Data processing method, first device, second device, and system

Abstract

The present invention provides a data processing method, a first device, a second device, and a system. The method comprises: determining first data, wherein the first data comprises channel measurement data; reducing the data volume of the first data; and sending the first data with the data volume reduced to a second device. In the present invention, a first device can reduce the data volume of first data and send the first data with the data volume reduced to a second device, thereby reducing the data transmission overhead for device sensing, reducing the complexity of device sensing processing, and improving the availability and reliability of integrated sensing and communication.

Description

Data processing method, first device, second device, and system Technical Field

[0001] This disclosure relates to the field of data processing, and more particularly to data processing methods, a first device, a second device, and a system. Background Technology

[0002] Integrated Sensing and Communication (ISAC) is a crucial research area in wireless communication. ISAC technology enables numerous application services, including detection, localization and tracking, environment reconstruction and target imaging, and gesture and posture recognition.

[0003] Summary of the Invention

[0004] To improve the reliability of integrated communication and sensing, embodiments of this disclosure provide a data processing method, a first device, a second device, and a system.

[0005] According to a first aspect of the present disclosure, a data processing method is provided, the method being executed by a first device, the method comprising:

[0006] Determine the first data; wherein the first data includes channel measurement data;

[0007] Reduce the amount of data in the first set of data;

[0008] Send the first data after the data volume has been reduced to the second device.

[0009] According to a second aspect of the present disclosure, a data processing method is provided, the method being executed by a second device, the method comprising:

[0010] The first data is received after the amount of data sent by the first device has been reduced; wherein, the first data is the data used by the first device to perform device sensing on the third device.

[0011] According to a third aspect of the present disclosure, a first device is provided, comprising:

[0012] The processing module is configured to determine first data; wherein the first data is data used for device sensing of the third device;

[0013] The processing module is also configured to reduce the amount of data in the first data;

[0014] The transceiver module is configured to send the first data, after the data volume has been reduced, to the second device.

[0015] According to a fourth aspect of the present disclosure, a second device is provided, comprising:

[0016] The transceiver module is configured to receive first data after the amount of data sent by the first device has been reduced; wherein the first data includes channel measurement data.

[0017] According to a fifth aspect of the present disclosure, a first device is provided, comprising:

[0018] One or more processors;

[0019] The processor is used to execute the data processing method described in any one of the first aspects.

[0020] According to a sixth aspect of the present disclosure, a second device is provided, comprising:

[0021] One or more processors;

[0022] The processor is used to execute the data processing method described in any one of the second aspects.

[0023] According to a seventh aspect of the present disclosure, a communication system is provided, comprising:

[0024] A first device, the first device being configured to implement the data processing method described in any one of the first aspects;

[0025] The second device is configured to implement the data processing method described in any one of the second aspects.

[0026] According to an eighth aspect of the present disclosure, a storage medium is provided that stores instructions that, when executed on a communication device, cause the communication device to perform a data processing method as described in any one of the first or second aspects.

[0027] According to a ninth aspect of the present disclosure, a computer program is provided that, when run on a computer, causes the computer to perform a data processing method as described in any one of the first or second aspects.

[0028] In this embodiment of the disclosure, the first device can reduce the amount of first data and then send the reduced amount of first data to the second device, thereby reducing the data transmission overhead of device sensing, which helps to reduce the complexity of device sensing processing and improves the availability and reliability of integrated communication and sensing.

[0029] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure. Attached Figure Description

[0030] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0031] Figure 1A is an exemplary schematic diagram of the architecture of a communication system provided according to an embodiment of the present disclosure.

[0032] Figure 1B is an exemplary scenario diagram of six sensing modes provided according to embodiments of the present disclosure.

[0033] Figure 1C is an exemplary schematic diagram of signal types in a communication and sensing channel model provided according to an embodiment of the present disclosure.

[0034] Figure 1D is an exemplary schematic diagram of determining the perception result based on the perception algorithm according to an embodiment of the present disclosure.

[0035] Figure 1E is an exemplary schematic diagram of obtaining perception results based on an artificial intelligence model according to an embodiment of the present disclosure.

[0036] Figure 2A is an exemplary interactive schematic diagram of a data processing method provided according to an embodiment of the present disclosure.

[0037] Figure 2B is an exemplary flowchart illustrating the training process of an AI model provided according to an embodiment of the present disclosure.

[0038] Figure 3A is an exemplary flowchart of a data processing method provided according to an embodiment of the present disclosure.

[0039] Figure 3B is an exemplary flowchart of a data processing method provided according to an embodiment of the present disclosure.

[0040] Figure 4A is an exemplary process diagram of a data processing method provided according to an embodiment of the present disclosure.

[0041] Figure 4B is an exemplary process diagram of a data processing method provided according to an embodiment of the present disclosure.

[0042] Figure 5 is an exemplary interactive schematic diagram of a data processing method provided according to an embodiment of the present disclosure.

[0043] Figure 6A is an exemplary block diagram of a first device provided according to an embodiment of the present disclosure.

[0044] Figure 6B is an exemplary block diagram of a second device provided according to an embodiment of the present disclosure.

[0045] Figure 7A is an exemplary block diagram of a communication device provided according to an embodiment of the present disclosure.

[0046] Figure 7B is an exemplary block diagram of a chip provided according to an embodiment of the present disclosure. Detailed Implementation

[0047] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numerals in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. Rather, they are merely examples of apparatuses and methods consistent with some aspects of the invention as detailed in the appended claims.

[0048] This disclosure provides a data processing method, a first device, a second device, and a system.

[0049] In a first aspect, embodiments of this disclosure provide a data processing method, which is executed by a first device. The method includes: determining first data; wherein the first data includes channel measurement data; reducing the amount of the first data; and sending the reduced first data to a second device.

[0050] In the above embodiments, the first device can perform data reduction processing on the first data, thereby reducing the device-aware data transmission overhead and improving availability.

[0051] In conjunction with some embodiments of the first aspect, in some embodiments, reducing the amount of the first data includes: reducing the amount of the first data based on a first method; wherein the first method includes at least one of the following: an aggregation method; a sampling method; and a truncation method.

[0052] In the above embodiments, the first device can use at least one of the above methods to reduce the amount of first data, thereby reducing the overhead of device-sensing data transmission.

[0053] In conjunction with some embodiments of the first aspect, in some embodiments, reducing the amount of the first data based on the first method includes: the first method includes the aggregation method, selecting m consecutive first values ​​from a total of M first values ​​each time; wherein, the first value is any value of the first data in the first dimension; wherein, M is a positive integer, m is a positive integer, and M is a positive integer multiple of m; determining a second value based on the sum of the m consecutive first values ​​selected each time; wherein, the second value is the value of the first data in the first dimension after the data amount is reduced.

[0054] In the above embodiments, the first device can aggregate multiple data into one data by means of aggregation, thereby reducing the amount of data in the first data, which is simple and highly usable.

[0055] In conjunction with some embodiments of the first aspect, in some embodiments, reducing the amount of the first data based on the first method includes: the first method includes the sampling method, selecting one first value from a total of M first values ​​at intervals of n; wherein, the first value is any value of the first data in the first dimension; wherein, M is a positive integer, n is a positive integer, and M is a positive integer multiple of n; determining a second value based on the first value selected at intervals of n; wherein, the second value is the value of the first data in the first dimension after the data amount is reduced.

[0056] In the above embodiments, the first device can use a sampling method to reduce the amount of data in the first data, which is simple to implement and highly usable.

[0057] In conjunction with some embodiments of the first aspect, in some embodiments, reducing the amount of the first data based on the first method includes: the first method includes the truncation method, selecting p consecutive first values ​​from a total of M first values; wherein, the first value is any value of the first data in the first dimension; wherein, M is a positive integer, p is a positive integer, and p is less than M; and determining p second values ​​based on the p consecutive first values; wherein, the second value is the value of the first data in the first dimension after the data amount is reduced.

[0058] In the above embodiments, the first device can use a interception method to intercept a portion of the first data, thereby reducing the amount of data in the first data, which is simple and highly usable.

[0059] In conjunction with some embodiments of the first aspect, in some embodiments, the method further includes: receiving first indication information sent by the second device; wherein the first indication information is used to indicate the first mode.

[0060] In the above embodiments, the second device can inform the first device of the first method used by the first device, so that the first device and the second device have a consistent understanding of the first method used by the first device, thereby improving the availability and reliability of the integrated communication and sensing.

[0061] In conjunction with some embodiments of the first aspect, in some embodiments, the method further includes: sending second indication information to the second device; wherein the second indication information is used to indicate the first method.

[0062] In the above embodiments, the first device can inform the second device of the first method it uses, so that the first device and the second device have a consistent understanding of the first method used by the first device, thereby improving the availability and reliability of the integrated communication and sensing.

[0063] In conjunction with some embodiments of the first aspect, in some embodiments, determining the first data includes: receiving a second signal; wherein the second signal is determined based on the first signal; wherein the first signal is a signal used to initiate device sensing; and performing channel measurement based on the second signal to obtain the first data.

[0064] In the above embodiments, the first device can obtain the first data in the above manner, which is simple to implement and highly usable.

