Sensing communication method, communication devices and storage medium

Abstract

The present disclosure relates to a sensing communication method, communication devices, and a storage medium. The method comprises: obtaining a sensing result on the basis of sensing information and an artificial intelligence (AI) model, wherein an input of the AI model is the sensing information. According to the embodiments of the present disclosure, the sensing information is used as the input of the AI model, and the sensing result is obtained on the basis of the sensing information and the AI model, such that errors caused by channel estimation in the sensing process can be reduced, thereby improving the accuracy of sensing communication.

Description

Perception communication method, communication device and storage medium Technical Field

[0001] The present disclosure relates to the field of communication technology, and in particular to a perception communication method, a communication device, and a storage medium. Background Art

[0002] With the development of communication technology, artificial intelligence (AI) technology has made continuous breakthroughs in many fields, including but not limited to the field of perceptual communication.

[0003] Due to the complexity of the perception environment, using a perception algorithm for channel estimation will result in a high channel estimation error, thereby affecting the accuracy of the perception results obtained based on the channel estimation results.

[0004] Summary of the Invention

[0005] In the communication perception scenario, how to reduce the channel estimation error during the perception process is a technical problem that needs to be solved.

[0006] The embodiments of the present disclosure provide a perception communication method, a communication device, and a storage medium.

[0007] According to a first aspect of an embodiment of the present disclosure, a perception communication method is proposed, the method comprising: obtaining a perception result based on perception information and an artificial intelligence (AI) model, wherein the input of the AI ​​model is the perception information.

[0008] According to a second aspect of an embodiment of the present disclosure, a communication device is proposed, comprising: a transceiver module for acquiring perception information; a processing module for obtaining a perception result based on the perception information and an AI model, wherein the input of the AI ​​model is the perception information.

[0009] According to a third aspect of an embodiment of the present disclosure, a communication device is proposed, comprising: one or more processors; wherein the processor is configured to execute the method of the first aspect.

[0010] According to a fourth aspect of an embodiment of the present disclosure, a storage medium is proposed, wherein the storage medium stores instructions, wherein when the instructions are executed on a communication device, the communication device executes the method of the first aspect.

[0011] According to a fifth aspect of an embodiment of the present disclosure, a program product is proposed, comprising: a computer program, which, when executed by a communication device, causes the communication device to execute the method of the first aspect.

[0012] Through the embodiments of the present disclosure, the perception information is used as the input of the AI ​​model, and the perception results are obtained based on the perception information and the AI ​​model, which can reduce the error caused by channel estimation in the perception process, thereby improving the accuracy of perception communication. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] In order to more clearly illustrate the technical solutions in the embodiments of the present disclosure, the following drawings required for describing the embodiments are introduced. The following drawings are merely some embodiments of the present disclosure and do not impose specific limitations on the protection scope of the present disclosure.

[0014] FIG1A is a schematic diagram of a perception mode in a perception-integrated scenario.

[0015] FIG1B shows four types of signals of the synaesthesia channel in a self-transmitting and self-receiving scenario.

[0016] FIG1C is a schematic diagram showing a communication system architecture according to an embodiment of the present disclosure.

[0017] FIG2A is a flow chart of a perceptual communication method according to an embodiment of the present disclosure.

[0018] FIG2B is a flow chart of a perceptual communication method according to an embodiment of the present disclosure.

[0019] FIG3A is a flow chart of a perceptual communication method according to an embodiment of the present disclosure.

[0020] FIG3B is a flow chart of a perceptual communication method according to an embodiment of the present disclosure.

[0021] FIG3C is a flow chart of a perceptual communication method according to an embodiment of the present disclosure.

[0022] FIG4A is a flow chart of a perceptual communication method according to an embodiment of the present disclosure.

[0023] FIG4B is a flow chart of a perceptual communication method according to an embodiment of the present disclosure.

[0024] FIG5A is a schematic diagram showing a perceptual communication method according to an embodiment of the present disclosure.

[0025] FIG5B is a schematic diagram illustrating a perceptual communication method according to an embodiment of the present disclosure.

[0026] FIG6 is a schematic structural diagram of a communication device proposed in an embodiment of the present disclosure.

[0027] FIG7A is a schematic structural diagram of a communication device proposed in an embodiment of the present disclosure.

[0028] FIG7B is a schematic diagram of the structure of the chip proposed in an embodiment of the present disclosure. DETAILED DESCRIPTION

[0029] The embodiments of the present disclosure provide a perception communication method, a communication device, and a storage medium.

[0030] In a first aspect, an embodiment of the present disclosure proposes a perception communication method, which includes: obtaining a perception result based on perception information and an artificial intelligence (AI) model, wherein the input of the AI ​​model is the perception information.

[0031] In the above embodiment, the perception information is used as the input of the AI ​​model, and the perception results are obtained based on the perception information and the AI ​​model, which can reduce the error caused by channel estimation in the perception process, thereby improving the accuracy of perception communication.

[0032] In combination with some embodiments of the first aspect, in some embodiments, the perception information includes at least one of the following: a reference signal received by a perception receiving end; a reference signal sent by a perception transmitting end; a transmission resource of a reference signal; the number of transmitting antennas; the number of receiving antennas; reference signal measurement information; reference signal configuration information; synaesthesia resource configuration information; and antenna configuration information.

[0033] In the above embodiment, the perception information includes the above content, and processing the above content through the AI ​​model can improve the accuracy of the perception results.

[0034] In combination with some embodiments of the first aspect, in some embodiments, the AI ​​model includes a first AI model and a second AI model; the input of the first AI model is the perception information, the first AI model performs channel estimation based on the perception information, and outputs a channel matrix; the second AI model outputs a perception result based on the channel matrix.

[0035] In the above embodiment, the AI ​​model includes a first AI model and a second AI model. The first AI model is used to perform channel estimation on the perception information, which can improve the accuracy of the channel estimation; the second AI model outputs the perception result based on the channel matrix output by the first AI model, which can improve the accuracy of the perception result.

[0036] In combination with some embodiments of the first aspect, in some embodiments, the first AI model is trained based on the first channel matrix and the first perception information.

[0037] In the above embodiment, training the first AI model based on the first channel matrix and the first perception information can improve the accuracy of channel estimation performed by the first AI model.

[0038] In combination with some embodiments of the first aspect, in some embodiments, the second AI model is trained based on the first perception result and the channel matrix output by the first AI model.

[0039] In the above embodiment, training the second AI model based on the first perception result and the channel matrix output by the first AI model can improve the accuracy of the second AI model in predicting the perception result.

[0040] In combination with some embodiments of the first aspect, in some embodiments, the AI ​​model is trained based on the first perception information and the first perception result.

[0041] In the above embodiment, training the AI ​​model based on the first perception information and the first perception result can improve the accuracy of the AI ​​model in predicting the perception result.

[0042] In combination with some embodiments of the first aspect, in some embodiments, the perception result includes at least one of the distance, speed and angle corresponding to the perceived target, and / or the perception result includes the number of perceived targets.

[0043] In a second aspect, an embodiment of the present disclosure proposes a communication device, comprising: a transceiver module for acquiring perception information; a processing module for obtaining a perception result based on the perception information and an AI model, wherein the input of the AI ​​model is the perception information.

[0044] In the above embodiment, the perception information is used as the input of the AI ​​model, and the perception results are obtained based on the perception information and the AI ​​model, which can reduce the error caused by channel estimation in the perception process, thereby improving the accuracy of perception communication.

[0045] In combination with some embodiments of the second aspect, in some embodiments, the perception information includes at least one of the following: a reference signal received by the perception receiving end; a reference signal sent by the perception transmitting end; a transmission resource of the reference signal; the number of transmitting antennas; the number of receiving antennas; reference signal measurement information; reference signal configuration information; synaesthesia resource configuration information; and antenna configuration information.

[0046] In combination with some embodiments of the second aspect, in some embodiments, the AI ​​model includes a first AI model and a second AI model; the input of the first AI model is the perception information, the first AI model performs channel estimation based on the perception information, and outputs a channel matrix; the second AI model outputs a perception result based on the channel matrix.

[0047] In combination with some embodiments of the second aspect, in some embodiments, the first AI model is trained based on the first channel matrix and the first perception information.

[0048] In combination with some embodiments of the second aspect, in some embodiments, the second AI model is trained based on the first perception result and the channel matrix output by the first AI model.

[0049] In combination with some embodiments of the second aspect, in some embodiments, the AI ​​model is trained based on the first perception information and the first perception result.

