to update from the network side device via other electronic devices. As an example, the processing unit 9003 can be configured to receive an updated semantic library and / or reasoning model from the network side device via other electronic devices when an update is required. Please refer to the corresponding part described in conjunction with Figure 4B in the embodiment of the electronic device 100, which will not be repeated here.

[0251] The present disclosure also provides an electronic device 1000 for wireless communication according to another embodiment of the present disclosure. The electronic device 1000 includes at least one processor and at least one memory, the at least one memory including computer program code, wherein the at least one memory and the computer program code are configured to, through the at least one processor, cause the electronic device 1000 to execute: transmitting semantic communication error rates received from other electronic devices to a network-side device, so that the network-side device determines whether to update a semantic library used for semantic communication of the other electronic devices and / or an inference model related to the semantic library.

[0252] FIG10 shows a functional module block diagram of a wireless electronic device 1000 according to yet another embodiment of the present disclosure.

[0253] As shown in Figure 10, the electronic device 1000 includes: a control unit 1001, which performs control; a communication unit 1003, which, under the control of the control unit 1001, transmits the semantic communication error rate received from other electronic devices to the network side device, so that the network side device can determine whether to update the semantic library used for semantic communication of other electronic devices and / or the reasoning model related to the semantic library.

[0254] The control unit 1001 and the communication unit 1003 may be implemented as one or more processing circuits and at least one memory. The processing circuit may be implemented as a processor or chip, for example. The at least one memory may be a RAM, a ROM, etc. The at least one memory is used to store computer program code and data required for the processing circuit to perform processing. It should be understood that the various functional units in the electronic device 1000 shown in FIG10 are merely logical modules divided according to the specific functions they implement, and are not intended to limit specific implementation methods.

[0255] For example, the electronic device 1000 may operate as a user device itself and may also include external devices such as a memory and a transceiver (not shown). The memory may be used to store programs and related data information required for the user device to implement various functions. The transceiver may include one or more communication interfaces to support communication with different devices (e.g., base stations, other user devices, etc.), and the implementation form of the transceiver is not specifically limited here.

[0256] As an example, the network-side device in the embodiment of the electronic device 1000 may be the electronic device 100 mentioned above, and the other electronic devices in the embodiment of the electronic device 1000 may be the electronic device 9000 mentioned above. As an example, the electronic device 1000 may be the other user devices involved in the embodiment of the electronic device 100 and the other electronic devices involved in the embodiment of the electronic device 9000.

[0257] According to the embodiment of the present disclosure, the electronic device 1000 forwards the semantic communication error rate of other electronic devices to the network side device, so that the network side device can more intelligently judge whether to update the semantic library and / or reasoning model of other electronic devices based on the semantic communication error rate, thereby ensuring that other electronic devices maintain continuous and high-quality communication in semantic communication, that is, it can improve the efficiency and reliability of semantic communication.

[0258] In a connected vehicle scenario, electronic device 1000 according to an embodiment of the present disclosure forwards the semantic communication error rates of other electronic devices to a network device, which then uses the semantic communication error rates to determine the need for semantic communication cell handover, thereby better addressing cell management issues in semantic communication. This allows the network device to more intelligently determine whether semantic cell handover is necessary based on the semantic communication error rates, thereby ensuring continuous and high-quality communication between vehicles in semantic communication service areas.

[0259] As an example, the semantic library includes a relationship mapping between data information and semantic information, a machine learning model for generating a relationship mapping between data information and semantic information, a deep learning model, a mapping table, and at least one of matrix compression.

[0260] For examples of the semantic library, please refer to the corresponding part described in conjunction with Figure 2 in the embodiment of the electronic device 100, which will not be repeated here.

[0261] As an example, the inference model is used to transform data information into semantic information.

[0262] As an example, the communication unit 1003 is configured to forward signaling received from the network-side device indicating whether to perform an update to other electronic devices, and, if an update is required, forward the updated semantic repository and / or inference model received from the network-side device to other electronic devices. For details, please refer to the corresponding portion described in conjunction with FIG. 4B in the embodiment of the electronic device 100, which will not be repeated here.

[0263] Hereinafter, the electronic devices 9000 and 1000 according to the embodiments of the present disclosure are described in the form of configuration of another form of unit modules.

[0264] Electronic devices 9000 and 1000 may include: an information acquisition unit, which is used to obtain the vehicle's scheduling request, information data and pilot signaling (for example, including receiving semantic communication error rate signaling and signaling for indicating whether the knowledge base is to be updated) during the vehicle semantic communication process; a channel measurement unit, which is used to measure the channel status in real time and generate a generalized channel model; an information sending unit, which is used to send scheduling signaling to other vehicles and base stations for resource allocation (for example, including sending semantic communication error rate signaling and signaling for indicating whether the knowledge base is to be updated); a semantic communication unit, which is used to extract semantic communication information and convert the collected data into semantic communication information. In this way, the channel can be measured in real time based on the signaling received by the vehicle, and then resources can be configured according to the user status.

[0265] As an example, the information acquisition unit is used to receive scheduling signaling and receive a scheduling request for establishing a downlink, a sidelink, etc.