[0065] Secondly, embodiments of this disclosure provide a data processing method, which is executed by a second device. The method includes: receiving first data after a reduction in the amount of data sent by a first device; wherein the first data includes channel measurement data.

[0066] In the above embodiments, the second device can receive the first data after the amount of data sent by the first device has been reduced, thereby reducing the overhead of device-sensing data transmission.

[0067] In conjunction with some embodiments of the second aspect, in some embodiments, the method further includes: performing device sensing on the third device based on the first data after the data volume has been reduced, and determining the device sensing result.

[0068] In the above embodiments, the second device can perform device sensing on the third device based on the first data after the data volume has been reduced, and determine the device sensing result. This helps to reduce the complexity of device sensing processing and improves the availability and reliability of integrated communication and sensing.

[0069] In conjunction with some embodiments of the second aspect, in some embodiments, the step of performing device perception on the third device based on the first data after the data volume has been reduced, and determining the device perception result, includes: using the first data after the data volume has been reduced as the input value of an artificial intelligence (AI) model, and obtaining the device perception result output by the AI ​​model; wherein, the AI ​​model is an AI model used for device perception.

[0070] In the above embodiments, the second device can obtain the device perception results through an artificial intelligence model, which improves the integrated development of communication, perception and artificial intelligence.

[0071] In conjunction with some embodiments of the second aspect, in some embodiments, the method further includes: sending first indication information to the first device; wherein the first indication information is used to indicate a first mode; wherein the first mode is a processing mode for reducing the amount of data in the first data.

[0072] In conjunction with some embodiments of the second aspect, in some embodiments, the method further includes: receiving second indication information sent by the first device; wherein the second indication information is used to indicate a first mode; wherein the first mode is a processing mode for reducing the amount of data in the first data.

[0073] In conjunction with some embodiments of the second aspect, in some embodiments, the first method includes at least one of the following: an aggregation method; a sampling method; and a truncation method. In a third aspect, embodiments of this disclosure provide a first device, including: a processing module configured to determine first data; wherein the first data includes channel measurement data; the processing module is further configured to reduce the amount of the first data; and a transceiver module configured to send the reduced first data to a second device.

[0074] Fourthly, embodiments of this disclosure provide a second device, including: a transceiver module configured to receive first data after a reduction in the amount of data transmitted by a first device; wherein the first data includes channel measurement data.

[0075] Fifthly, embodiments of this disclosure provide a first device comprising: one or more processors; wherein the processors are configured to perform the data processing method described in any one of the first aspects.

[0076] In a sixth aspect, embodiments of this disclosure provide a second device comprising: one or more processors; wherein the processors are configured to perform the data processing method described in any one of the second aspects.

[0077] In a seventh aspect, embodiments of this disclosure provide a communication system comprising: a first device configured to implement the data processing method described in any one aspect; and a second device configured to implement the data processing method described in any one aspect.

[0078] Eighthly, embodiments of this disclosure provide a storage medium storing instructions that, when executed on a communication device, cause the communication device to perform a data processing method as described in any one of the first or second aspects.

[0079] In a ninth aspect, embodiments of this disclosure provide a computer program that, when run on a computer, causes the computer to perform the data processing method as described in any one of the first or second aspects.

[0080] In a tenth aspect, embodiments of this disclosure provide a program product that, when executed by a communication device, causes the communication device to perform the method described in the optional implementation of the first aspect.

[0081] Eleventhly, embodiments of this disclosure provide a chip or chip system. The chip or chip system includes processing circuitry configured to perform the method described in the optional implementation of the first aspect above.

[0082] It is understood that the aforementioned first device, communication system, storage medium, program product, computer program, chip, or chip system are all used to perform the methods proposed in the embodiments of this disclosure. Therefore, the beneficial effects that can be achieved can be referred to the beneficial effects in the corresponding methods, and will not be repeated here.

[0083] This disclosure provides a data processing method, a first device, a communication system, and a storage medium. In some embodiments, the terms "data processing method" can be used interchangeably with "information processing method," "device sensing method," and "communication method," and the terms "data processing device" can be used interchangeably with "information processing device," "device sensing device," and "communication device," and the terms "information processing system," "device sensing system," and "communication system" can be used interchangeably.

[0084] This disclosure is not exhaustive, but merely illustrative of some embodiments, and is not intended to limit the scope of protection of this disclosure. Unless otherwise specified, each step in a particular 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 particular embodiment can also be implemented as an independent embodiment, and the order of the steps in a particular embodiment can be arbitrarily interchanged. Furthermore, the optional implementation methods in a particular embodiment can be arbitrarily combined; moreover, the embodiments can be arbitrarily combined, for example, some or all steps of different embodiments can be arbitrarily combined, and a particular embodiment can be arbitrarily combined with the optional implementation methods of other embodiments.

[0085] In each of the disclosed embodiments, unless otherwise specified or in case of logical conflict, the terminology and / or descriptions of the embodiments are consistent and can be referenced by each other. Technical features in different embodiments can be combined to form new embodiments based on their inherent logical relationships.

[0086] The terminology used in the embodiments of this disclosure is for the purpose of describing particular embodiments only and is not intended to limit the scope of this disclosure.

[0087] In this embodiment of the disclosure, unless otherwise stated, elements expressed in the singular form, such as "a," "an," "the," "the," "the," "the," "the," "the," "this," etc., can mean "one and only one," or "one or more," "at least one," etc. For example, when using articles such as "a," "an," "the," etc. in translation, the noun following the article can be understood as either a singular expression or a plural expression.

[0088] In the embodiments disclosed herein, "multiple" refers to two or more.

[0089] In some embodiments, the terms “at least one of”, “one or more”, “a plurality of”, “multiple”, etc., may be used interchangeably.

[0090] In some embodiments, the notation "at least one of A and B", "A and / or B", "A in one case, B in another", "in response to one case A, in response to another case B", etc., may include the following technical solutions depending on the situation: in some embodiments, A (execute A regardless of B); in some embodiments, B (execute B regardless of A); in some embodiments, execution is selected from A and B (A and B are selectively executed); in some embodiments, A and B (both A and B are executed). The same applies when there are more branches such as A, B, C, etc.

[0091] In some embodiments, the notation "A or B" may include the following technical solutions, depending on the situation: in some embodiments, A (execution of A regardless of B); in some embodiments, B (execution of B regardless of A); in some embodiments, execution is selected from A and B (A and B are selectively executed). The same applies when there are more branches such as A, B, C, etc.

[0092] The prefixes "first," "second," etc., used in the embodiments of this disclosure are merely for distinguishing different descriptive objects and do not impose restrictions on the position, order, priority, quantity, or content of the descriptive objects. The description of the descriptive objects is found in the claims or the context of the embodiments, and the use of prefixes should not constitute unnecessary restrictions. For example, if the descriptive object is a "field," the ordinal numbers preceding "field" in "first field" and "second field" do not restrict the position or order of the "fields." "First" and "second" do not restrict whether the "fields" they modify are in the same message, nor do they restrict the order of "first field" and "second field." Similarly, if the descriptive object is a "level," the ordinal numbers preceding "level" in "first level" and "second level" do not restrict the priority between "levels." Furthermore, the number of descriptive objects is not limited by ordinal numbers and can be one or more. For example, in "first device," the number of "devices" can be one or more. Furthermore, the objects modified by different prefixes can be the same or different. For example, if the object being described is "device", then "first device" and "second device" can be the same device or different devices, and their types can be the same or different. Similarly, if the object being described is "information", then "first information" and "second information" can be the same information or different information, and their content can be the same or different.

[0093] In some embodiments, “including A,” “containing A,” “for indicating A,” and “carrying A” can be interpreted as directly carrying A or indirectly indicating A.

[0094] 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”, “circuit”, “network element”, “node”, “function”, “unit”, “section”, “system”, “network”, “chip”, “chip system”, “entity”, and “subject” can be used interchangeably.

[0095] In some embodiments, "device" may be interpreted as "network" or "terminal".

[0096] In some embodiments, "network" can be interpreted as devices included in a network (e.g., access network devices, core network devices, etc.).

[0097] 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," and "bandwidth part (BWP)" can be used interchangeably.

[0098] 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", and "client" can be used interchangeably.

[0099] In some embodiments, access network devices, core network devices, or network devices can be replaced by terminals. For example, embodiments of this disclosure can also be applied to structures where communication between access network devices, core network devices, or network devices and terminals is replaced by communication between multiple terminals (e.g., device-to-device (D2D), vehicle-to-everything (V2X), etc.). In this case, the structure can also be configured such that the terminal has all or part of the functions of the access network device. Furthermore, terms such as "uplink" and "downlink" can be replaced with terms corresponding to communication between terminals (e.g., "sidelink"). For example, uplink channel, downlink channel, etc., can be replaced with sidelink channel, and uplink link, downlink, etc., can be replaced with sidelink link.

[0100] 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, core network device, or network device may also be configured to have all or some of the functions of the terminal.

[0101] In some embodiments, the acquisition of data, information, etc., may comply with the laws and regulations of the country where the location is situated.

[0102] In some embodiments, data, information, etc., may be obtained with the user's consent.