[0050] In combination with some embodiments of the second aspect, in some embodiments, the perception result includes at least one of the distance, speed and angle corresponding to the perceived target, and / or the perception result includes the number of perceived targets.

[0051] In a third aspect, an embodiment of the present disclosure proposes a communication device, comprising: one or more processors; wherein the processor is used to execute the perceptual communication method of the first aspect.

[0052] In a fourth aspect, an embodiment of the present disclosure proposes a storage medium storing instructions, wherein the storage medium is characterized in that when the instructions are executed on a communication device, the communication device executes the method of the first aspect.

[0053] In a fifth aspect, an embodiment of the present disclosure proposes a program product, including: a computer program, which, when executed by a communication device, enables the communication device to execute the method described in the optional implementation manner of the first aspect.

[0054] Sixthly, an embodiment of the present disclosure proposes a computer program, which, when executed on a computer, enables the computer to execute the method described in the optional implementation manner of the first aspect.

[0055] In a seventh aspect, an embodiment of the present disclosure provides a chip or a chip system, which includes a processing circuit configured to execute the method described in the optional implementation of the first aspect.

[0056] It is understandable that the above-mentioned communication devices, storage media, program products, computer programs, chips, or chip systems are all used to perform the methods proposed in the embodiments of the present disclosure. Therefore, the beneficial effects that can be achieved can refer to the beneficial effects of the corresponding methods and will not be repeated here.

[0057] The embodiments of the present disclosure provide a perception communication method, a communication device, and a storage medium. In some embodiments, the perception communication method and the terms communication method, communication perception method, information transmission method, information reporting method, and information receiving method are interchangeable.

[0058] The embodiments of the present disclosure are not exhaustive and are merely illustrative of some embodiments, and are not intended to be a specific limitation on the scope of protection of the present disclosure. In the absence of contradiction, each step in a certain embodiment can be implemented as an independent embodiment, and the steps can be arbitrarily combined. For example, a solution after removing some steps in a certain embodiment can also be implemented as an independent embodiment, and the order of the steps in a certain embodiment can be arbitrarily exchanged. In addition, the optional implementation methods in a certain embodiment can be arbitrarily combined; in addition, the embodiments can be arbitrarily combined. For example, some or all steps of different embodiments can be arbitrarily combined, and a certain embodiment can be arbitrarily combined with the optional implementation methods of other embodiments.

[0059] In each embodiment of the present disclosure, unless otherwise specified or provided for by logic, the terms and / or descriptions between the embodiments are consistent and can be referenced by each other. The technical features in different embodiments can be combined to form a new embodiment based on their inherent logical relationships.

[0060] The terms used in the embodiments of the present disclosure are only for the purpose of describing specific embodiments and are not intended to limit the present disclosure.

[0061] In the embodiments of the present disclosure, unless otherwise specified, elements expressed in the singular, such as "a", "an", "the", "above", "said", "the", "the", etc., may mean "one and only one", or "one or more", "at least one", etc. For example, when using articles such as "a", "an", "the" in English in translation, the noun following the article may be understood as a singular expression or a plural expression.

[0062] In the embodiments of the present disclosure, “plurality” refers to two or more.

[0063] In some embodiments, the terms "at least one," "one or more," "a plurality of," "multiple," etc. may be used interchangeably.

[0064] In some embodiments, descriptions such as "at least one of A and B," "A and / or B," "A in one case, B in another case," or "in response to one case A, in response to another case B" may include the following technical solutions depending on the situation: in some embodiments, A (A is executed independently of B); in some embodiments, B (B is executed independently of A); in some embodiments, execution is selected from A and B (A and B are selectively executed); and in some embodiments, A and B (both A and B are executed). The above is also applicable when there are more branches such as A, B, and C.

[0065] In some embodiments, "A or B" and other descriptions may include the following technical solutions depending on the situation: in some embodiments, A (A is executed independently of B); in some embodiments, B (B is executed independently of A); in some embodiments, execution is selected from A and B (A and B are selectively executed). The above is also applicable when there are more branches such as A, B, C, etc.

[0066] The prefixes such as "first" and "second" in the embodiments of the present disclosure are only used to distinguish different description objects and do not constitute any restriction on the position, order, priority, quantity or content of the description objects. For the statement of the description object, please refer to the description in the context of the claims or embodiments, and no unnecessary restriction should be constituted due to the use of prefixes. For example, if the description object is a "field", the ordinal number before the "field" in the "first field" and the "second field" does not limit the position or order between the "fields". "First" and "second" do not limit whether the "fields" they modify are in the same message, nor do they limit the order of the "first field" and the "second field". For another example, if the description object is a "level", the ordinal number before the "level" in the "first level" and the "second level" does not limit the priority between the "levels". For another example, the number of description objects is not limited by the ordinal number and can be one or more. Taking "first device" as an example, the number of "devices" can be one or more. In addition, the objects modified by different prefixes can be the same or different. For example, if the description object is "device", then the "first device" and the "second device" can be the same device or different devices, and their types can be the same or different; for another example, if the description object is "information", then the "first information" and the "second information" can be the same information or different information, and their contents can be the same or different.

[0067] In some embodiments, “including A,” “comprising A,” “used to indicate A,” and “carrying A” can be interpreted as directly carrying A or indirectly indicating A.

[0068] In some embodiments, terms such as "in response to...", "in response to determining...", "in the case of...", "at the time of...", "when...", "if...", "if...", etc. can be used interchangeably.

[0069] In some embodiments, terms such as "greater than", "greater than or equal to", "not less than", "more than", "more than or equal to", "not less than", "higher than", "higher than or equal to", "not less than", and "above" can be replaced with each other, and terms such as "less than", "less than or equal to", "not greater than", "less than", "less than or equal to", "not more than", "lower than", "lower than or equal to", "not higher than", and "below" can be replaced with each other.

[0070] In some embodiments, devices, etc. can be interpreted as physical or virtual, and their names are not limited to the names recorded in the embodiments. Terms such as "device", "equipment", "device", "circuit", "network element", "node", "function", "unit", "section", "system", "network", "chip", "chip system", "entity", and "subject" can be used interchangeably.

[0071] In some embodiments, "network" can be interpreted as devices included in the network (eg, access network equipment, core network equipment, etc.).

[0072] In some embodiments, the terms "access network device (AN device)", "radio access network device (RAN device)", "base station (BS)", "radio base station" "fixed station", "node", "access point", "transmission point (TP)", "reception point (RP)", "transmission / reception point (TRP)" "panel", "antenna panel", "antenna array", "cell", "macro cell", "small cell", "femto cell", "pico cell", "sector", "cell group", "serving cell", "carrier", "component carrier", "bandwidth part (BWP)" and the like may be used interchangeably.

[0073] In some embodiments, the terms "terminal", "terminal device", "user equipment (UE)", "user terminal", "mobile station (MS)", "mobile terminal (MT)", subscriber station, mobile unit, subscriber unit, wireless unit, remote unit, mobile device, wireless device, wireless communication device, remote device, mobile subscriber station, access terminal, mobile terminal, wireless terminal, remote terminal, handset, user agent, mobile client, client, etc. can be used interchangeably.

[0074] In some embodiments, the access network device, the core network device, or the network device can be replaced by a terminal. For example, the various embodiments of the present disclosure can also be applied to a structure in which the communication between the access network device, the core network device, or the network device and the terminal is replaced by communication between multiple terminals (for example, device-to-device (D2D), vehicle-to-everything (V2X), etc.). In this case, it is also possible to set the structure in which the terminal has all or part of the functions of the access network device. In addition, terms such as "uplink" and "downlink" can also be replaced by terms corresponding to communication between terminals (for example, "side"). For example, uplink channels, downlink channels, etc. can be replaced by side channels, and uplinks, downlinks, etc. can be replaced by side links.

[0075] In some embodiments, the terminal may be replaced by an access network device, a core network device, or a network device. In this case, the access network device, the core network device, or the network device may have a structure that has all or part of the functions of the terminal.

[0076] In some embodiments, obtaining data, information, etc. may comply with the laws and regulations of the country where the data is obtained.

[0077] In some embodiments, data, information, etc. may be obtained with the user's consent.

[0078] In addition, each element, each row, or each column in the table of the embodiment of the present disclosure can be implemented as an independent embodiment, and the combination of any elements, any rows, and any columns can also be implemented as an independent embodiment.