[0266] For example, the channel measurement unit obtains pilot information from the information acquisition unit and models the channel as a generalized channel model based on the pilot information. After modeling, the model is sent to the information transmission unit. This unit is used to estimate the channel model, for example, using methods such as deep learning and reinforcement learning.

[0267] For example, the information sending unit receives the status from the information acquisition unit and the channel measurement unit, and sends the scheduling strategy decision to the base station and the vehicle user. This unit informs the base station and the vehicle user how to configure the channel resources and whether to transmit data.

[0268] As an example, the semantic communication unit may include a deep neural network to process the user's own data and extract semantic communication information. This unit is used to process semantic communication related information.

[0269] In the process of describing the electronic devices 100, 9000, and 1000 in the above embodiments, it is obvious that some processes or methods are also disclosed. Below, an overview of these methods is given without repeating some of the details already discussed above, but it should be noted that although these methods are disclosed in the process of describing the above electronic devices, these methods do not necessarily use the components described or are not necessarily performed by those components. For example, the embodiments of the above electronic devices can be partially or completely implemented using hardware and / or firmware, while the methods discussed below can be completely implemented by computer-executable programs, although these methods can also use the hardware and / or firmware of the electronic device.

[0270] Figure 11 illustrates a flowchart of a method S1100 for wireless communication according to an embodiment of the present disclosure. Method S1100 begins at step S1102. At step S1104, based on a semantic communication error rate associated with a user device performing semantic communication, a determination is made as to whether to update a semantic repository and / or a reasoning model associated with the semantic repository of the user device. Method S1100 concludes at step S1106.

[0271] The method may be executed, for example, by the electronic device 100 described above. For specific details, please refer to the description of the related processing of the electronic device 100, which will not be repeated here.

[0272] Figure 12 shows a flowchart of a method S1200 for wireless communication according to another embodiment of the present disclosure. Method S1200 begins at step S1202. At step S1204, a semantic communication error rate related to semantic communication is calculated, allowing the network device to determine whether to update the semantic library used for semantic communication and / or the reasoning model associated with the semantic library of electronic device 9000. Method S1200 ends at step S1206.

[0273] The method may be executed, for example, by the electronic device 9000 described above. For specific details, please refer to the description of the related processing of the electronic device 9000, which will not be repeated here.

[0274] FIG13 illustrates a flowchart of a method S1300 for wireless communication according to another embodiment of the present disclosure. Method S1300 begins at step S1302. At step S1304, the semantic communication error rate received from other electronic devices is transmitted to a network device, allowing the network device to determine whether to update the semantic repository and / or reasoning model associated with the semantic repository used for semantic communication in the other electronic devices. Method S1300 concludes at step S1306.

[0275] The method may be executed, for example, by the electronic device 1000 described above. For specific details, please refer to the description of the related processing of the electronic device 1000, which will not be repeated here.

[0276] The technology of the present disclosure can be applied to various products.

[0277] The electronic device 100 can be set on the base station side or connected to the base station. The base station can be implemented as any type of evolved Node B (eNB) or gNB (5G base station). eNB includes, for example, macro eNB and small eNB. Small eNB can be an eNB that covers a cell smaller than a macro cell, such as a pico eNB, micro eNB, and home (femto) eNB. Similar situations can also be encountered for gNB. Alternatively, the base station can be implemented as any other type of base station, such as a NodeB and a base transceiver station (BTS). The base station may include: a main body (also called a base station device) configured to control wireless communication; and one or more remote radio heads (RRHs) arranged in a place different from the main body. In addition, various types of electronic devices can work as a base station by temporarily or semi-permanently performing base station functions.

[0278] The electronic devices 9000 and 1000 can be implemented as various user devices. The user device can be implemented as a mobile terminal (such as a smartphone, a tablet personal computer (PC), a notebook PC, a portable game terminal, a portable / dongle-type mobile router, and a digital camera) or an in-vehicle terminal (such as a car navigation device). The user device can also be implemented as a terminal that performs machine-to-machine (M2M) communication (also known as a machine type communication (MTC) terminal). In addition, the user device can be a wireless communication module (such as an integrated circuit module including a single chip) installed on each of the above terminals.

[0279] [Application examples for base stations]

[0280] (First application example)

[0281] FIG14 is a block diagram illustrating a first example of a schematic configuration of an eNB or gNB to which the techniques of this disclosure can be applied. Note that the following description uses an eNB as an example, but is equally applicable to a gNB. An eNB 800 includes one or more antennas 810 and a base station device 820. The base station device 820 and each antenna 810 can be connected to each other via an RF cable.

[0282] Each of the antennas 810 includes a single or multiple antenna elements (such as multiple antenna elements included in a multiple-input multiple-output (MIMO) antenna) and is used for base station device 820 to transmit and receive wireless signals. As shown in FIG14 , eNB 800 may include multiple antennas 810. For example, multiple antennas 810 may be compatible with multiple frequency bands used by eNB 800. Although FIG14 shows an example in which eNB 800 includes multiple antennas 810, eNB 800 may also include a single antenna 810.