[0103] Furthermore, each element, each row, or each column in the table of this disclosure can be implemented as an independent embodiment, and any combination of any element, any row, or any column can also be implemented as an independent embodiment.

[0104] Figure 1A is a schematic diagram of the architecture of a communication system according to an embodiment of the present disclosure.

[0105] As shown in Figure 1A, the communication system 100 includes a first device 101, a second device 102, a third device 103, and a fourth device 104.

[0106] In some embodiments, the first device 101 may be a receiving device for sensing signals.

[0107] In some embodiments, the first device 101 may obtain first data after performing channel measurements based on the received signal.

[0108] For example, the name of the received signal is not limited and can be interchanged with the second signal, the propagation signal, etc.

[0109] In some embodiments, the first data may include channel measurement data for device awareness. Exemplarily, the first data may include, but is not limited to, channel state information (CSI).

[0110] For example, the name of the first data is not limited and can be interchanged with channel state information, device sensing data, channel measurement data, etc.

[0111] In some embodiments, the first device 101 may be a terminal or a network device, such as an access network device or a core network device. In some embodiments, the terminal includes, but is not limited to, at least one of the following: mobile phone, wearable device, Internet of Things device, car with communication function, smart car, tablet computer, computer with wireless transceiver function, virtual reality (VR) terminal device, augmented reality (AR) terminal device, wireless terminal device in industrial control, wireless terminal device in self-driving, wireless terminal device in remote medical surgery, wireless terminal device in smart grid, wireless terminal device in transportation safety, wireless terminal device in smart city, and wireless terminal device in smart home.

[0112] In some embodiments, the access network device is, for example, a node or device that connects a terminal to a wireless network. The access network device may include, but is not limited to, at least one of the following in a 5G communication system: evolved Node B (eNB), next-generation eNB (ng-eNB), next-generation Node B (gNB), node B (NB), home node B (HNB), home evolved node B (HeNB), radio backhaul device, radio network controller (RNC), base station controller (BSC), base transceiver station (BTS), base band unit (BBU), mobile switching center, base station in a 6G communication system, open RAN, cloud RAN, base station in other communication systems, and access node in a Wi-Fi system.

[0113] In some embodiments, the first device 101 may be an autonomous driving device, an Internet of Things (IoT) device, an environmental IoT device, etc. Autonomous driving devices include, but are not limited to, driverless cars and drones. IoT devices include, but are not limited to, sensors using radio frequency identification (RFID), scanners, smart home devices, and robots. Environmental IoT devices include, but are not limited to, IoT devices that can obtain energy from the outside environment and be charged.

[0114] In some embodiments, the technical solutions of this disclosure can be applied to the Open RAN architecture. In this case, the interfaces between or within access network devices involved in the embodiments of this disclosure can be transformed into internal interfaces of Open RAN. The processes and information interactions between these internal interfaces can be implemented by software or programs.

[0115] In some embodiments, the access network device may be composed of a central unit (CU) and a distributed unit (DU). The CU may also be called a control unit. The CU-DU structure can separate the protocol layer of the access network device. Some of the protocol layer functions are centrally controlled by the CU, while the remaining part or all of the protocol layer functions are distributed in the DU and centrally controlled by the CU. However, this is not the only possibility.

[0116] In some embodiments, the core network equipment can be a single device, including a first network element, a second network element, etc., or it can be multiple devices or a group of devices, each including all or part of the first network element, the second network element, etc. Network elements can be virtual or physical. The core network includes, for example, at least one of the Evolved Packet Core (EPC), 5G Core Network (5GCN), and Next Generation Core (NGC).

[0117] In some embodiments, the core network device is, for example, a Location Management Function (LMF).

[0118] In some embodiments, core network devices are used for device awareness, and the names are not limited to device awareness network elements or functions.

[0119] In some embodiments, the second device 102 may be a device for performing device sensing and determining the results of device sensing.

[0120] In some embodiments, an artificial intelligence (AI) model may be deployed on the second device 102 to perform device perception.

[0121] In some embodiments, the second device 102 may be a terminal or network device, or an autonomous driving device, an IoT device, an environmental IoT device, etc. For example, the second device 102 may be a network device, such as an LMF (Local Multi-Functional Function) of a core network device.

[0122] In some embodiments, the third device 103 may be a device that needs to be sensed.

[0123] In some embodiments, the third device 103 may be a sensing target device. "Sensing target" may refer to a device or terminal that is expected to determine the sensing results of its device.

[0124] In some embodiments, the third device 103 may be a terminal, a network device, an autonomous driving device, an IoT device, an environmental IoT device, etc. Specific details will not be elaborated further here.

[0125] In some embodiments, the fourth device 104 may be a device that transmits sensing signals.

[0126] In some embodiments, the sensing signal (i.e., the first signal) is a signal capable of determining the device sensing result of the third device 103.

[0127] For example, the sensing signal (i.e., the first signal) sent by the fourth device 104 is received by the first device 101 after propagation through reflection, refraction, and / or scattering by the third device 103 or other objects. The signal received by the first device 101 is the second signal. The first device 101 can determine first data based on the second signal and send it to the second device 102. The second device 102 can then sense the distance (distance of the third device 103 relative to the specified object), speed (moving speed of the third device 103), and angle (horizontal angle and / or zenith angle of the third device 103 relative to the specified object) of the third device 103 based on the first data.

[0128] For example, the horizontal angle refers to the angle obtained by projecting the lines connecting the reference point, such as the Earth's center, or other specified location points to the third device 103 and the specified object onto the horizontal plane. The zenith angle refers to the angle of the line connecting the third device 103 and the specified object relative to the ground normal.

[0129] For example, if the first signal is sent by the third device 103 itself, and the third device 103 receives the second signal, the specified object can be other objects that propagate the first signal through the above-mentioned propagation methods such as emission, refraction and / or scattering, such as network devices, vehicles, IoT devices, environmental IoT devices, etc.

[0130] For example, if the first signal is sent by the fourth device 104 (a device different from the third device 103), the designated object may refer to the third device 103.

[0131] Specifically, determining the perception results of the third device can refer to determining the specific values ​​of the three parameters of the third device: distance, speed, and angle.

[0132] For example, the name of the sensing signal is not limited and can be interchanged with the first signal, the transmitted signal, etc.

[0133] In some embodiments, the first device 101, the fourth device 104, and the third device 103 may be the same device. For example, after the third device 103 sends a first signal, the first signal propagates through reflection, refraction, and / or scattering by other devices, and the third device 103 receives a second signal. The third device 103 then performs channel measurement based on the second signal to obtain first data.

[0134] Furthermore, if the third device 103 and the second device 102 are the same device, the third device 103 directly performs device perception on itself based on the first data and determines the device perception result. The device perception result may include, but is not limited to, the values ​​of parameters such as distance, speed and angle of the third device 103.

[0135] If the third device 103 and the second device 102 are different devices, the third device 103 sends the first data to the second device 102, and the second device 102 performs device perception on the third device 103 based on the first data to determine the device perception result. The device perception result may include, but is not limited to, the values ​​of parameters such as distance, speed and angle of the third device 103.

[0136] In some embodiments, the first device 101, the fourth device 104, and the third device 103 can be different devices. For example, after the fourth device 104 sends a first signal, the first signal is propagated through the third device 103 (i.e., the sensing target) by reflection, refraction, and / or scattering, and the first device 101 receives the second signal. The first device 101 performs channel measurement based on the second signal to obtain the first data.

[0137] Furthermore, if the first device 101 and the second device 102 are the same device, the first device 101 directly performs device perception on the third device 103 based on the first data and determines the device perception result. The device perception result may include, but is not limited to, the values ​​of parameters such as distance, speed and angle of the third device 103.

[0138] If the first device 101 and the second device 102 are different devices, the first device 101 sends the first data to the second device 102, and the second device 102 performs device perception on the third device 103 based on the first data to determine the device perception result. The device perception result may include, but is not limited to, the values ​​of parameters such as distance, speed and angle of the third device 103.

[0139] It is understood that the communication system described in this disclosure is for the purpose of more clearly illustrating the technical solutions of this disclosure, and does not constitute a limitation on the technical solutions proposed in this disclosure. As those skilled in the art will know, with the evolution of system architecture and the emergence of new business scenarios, the technical solutions proposed in this disclosure are also applicable to similar technical problems.

[0140] The following embodiments of this disclosure can be applied to the communication system 100 shown in FIG1A, or to some of the main bodies, but are not limited thereto. The main bodies shown in FIG1A are illustrative. The communication system may include all or some of the main bodies in FIG1A, or it may include other main bodies outside of FIG1A. The number and form of each main body are arbitrary. Each main body may be physical or virtual. The connection relationship between the main bodies is illustrative. The main bodies may not be connected or may be connected. The connection can be in any way, it can be a direct connection or an indirect connection, it can be a wired connection or a wireless connection.

[0141] The embodiments disclosed herein 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), CDMA2000, Ultra Mobile Broadband (UMB), IEEE 802.11 (Wi-Fi), IEEE 802.16 (WiMAX), and IEEE 802.20, Ultra-Wideband (UWB), Bluetooth (a 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, systems utilizing other communication methods, and next-generation systems built upon them, etc. Furthermore, multiple systems can be combined (e.g., a combination of LTE or LTE-A with 5G).