[0079] Integrated Sensing and Communication (ISAC) is a crucial research area in wireless communications and a key enabling technology for 5G-A (5G-Advanced) and future 6G mobile communications. ISAC enables sensing services based on mobile communications infrastructure, leveraging the advantages of mobile communication networks to meet the sensing needs of various service scenarios. This enhanced sensing capability improves communication performance, enabling numerous application services such as detection, positioning and tracking, environmental reconstruction and target imaging, and gesture and posture recognition.

[0080] For integrated communication and perception applications, comprehensive synaesthesia system channel modeling has been conducted based on 3GPP channel modeling and related standards, providing strong support for the evaluation of synaesthesia technology solutions. Related research has proposed and designed various estimation algorithms for target speed, distance, angle, and other parameters, targeting scenarios such as highways. These algorithms include Multiple Signal Classification (MUSIC) and Estimation of Signal Parameters via Rotational Invariance Techniques (ESPRIT). These algorithms can achieve good target perception in some synaesthesia-integrated simulation scenarios.

[0081] The application of artificial intelligence (AI) technology in wireless communications is a key research area and a key research topic in 3GPP standardization. Machine learning (ML) and deep learning (DL) technologies have been widely applied in many fields. The powerful feature extraction and mapping relationship modeling capabilities of neural network models can provide new solutions to key problems in wireless communication systems. They are currently being widely applied and researched in high-precision terminal positioning, channel state information feedback, and beam management.

[0082] Realizing integrated communication and perception based on AI technology is a potential application in future 6G wireless communication systems. By utilizing the powerful computing power of dedicated devices such as graphics processing units (GPUs), AI perception models are acquired based on training of certain data sets to achieve optimization in perception accuracy, computational complexity, and scope of application. This will promote the in-depth application of integrated communication and perception technology, support more intelligent business applications and services, and thus promote the development of integrated communication, perception, computing, and intelligence.

[0083] FIG1A is a schematic diagram of a perception mode in a perception-integrated scenario.

[0084] As shown in Figure 1A, the following six sensing modes are primarily considered in the discussion of sensing integration standards. Options 1 and 2 are self-transmitting and self-receiving modes, which can be represented using Mono-static. In these modes, the transmitter and receiver are identical. Options 4 through 6 are inter-station transmission and reception modes, which can be represented using Bi-static. In these modes, the transmitter and receiver are different.

[0085] Option 1: Self-transmission and self-reception: A UE (e.g., a person) sends a signal and then receives the reflected echo from the UE (car) to sense its own distance, speed, angle, and other information.

[0086] Option 2: Self-transmission and self-reception: Through the gNB, for example, a user (human) sends a signal and receives the reflected echo from the gNB to sense its own distance, speed, angle, and other information.

[0087] Option 3: Inter-site transmission and reception, from the gNB to the UE. For example, the gNB sends a signal, and the UE (car) receives the signal. The UE (car) calculates the channel matrix based on the received signal to perceive the distance, speed, angle, and other information of the target in the environment, such as the user (person).

[0088] Option 4: Inter-site transmission and reception, from the UE to the gNB. For example, the UE (car) sends signals, and the gNB receives the signals. The gNB calculates the channel matrix based on the received signals to perceive the distance, speed, angle, and other information of the target in the environment, such as the user (person).

[0089] Option 5: Inter-site transmission and reception, from gNB to gNB. For example, gNB1 transmits a signal, gNB2 receives it, and gNB2 calculates the channel matrix based on the received signal to sense the distance, speed, angle, and other information of targets in the environment, such as users (people).

[0090] Option 6: Inter-station transmission and reception, from UE (car) to UE (car). For example, UE1 (car) sends a signal, UE2 (car) receives the signal, and UE2 (car) calculates the channel matrix based on the received signal to perceive the distance, speed, angle, and other information of targets in the environment, such as users (people).

[0091] Perceived targets may include, but are not limited to, drones, people in both indoor and outdoor scenarios, cars in outdoor scenarios (e.g., on highways), automated guided vehicles in indoor scenarios (e.g., factories), and dangerous objects on roadways or railways. Synaesthesia integration primarily involves the receiver calculating information such as the distance, speed, and angle of perceived targets in the environment based on received signals. The perceived target can be the receiver itself or another object, and this disclosure does not limit this.

[0092] In a synaesthesia-integrated simulation scenario, there may be sensing targets (e.g., UEs), base stations (BSs), and obstructions, which may be other UEs or debris. Therefore, the synaesthesia channel model primarily considers four types of signals: line-of-sight (LOS) sensing paths, non-line-of-sight (NLOS) sensing paths, clutter LOS paths, and clutter NLOS paths.

[0093] FIG1B shows four types of signals of the synaesthesia channel in a self-transmitting and self-receiving scenario.

[0094] As shown in Figure 1B, the synaesthesia channel model includes four types of signals:

[0095] Perception LOS path: The LOS path is between the perception signal transmitter and the perception target, and the LOS path is between the perception target and the perception signal receiver (solid line path).

[0096] Perception NLOS path: The path between the sensing signal transmitter and the sensing target is the NLOS path, and the path between the sensing target and the sensing signal receiver is also the NLOS path (bold solid line path);

[0097] Clutter LOS path: The LOS path is between the sensing signal transmitter and the scattering cluster in the environment, and the LOS path is also between the scattering cluster in the environment and the sensing signal receiver (dashed path);

[0098] Clutter NLOS path: The NLOS path is between the sensing signal transmitter and the scattering cluster in the environment, and the NLOS path is between the scattering cluster in the environment and the sensing signal receiver (bold dashed path).

[0099] Typically, the impact of channel noise also needs to be considered.

[0100] Due to the complexity of the perception environment, it is difficult to achieve good perception performance using traditional perception algorithms. Therefore, a perception method based on AI models is proposed.

[0101] In the perception method based on the AI ​​model, the estimated channel matrix is ​​processed and input into the AI ​​model to obtain the perception result or the intermediate result required for perception (such as the number of perceived objects).

[0102] In the above-mentioned AI model-based perception method, the input of the AI ​​model is the estimated channel matrix. However, in actual deployment scenarios, due to factors such as channel estimation errors and non-ideal receiving devices, there are significant differences between the estimated channel and the actual perceived channel, and these differences will further affect the perception accuracy.

[0103] In the application scenario of integrated communication and perception, how to reduce the channel estimation error in the perception process is a technical problem that needs to be solved.

[0104] The embodiments of the present disclosure provide a perceptual communication method, which uses perceptual information as the input of an AI model and obtains a perceptual result based on the perceptual information and the AI ​​model. This method can reduce the error caused by channel estimation during the perceptual process, thereby improving the accuracy of perceptual communication.

[0105] FIG1C is a schematic diagram showing a communication system architecture according to an embodiment of the present disclosure.

[0106] As shown in FIG1C , the communication system 100 includes a perception sending end 101 , a perception target 102 , and a perception receiving end 103 .

[0107] Among them, the perception sending end 101 is used to send a perception signal, and the perception receiving end 103 is used to receive a signal reflected by the perception target 102. The perception receiving end 103 can determine the perception result of the perception target 102 based on the received signal. The perception receiving end 103 can also forward the received signal to any entity capable of performing perception prediction function to determine the perception result of the perception target 102.

[0108] In some embodiments, the perception target 102 may be the perception receiving end 103 itself, or another object.

[0109] In some embodiments, the perception sending end 101 may be an access network device and / or a terminal, and the perception receiving end 103 may be an access network device and / or a terminal.

[0110] In some embodiments, the terminal includes, for example, a mobile phone, a wearable device, an Internet of Things device, a car with communication function, a smart car, a tablet computer, a computer with wireless transceiver function, a virtual reality (VR) terminal device, an augmented reality (AR) terminal device, a wireless terminal device in industrial control, a wireless terminal device in self-driving, a wireless terminal device in remote medical surgery, a wireless terminal device in a smart grid, a wireless terminal device in transportation safety, a wireless terminal device in a smart city, and at least one of a wireless terminal device in a smart home, but is not limited thereto.

[0111] In some embodiments, the access network device is, for example, a node or device that accesses a terminal to a wireless network. The access network device may include an evolved NodeB (eNB) in a fifth generation mobile communication technology (5G) communication system, a next generation evolved NodeB (ng-eNB), a next generation NodeB (gNB), a node B (NB), a home node B (HNB), a home evolved nodeB (HeNB), a wireless backhaul device, a radio network controller (RNC), a base station controller (BSC), a base transceiver station (BTS), a base band unit (BBU), a mobile switching center, a base station in a 6G communication system, an open base station (Open RAN), a cloud base station (Cloud RAN), a base station in other communication systems, and at least one of an access node in a Wi-Fi system, but is not limited thereto.