[0283] The base station device 820 includes a controller 821 , a memory 822 , a network interface 823 , and a wireless communication interface 825 .

[0284] The controller 821 may be, for example, a CPU or a DSP, and operates various functions of the higher layers of the base station device 820. For example, the controller 821 generates data packets based on the data in the signal processed by the wireless communication interface 825, and transmits the generated packets via the network interface 823. The controller 821 may bundle data from multiple baseband processors to generate bundled packets, and transmit the generated bundled packets. The controller 821 may have logic functions for performing the following controls: the control may be radio resource control, radio bearer control, mobility management, admission control, and scheduling. The control may be performed in conjunction with a nearby eNB or core network node. The memory 822 includes RAM and ROM, and stores programs executed by the controller 821 and various types of control data (such as a terminal list, transmission power data, and scheduling data).

[0285] The network interface 823 is a communication interface for connecting the base station device 820 to the core network 824. The controller 821 can communicate with the core network node or another eNB via the network interface 823. In this case, the eNB 800 and the core network node or other eNBs can be connected to each other through a logical interface (such as an S1 interface and an X2 interface). The network interface 823 can also be a wired communication interface or a wireless communication interface for a wireless backhaul line. If the network interface 823 is a wireless communication interface, the network interface 823 can use a higher frequency band for wireless communication than the frequency band used by the wireless communication interface 825.

[0286] The wireless communication interface 825 supports any cellular communication scheme, such as Long Term Evolution (LTE) and LTE-Advanced, and provides wireless connectivity to terminals located in the cell of the eNB 800 via the antenna 810. The wireless communication interface 825 may typically include, for example, a baseband (BB) processor 826 and RF circuitry 827. The BB processor 826 can perform various signal processing functions, such as encoding / decoding, modulation / demodulation, and multiplexing / demultiplexing, and performs various types of signal processing for layers such as Layer 1, Medium Access Control (MAC), Radio Link Control (RLC), and Packet Data Convergence Protocol (PDCP). In place of the controller 821, the BB processor 826 may have some or all of the aforementioned logical functions. The BB processor 826 may be a memory that stores communication control programs, or a module including a processor configured to execute programs and associated circuitry. Program updates can modify the functionality of the BB processor 826. This module may be a card or blade inserted into a slot in the base station device 820. Alternatively, the module may be a chip mounted on the card or blade. Meanwhile, the RF circuit 827 may include, for example, a mixer, a filter, and an amplifier, and transmit and receive wireless signals via the antenna 810 .

[0287] As shown in FIG14 , the wireless communication interface 825 may include multiple BB processors 826. For example, multiple BB processors 826 may be compatible with multiple frequency bands used by the eNB 800. As shown in FIG14 , the wireless communication interface 825 may include multiple RF circuits 827. For example, multiple RF circuits 827 may be compatible with multiple antenna elements. Although FIG14 illustrates an example in which the wireless communication interface 825 includes multiple BB processors 826 and multiple RF circuits 827, the wireless communication interface 825 may also include a single BB processor 826 or a single RF circuit 827.

[0288] When the electronic device 100 shown in FIG1 is implemented as the eNB 800 shown in FIG14 , its transceiver may be implemented by the wireless communication interface 825. At least a portion of the functionality may also be implemented by the controller 821. For example, the controller 821 may improve the continuity and reliability of semantic communication by executing the functions of the units in the electronic device 100.

[0289] (Second application example)

[0290] FIG15 is a block diagram illustrating a second example of a schematic configuration of an eNB or gNB to which the techniques of this disclosure can be applied. Note that similarly, the following description uses an eNB as an example, but is equally applicable to a gNB. An eNB 830 includes one or more antennas 840, a base station device 850, and an RRH 860. The RRH 860 and each antenna 840 can be connected to each other via an RF cable. The base station device 850 and the RRH 860 can be connected to each other via a high-speed line such as an optical fiber cable.

[0291] Each of the antennas 840 includes a single or multiple antenna elements (such as multiple antenna elements included in a MIMO antenna) and is used for RRH 860 to transmit and receive wireless signals. As shown in FIG15 , eNB 830 may include multiple antennas 840. For example, multiple antennas 840 may be compatible with multiple frequency bands used by eNB 830. Although FIG15 shows an example in which eNB 830 includes multiple antennas 840, eNB 830 may also include a single antenna 840.

[0292] Base station device 850 includes a controller 851, a memory 852, a network interface 853, a wireless communication interface 855, and a connection interface 857. Controller 851, memory 852, and network interface 853 are the same as controller 821, memory 822, and network interface 823 described with reference to FIG.

[0293] The wireless communication interface 855 supports any cellular communication scheme (such as LTE and LTE-Advanced) and provides wireless communication to terminals located in the sector corresponding to the RRH 860 via the RRH 860 and the antenna 840. The wireless communication interface 855 may generally include, for example, a BB processor 856. The BB processor 856 is the same as the BB processor 826 described with reference to FIG. 15 , except that the BB processor 856 is connected to the RF circuit 864 of the RRH 860 via the connection interface 857. As shown in FIG. 15 , the wireless communication interface 855 may include multiple BB processors 856. For example, the multiple BB processors 856 may be compatible with multiple frequency bands used by the eNB 830. Although FIG. 15 shows an example in which the wireless communication interface 855 includes multiple BB processors 856, the wireless communication interface 855 may also include a single BB processor 856.