[0142] In this disclosed embodiment, ISAC is one of the most important research directions in wireless communication and a key enabling technology in mobile communication. Sensor-integrated communication technology can enable sensing services based on existing mobile communication infrastructure, fully leverage the advantages of mobile communication networks to meet the sensing needs of various service scenarios, and improve communication performance by relying on sensing capabilities. This, in turn, helps to realize numerous application services such as detection, localization and tracking, environmental reconstruction and target imaging, gesture and posture recognition, etc.

[0143] In some embodiments, for integrated communication and sensing applications, relatively complete channel modeling of the sensing system has been carried out based on relevant channel modeling standards, providing strong support for the evaluation of sensing technology solutions. For scenarios such as highways, various estimation algorithms for parameters such as target speed, distance, and angle have been proposed and designed, including but not limited to the Multiple Signal Classification (MUSIC) algorithm and the Estimation of Signal Parameters using Rotational Invariance Techniques (ESPRIT) algorithm. These algorithms can achieve good target perception results in integrated communication and sensing simulation scenarios.

[0144] The application of AI technology in the field of wireless communication is also one of the important research topics. AI technology has been widely used 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. It can be widely used and studied in problems such as high-precision terminal positioning, channel state information feedback, and beam management.

[0145] The integration of communication and sensing based on AI technology is a potential application in future wireless communication systems. By leveraging the powerful computing capabilities of graphics processing units (GPUs), AI sensing models can be trained and acquired based on sample datasets. This can optimize aspects such as sensing accuracy, computational complexity, and applicability, promoting the deep application of communication and sensing integration technology, supporting more intelligent business applications and services, and thus contributing to the development of the integration of communication, sensing, algorithms, and AI.

[0146] In this embodiment, the following six sensing modes are provided, as shown in Figure 1B. Mono-static indicates self-transmission and self-reception, meaning the sending and receiving ends are the same; Bi-static indicates cross-station transmission and reception, meaning the sending and receiving ends are different.

[0147] Sensing Mode 1 is a self-transmitting and self-receiving mode where both the transmitter and receiver are User Equipment (UE), such as a user (person). The transmitter sends a sensing signal to another UE (e.g., a car) and receives the reflected echo from the other UE (car), thereby sensing the user's (person's) device sensing result.

[0148] Sensing Mode 2 is a self-transmitting and self-receiving mode where both the transmitter and receiver are User Equipment (UE), such as a user (person). The transmitter sends a sensing signal to the access network device, such as a gNB, and receives the reflected echo from the gNB to perceive the sensing results of itself (the person).

[0149] Sensing mode 3, cross-site transceiver, the transmitting end is gNB and the receiving end is UE (vehicle). For example, gNB sends a sensing signal and UE (vehicle) receives the signal forwarded by the user (person). UE (vehicle) calculates the channel matrix based on the received signal to sense the target in the environment, such as the user (person), and the device sensing result.

[0150] Sensing mode 4, cross-site transceiver, the transmitting end is UE (vehicle) and the receiving end is gNB. For example, the UE (vehicle) sends a sensing signal and the gNB receives the signal forwarded by the user (person). The gNB calculates the channel matrix based on the received signal to sense the target in the environment, such as the user (person), and the device sensing result.

[0151] Sensing mode 5, cross-site transmission and reception, with gNB#1 as the transmitter and gNB#2 as the receiver. For example, gNB#1 transmits a sensing signal, and gNB#2 receives the signal forwarded by the user (person). gNB#2 calculates the channel matrix based on the received signal to sense the device sensing results of targets in the environment, such as users (persons).

[0152] Sensing mode 6, cross-site transceiver, the transmitting end is UE#1 (vehicle #1) and the receiving end is UE#2 (vehicle #2). For example, UE#1 (vehicle #1) sends a sensing signal and UE#2 (vehicle #2) receives the signal forwarded by the user (person). UE#2 (vehicle #2) calculates the channel matrix based on the received signal to sense the target in the environment, such as the user (person), and the device sensing result.

[0153] It is understood that the aforementioned sensing target, namely the third device 103, can include: drones, people in indoor and outdoor scenarios, cars in outdoor scenarios such as highways, automated guided vehicles in indoor scenarios such as factories, and targets that create danger on roads or railways. Sensor integration mainly involves the receiver calculating and acquiring information such as the distance, speed, and angle of the sensing target in the environment based on the received signal. The sensing target can be the receiver itself or other objects.

[0154] In some embodiments, taking self-transmitting and self-receiving as an example, the fourth device 104, the first device 101, and the third device 103 are the same device, assumed to be vehicles. Other objects (i.e., devices that propagate sensing signals through reflection, scattering, and / or refraction, also referred to as "sensing signal reflection ends") are assumed to be gNBs, as shown in Figure 1C. In this scenario, there are obstructions between the vehicle and the gNB, which may be other UEs or debris. Four types of signals can be considered in the sensing channel model:

[0155] Line of sight (LOS) path; Non-line of sight (NLOS) path; Clutter LOS path; Clutter NLOS path.

[0156] For example, in addition to the four types of signals mentioned above, the impact of channel noise usually needs to be considered.

[0157] For example, as shown in Figure 1C, the above four types of signals are described below:

[0158] The path of perception LOS is the path between the sensing signal reflector (e.g., gNB) and the sensing target (e.g., vehicle). The path between the sensing target (e.g., vehicle) and the sensing signal receiver (e.g., gNB) is also a path of perception LOS (e.g., solid line path #1 between gNB and vehicle in Figure 1C).

[0159] Sensing NLOS path: The path between the sensing signal reflector (e.g., gNB) and the sensing target (e.g., vehicle) that needs to pass through clutter (e.g., clutter #1) is the sensing NLOS path. The path between the sensing target (e.g., vehicle) and the sensing signal receiver (e.g., gNB) that needs to pass through clutter is also the sensing NLOS path (e.g., solid line path #2 in Figure 1C).

[0160] Clutter LOS path: The path between the sensing signal reflector (e.g., gNB) and the scattering cluster in the environment (e.g., clutter #2) is the clutter LOS path. The path between the scattering cluster in the environment (e.g., clutter #2) and the sensing signal receiver (e.g., vehicle) is also the clutter LOS path (e.g., the dashed path #1 in Figure 1C).

[0161] Clutter NLOS path: The path between the sensing signal reflector (e.g., gNB) and the scattering cluster in the environment (e.g., clutter #2) that still needs to pass through clutter (e.g., clutter #3) is the sensing NLOS path. The path between the scattering cluster in the environment (e.g., clutter #3) and the sensing signal receiver (e.g., vehicle) that still needs to pass through clutter (e.g., clutter #2) is also the sensing NLOS path (e.g., the dashed path #2 in Figure 1C).

[0162] In this embodiment, the main information for device sensing is the Channel State Information (CSI) matrix H of the receiving end. This CSI matrix H is a superposition of the sensing target channel, clutter channel, and noise, reflecting the channel environment in which the sensing signal resides and the state information of the sensing target within that environment. The dimensions of the CSI matrix H are related to the specific parameter settings in the actual sensing scenario; the information in the data on different dimensions can reflect different types of state information of the sensing target. This embodiment presents one possible scenario:

[0163] The CSI matrix H typically includes three dimensions: subcarriers, Orthogonal Frequency Division Multiplexing (OFDM) symbols, and receiver antenna ports.

[0164] Specifically, the CSI matrix H can be an L×S×P complex matrix, where L is the number of OFDM symbols, S is the number of subcarriers, and P is the number of receive antenna ports. The element H at the position of the l-th OFDM symbol, the s-th subcarrier, and the p-th receive antenna port in the CSI matrix H is... l,s,p For a complex number Z l,s,p For example, as shown in Equation 1, it represents the influence of the signal's amplitude and phase on its propagation from the transmitting antenna to the receiving antenna: H l,s,p =Z l,s,p =a l,s,p +i×b l,s,p Formula 1

[0165] Among them, a l,s,p b l,s,p Used to represent the complex number Z l,s,p The values ​​of the real part and the imaginary part. Where, a l,s,p b l,s,p It can be a real number.

[0166] According to the received signal model, the phase shifts in the three dimensions of the channel matrix are independent of each other, and different types of sensing results for the target can be obtained based on the phase changes in the three dimensions. For example, the distance value of the sensing device can be estimated based on the phase difference caused by the time delay between subcarriers, the velocity value of the sensing device can be estimated based on the phase difference caused by the Doppler effect between OFDM symbols, and the angle value of the sensing device can be estimated based on the phase changes between the received signals of multiple antenna ports. Sensing algorithms such as 3D-MUSIC (3-Dimensional Multiple Signal Classification) and 3D-ESPRIT (3-Dimensional Estimation of Signal Parameters using Rotational Invariance Techniques) can obtain the sensing results of the distance, velocity, and angle of the sensing device based on the channel state information matrix, as shown in Figure 1D.

[0167] In some embodiments, the scene applicability of the perception algorithm is affected by factors such as clutter and noise in the scene, and its performance will be significantly reduced. In the case of multiple targets, the direct path of non-perceived targets will have a certain impact on the perception results of perceived targets, and when the perceived target is far away, the impact of noise will be greater.