[0112] In some embodiments, the technical solution of the present disclosure can be applied to the Open RAN architecture. In this case, the interfaces between or within the access network devices involved in the embodiments of the present disclosure can be transformed into internal interfaces of Open RAN, and the processes and information interactions between these internal interfaces can be implemented through software or programs.

[0113] In some embodiments, the access network device can be composed of a centralized unit (CU) and a distributed unit (DU), where the CU can also be called a control unit. The CU-DU structure can be used to split the protocol layer of the access network device, with the functions of some protocol layers centrally controlled by the CU, and the functions of the remaining part or all of the protocol layers distributed in the DU, which is centrally controlled by the CU, but is not limited to this.

[0114] In some embodiments, a core network device may be a device including one or more network elements, or may be multiple devices or device groups, each including all or part of the one or more network elements. The network element may be virtual or physical. The core network may include, for example, at least one of an Evolved Packet Core (EPC), a 5G Core Network (5GCN), and a Next Generation Core (NGC).

[0115] It can be understood that the communication system described in the embodiment of the present disclosure is for the purpose of more clearly illustrating the technical solution of the embodiment of the present disclosure, and does not constitute a limitation on the technical solution proposed in the embodiment of the present disclosure. Ordinary technicians in this field can know that with the evolution of the system architecture and the emergence of new business scenarios, the technical solution proposed in the embodiment of the present disclosure is also applicable to similar technical problems.

[0116] The following embodiments of the present disclosure may be applied to the communication system 100 shown in FIG1C , or a portion thereof, but are not limited thereto. The entities shown in FIG1C are illustrative only. The communication system may include all or part of the entities shown in FIG1C , or may include other entities other than those shown in FIG1 . The number and form of the entities are arbitrary, and the entities may be physical or virtual. The connection relationships between the entities are illustrative only. The entities may be connected or disconnected, and the connection may be in any manner, including direct or indirect, wired or wireless.

[0117] The embodiments of the present disclosure can be applied to Long Term Evolution (LTE), LTE-Advanced (LTE-A), LTE-Beyond (LTE-B), SUPER 3G, IMT-Advanced, 4th generation mobile communication system (4G), 5th generation mobile communication system (5G), 5G new radio (NR), future radio access (FRA), new radio access technology (RAT), new radio (NR), new radio access (NX), future generation radio access (FX), Global System for Mobile Communications (GSM (registered trademark)), CDMA2000, Ultra Mobile Broadband (UMB), IEEE 802.11 (Wi-Fi (registered trademark)), IEEE 802.16 (WiMAX (registered trademark)), IEEE 802.20, Ultra-WideBand (UWB), Bluetooth (registered trademark), Public Land Mobile Network (PLMN) networks, Device-to-Device (D2D) systems, Machine-to-Machine (M2M) systems, Internet of Things (IoT) systems, Vehicle-to-Everything (V2X), systems utilizing other communication methods, and next-generation systems based on and extending these methods. Furthermore, multiple systems may be combined (for example, a combination of LTE or LTE-A with 5G).

[0118] FIG2A is a flow chart of a perceptual communication method according to an embodiment of the present disclosure.

[0119] In the embodiment of the present disclosure, the executor of the perception communication method is an entity that predicts / estimates the perception results. This entity can be a perception receiving end, or any other entity that has the function of performing perception prediction, or it can be jointly executed by the perception receiving end and any other entity that has the function of performing perception prediction. The present disclosure does not limit this.

[0120] As shown in FIG2A , an embodiment of the present disclosure relates to a perceptual communication method, which includes:

[0121] Step S2101: Acquire a first channel matrix and first perception information.

[0122] In some embodiments, the first channel matrix may be an ideal channel matrix, which may also be referred to as a target channel matrix.

[0123] In some embodiments, the first channel matrix can be used as a label during the training process of the first AI model.

[0124] In some embodiments, the first perception information can be used as input in the first AI model training process.

[0125] In some embodiments, the first perception information may include at least one of the following:

[0126] Sensing the reference signal (RS) received by the receiving end;

[0127] sensing a reference signal sent by a transmitter;

[0128] Transmission resources for reference signals;

[0129] Number of transmitting antennas;

[0130] Number of receiving antennas;

[0131] Reference signal measurement information;

[0132] Reference signal configuration information;

[0133] Synaesthesia resource configuration information;

[0134] Antenna configuration information.

[0135] In some embodiments, the transmission resource of the reference signal may be a time-frequency resource used to transmit the reference signal.

[0136] In some embodiments, the reference signal measurement information may include a reference signal received by the sensing receiving end, and may also include a reference signal sent by the sensing transmitting end.

[0137] In some embodiments, the reference signal configuration information may include time-frequency resources for reference signal transmission, and may also include a usage sequence, etc.

[0138] In some embodiments, the synaesthesia resource configuration information may include time, frequency, antenna resources, etc. for communication perception.

[0139] Step S2102: Train a first AI model based on the first channel matrix and the first perception information.

[0140] In some embodiments, the training data of the first AI model may include a first channel matrix and first perception information, and the first channel matrix and the first perception information have a corresponding relationship.

[0141] For example, an ideal channel matrix corresponding to the reference signal received by the perception receiving end and the reference signal sent by the perception transmitting end included in the first perception information may be determined as the first channel matrix.

[0142] In other embodiments, the training data of the first AI model may include first perception information and information on antenna dimension, time dimension, and frequency dimension based on decomposition of the first channel matrix.

[0143] In some embodiments, the first perception information can be input into the first AI model to be trained for channel estimation processing to obtain a predicted channel matrix; the first AI model to be trained is trained based on the predicted channel matrix and the first channel matrix to obtain a trained first AI model.

[0144] In some embodiments, training a first AI model to be trained based on the predicted channel matrix and the first channel matrix may include: determining a first loss based on the predicted channel matrix and the first channel matrix, and adjusting the model parameters of the first AI model when the first loss meets or does not meet a preset condition, until the first loss between the predicted channel matrix output by the first AI model and the first channel matrix meets the preset condition, thereby completing the training of the first AI model.

[0145] Step S2103: Obtain the first perception result.

[0146] In some embodiments, the first perception result may be an ideal perception result, which may also be referred to as a target perception result.

[0147] In some embodiments, the first perception result may include location information of the perception target, and / or the perception result may include the number of the perception targets. The location information of the perception target may include at least one of the distance, speed, and angle corresponding to the perception target.

[0148] In some embodiments, the first perception result can be used as a label in the training process of the second AI model. The first perception result can be the actual location information of the perception target (such as distance, speed and angle), or it can be the actual number of perception targets, or it can be the actual number of perception targets and the actual location information of each perception target.

[0149] Step S2104: Train the second AI model based on the first perception result and the channel matrix output by the first AI model.

[0150] In some embodiments, the training data of the second AI model may include the first perception result and the channel matrix output by the first AI model. The channel matrix output by the first AI model serves as input in the training process of the second AI model. The first perception result and the channel matrix output by the first AI model have a corresponding relationship. The first AI model may be the first AI model that has completed training.

[0151] In other embodiments, the training data of the first AI model may include the first perception information and information on antenna dimensions, time dimensions, and frequency dimensions based on the decomposition of the channel matrix output by the first AI model. The information on antenna dimensions, time dimensions, and frequency dimensions based on the decomposition of the channel matrix output by the first AI model is used as input in the training process of the second AI model, and the first AI model may be the first AI model that has completed training.

[0152] In some embodiments, the channel matrix output by the first AI model can be input into the second AI model to be trained for processing to obtain a predicted perception result; the second AI model to be trained is trained based on the predicted perception result and the first perception result to obtain a trained second AI model.

[0153] In some embodiments, training a second AI model to be trained based on the predicted perception result and the first perception result may include: determining a second loss based on the predicted perception result and the first perception result, and adjusting the model parameters of the second AI model when the second loss meets or does not meet a preset condition, until the second loss between the predicted perception result output by the second AI model and the first perception result meets the preset condition, thereby completing the training of the second AI model.

[0154] In some embodiments, after the training of the first AI model and the second AI model is completed, the first AI model and the second AI model can be deployed in the same device or different devices, which is not limited in this disclosure.