[0294] The connection interface 857 is an interface for connecting the base station device 850 (wireless communication interface 855) to the RRH 860. The connection interface 857 may also be a communication module for connecting the base station device 850 (wireless communication interface 855) to the RRH 860 for communication in the high-speed line.

[0295] The RRH 860 includes a connection interface 861 and a wireless communication interface 863 .

[0296] The connection interface 861 is an interface for connecting the RRH 860 (wireless communication interface 863) to the base station device 850. The connection interface 861 may also be a communication module for communication in the above-mentioned high-speed line.

[0297] The wireless communication interface 863 transmits and receives wireless signals via the antenna 840. The wireless communication interface 863 may generally include, for example, an RF circuit 864. The RF circuit 864 may include, for example, a mixer, a filter, and an amplifier, and transmits and receives wireless signals via the antenna 840. As shown in FIG15 , the wireless communication interface 863 may include multiple RF circuits 864. For example, multiple RF circuits 864 may support multiple antenna elements. Although FIG15 shows an example in which the wireless communication interface 863 includes multiple RF circuits 864, the wireless communication interface 863 may also include a single RF circuit 864.

[0298] When the electronic device 100 shown in FIG1 is implemented as the eNB 830 shown in FIG15 , its transceiver may be implemented by the wireless communication interface 855. At least a portion of the functionality may also be implemented by the controller 851. For example, the controller 851 may improve the continuity and reliability of semantic communication by executing the functions of the units in the electronic device 100.

[0299] [Application examples on user devices]

[0300] (First application example)

[0301] 16 is a block diagram showing an example of a schematic configuration of a smartphone 900 to which the technology of the present disclosure can be applied. The smartphone 900 includes a processor 901, a memory 902, a storage device 903, an external connection interface 904, a camera 906, a sensor 907, a microphone 908, an input device 909, a display device 910, a speaker 911, a wireless communication interface 912, one or more antenna switches 915, one or more antennas 916, a bus 917, a battery 918, and an auxiliary controller 919.

[0302] The processor 901 may be, for example, a CPU or a system on a chip (SoC), and controls the functions of the application layer and other layers of the smartphone 900. The memory 902 includes RAM and ROM, and stores data and programs executed by the processor 901. The storage device 903 may include storage media such as semiconductor memories and hard disks. The external connection interface 904 is an interface for connecting external devices (such as memory cards and universal serial bus (USB) devices) to the smartphone 900.

[0303] The camera 906 includes an image sensor such as a charge coupled device (CCD) and a complementary metal oxide semiconductor (CMOS) and generates a captured image. The sensor 907 may include a group of sensors such as a measurement sensor, a gyroscope sensor, a geomagnetic sensor, and an acceleration sensor. The microphone 908 converts the sound input to the smartphone 900 into an audio signal. The input device 909 includes, for example, a touch sensor, a keypad, a keyboard, a button, or a switch configured to detect a touch on the screen of the display device 910, and receives an operation or information input from the user. The display device 910 includes a screen such as a liquid crystal display (LCD) and an organic light emitting diode (OLED) display and displays an output image of the smartphone 900. The speaker 911 converts the audio signal output from the smartphone 900 into sound.

[0304] The wireless communication interface 912 supports any cellular communication scheme (such as LTE and LTE-Advanced) and performs wireless communications. The wireless communication interface 912 may typically include, for example, a BB processor 913 and an RF circuit 914. The BB processor 913 may perform, for example, encoding / decoding, modulation / demodulation, and multiplexing / demultiplexing, and may also perform various types of signal processing for wireless communications. Meanwhile, the RF circuit 914 may include, for example, mixers, filters, and amplifiers, and transmit and receive wireless signals via an antenna 916. Note that while the figure shows a scenario where one RF link is connected to one antenna, this is merely illustrative, and also encompasses scenarios where one RF link is connected to multiple antennas via multiple phase shifters. The wireless communication interface 912 may be a chip module on which the BB processor 913 and the RF circuit 914 are integrated. As shown in FIG16 , the wireless communication interface 912 may include multiple BB processors 913 and multiple RF circuits 914. While FIG16 illustrates an example in which the wireless communication interface 912 includes multiple BB processors 913 and multiple RF circuits 914, the wireless communication interface 912 may also include a single BB processor 913 or a single RF circuit 914.

[0305] In addition, in addition to the cellular communication scheme, the wireless communication interface 912 can support other types of wireless communication schemes, such as a short-range wireless communication scheme, a near-field communication scheme, and a wireless local area network (LAN) scheme. In this case, the wireless communication interface 912 may include a BB processor 913 and an RF circuit 914 for each wireless communication scheme.

[0306] Each of the antenna switches 915 switches a connection destination of the antenna 916 between a plurality of circuits (eg, circuits for different wireless communication schemes) included in the wireless communication interface 912 .