[0168] In some embodiments, the computational complexity of the perception algorithm is high. In order to obtain a high-accuracy perception result, the perception algorithm is complex and the application process of the perception algorithm is complicated.

[0169] In some embodiments, the accuracy needs to be improved in the case of multi-target perception. In actual integrated sensing application scenarios, existing multi-target perception algorithms usually have certain errors when estimating the number of sensing targets, resulting in large errors in the algorithm estimation results.

[0170] Leveraging the strong learning and modeling capabilities of neural network models, an AI perception model can be trained and acquired based on a sample dataset. This model can estimate the number of perceived targets and perceive information such as distance, velocity, and angle for each target in multi-target perception scenarios. For example, by directly using the channel state information matrix H as input data to the AI ​​perception model, a model can be trained that can simultaneously output the distance to the targets. speed angle The three types of perception results are shown in Figure 1E.

[0171] AI-based neural network models can effectively overcome the main problems of traditional target perception algorithms and achieve higher perception accuracy.

[0172] AI-based target perception models may be deployed on user equipment, including mobile terminals and vehicles, or on the network side specifically for implementing sensing functions. The equipment acquiring channel measurement data for AI perception may differ from the equipment performing the AI ​​perception model inference application. When the AI ​​perception model is deployed on user equipment, the user equipment can acquire channel measurement data (e.g., receiving the channel matrix) and directly input it into the AI ​​perception model to obtain the perception result. However, when the AI ​​perception model is deployed on the network-side sensing function unit, the sensing function unit first needs to acquire channel measurement data reported by the user equipment or base station for perception, and then input the measurement data into the AI ​​perception model to obtain the perception result.

[0173] Because AI neural network models have high computational requirements, they often need to be deployed on network-side functional units with abundant computing resources. In this case, reporting channel measurement data for sensing will result in significant channel resource consumption overhead, and the computational complexity of the AI ​​model will also significantly affect the energy consumption and latency of the model application.

[0174] In order to reduce the overhead of device sensing data transmission, reduce the complexity of device sensing processing, and improve the availability and reliability of integrated communication and sensing, this disclosure provides the following data processing method, first device, second device, and system.

[0175] Figure 2A is an interactive schematic diagram of a data processing method according to an embodiment of the present disclosure. As shown in Figure 2A, the embodiments of the present disclosure relate to a data processing method, which includes:

[0176] In step S2101, the fourth device 104 sends the first signal.

[0177] In some embodiments, the first signal may be a signal used to initiate device sensing.

[0178] In some embodiments, the name of the first signal is not limited and can be interchanged with sensing signal, sensing information, etc.

[0179] In some embodiments, the first signal may be, for example, a probe reference signal, a random sequence, etc.

[0180] In some embodiments, the fourth device 104 may be a device that sends the first signal, such as a terminal, vehicle, autonomous driving device, Internet of Things (IoT) device, Ambient Internet of Things (Ambient IoT) device, etc.

[0181] In some embodiments, the first device 101 may be a device that receives the second signal, such as an access network device, a terminal, a vehicle, an autonomous driving device, an IoT device, an Ambient IoT device, etc.

[0182] In some embodiments, the second signal is determined based on the first signal.

[0183] In some embodiments, the first device 101 and the fourth device 104 may be the same device, corresponding to the self-transmitting and self-receiving mode 1 or mode 2 described above. In this case, the first device 101 sends a first signal, which is then propagated through reflection, refraction and / or scattering by other devices such as network devices, vehicles, IoT devices, etc., and then the first device 101 receives the second signal.

[0184] In some embodiments, the first device 101 may be a different device from the fourth device 104, corresponding to any of the modes 3 to 6 of the above-mentioned inter-station transceiver. In this case, the fourth device 104 sends a first signal, which is propagated through the third device 103 by reflection, refraction and / or scattering, and then received by the first device 101 as a second signal.

[0185] In some embodiments, the third device 103 is a device that needs to be sensed.

[0186] In some embodiments, the third device 103 may be, for example, a terminal, a vehicle, an autonomous driving device, an IoT device, an Ambient IoT device, etc.

[0187] In some embodiments, this disclosure does not limit whether the first device 101 and the fourth device 104 are the same device.

[0188] In step S2102, the first device 101 determines the first data.

[0189] In some embodiments, the name of the second signal is not limited and can be interchanged with received signal, received information, etc.

[0190] In some embodiments, the first device 101 performs channel measurements based on the second signal to obtain first data.

[0191] In some embodiments, the first data includes channel measurement data.

[0192] In some embodiments, the first data may include, but is not limited to, the CSI matrix H.

[0193] In some embodiments, the first data may be data used to determine the device perception results of the third device 103.

[0194] In some embodiments, the name of the first data is not limited and can be interchanged with device sensing data, device sensing measurement data, etc.

[0195] In step S2103, the second device 102 sends the first instruction information to the first device 101.

[0196] In some embodiments, the second device 102 may be a device for performing device sensing and determining the results of device sensing.

[0197] In some embodiments, the first device 101 receives first instruction information.

[0198] In some embodiments, the first indication information may be used to indicate a first manner.

[0199] In some embodiments, the second device 102 may send a first method that the first device 101 is expected to use through a first instruction message.

[0200] In some embodiments, the first approach may be a processing method for reducing the amount of data in the first data.

[0201] In one example, the first approach may include, but is not limited to, at least one of the following:

[0202] Aggregation method;

[0203] Sampling method;

[0204] Method of interception.

[0205] For example, aggregation can refer to a processing method that combines multiple data sets into one data set, thereby reducing the amount of data.

[0206] For example, the sum of multiple data points can be determined as the data after the data volume has been reduced.

[0207] For example, sampling method can refer to a processing method that randomly selects one data point from multiple data points, thereby reducing the amount of data.

[0208] For example, a truncation method can refer to a processing method that extracts a portion of continuous data from multiple datasets, thereby reducing the amount of data.

[0209] In one example, the first approach could include two of the above approaches, such as: first performing aggregation, then sampling all the aggregated data; or first sampling, then aggregating the sampled data; or first performing aggregation, then truncating all the aggregated data; or first truncating, then aggregating the truncated data; or first sampling, then truncating the sampled data; or first truncating the data, then sampling the truncated data.

[0210] In one example, the first approach may include the three approaches mentioned above, such as first performing aggregation, then sampling all the aggregated data, and then truncating the sampled data.

[0211] The above are merely illustrative examples, and this disclosure does not limit the combination of the above methods.

[0212] In some embodiments, the first indication method can be indicated by two bit values. For example, if the first method is an aggregation method, the bit value can be "00"; if the first method is a sampling method, the bit value can be "01"; and if the first method is a truncation method, the bit value can be "10".

[0213] In some embodiments, the first indication method can be indicated by more bit values. For example, the first method is a combination of aggregation and sampling methods. The bit value "011" can be used to indicate aggregation before sampling, and the bit value "100" can be used to indicate sampling before aggregation.

[0214] In some embodiments, the first indication method may be a Boolean method, for example, setting the information unit corresponding to the aggregation method to "true" to indicate that the first method is an aggregation method.

[0215] In some embodiments, the second device 102 may determine the first manner indicated by the first instruction information based on the capabilities of its own deployed AI model for device awareness.

[0216] For example, if the AI ​​model used for device perception supports extracting a portion of continuous data to infer the device perception result, then the first method indicated by the first indication information can be the extraction method.

[0217] The above is merely an illustrative example, and this disclosure does not limit the scheme of how the second device 102 indicates the first mode.

[0218] In step S2104, the first device 101 sends a second instruction message to the second device 102.

[0219] In some embodiments, the second device 102 receives second instruction information.

[0220] In some embodiments, the second indication information may be used to indicate the first manner.

[0221] In some embodiments, the first device 101 may send the first method it uses to the second device 102 through the second instruction information.

[0222] In some embodiments, the specific content of the first method and the indication method of the second indication information are similar to those in the foregoing embodiments, and will not be repeated here.

[0223] In some embodiments, the first device 101 may assume that multiple AI models corresponding to the first method are deployed on the second device 102. In this case, the first device 101 can send a first method it supports to the second device 102 through the second instruction information. The second device 102 can then select the AI ​​model corresponding to the first method indicated by the second instruction information based on the second instruction information, and use the selected AI model to perform device perception on the third device 103.

[0224] In some embodiments, the first device 101 and the second device 102 are different devices.

[0225] In step S2105, the first device 101 reduces the amount of data in the first data.

[0226] In some embodiments, the first device 101 reduces the amount of data in the first data based on a first method.

[0227] In some embodiments, where the first mode includes the aggregation mode, the first device 101 may select m consecutive first values ​​from a total of M first values ​​each time. Further, a second value is determined based on the sum of the m consecutive first values ​​selected each time.

[0228] Wherein, the first value is any value of the first data in the first dimension.

[0229] The second value is the value of the first data in the first dimension after the data volume is reduced.

[0230] Where M is a positive integer. Where m is a positive integer. Where M is a positive integer multiple of m.

[0231] The value of M can be different when the first dimension is different.