[0155] In some embodiments, the first AI model and the second AI model can both be deployed at the perception receiving end; or both can be deployed at any entity other than the perception receiving end that has the function of performing perception prediction; or one of the models can be deployed at the perception receiving end, and the other model can be deployed at any entity other than the perception receiving end that has the function of performing perception prediction.

[0156] In some embodiments, when the first AI model and the second AI model are deployed in different devices, the device where the first AI model is deployed sends output information of the first AI model to the device where the second AI model is deployed.

[0157] In some embodiments, the input generation device of the first AI model may be different from the device on which the first AI model is deployed. When the two devices are different, the input generation device sends the input to the device on which the first AI model is deployed.

[0158] Step S2105, obtaining perception information.

[0159] In some embodiments, the perception information may include at least one of the following:

[0160] sensing a reference signal received by a receiving end;

[0161] sensing a reference signal sent by a transmitter;

[0162] Transmission resources for reference signals;

[0163] Number of transmitting antennas;

[0164] Number of receiving antennas;

[0165] Reference signal measurement information;

[0166] Reference signal configuration information;

[0167] Synaesthesia resource configuration information;

[0168] Antenna configuration information.

[0169] In some embodiments, during the model application stage, perception information can be obtained, and the perception information can be processed by the trained first AI model and the second AI model to obtain perception results.

[0170] Step S2106: Obtain a perception result based on the perception information, the first AI model, and the second AI model.

[0171] Among them, the input of the first AI model is perception information.

[0172] In some embodiments, the perception information is information used for channel matrix estimation.

[0173] In some embodiments, the perception information includes at least one of the following:

[0174] sensing a reference signal received by a receiving end;

[0175] sensing a reference signal sent by a transmitter;

[0176] Transmission resources for reference signals;

[0177] Number of transmitting antennas;

[0178] Number of receiving antennas;

[0179] Reference signal measurement information;

[0180] Reference signal configuration information;

[0181] Synaesthesia resource configuration information;

[0182] Antenna configuration information.

[0183] In some embodiments, the sensing result may include location information of the sensing target, and / or the sensing result may include the number of the sensing targets. The location information of the sensing target may include at least one of the distance, speed, and angle corresponding to the sensing target.

[0184] In some embodiments, the perception information can be input into a trained first AI model for channel estimation, and a channel matrix can be output; the channel matrix can be input into a trained second AI model, and the perception result can be output.

[0185] In some embodiments, the perception information can be input into a trained first AI model for channel estimation, and information on the antenna dimension, time dimension, and frequency dimension based on the channel matrix decomposition can be output; the information on the antenna dimension, time dimension, and frequency dimension based on the channel matrix decomposition can be input into a trained second AI model to output the perception results.

[0186] In some embodiments, the perception receiving end is deployed with a trained first AI model and a trained second AI model. The perception receiving end can obtain perception information and obtain a perception result based on the perception information, the trained first AI model, and the trained second AI model.

[0187] In some embodiments, the perception receiving end can obtain perception information and send the perception information to a device deployed with the trained first AI model and the second AI model to obtain a perception result.

[0188] In some embodiments, a perception receiving end can obtain perception information and send the perception information to a first device deployed with a trained first AI model. The first device inputs the perception information into the trained first AI model, outputs a channel matrix, and sends the channel matrix to a second device deployed with a trained second AI model. The second device inputs the channel matrix into the trained second AI model and outputs a perception result.

[0189] The perceptual communication method provided by the embodiments of the present disclosure uses perceptual information as the input of an AI model, and obtains perceptual results based on the perceptual information and the AI ​​model, which can reduce the error caused by channel estimation in the perceptual process, thereby improving the accuracy of perceptual communication.

[0190] The perception communication method provided by the embodiments of the present disclosure utilizes the strong learning and modeling capabilities of the neural network model in the integrated communication and perception application scenario, obtains an AI perception model based on a certain amount of data training, and can overcome the influence of channel estimation errors and device non-idealities to obtain higher-precision perception results.

[0191] The perceptual communication method according to the embodiments of the present disclosure may include at least one of steps S2101 to S2106. For example, step S2103 may be implemented as an independent embodiment, step S2104 may be implemented as an independent embodiment, step S2105 may be implemented as an independent embodiment, and step S2106 may be implemented as an independent embodiment, but the present invention is not limited thereto.

[0192] In some embodiments, steps S2101 and S2103 may be executed in an exchanged order or simultaneously, and steps S2102 and S2104 may be executed in an exchanged order or simultaneously.

[0193] In some embodiments, steps S2101, S2102, S2103, S2104, and S2105 are optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0194] In some embodiments, steps S2101 and S2102 are optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0195] In some embodiments, steps S2103 and S2104 are optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0196] In some embodiments, step S2105 is optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0197] In some embodiments, reference may be made to other optional implementations described before or after the description corresponding to FIG. 2A .

[0198] FIG2B is a flow chart of a perceptual communication method according to an embodiment of the present disclosure. As shown in FIG2B , the embodiment of the present disclosure relates to a perceptual communication method, which includes:

[0199] Step S2201: Acquire first perception information and first perception results.

[0200] In some embodiments, the first perception information can be used as input in the AI ​​model training process.

[0201] In some embodiments, the first perception information may include at least one of the following:

[0202] sensing a reference signal received by a receiving end;

[0203] sensing a reference signal sent by a transmitter;

[0204] Transmission resources for reference signals;

[0205] Number of transmitting antennas;

[0206] Number of receiving antennas;

[0207] Reference signal measurement information;

[0208] Reference signal configuration information;

[0209] Synaesthesia resource configuration information;

[0210] Antenna configuration information.

[0211] In some embodiments, the first perception result may be an ideal perception result, which may also be referred to as a target perception result.

[0212] In some embodiments, the first perception result may include at least one of a distance, a speed, and an angle corresponding to the perception target, and / or the perception result includes the number of perception targets.

[0213] In some embodiments, the first perception result can be used as a label in the AI ​​model training process. The first perception result can be the actual distance, speed and angle of the perceived target, or the actual number of perceived targets, or the actual number of perceived targets and the actual distance, speed and angle of each perceived target.

[0214] Step S2202: Train the AI ​​model based on the first perception information and the first perception result.

[0215] In some embodiments, the training data of the AI ​​model may include first perception information and first perception results, and the first perception information and the first perception results have a corresponding relationship.

[0216] For example, in a perception scenario, the first perception information includes a reference signal received by a perception receiving end and a reference signal sent by a perception transmitting end, and the first perception result is the location information (such as distance, speed, and angle) of the perception target and / or the number of perception targets.

[0217] In some embodiments, the first perception information can be input into the AI ​​model to be trained for processing to obtain a predicted perception result; the AI ​​model to be trained is trained based on the predicted perception result and the first perception result to obtain a trained AI model.

[0218] In some embodiments, training the AI ​​model to be trained based on the predicted perception result and the first perception result may include: determining a third loss based on the predicted perception result and the first perception result, and adjusting the model parameters of the AI ​​model when the third loss meets or does not meet a preset condition, until the third loss between the predicted perception result output by the AI ​​model and the first perception result meets the preset condition, thereby completing the training of the AI ​​model.

[0219] Step S2203, obtaining perception information.

[0220] In some embodiments, the perception information may include at least one of the following:

[0221] sensing a reference signal received by a receiving end;

[0222] sensing a reference signal sent by a transmitter;

[0223] Transmission resources for reference signals;

[0224] Number of transmitting antennas;

[0225] Number of receiving antennas;

[0226] Reference signal measurement information;

[0227] Reference signal configuration information;

[0228] Synaesthesia resource configuration information;

[0229] Antenna configuration information.

[0230] In some embodiments, during the model application stage, perception information can be obtained, and the perception information can be processed by the trained AI model to obtain perception results.

[0231] Step S2204: Obtain a perception result based on the perception information and the AI ​​model.

[0232] Among them, the input of the AI ​​model is perception information.

[0233] In some embodiments, the sensory information includes at least one of the following:

[0234] sensing a reference signal received by a receiving end;

[0235] sensing a reference signal sent by a transmitter;

[0236] Transmission resources for reference signals;

[0237] Number of transmitting antennas;

[0238] Number of receiving antennas;

[0239] Reference signal measurement information;

[0240] Reference signal configuration information;

[0241] Synaesthesia resource configuration information;

[0242] Antenna configuration information.