[0307] Each of the antennas 916 includes a single or multiple antenna elements (such as multiple antenna elements included in a MIMO antenna) and is used for transmitting and receiving wireless signals via the wireless communication interface 912. As shown in FIG16 , the smartphone 900 may include multiple antennas 916. Although FIG16 shows an example in which the smartphone 900 includes multiple antennas 916, the smartphone 900 may also include a single antenna 916.

[0308] In addition, the smartphone 900 may include an antenna 916 for each wireless communication scheme. In this case, the antenna switch 915 may be omitted from the configuration of the smartphone 900.

[0309] The bus 917 connects the processor 901, the memory 902, the storage device 903, the external connection interface 904, the camera 906, the sensor 907, the microphone 908, the input device 909, the display device 910, the speaker 911, the wireless communication interface 912, and the auxiliary controller 919. The battery 918 supplies power to the various blocks of the smartphone 900 shown in FIG16 via feeders, which are partially shown as dotted lines in the figure. The auxiliary controller 919 operates the minimum necessary functions of the smartphone 900, for example, in sleep mode.

[0310] When the electronic devices 9000 and 1000 shown in Figures 9 and 10 are respectively implemented as smartphones serving as user devices, such as the smartphone 900 shown in Figure 16 , the transceivers of the electronic devices 9000 and 1000 may be implemented by the wireless communication interface 912. At least a portion of the functions may also be implemented by the processor 901 or the auxiliary controller 919. For example, by executing the functions of the units in the electronic devices 9000 and 1000, the processor 901 or the auxiliary controller 919 can improve the continuity and reliability of semantic communication.

[0311] (Second application example)

[0312] 17 is a block diagram showing an example of a schematic configuration of a car navigation device 920 to which the technology of the present disclosure can be applied. The car navigation device 920 includes a processor 921, a memory 922, a global positioning system (GPS) module 924, a sensor 925, a data interface 926, a content player 927, a storage medium interface 928, an input device 929, a display device 930, a speaker 931, a wireless communication interface 933, one or more antenna switches 936, one or more antennas 937, and a battery 938.

[0313] The processor 921 may be, for example, a CPU or an SoC, and controls a navigation function and other functions of the car navigation apparatus 920. The memory 922 includes a RAM and a ROM, and stores data and programs executed by the processor 921.

[0314] The GPS module 924 measures the position (such as latitude, longitude, and altitude) of the car navigation device 920 using GPS signals received from GPS satellites. The sensor 925 may include a group of sensors such as a gyroscope sensor, a geomagnetic sensor, and an air pressure sensor. The data interface 926 is connected to, for example, the in-vehicle network 941 via an unillustrated terminal and acquires data generated by the vehicle (such as vehicle speed data).

[0315] The content player 927 reproduces content stored in a storage medium (such as a CD or DVD) inserted into the storage medium interface 928. The input device 929 includes, for example, a touch sensor, button, or switch configured to detect a touch on the screen of the display device 930, and receives an operation or information input from the user. The display device 930 includes a screen such as an LCD or OLED display and displays an image of a navigation function or reproduced content. The speaker 931 outputs the sound of the navigation function or the reproduced content.

[0316] The wireless communication interface 933 supports any cellular communication scheme (such as LTE and LTE-Advanced) and performs wireless communication. The wireless communication interface 933 may generally include, for example, a BB processor 934 and an RF circuit 935. The BB processor 934 may perform, for example, encoding / decoding, modulation / demodulation, and multiplexing / demultiplexing, and perform various types of signal processing for wireless communication. Meanwhile, the RF circuit 935 may include, for example, a mixer, a filter, and an amplifier, and transmit and receive wireless signals via an antenna 937. The wireless communication interface 933 may also be a chip module on which the BB processor 934 and the RF circuit 935 are integrated. As shown in Figure 17, the wireless communication interface 933 may include multiple BB processors 934 and multiple RF circuits 935. Although Figure 17 shows an example in which the wireless communication interface 933 includes multiple BB processors 934 and multiple RF circuits 935, the wireless communication interface 933 may also include a single BB processor 934 or a single RF circuit 935.

[0317] In addition, in addition to the cellular communication scheme, the wireless communication interface 933 can support other types of wireless communication schemes, such as a short-range wireless communication scheme, a near field communication scheme, and a wireless LAN scheme. In this case, for each wireless communication scheme, the wireless communication interface 933 can include a BB processor 934 and an RF circuit 935.

[0318] Each of the antenna switches 936 switches a connection destination of the antenna 937 between a plurality of circuits included in the wireless communication interface 933 , such as circuits for different wireless communication schemes.

[0319] Each of the antennas 937 includes a single or multiple antenna elements (such as multiple antenna elements included in a MIMO antenna) and is used for transmitting and receiving wireless signals via the wireless communication interface 933. As shown in FIG17 , the car navigation device 920 may include multiple antennas 937. Although FIG17 shows an example in which the car navigation device 920 includes multiple antennas 937, the car navigation device 920 may also include a single antenna 937.

[0320] Furthermore, the car navigation device 920 may include an antenna 937 for each wireless communication scheme. In this case, the antenna switch 936 may be omitted from the configuration of the car navigation device 920.