[0232] In one example, the first data includes three dimensions, such as symbol, subcarrier, and antenna port (corresponding to the time domain, frequency domain, and spatial domain, respectively). Assuming the first dimension is symbol, the value of M can be 10; the first dimension is subcarrier, the value of M can be 6; and the first dimension is antenna port, the value of M can be 8.

[0233] For example, assuming the first dimension is the symbol, M is 10, and m can be 2, the first device 101 can select two consecutive first values ​​from the total of 10 first values ​​corresponding to the symbol, calculate the sum of the two consecutive first values, and obtain a second value. Finally, a total of M / m second values ​​are obtained, for example, 5 second values, where the second values ​​are (M... 11 +M 12 ), (M 13 +M 14 ), (M15 +M 16 ), (M 17 +M 18 ), (M 19 +M 110 ).

[0234] The above is merely an illustrative example. The first method includes the aggregation method, and the scheme of determining a second value based on m first values ​​should all be protected by this disclosure.

[0235] In some embodiments, where the first method includes the sampling method, the first device 101 may select one of the first values ​​from a total of M first values, at intervals of n first values ​​each time. Further, a second value is determined based on the first value selected at intervals of n first values ​​each time.

[0236] Wherein, the first value is any value of the first data in the first dimension.

[0237] The second value is the value of the first data in the first dimension after the data volume is reduced.

[0238] Where M is a positive integer. Where n is a positive integer. Where M is a positive integer multiple of n.

[0239] The value of M can be different when the first dimension is different.

[0240] For example, assuming the first dimension is a subcarrier, M is 6, and n can be 3, the first device 101 can select one first value from a total of 6 first values ​​corresponding to the symbol, with each selection occurring at intervals of 3 first values. This selected first value is then used as the second value, resulting in a total of M / n second values. For example, if two second values ​​are ultimately obtained, the second values ​​are M... 21 M 24 .

[0241] The above is merely an illustrative example. The first method includes sampling methods, and schemes that determine a second value based on one of every n first values ​​should all fall under the protection schemes of this disclosure.

[0242] In some embodiments, where the first method includes the interception method, the first device 101 may select p consecutive first values ​​from a total of M first values. Further, based on the p consecutive first values, p second values ​​are determined.

[0243] Wherein, the first value is any value of the first data in the first dimension.

[0244] The second value is the value of the first data in the first dimension after the data volume is reduced.

[0245] Where M is a positive integer. Where p is a positive integer. Where p is less than M.

[0246] The value of M can be different when the first dimension is different.

[0247] In one example, p consecutive first values ​​can be selected starting from the s-th first value. S can be a positive integer, and 1 ≤ s ≤ (Mp).

[0248] For example, assuming the first dimension is the antenna port, M is 8, and p can be 5, the first device 101 can select 5 consecutive first values ​​M from a total of 8 first values ​​corresponding to the symbol. 31 To M 38 This yields five second values, for example, the second value is M. 32 To M 36 .

[0249] The above is merely an illustrative example. The first method includes the interception method, and the scheme of determining p second values ​​based on p consecutive first values ​​should all be considered as protected schemes of this disclosure.

[0250] In some embodiments, the first method may include a combination of the two methods described above. In this case, the first device 101 may execute one processing method first, and then execute the other processing method, according to the execution order of the two methods. The execution order of the two methods may be determined by negotiation between the first device 101 and the second device 102, for example, by indication through the first indication information and / or the second indication information described above, or it may be determined by a predefined method, such as by an agreement on the execution order.

[0251] In some embodiments, the first method may include a combination of the three methods described above. In this case, the first device 101 may execute the three methods sequentially according to their execution order. The execution order may be determined by negotiation between the first device 101 and the second device 102, or it may be determined by a predefined method.

[0252] In step S2106, the first device 101 sends the first data after the data volume has been reduced to the second device 102.

[0253] In some embodiments, the second device 102 receives the first data after the data volume has been reduced.

[0254] In step S2107, the second device 102 performs device sensing on the third device 103 and determines the device sensing result.

[0255] In some embodiments, an AI model is deployed on the second device 102. This AI model is used for device perception. The second device 102 uses the first data, after reducing the amount of data, as the input value of the AI ​​model and obtains the device perception result output by the AI ​​model.

[0256] The training process of this AI model will be described in subsequent embodiments and will not be described here.

[0257] In some embodiments, the device perception result is the result obtained by performing device perception on the third device 103.

[0258] In some embodiments, the device perception result includes at least one of the distance, speed, and angle corresponding to the third device 103.

[0259] In some embodiments, step S2107 is an optional step. For example, when the second device 102 performs other operations based on the reduced amount of first data, including but not limited to storage, sending to other devices, step S2107 may not be executed.

[0260] In some embodiments, the data processing method provided in this disclosure can be applied to other scenarios besides device sensing, including but not limited to other scenarios where data interaction is required between two devices and the amount of data is large. In such cases, the data processing method provided in this disclosure can be used to reduce the amount of data interaction.

[0261] In some embodiments, the results of device perception can be used for autonomous driving control, device localization and tracking, environmental reconstruction, etc., and this disclosure does not limit them.

[0262] In some embodiments, the names of signals, etc., are not limited to those described in the embodiments. Terms such as "information", "message", "signal", "signaling", "report", "configuration", "indication", "instruction", "command", "channel", "parameter", "domain", "field", "symbol", "symbol", "codebook", "codeword", "codepoint", "bit", "data", "program", and "chip" can be used interchangeably.

[0263] In some embodiments, the terms "codebook," "codeword," and "precoding matrix" can be used interchangeably. For example, a codebook can be a collection of one or more codewords / precoding matrices.

[0264] In some embodiments, “get,” “obtain,” “receive,” “transmit,” “bidirectional transmission,” and “send and / or receive” can be used interchangeably and can be interpreted as receiving from other entities, obtaining from protocols, obtaining from higher layers, obtaining through self-processing, or autonomous implementation, among other meanings.

[0265] In some embodiments, terms such as “send,” “transmit,” “report,” “distribute,” “transfer,” “bidirectional transmission,” “send and / or receive” can be used interchangeably.

[0266] In some embodiments, terms such as "certain," "preset," "default," "set," "indicated," "a certain," "any," and "first" can be used interchangeably. "Certain A," "preset A," "default A," "set A," "indicated A," "a certain A," "any A," and "first A" can be interpreted as A pre-defined in a protocol or the like, or as A obtained through setting, configuration, or instruction, or as specific A, a certain A, any A, or first A, but are not limited thereto.

[0267] The data processing method involved in the embodiments of this disclosure may include at least one of steps S2101 to S2107. For example, step S2105 may be implemented as an independent embodiment, step S2106 may be implemented as an independent embodiment, step S2107 may be implemented as an independent embodiment, steps S2106+S2107 may be implemented as an independent embodiment, steps S2101 to S2103 may be implemented as independent embodiments, step S2103 may be implemented as an independent embodiment, step S2104 may be implemented as an independent embodiment, and steps S2101 to S2107 may be implemented as independent embodiments, but are not limited thereto.

[0268] In some embodiments, steps S2103 and S2104 may be performed in an alternate order or simultaneously.

[0269] In some embodiments, steps S2103 and S2104 may be performed selectively.

[0270] In some embodiments, steps S2101, S2102, S2103, and S2104 are optional, and one or more of these steps may be omitted or substituted in different embodiments.

[0271] In some embodiments, step S2107 is optional, and one or more of these steps may be omitted or substituted in different embodiments.

[0272] In some embodiments, other alternative implementations may be described before or after the specification corresponding to FIG2A.

[0273] In the above embodiments, the first device can reduce the amount of the first data and then send the reduced amount of the first data to the second device, thereby reducing the data transmission overhead of the device sensing data, which helps to reduce the complexity of the device sensing processing and improves the availability and reliability of the integrated communication and sensing.

[0274] Figure 2B is a flowchart illustrating a data processing method according to an embodiment of the present disclosure. As shown in Figure 2B, the present disclosure relates to a data processing method, which can be executed by a second device 102. The method includes:

[0275] Step S2201: Obtain the device perception prediction results output by the initial AI model.

[0276] In some embodiments, the sample data may include, but is not limited to, multiple CSI sample matrices.

[0277] In some embodiments, one or more first methods can be used to process the sample data to obtain sample data with a reduced data volume.

[0278] In some embodiments, sample data can be used as input to an initial AI model to obtain the device perception prediction results output by the initial AI model.

[0279] In some embodiments, the sample data after the data volume has been reduced can be used as the input value of the initial AI model to obtain the device perception prediction result output by the initial AI model.

[0280] In some embodiments, the initial AI model may use a Residual Network (ResNet), Visual Geometry Group (VGG) network, or similar networks as its backbone, and may include, but is not limited to, at least one of the following network layers: input layer; convolutional layer; pooling layer; activation function layer; connection layer; and output layer. This disclosure does not limit the architecture of the initial AI model.

[0281] Step S2202: Determine the loss function based on the difference between the device perception prediction result and the true value of the device perception result.

[0282] In some embodiments, the second device can acquire the true value of the device perception result corresponding to the sample data. Specifically, the true value of the device perception result may include the true values ​​of three parameters: distance, speed, and angle corresponding to the "perceived target". The number of perceived targets can be one or more, and this disclosure does not limit this.