[0243] In some embodiments, the sensing result may include location information of the sensing target, and / or the sensing result may include the number of the sensing targets. The location information of the sensing target may include at least one of the distance, speed, and angle corresponding to the sensing target.

[0244] In some embodiments, the perception information can be input into a trained AI model for processing and output as a perception result.

[0245] In some embodiments, a trained AI model is deployed on the perception receiving end. The perception receiving end can obtain perception information and obtain a perception result based on the perception information and the trained AI model.

[0246] In some embodiments, the perception receiving end can obtain perception information and send the perception information to a device deployed with a trained AI model to obtain a perception result.

[0247] The perceptual communication method provided by the embodiments of the present disclosure uses perceptual information as the input of an AI model, and obtains perceptual results based on the perceptual information and the AI ​​model, which can reduce the error caused by channel estimation in the perceptual process, thereby improving the accuracy of perceptual communication.

[0248] The perception communication method involved in the embodiment of the present disclosure may include at least one of steps S2201 to S2204. For example, step S2202 may be implemented as an independent embodiment, and step S2204 may be implemented as an independent embodiment, but the present invention is not limited thereto.

[0249] In some embodiments, steps S2201, S2202, and S2203 are optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0250] In some embodiments, steps S2201 and S2202 are optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0251] In some embodiments, step S2203 is optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0252] In some embodiments, the names of information, etc. are not limited to the names described in the embodiments, and terms such as "information", "message", "signal", "signaling", "report", "configuration", "indication", "instruction", "command", "channel", "parameter", "domain", "field", "symbol", "symbol", "code element", "codebook", "codeword", "codepoint", "bit", "data", "program", and "chip" can be used interchangeably.

[0253] In some embodiments, terms such as "moment", "time point", "time", and "time position" can be replaced with each other, and terms such as "duration", "period", "time window", "window", and "time" can be replaced with each other.

[0254] In some embodiments, "obtain", "get", "get", "receive", "transmit", "bidirectional transmission", "send and / or receive" can be interchangeable, and can be interpreted as receiving from other entities, obtaining from protocols, obtaining from higher layers, obtaining by self-processing, autonomous implementation, etc.

[0255] In some embodiments, terms such as "send", "transmit", "report", "download", "transmit", "bidirectional transmission", "send and / or receive" can be used interchangeably.

[0256] In some embodiments, terms such as "certain", "preset", "preset", "setting", "indicated", "a certain", "any", and "first" can be interchangeable. "Specific A", "preset A", "preset A", "setting A", "indicated A", "a certain A", "any A", and "first A" can be interpreted as A pre-specified in a protocol, etc., or as A obtained through setting, configuration, or indication, etc., or as specific A, a certain A, any A, or first A, etc., but not limited to this.

[0257] In some embodiments, the determination or judgment can be performed by a value represented by 1 bit (0 or 1), or by a true or false value (Boolean value (bool)) represented by true (true) or false (false), or by comparison of numerical values ​​(for example, comparison with a predetermined value), but is not limited thereto.

[0258] In some embodiments, "not expecting to receive" can be interpreted as not receiving on time domain resources and / or frequency domain resources, or as not performing subsequent processing on the data after receiving it; "not expecting to send" can be interpreted as not sending, or as sending but not expecting the recipient to respond to the content sent.

[0259] FIG3A is a flow chart of a perceptual communication method according to an embodiment of the present disclosure. As shown in FIG3A , the embodiment of the present disclosure relates to a perceptual communication method, which includes:

[0260] Step S3101: Acquire a first channel matrix and first perception information.

[0261] The optional implementation of step S3101 can refer to the optional implementation of step S2101 in Figure 2A and other related parts in the embodiment involved in Figure 2A, which will not be repeated here.

[0262] Step S3102: Train a first AI model based on the first channel matrix and the first perception information.

[0263] The optional implementation of step S3102 can refer to the optional implementation of step S2102 in Figure 2A and other related parts in the embodiment involved in Figure 2A, which will not be repeated here.

[0264] The perception communication method involved in the embodiment of the present disclosure may include at least one of steps S3101 to S3102. For example, step S3102 may be implemented as an independent embodiment, but is not limited thereto.

[0265] In some embodiments, step S3101 is optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0266] FIG3B is a flow chart of a perceptual communication method according to an embodiment of the present disclosure. As shown in FIG3B , the embodiment of the present disclosure relates to a perceptual communication method, which includes:

[0267] Step S3201, obtain the first perception result.

[0268] The optional implementation of step S3201 can refer to the optional implementation of step S2103 in Figure 2A and other related parts in the embodiment involved in Figure 2A, which will not be repeated here.

[0269] Step S3202: Train the second AI model based on the first perception result and the channel matrix output by the first AI model.

[0270] The optional implementation of step S3202 can refer to the optional implementation of step S2104 in Figure 2A and other related parts in the embodiment involved in Figure 2A, which will not be repeated here.

[0271] The perception communication method involved in the embodiment of the present disclosure may include at least one of steps S3201 to S3202. For example, step S3202 may be implemented as an independent embodiment, but is not limited thereto.

[0272] In some embodiments, step S3201 is optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0273] FIG3C is a flow chart of a perceptual communication method according to an embodiment of the present disclosure. As shown in FIG3C , the embodiment of the present disclosure relates to a perceptual communication method, which includes:

[0274] Step S3301, obtaining perception information.

[0275] The optional implementation of step S3301 can refer to the optional implementation of step S2105 in Figure 2A and other related parts in the embodiment involved in Figure 2A, which will not be repeated here.

[0276] Step S3302: Obtain a perception result based on the perception information, the first AI model, and the second AI model.

[0277] The optional implementation of step S3302 can refer to the optional implementation of step S2106 in Figure 2A and other related parts in the embodiment involved in Figure 2A, which will not be repeated here.

[0278] The perception communication method involved in the embodiment of the present disclosure may include at least one of steps S3301 to S3302. For example, step S3302 may be implemented as an independent embodiment, but is not limited thereto.

[0279] In some embodiments, step S3301 is optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0280] FIG4A is a flow chart of a perceptual communication method according to an embodiment of the present disclosure. As shown in FIG4A , the embodiment of the present disclosure relates to a perceptual communication method, which includes:

[0281] Step S4101: Acquire first perception information and first perception results.

[0282] The optional implementation of step S4101 can refer to the optional implementation of step S2201 in Figure 2B and other related parts in the embodiment involved in Figure 2B, which will not be repeated here.

[0283] Step S4102: training an AI model based on the first perception information and the first perception result.

[0284] The optional implementation of step S4102 can refer to the optional implementation of step S2202 in Figure 2B and other related parts in the embodiment involved in Figure 2B, which will not be repeated here.

[0285] The perception communication method involved in the embodiment of the present disclosure may include at least one of steps S4101 to S4102. For example, step S4102 may be implemented as an independent embodiment, but is not limited thereto.

[0286] In some embodiments, step S4101 is optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0287] FIG4B is a flow chart of a perceptual communication method according to an embodiment of the present disclosure. As shown in FIG4B , the embodiment of the present disclosure relates to a perceptual communication method, which includes:

[0288] Step S4201, obtaining perception information.

[0289] The optional implementation of step S4201 can refer to the optional implementation of step S2203 in Figure 2B and other related parts in the embodiment involved in Figure 2B, which will not be repeated here.

[0290] Step S4202: Obtain a perception result based on the perception information and the AI ​​model.

[0291] The optional implementation of step S4202 can refer to the optional implementation of step S2204 in Figure 2B and other related parts in the embodiment involved in Figure 2B, which will not be repeated here.

[0292] The perception communication method involved in the embodiment of the present disclosure may include at least one of steps S4201 to S4202. For example, step S4202 may be implemented as an independent embodiment, but is not limited thereto.

[0293] In some embodiments, step S4201 is optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0294] FIG5A is a schematic diagram showing a perceptual communication method according to an embodiment of the present disclosure.

[0295] In an embodiment of the present disclosure, referring to Figure 5A, a two-step processing method can be used, using two AI models. The first model (also referred to as the first AI model) is used to estimate the channel, and the second model (also referred to as the second AI model) estimates the perception results (such as speed, angle, distance) based on the output of the first model. The first model inputs the channel information (such as the channel matrix) obtained by the channel estimation into the second model for processing.

[0296] In some embodiments, the input of the first model may include at least one of the following information:

[0297] RS signal received by the receiving end;

[0298] RS signal sent by the transmitter;

[0299] Transmission resources of RS signals;

[0300] Number of transmitting antennas;

[0301] Number of receiving antennas.