[0321] The battery 938 supplies power to the respective blocks of the car navigation device 920 shown in Fig. 17 via a feeder line, which is partially shown as a dotted line in the figure. The battery 938 accumulates the power supplied from the vehicle.

[0322] When the electronic devices 9000 and 1000 shown in Figures 9 and 10 are respectively implemented as user equipment-side car navigation devices, such as the car navigation device 920 shown in Figure 17, the transceivers of the electronic devices 9000 and 1000 can be implemented by the wireless communication interface 933. At least part of the functions can also be implemented by the processor 921. For example, by executing the functions of the units in the electronic devices 9000 and 1000, the processor 921 can improve the continuity and reliability of semantic communication.

[0323] The technology of the present disclosure can also be implemented as an in-vehicle system (or vehicle) 940 including a car navigation device 920, an in-vehicle network 941, and one or more blocks of a vehicle module 942. The vehicle module 942 generates vehicle data (such as vehicle speed, engine speed, and fault information) and outputs the generated data to the in-vehicle network 941.

[0324] The basic principles of the present invention are described above in conjunction with specific embodiments. However, it should be pointed out that those skilled in the art will understand that all or any steps or components of the methods and devices of the present invention can be implemented in any computing device (including a processor, storage medium, etc.) or a network of computing devices in the form of hardware, firmware, software, or a combination thereof. This can be achieved by those skilled in the art using their basic circuit design knowledge or basic programming skills after reading the description of the present invention.

[0325] Furthermore, the present invention also provides a program product storing machine-readable instruction codes. When the instruction codes are read and executed by a machine, the method according to the embodiment of the present invention can be executed.

[0326] Accordingly, the storage medium for carrying the program product storing the machine-readable instruction code is also included in the disclosure of the present invention. The storage medium includes but is not limited to a floppy disk, an optical disk, a magneto-optical disk, a memory card, a memory stick, and the like.

[0327] When the present invention is implemented through software or firmware, the programs constituting the software are installed from a storage medium or a network to a computer with a dedicated hardware structure (such as the general-purpose computer 1800 shown in Figure 18). When various programs are installed on the computer, it can perform various functions, etc.

[0328] In FIG18 , a central processing unit (CPU) 1801 executes various processes according to a program stored in a read-only memory (ROM) 1802 or a program loaded from a storage section 1808 to a random access memory (RAM) 1803. Data required when the CPU 1801 executes various processes, etc., is also stored in the RAM 1803 as needed. The CPU 1801, the ROM 1802, and the RAM 1803 are connected to each other via a bus 1804. An input / output interface 1805 is also connected to the bus 1804.

[0329] The following components are connected to the input / output interface 1805: an input section 1806 (including a keyboard, a mouse, etc.), an output section 1807 (including a display such as a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and speakers, etc.), a storage section 1808 (including a hard disk, etc.), and a communication section 1809 (including a network interface card such as a LAN card, a modem, etc.). The communication section 1809 performs communication processing via a network such as the Internet. A drive 1810 may also be connected to the input / output interface 1805 as needed. Removable media 1811 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc. are installed in the drive 1810 as needed, so that computer programs read therefrom are installed in the storage section 1808 as needed.

[0330] In the case of realizing the above-described series of processing by software, a program constituting the software is installed from a network such as the Internet or a storage medium such as the removable medium 1811 .

[0331] It should be understood by those skilled in the art that such storage media is not limited to the removable medium 1811 shown in FIG. 18 , which stores the program and is distributed separately from the device to provide the program to the user. Examples of the removable medium 1811 include magnetic disks (including floppy disks (registered trademark)), optical disks (including compact disk read-only memories (CD-ROMs) and digital versatile disks (DVDs)), magneto-optical disks (including minidiscs (MDs) (registered trademark)), and semiconductor memories. Alternatively, the storage medium may be the ROM 1802, a hard disk included in the storage section 1808, or the like, in which the program is stored and distributed to the user together with the device containing the program.

[0332] It should also be noted that in the apparatus, method, and system of the present invention, each component or step can be decomposed and / or recombined. Such decomposition and / or recombination should be considered equivalent solutions of the present invention. Furthermore, the steps of performing the above series of processes can naturally be performed in chronological order according to the order described, but do not necessarily need to be performed in chronological order. Certain steps can be performed in parallel or independently of each other.

[0333] Finally, it should be noted that the terms "comprises," "comprising," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus. Furthermore, in the absence of further limitations, an element defined by the phrase "comprising a ..." does not preclude the presence of additional identical elements in the process, method, article, or apparatus that includes the element.

[0334] Although the embodiments of the present invention have been described in detail above with reference to the accompanying drawings, it should be understood that the embodiments described above are merely illustrative of the present invention and are not intended to limit the present invention. It will be apparent to those skilled in the art that various modifications and variations may be made to the embodiments described above without departing from the spirit and scope of the present invention. Therefore, the scope of the present invention is limited solely by the appended claims and their equivalents.