[0283] In some embodiments, the loss function can be determined based on the difference between the device perception prediction output by the initial AI model and the true value of the device perception result.

[0284] Step S2203: Based on the loss function, train the initial AI model until the stopping condition is met, and then stop training to obtain the second AI model.

[0285] The stopping condition includes at least one of the following:

[0286] Reach the first training cycle number;

[0287] The loss function decreases to the first fault tolerance range;

[0288] The initial AI model determines that the accuracy of the device's perception results reaches a first value.

[0289] In some embodiments, the second device may reduce the loss function by employing, for example, a stochastic gradient descent (SGD) algorithm based on the loss function described above.

[0290] In some embodiments, the first number of training epochs may be agreed upon by a protocol or configured by the network device, and this disclosure does not limit this. Specifically, to reduce the loss function, the second device increments the number of training epochs by 1 each time it adjusts the parameters of each network layer of the initial AI model. The first device 101 can stop training when the first number of training epochs of the initial AI model reaches the specified number of training epochs, at which point the AI ​​model can be obtained.

[0291] In some embodiments, the first fault tolerance range may be defined by a protocol or configured by the network device, and this disclosure does not limit this. The second device may use the true value of the device perception result as a label, and determine when the loss function decreases to the first fault tolerance range, at which point training stops, thus obtaining the AI ​​model. Using the true value of the device perception result as a label can be understood as using a training target that is infinitely close to the true value of the device perception result.

[0292] In some embodiments, the first value may be agreed upon by a protocol or configured by a network device, and this disclosure does not limit this. The first device 101 may stop training and obtain the AI ​​model when it determines that the accuracy of the initial AI model's device perception reaches the first value.

[0293] In some embodiments, steps S2201 to S2203 are optional, and one or more of these steps may be omitted or substituted in different embodiments.

[0294] In some embodiments, the execution order of steps S2201 to S2203 is not limited.

[0295] In the above embodiments, the AI ​​model can be trained in the above manner to improve the reliability of device perception.

[0296] Figure 3A is a flowchart illustrating a data processing method according to an embodiment of the present disclosure. As shown in Figure 3A, the present disclosure relates to a data processing method, which can be executed by a first device 101. The method includes:

[0297] Step S3101: Obtain the second signal.

[0298] In some embodiments, optional implementations of step S3101 can be found in optional implementations of step S2101 in FIG2A and other related parts in the embodiments involved in FIG2A, which will not be repeated here.

[0299] In some embodiments, the second signal is determined based on the first signal, which may be a signal used to initiate device sensing.

[0300] In some embodiments, the first device 101 receives a second signal, but is not limited thereto; it may also receive a second signal sent by another entity.

[0301] In some embodiments, the first device 101 acquires a second signal defined by a protocol.

[0302] In some embodiments, the first device 101 acquires a second signal from an upper layer(s).

[0303] In some embodiments, the first device 101 processes the signal to obtain the second signal.

[0304] In some embodiments, step S3101 is omitted, and the first device 101 autonomously implements the function indicated by the second signal, or the above function is defaulted or set to default.

[0305] Step S3102: Determine the first data.

[0306] In some embodiments, optional implementations of step S3102 can be found in optional implementations of step S2102 in FIG2A and other related parts in the embodiments involved in FIG2A, which will not be repeated here.

[0307] In some embodiments, the first data may include channel measurement data.

[0308] Step S3103: Obtain the first instruction information.

[0309] In some embodiments, optional implementations of step S3103 can be found in optional implementations of step S2103 in FIG2A and other related parts in the embodiments involved in FIG2A, which will not be repeated here.

[0310] In some embodiments, the first indication information may be used to indicate a first mode supported by the second device 102.

[0311] In some embodiments, the first device 101 receives first indication information sent by the second device 102, but is not limited thereto, and may also receive first indication information sent by other entities.

[0312] In some embodiments, the first device 101 acquires first instruction information as defined by the protocol.

[0313] In some embodiments, the first device 101 obtains first indication information from the upper layer(s).

[0314] In some embodiments, the first device 101 processes information to obtain first instruction information.

[0315] In some embodiments, step S3103 is omitted, and the first device 101 autonomously implements the function indicated by the first instruction information, or the above function is a default or default setting.

[0316] Step S3104: Send the second instruction information.

[0317] In some embodiments, optional implementations of step S3104 can be found in optional implementations of step S2104 in FIG2A and other related parts in the embodiments involved in FIG2A, which will not be repeated here.

[0318] In some embodiments, the second indication information may be used to indicate the first mode supported by the first device 101.

[0319] Step S3105: Determine the first data after the data volume is reduced.

[0320] In some embodiments, optional implementations of step S3105 can be found in optional implementations of step S2105 in FIG2A and other related parts in the embodiments involved in FIG2A, which will not be repeated here.

[0321] Step S3106: Send the first data after the data volume has been reduced.

[0322] In some embodiments, optional implementations of step S3106 can be found in optional implementations of step S2106 in FIG2A and other related parts in the embodiments involved in FIG2A, which will not be repeated here.

[0323] In some embodiments, the first device 101 sends the first data, after the data volume has been reduced, to the second device 102.

[0324] In some embodiments, the second device 102 receives the first data after the data volume has been reduced.

[0325] The data processing method involved in the embodiments of this disclosure may include at least one of steps S3101 to S3106. For example, step S3105 may be implemented as an independent embodiment, step S3106 may be implemented as an independent embodiment, steps S3101+S3102+S3103 may be implemented as an independent embodiment, step S3103 may be implemented as an independent embodiment, step S3104 may be implemented as an independent embodiment, and steps S3101 to S3106 may be implemented as independent embodiments, but are not limited thereto.

[0326] In some embodiments, steps S3103 and S3104 may be performed in an alternate order or simultaneously.

[0327] In some embodiments, steps S3103 and S3104 may be performed selectively.

[0328] In some embodiments, steps S3101 to S2104 are optional, and one or more of these steps may be omitted or substituted in different embodiments.

[0329] In the above embodiments, the first device can reduce the amount of the first data and then send the reduced amount of the first data to the second device, thereby reducing the data transmission overhead of the device sensing data, which helps to reduce the complexity of the device sensing processing and improves the availability and reliability of the integrated communication and sensing.

[0330] Figure 3B is a flowchart illustrating a data processing method according to an embodiment of the present disclosure. As shown in Figure 3B, the present disclosure relates to a data processing method, which can be executed by a first device 101. The method includes:

[0331] Step S3201: Determine the first data.

[0332] In some embodiments, optional implementations of step S3201 can be found in optional implementations of step S2102 in FIG2A and other related parts in the embodiments involved in FIG2A, which will not be repeated here.

[0333] In some embodiments, the first data may be data used for device sensing of a third device.

[0334] Step S3202: Determine the first data after the data volume is reduced.

[0335] In some embodiments, optional implementations of step S3202 can be found in optional implementations of step S2105 in FIG2A and other related parts in the embodiments involved in FIG2A, which will not be repeated here.

[0336] Step S3203: Send the first data after the data volume has been reduced.

[0337] In some embodiments, optional implementations of step S3203 can be found in optional implementations of step S2106 in FIG2A and other related parts in the embodiments involved in FIG2A, which will not be repeated here.

[0338] In some embodiments, the first device 101 sends the first data, after the data volume has been reduced, to the second device 102.

[0339] In some embodiments, the second device 102 receives the first data after the data volume has been reduced.

[0340] The data processing method involved in the embodiments of this disclosure may include at least one of steps S3201 to S3203. For example, step S3202 may be implemented as an independent embodiment, step S3203 may be implemented as an independent embodiment, step S3202+S3203 may be implemented as an independent embodiment, step S3201 may be implemented as an independent embodiment, and steps S3201 to S3203 may be implemented as independent embodiments, but are not limited thereto.

[0341] In some embodiments, steps S3201 to S3203 are optional, and one or more of these steps may be omitted or substituted in different embodiments.

[0342] In the above embodiments, the first device can reduce the amount of the first data and then send the reduced amount of the first data to the second device, thereby reducing the data transmission overhead of the device sensing data, which helps to reduce the complexity of the device sensing processing and improves the availability and reliability of the integrated communication and sensing.

[0343] Figure 4A is a flowchart illustrating a data processing method according to an embodiment of the present disclosure. As shown in Figure 4A, the present disclosure relates to a data processing method, which can be executed by a second device 102, and the method includes:

[0344] Step S4101: Obtain the first data after the data volume has been reduced.

[0345] In some embodiments, optional implementations of step S4101 can be found in optional implementations of step S2106 in FIG2A and other related parts in the embodiments involved in FIG2A, which will not be repeated here.

[0346] In some embodiments, the second device 102 receives the first data after the amount of data sent by the first device 101 has been reduced, but is not limited thereto, and may also receive the first data after the amount of data sent by other entities has been reduced.

[0347] In some embodiments, the second device 102 acquires first data after the amount of data is reduced as specified in the protocol.

[0348] In some embodiments, the second device 102 obtains the first data after a reduction in data volume from the upper layer(s).