[0302] In some embodiments, the input of the first model may also include at least one of the following information:

[0303] RS measurement information;

[0304] RS configuration information;

[0305] Synaesthesia resource allocation;

[0306] Antenna configuration.

[0307] In some embodiments, the RS measurement information may include an RS signal received by a sensing receiving end, and may also include an RS signal sent by a sensing transmitting end.

[0308] In some embodiments, the RS configuration information may include the time-frequency resources for RS transmission, and may also include a usage sequence, etc.

[0309] In some embodiments, the synaesthesia resource configuration information may include time, frequency, antenna resources, etc. for communication perception.

[0310] In some embodiments, RS signals are transmitted on corresponding transmission resources and antennas.

[0311] In some embodiments, the corresponding model output during the first model training is an ideal channel matrix, or information on antenna dimensions, time dimensions, and frequency dimensions decomposed based on the ideal channel matrix.

[0312] In some embodiments, the input information of the second model is the channel matrix output by the trained first model, or information on the antenna dimension, time dimension, and frequency dimension decomposed based on the channel matrix output by the first model, or information on the antenna dimension, time dimension, and frequency dimension decomposed based on the ideal channel matrix.

[0313] In some embodiments, the output information of the second model is a perception result, such as the distance, speed, angle, etc. corresponding to the perceived target. Alternatively, the output information of the second model is an intermediate result required for perception, such as the number of perceived targets.

[0314] In some embodiments, the distance corresponding to the perceived target may refer to the distance between two objects, the speed corresponding to the perceived target may refer to the relative speed between the two objects, and the angle corresponding to the perceived target may refer to the horizontal angle between the two objects.

[0315] In some embodiments, during the model training phase, the mapping relationship between the ideal channel and the transmitted signal and the received signal can be abstracted.

[0316] In some embodiments, in actual deployment, the first model and the second model can be deployed on the same device or on different devices. When the first model and the second model are deployed on different devices, the device where the first model is deployed sends the output information of the first model to the device where the second model is deployed.

[0317] In some embodiments, the input generation device of the first model may be different from the deployment device of the first model. When the two devices are different, the input generation device sends the input to the deployment device.

[0318] FIG5B is a schematic diagram illustrating a perceptual communication method according to an embodiment of the present disclosure.

[0319] In the embodiment of the present disclosure, referring to FIG5B , an integrated processing method may be used, using one AI model, which may be referred to as a third model for ease of distinction.

[0320] In some embodiments, the input of the third model may include at least one of the following information:

[0321] RS signal received by the receiving end;

[0322] RS signal sent by the transmitter;

[0323] Transmission resources of RS signals;

[0324] Number of transmitting antennas;

[0325] Number of receiving antennas.

[0326] In some embodiments, the input of the third model may also include at least one of the following information:

[0327] RS measurement information;

[0328] RS configuration information;

[0329] Synaesthesia resource allocation;

[0330] Antenna configuration.

[0331] In some embodiments, during the model training phase, the mapping relationship between the ideal channel and the transmitted signal and the received signal can be abstracted.

[0332] In some embodiments, the output information of the third model is a perception result, such as the distance, speed, angle, etc. corresponding to the perceived target. Alternatively, the output information of the second model is an intermediate result required for perception, such as the number of perceived targets.

[0333] In some embodiments, the input generating device of the third model may be different from the deployment device of the third model. When the two devices are different, the input generating device sends the input to the deployment device.

[0334] The perception communication method provided by the embodiment of the present disclosure can be applied to the application scenario of integrated communication and perception, and can perform multi-target perception based on the AI ​​model, and use the neural network model to respectively realize the estimation of the number of targets and the perception of the distance, speed, angle and other information of multiple targets. In the application scenario of multi-target synaesthesia integration, the AI ​​perception model can be trained and acquired based on the channel state information matrix of the receiving end and the data set composed of the actual number of perceived targets and the actual distance, speed, angle and other parameter information of each perceived target, so as to realize the perception of the distance, speed and angle of multiple targets with high accuracy. The multi-target perception solution based on AI provided by the embodiment of the present disclosure has a large scope of application, which is conducive to solving the shortcomings of the traditional perception algorithm in the case of multiple targets with poor perception accuracy, thereby promoting the development and application of communication perception integration technology.

[0335] In the embodiments of the present disclosure, some or all of the steps and their optional implementations may be arbitrarily combined with some or all of the steps in other embodiments, or may be arbitrarily combined with the optional implementations of other embodiments.

[0336] The embodiments of the present disclosure further provide an apparatus for implementing any of the above methods. For example, an apparatus is provided, comprising units or modules for implementing each step performed by a terminal in any of the above methods. For another example, another apparatus is provided, comprising units or modules for implementing each step performed by a network device (e.g., an access network device, a core network function node, a core network device, etc.) in any of the above methods.

[0337] It should be understood that the division of the various units or modules in the above device is merely a division of logical functions. In actual implementation, they may be fully or partially integrated into a physical entity, or they may be physically separated. In addition, the units or modules in the device may be implemented in the form of a processor calling software: for example, the device includes a processor, the processor is connected to a memory, and the memory stores instructions. The processor calls the instructions stored in the memory to implement any of the above methods or implement the functions of the various units or modules of the above device, wherein the processor is, for example, a general-purpose processor, such as a central processing unit (CPU) or a microprocessor, and the memory is a memory within the device or a memory outside the device. Alternatively, the units or modules in the device can be implemented in the form of hardware circuits, and the functions of some or all of the units or modules can be realized by designing the hardware circuits. The above-mentioned hardware circuits can be understood as one or more processors; for example, in one implementation, the above-mentioned hardware circuit is an application-specific integrated circuit (ASIC), which realizes the functions of some or all of the above units or modules by designing the logical relationship of the components in the circuit; for example, in another implementation, the above-mentioned hardware circuit can be realized by a programmable logic device (PLD). Taking a field programmable gate array (FPGA) as an example, it can include a large number of logic gate circuits, and the connection relationship between the logic gate circuits is configured by configuring the configuration file, thereby realizing the functions of some or all of the above units or modules. All units or modules of the above devices can be realized in the form of software called by the processor, or in the form of hardware circuits, or in part by the form of software called by the processor, and the rest by hardware circuits.

[0338] In the embodiments of the present disclosure, the processor is a circuit with signal processing capabilities. In one implementation, the processor can be a circuit with instruction reading and execution capabilities, such as a central processing unit (CPU), a microprocessor, a graphics processing unit (GPU) (which can be understood as a microprocessor), or a digital signal processor (DSP). In another implementation, the processor can implement certain functions through the logical relationship of the hardware circuit. The logical relationship of the above-mentioned hardware circuit is fixed or reconfigurable. For example, the processor is a hardware circuit implemented by an application-specific integrated circuit (ASIC) or a programmable logic device (PLD), such as an FPGA. In a reconfigurable hardware circuit, the process of the processor loading a configuration document and implementing the hardware circuit configuration can be understood as the process of the processor loading instructions to implement the functions of some or all of the above units or modules. In addition, it can also be a hardware circuit designed for artificial intelligence, which can be understood as an ASIC, such as a neural network processing unit (NPU), a tensor processing unit (TPU), a deep learning processing unit (DPU), etc.

[0339] Figure 6 is a structural diagram of a communication device proposed in an embodiment of the present disclosure. As shown in Figure 6, the communication device 6100 may include: a transceiver module 6101 and a processing module 6102. In some embodiments, the above-mentioned transceiver module 6101 is used to obtain perception information; the processing module 6102 is used to obtain perception results based on the perception information and the AI ​​model, and the input of the AI ​​model is the perception information. Optionally, the above-mentioned transceiver module is used to execute at least one of the processing steps (such as steps S2101, S2103, S2105, but not limited to this) performed by the communication device in any of the above methods, and the above-mentioned transceiver module is used to execute at least one of the processing steps (such as steps S2102, S2104, S2106, but not limited to this) performed by the communication device in any of the above methods, which will not be repeated here.

[0340] In some embodiments, the processing module can be a single module or can include multiple submodules. Optionally, the multiple submodules respectively execute all or part of the steps required to be executed by the processing module. Optionally, the processing module can be interchangeable with the processor.