[0335] The present technology can also be implemented as follows. Solution 1. An electronic device for wireless communication, comprising: at least one processor; and at least one memory including computer program code, wherein the at least one memory and the computer program code are configured to, through the at least one processor, cause the electronic device to execute: based on the semantic communication error rate associated with the user device for semantic communication, determine whether to update the semantic library for semantic communication of the user device and / or the inference model associated with the semantic library. Solution 2. The electronic device according to Solution 1, wherein the semantic library includes at least one of a relationship mapping between data information and semantic information, a machine learning model for generating a relationship mapping between data information and semantic information, a deep learning model, a mapping table, and matrix compression. Solution 3. The electronic device according to Solution 1 or 2, wherein the inference model is used to convert data information into semantic information. Solution 4. The electronic device according to any one of Solutions 1 to 3, wherein the semantic communication error rate is obtained by the user device calculating the proportion of erroneous information in the semantic information received from other user devices within a predetermined time period based on its original semantic library and / or original inference model. Solution 5. The electronic device according to any one of Solutions 1 to 3, wherein the semantic communication error rate is calculated by the user device, based on its original semantic library and / or original reasoning model, calculating the proportion of erroneous information among semantic information received from other user devices within a predetermined time period, the correctness of which can be determined by the user device. Solution 6. The electronic device according to any one of Solutions 1 to 3, wherein the semantic communication error rate is calculated by the user device, based on its original semantic library and / or original reasoning model, calculating the proportion of erroneous information among a predetermined amount of flag information obtained, wherein the flag information is information for which the user device can pre-determine the correct semantics. Solution 7. An electronic device for wireless communication, comprising: at least one processor; and at least one memory including computer program code, wherein the at least one memory and the computer program code are configured to, through the at least one processor, cause the electronic device to: calculate a semantic communication error rate related to semantic communication, for a network-side device to determine whether to update the semantic library used for semantic communication of the electronic device and / or the reasoning model related to the semantic library. Solution 8. The electronic device according to Solution 7, wherein the semantic library includes at least one of a relationship mapping between data information and semantic information, a machine learning model for generating the relationship mapping between data information and semantic information, a deep learning model, a mapping table, and matrix compression. Solution 9. The electronic device according to Solution 7 or 8, wherein the inference model is used to convert data information into semantic information.Solution 10. An electronic device according to any one of Solutions 7 to 9, wherein the at least one memory and the computer program code are configured to, through the at least one processor, cause the electronic device to execute: Based on the original semantic library and / or original reasoning model of the electronic device, calculate the proportion of erroneous information among the semantic information received from other electronic devices within a predetermined time period as the semantic communication error rate. Solution 11. An electronic device according to any one of Solutions 7 to 9, wherein the at least one memory and the computer program code are configured to, through the at least one processor, cause the electronic device to execute: Based on the original semantic library and / or original reasoning model of the electronic device, calculate the proportion of erroneous information among the semantic information received from other electronic devices within a predetermined time period, the correctness of which the electronic device can determine, as the semantic communication error rate. Solution 12. The electronic device according to any one of Solutions 7 to 9, wherein the at least one memory and the computer program code are configured to, through the at least one processor, cause the electronic device to: calculate, based on the original semantic library and / or original reasoning model of the electronic device, the proportion of error information among a predetermined amount of acquired marker information as the semantic communication error rate, wherein the marker information is information for which the electronic device can pre-determine the correct semantics. Solution 13. An electronic device for wireless communication, comprising: at least one processor; and at least one memory including computer program code, wherein the at least one memory and the computer program code are configured to, through the at least one processor, cause the electronic device to: transmit the semantic communication error rate received from another electronic device to a network-side device, so that the network-side device determines whether to update the semantic library for semantic communication of the other electronic device and / or the reasoning model associated with the semantic library. Solution 14. The electronic device according to Solution 13, wherein the semantic library includes at least one of a relationship mapping between data information and semantic information, a machine learning model for generating the relationship mapping between data information and semantic information, a deep learning model, a mapping table, and matrix compression. Solution 15. The electronic device according to Solution 13 or 14, wherein the reasoning model is used to convert data information into semantic information. Solution 16. The electronic device according to any one of Solutions 13 to 15, wherein the at least one memory and the computer program code are configured to, through the at least one processor, cause the electronic device to execute: forwarding signaling received from the network-side device indicating whether to perform the update to the other electronic device, and, if the update is required, forwarding the updated semantic library and / or reasoning model received from the network-side device to the other electronic device.Solution 17. A method for wireless communication, comprising: based on a semantic communication error rate associated with a user device for semantic communication, determining whether to update a semantic library for semantic communication of the user device and / or an inference model associated with the semantic library. Solution 18. A method for wireless communication, comprising: calculating a semantic communication error rate associated with semantic communication, for a network-side device to determine whether to update the semantic library for semantic communication of the electronic device and / or an inference model associated with the semantic library. Solution 19. A method for wireless communication, comprising: transmitting a semantic communication error rate received from other electronic devices to a network-side device, for the network-side device to determine whether to update the semantic library for semantic communication of the other electronic device and / or an inference model associated with the semantic library. Solution 20. A computer-readable storage medium having computer-executable instructions stored thereon, wherein when the computer-executable instructions are executed, the method according to any one of Solutions 17 to 19 is executed.