[0341] Figure 7A is a schematic diagram of the structure of a communication device 7100 proposed in an embodiment of the present disclosure. Communication device 7100 can be a network device (e.g., an access network device, a core network device, etc.), a terminal (e.g., a user equipment, etc.), a chip, a chip system, or a processor that supports a network device to implement any of the above methods, or a chip, a chip system, or a processor that supports a terminal to implement any of the above methods. Communication device 7100 can be used to implement the methods described in the above method embodiments. For details, please refer to the description of the above method embodiments.

[0342] As shown in Figure 7A, the communication device 7100 includes one or more processors 7101. The processor 7101 can be a general-purpose processor or a dedicated processor, for example, a baseband processor or a central processing unit. The baseband processor can be used to process the communication protocol and communication data, and the central processing unit can be used to control the communication device (such as a base station, a baseband chip, a terminal device, a terminal device chip, a DU or a CU, etc.), execute programs, and process program data. Optionally, the communication device 7100 is used to perform any of the above methods. Optionally, one or more processors 7101 are used to call instructions to enable the communication device 7100 to perform any of the above methods.

[0343] In some embodiments, the communication device 7100 further includes one or more transceivers 7102. When the communication device 7100 includes one or more transceivers 7102, the transceiver 7102 performs at least one of the communication steps such as sending and / or receiving in the above method (for example, steps S2101, S2103, and S2105, but not limited thereto), and the processor 7101 performs at least one of the other steps (for example, steps S2102, S2104, and S2106, but not limited thereto). In an optional embodiment, the transceiver may include a receiver and / or a transmitter, and the receiver and transmitter may be separate or integrated. Optionally, the terms transceiver, transceiver unit, transceiver, transceiver circuit, interface circuit, and interface may be interchangeable, the terms transmitter, transmitting unit, transmitter, and transmitting circuit may be interchangeable, and the terms receiver, receiving unit, receiver, and receiving circuit may be interchangeable.

[0344] In some embodiments, the communication device 7100 further includes one or more memories 7103 for storing data. Alternatively, all or part of the memories 7103 may be located outside the communication device 7100. In alternative embodiments, the communication device 7100 may include one or more interface circuits 7104. Optionally, the interface circuits 7104 are connected to the memories 7103 and may be configured to receive data from the memories 7103 or other devices, or to send data to the memories 7103 or other devices. For example, the interface circuits 7104 may read data stored in the memories 7103 and send the data to the processor 7101.

[0345] The communication device 7100 described in the above embodiment may be a network device or a terminal, but the scope of the communication device 7100 described in the present disclosure is not limited thereto, and the structure of the communication device 7100 may not be limited by FIG. 7A. The communication device may be an independent device or may be part of a larger device. For example, the communication device may be: 1) an independent integrated circuit IC, or a chip, or a chip system or subsystem; (2) a collection of one or more ICs, optionally, the above IC collection may also include a storage component for storing data or programs; (3) an ASIC, such as a modem; (4) a module that can be embedded in other devices; (5) a receiver, a terminal device, an intelligent terminal device, a cellular phone, a wireless device, a handheld device, a mobile unit, an in-vehicle device, a network device, a cloud device, an artificial intelligence device, etc.; (6) others, etc.

[0346] 7B is a schematic diagram of the structure of a chip 7200 proposed in an embodiment of the present disclosure. If the communication device 7100 can be a chip or a chip system, please refer to the schematic diagram of the structure of the chip 7200 shown in FIG7B , but the present disclosure is not limited thereto.

[0347] The chip 7200 includes one or more processors 7201. The chip 7200 is configured to execute any of the above methods.

[0348] In some embodiments, chip 7200 further includes one or more interface circuits 7202. Alternatively, terms such as interface circuit, interface, and transceiver pins may be used interchangeably. In some embodiments, chip 7200 further includes one or more memories 7203 for storing data. Alternatively, all or part of memory 7203 may be located external to chip 7200. Optionally, interface circuit 7202 is connected to memory 7203 and may be used to receive data from memory 7203 or other devices, or may be used to send data to memory 7203 or other devices. For example, interface circuit 7202 may read data stored in memory 7203 and send the data to processor 7201.

[0349] In some embodiments, the interface circuit 7202 performs at least one of the communication steps (e.g., steps S2101, S2103, and S2105, but not limited thereto) in the above method. The interface circuit 7202 performing the communication steps (e.g., steps S2101, S2103, and S2105, but not limited thereto) in the above method, for example, means that the interface circuit 7202 performs data exchange between the processor 7201, the chip 7200, the memory 7203, or the transceiver device. In some embodiments, the processor 7201 performs at least one of the other steps (e.g., steps S2102, S2104, and S2106, but not limited thereto).

[0350] The modules and / or devices described in various embodiments, such as virtual devices, physical devices, and chips, can be arbitrarily combined or separated according to circumstances. Optionally, some or all steps can also be performed collaboratively by multiple modules and / or devices, which is not limited here.

[0351] The present disclosure also proposes a storage medium having instructions stored thereon. When the instructions are executed on the communication device 7100, the communication device 7100 executes any of the above methods. Optionally, the storage medium is an electronic storage medium. Optionally, the storage medium is a computer-readable storage medium, but is not limited thereto and may also be a storage medium readable by other devices. Optionally, the storage medium may be a non-transitory storage medium, but is not limited thereto and may also be a temporary storage medium.

[0352] The present disclosure also provides a program product, which, when executed by the communication device 7100, enables the communication device 7100 to perform any of the above methods. Optionally, the program product is a computer program product.

[0353] The present disclosure also proposes a computer program, which, when executed on a computer, causes the computer to perform any one of the above methods.

Claims

1. A perception communication method, characterized in that: The method comprises: A perception result is obtained based on the perception information and an artificial intelligence (AI) model, wherein the input of the AI ​​model is the perception information.

2. The method according to claim 1, characterized in that The perception information includes at least one of the following: sensing a reference signal received by a receiving end; sensing a reference signal sent by a transmitter; Transmission resources for reference signals; Number of transmitting antennas; Number of receiving antennas; Reference signal measurement information; Reference signal configuration information; Synaesthesia resource configuration information; Antenna configuration information.

3. The method according to claim 1 or 2, characterized in that The AI ​​model includes a first AI model and a second AI model; the input of the first AI model is the perception information, the first AI model performs channel estimation based on the perception information, and outputs a channel matrix; the second AI model outputs a perception result based on the channel matrix.

4. The method according to claim 3, characterized in that The first AI model is trained based on the first channel matrix and the first perception information.

5. The method according to claim 3 or 4, characterized in that The second AI model is trained based on the first perception result and the channel matrix output by the first AI model.

6. The method according to claim 1 or 2, characterized in that The AI ​​model is trained based on the first perception information and the first perception result.

7. The method according to any one of claims 1 to 6, characterized in that The perception result includes at least one of a distance, a speed, and an angle corresponding to the perceived target, and / or the perception result includes the number of perceived targets.

8. A communication device, characterized in that: include: Transceiver module, used to obtain perception information; A processing module is used to obtain a perception result based on the perception information and an AI model, wherein the input of the AI ​​model is the perception information.

9. The device according to claim 8, characterized in that The perception information includes at least one of the following: sensing a reference signal received by a receiving end; sensing a reference signal sent by a transmitter; Transmission resources for reference signals; Number of transmitting antennas; Number of receiving antennas; Reference signal measurement information; Reference signal configuration information; Synaesthesia resource configuration information; Antenna configuration information.

10. The device according to claim 8 or 9, characterized in that The AI ​​model includes a first AI model and a second AI model; the input of the first AI model is the perception information, the first AI model performs channel estimation based on the perception information, and outputs a channel matrix; the second AI model outputs a perception result based on the channel matrix.

11. The device according to claim 10, characterized in that The first AI model is trained based on the first channel matrix and the first perception information.

12. The device according to claim 10 or 11, characterized in that The second AI model is trained based on the first perception result and the channel matrix output by the first AI model.

13. The device according to claim 8 or 9, characterized in that The AI ​​model is trained based on the first perception information and the first perception result.

14. The device according to any one of claims 8 to 13, characterized in that The perception result includes at least one of a distance, a speed, and an angle corresponding to the perceived target, and / or the perception result includes the number of perceived targets.

15. A communication device, characterized in that: include: one or more processors; The communication device is configured to execute the method according to any one of claims 1 to 7.

16. A storage medium storing instructions, characterized in that: When the instruction is executed on a communication device, the communication device is caused to execute the method according to any one of claims 1 to 7.

17. A program product, characterized in that include: A computer program, which, when executed by a communication device, causes the communication device to perform the method according to any one of claims 1 to 7.

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