Claims

1. An electronic device for wireless communication, comprising: at least one processor; and at least one memory including computer program code, wherein the at least one memory and the computer program code are configured to, through the at least one processor, cause the electronic device to execute: Based on a semantic communication error rate associated with a user device used for semantic communication, it is determined whether to update a semantic library used for semantic communication of the user device and / or a reasoning model associated with the semantic library.

2. The electronic device according to claim 1, wherein The semantic library includes a relationship mapping between data information and semantic information, and at least one of a machine learning model, a deep learning model, a mapping table, and matrix compression for generating a relationship mapping between data information and semantic information.

3. The electronic device according to claim 1 or 2, wherein: The reasoning model is used to transform data information and semantic information.

4. The electronic device according to any one of claims 1 to 3, wherein: The semantic communication error rate is obtained by calculating, by the user equipment based on its original semantic library and / or original reasoning model, the proportion of error information in the semantic information received from other user equipment within a predetermined time period.

5. The electronic device according to any one of claims 1 to 3, wherein: The semantic communication error rate is obtained by calculating, based on the user device's original semantic library and / or original reasoning model, the proportion of erroneous information among the semantic information received from other user devices within a predetermined time period, the correctness of which the user device can judge.

6. The electronic device according to any one of claims 1 to 3, wherein: The semantic communication error rate is obtained by calculating the proportion of error information in a predetermined amount of acquired sign information based on the user equipment's original semantic library and / or original reasoning model. The flag information is information whose correct semantics can be obtained in advance by the user equipment.

7. An electronic device for wireless communication, comprising: at least one processor; and at least one memory including computer program code, wherein the at least one memory and the computer program code are configured to, through the at least one processor, cause the electronic device to execute: A semantic communication error rate related to semantic communication is calculated so that the network-side device can determine whether to update a semantic library used for semantic communication of the electronic device and / or an inference model related to the semantic library.

8. The electronic device according to claim 7, wherein: The semantic library includes a relationship mapping between data information and semantic information, and at least one of a machine learning model, a deep learning model, a mapping table, and matrix compression for generating a relationship mapping between data information and semantic information.

9. The electronic device according to claim 7 or 8, wherein: The reasoning model is used to transform data information and semantic information.

10. The electronic device according to any one of claims 7 to 9, wherein: The at least one memory and the computer program code are configured to, through the at least one processor, cause the electronic device to execute: Based on the original semantic library and / or original reasoning model of the electronic device, the proportion of error information in the semantic information received from other electronic devices within a predetermined time period is calculated as the semantic communication error rate.

11. The electronic device according to any one of claims 7 to 9, wherein: The at least one memory and the computer program code are configured to, through the at least one processor, cause the electronic device to execute: Based on the original semantic library and / or original reasoning model of the electronic device, the proportion of erroneous information among the semantic information received from other electronic devices within a predetermined time period, the correctness of which can be judged by the electronic device, is calculated as the semantic communication error rate.

12. The electronic device according to any one of claims 7 to 9, wherein: The at least one memory and the computer program code are configured to, through the at least one processor, cause the electronic device to execute: Based on the original semantic library and / or original reasoning model of the electronic device, the proportion of error information in the obtained predetermined amount of sign information is calculated as the semantic communication error rate, The flag information is information whose correct semantics can be obtained in advance by the electronic device.

13. An electronic device for wireless communication, comprising: at least one processor; and at least one memory including computer program code, wherein the at least one memory and the computer program code are configured to, through the at least one processor, cause the electronic device to execute: The semantic communication error rate received from other electronic devices is transmitted to the network side device so that the network side device can determine whether to update the semantic library used for semantic communication of the other electronic devices and / or the reasoning model related to the semantic library.

14. The electronic device according to claim 13, wherein: The semantic library includes a relationship mapping between data information and semantic information, and at least one of a machine learning model, a deep learning model, a mapping table, and matrix compression for generating a relationship mapping between data information and semantic information.

15. The electronic device according to claim 13 or 14, wherein: The reasoning model is used to transform data information and semantic information.

16. The electronic device according to any one of claims 13 to 15, wherein: The at least one memory and the computer program code are configured to, through the at least one processor, cause the electronic device to execute: forwarding the signaling received from the network side device for indicating whether to perform the update to the other electronic devices, and When the update is required, the updated semantic library and / or reasoning model received from the network-side device is forwarded to the other electronic devices.

17. A method for wireless communication, comprising: Based on a semantic communication error rate associated with a user device used for semantic communication, it is determined whether to update a semantic library used for semantic communication of the user device and / or a reasoning model associated with the semantic library.

18. A method for wireless communication, comprising: A semantic communication error rate related to semantic communication is calculated so that the network-side device can determine whether to update a semantic library used for semantic communication of the electronic device and / or an inference model related to the semantic library.

19. A method for wireless communication, comprising: The semantic communication error rate received from other electronic devices is transmitted to the network side device so that the network side device can determine whether to update the semantic library used for semantic communication of the other electronic devices and / or the reasoning model related to the semantic library.

20. A computer-readable storage medium having computer-executable instructions stored thereon, which, when executed, perform the method according to any one of claims 17 to 19.

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