[0349] In some embodiments, the second device 102 processes the data to obtain the first data after the data volume has been reduced.

[0350] In some embodiments, step S4101 is omitted, and the second device 102 autonomously implements the function indicated by the first data after the data volume is reduced, or the above function is the default or default.

[0351] Step S4102: Determine the device sensing results.

[0352] In some embodiments, optional implementations of step S4102 can be found in optional implementations of step S2107 in FIG2A and other related parts in the embodiments involved in FIG2A, which will not be repeated here.

[0353] The data processing method involved in the embodiments of this disclosure may include at least one of steps S4101 to S4102. For example, step S4101 may be implemented as an independent embodiment, step S4102 may be implemented as an independent embodiment, and steps S4101 to S4102 may be implemented as independent embodiments, but are not limited thereto.

[0354] In some embodiments, steps S4101 to S4102 are optional, and one or more of these steps may be omitted or substituted in different embodiments.

[0355] In the above embodiments, the second device can receive the first data after the amount of data sent by the first device has been reduced, thereby performing device sensing, reducing the data transmission overhead of device sensing, which helps to reduce the complexity of device sensing processing and improves the availability and reliability of integrated communication and sensing.

[0356] Figure 4B is a flowchart illustrating a data processing method according to an embodiment of the present disclosure. As shown in Figure 4B, the present disclosure relates to a data processing method, which can be executed by a second device 102, and the method includes:

[0357] Step S4201: Obtain the first data after the data volume has been reduced.

[0358] In some embodiments, optional implementations of step S4201 can be found in optional implementations of step S2106 in FIG2A and other related parts in the embodiments involved in FIG2A, which will not be repeated here.

[0359] In some embodiments, the second device 102 receives the first data after the amount of data sent by the first device 101 has been reduced, but is not limited thereto, and may also receive the first data after the amount of data sent by other entities has been reduced.

[0360] In some embodiments, the second device 102 acquires first data after the amount of data is reduced as specified in the protocol.

[0361] In some embodiments, the second device 102 obtains the first data after a reduction in data volume from the upper layer(s).

[0362] In some embodiments, the second device 102 processes the data to obtain the first data after the data volume has been reduced.

[0363] In some embodiments, step S4201 is omitted, and the second device 102 autonomously implements the function indicated by the first data after the data volume is reduced, or the above function is the default or default.

[0364] In the above embodiments, the second device can receive the first data after the amount of data sent by the first device has been reduced, thereby enabling device sensing and reducing the data transmission overhead of device sensing.

[0365] The above process is further illustrated with examples below.

[0366] This disclosure proposes a method for processing channel measurement data for AI perception. Partial data from each dimension of the channel measurement data acquired by the UE or BS for perception is reported to the LMF (second device) and used as input to the AI ​​perception model. This method helps reduce the reporting overhead of perception measurement data while ensuring perception accuracy. Furthermore, since the computational complexity of AI models is generally positively correlated with the dimensionality of the input data, the proposed model input data processing method can also reduce the computational complexity of the AI ​​perception model.

[0367] In some embodiments, the specific implementation method for channel measurement data processing includes, but is not limited to, at least one of the following:

[0368] Data aggregation: merging multiple consecutive data points along a certain dimension of the original data into a single data point;

[0369] Data sampling: Extracting data from a certain dimension of the original data at regular intervals;

[0370] Data extraction: Selecting a continuous portion of the original data along a certain dimension.

[0371] Taking the CSI matrix H as the first data as an example, an embodiment of the proposed solution is given by applying the channel measurement data processing method of this disclosure:

[0372] The CSI matrix H used for AI perception contains three dimensions: subcarriers, OFDM symbols, and receive antenna ports. It is a complex matrix of size L×N×P, meaning it contains L OFDM symbols, N subcarriers, and P data from the receive antenna ports.

[0373] It is understandable that when the dimensions are the three dimensions mentioned above, the value of M in this disclosure is equal to L, N, and P, respectively.

[0374] If the data aggregation method is applied: for data in the L OFDM symbol dimension, sum the m1 consecutive data points, where L is an integer multiple of m1, to obtain L / m1 processed OFDM symbol dimension data; for data in the N subcarrier dimension, sum the m2 consecutive data points, where N is a multiple of m2, to obtain N / m2 processed data; for data in the P receive antenna ports, sum the m3 consecutive data points, where P is a multiple of m3, to obtain P / m3 processed data.

[0375] If the data sampling method is applied: for data in the L OFDM symbol dimension, select and retain 1 data every n1 data intervals, where L is an integer multiple of n1, to obtain L / n1 processed OFDM symbol dimension data; for data in the N subcarrier dimension, select and retain 1 data every n2 data intervals, where N is a multiple of n2, to obtain N / n2 processed data; for data in the P receive antenna ports, select and retain 1 data every n3 data intervals, where P is a multiple of n3, to obtain P / n3 processed data.

[0376] If the data extraction method is applied: for data in the L OFDM symbol dimension, only the first p1 data points are retained, and p1 processed OFDM symbol dimension data are obtained; for data in the N subcarrier dimension, only the first p2 data points are retained, and p2 processed data are obtained; for data in the P receive antenna ports, only the first p3 data points are retained, and p3 processed data are obtained.

[0377] When processing the input data of an AI perception model using different methods for the same sample data, the dimensions and features of the processed sample data can differ. The trained AI perception model can process sample data processed using the corresponding input data processing method. Therefore, when applying the method for reducing the amount of channel measurement data for AI perception proposed in this invention, it is necessary to consider the method indication for reducing the amount of channel measurement data between the UE or BS and the LMF. That is, in practical applications, the LMF uses a method that matches the UE or BS to process the model input data.

[0378] When applying the method for reducing the amount of channel measurement data for AI perception proposed in this invention, the overall application process of the AI ​​target perception model is shown in Figure 5, including the following steps:

[0379] Step S5101, Instruction on channel measurement data processing method.

[0380] The perception model inference device, i.e., the network-side LMF, sends a channel measurement data processing method instruction to the perception signal receiving device, i.e., the channel measurement data collection device, based on the channel measurement data processing method adopted by its deployed AI perception model.

[0381] Of course, the sensing signal receiving device can also send this instruction to the sensing model inference device.

[0382] Step S5102: Acquisition of sensing measurement data.

[0383] The sensing signal receiving device acquires raw channel measurement data for AI sensing based on the sensing signals sent by the sensing signal transmitting device.

[0384] Step S5103: Sensing measurement data processing.

[0385] The sensing signal receiving device processes the raw channel measurement data according to the data processing method described above, and obtains the channel measurement data after the data volume is reduced.

[0386] Step S5104: Report the sensing measurement data.

[0387] The sensing signal receiving device reports the channel measurement data, after the data volume has been reduced, to the sensing model inference device.

[0388] Step S5105, AI perception model inference

[0389] The perception model inference device inputs the received channel measurement data into the AI ​​perception model to obtain the perception results.

[0390] In the embodiments disclosed herein, 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 in other embodiments.

[0391] This disclosure also provides embodiments of an apparatus for implementing any of the above methods. For example, an apparatus is provided that includes units or modules for implementing the steps performed by the first device in any of the above methods. Alternatively, another apparatus is provided that includes units or modules for implementing the steps performed by the second device in any of the above methods.

[0392] It should be understood that the division of units or modules in the above device is only a logical functional division. In actual implementation, they can be fully or partially integrated into a single physical entity, or they can be physically separated. Furthermore, the units or modules in the device can be implemented by a processor calling software: for example, the device includes a processor connected to a memory containing instructions. The processor calls the instructions stored in the memory to implement any of the above methods or to implement the functions of the units or modules in the above device. The processor can be, for example, a general-purpose processor, such as a Central Processing Unit (CPU) or a microprocessor, and the memory can be internal or external to the device. Alternatively, the units or modules in the device can be implemented in the form of hardware circuits. The functionality of some or all of the units or modules can be achieved through the design of these hardware circuits, which can be understood as one or more processors. For example, in one implementation, the hardware circuit is an application-specific integrated circuit (ASIC). The functionality of some or all of the units or modules is achieved through the design of the logical relationships between the components within the circuit. In another implementation, the hardware circuit can be implemented using a programmable logic device (PLD). Taking a field-programmable gate array (FPGA) as an example, it can include a large number of logic gates. The connection relationships between the logic gates are configured through configuration files, thereby achieving the functionality of some or all of the units or modules. All units or modules of the above device can be implemented entirely through processor-called software, entirely through hardware circuits, or partially through processor-called software with the remaining parts implemented through hardware circuits.

[0393] In this embodiment, the processor is a circuit with signal processing capabilities. In one implementation, the processor can be a circuit with instruction read and execute 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 relationships of hardware circuits. The logical relationships of the aforementioned hardware circuits are fixed or reconfigurable. For example, the processor is a hardware circuit implemented using 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 configuring the hardware circuit 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. Furthermore, 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), or a Deep Learning Processing Unit (DPU).

[0394] Figure 6A is a schematic diagram of the structure of the first device proposed in an embodiment of this disclosure. As shown in Figure 6A, the first device 6100 may include: a processing module 6101 and a transceiver module 6102.

[0395] In some embodiments, the processing module 6101 is used to determine first data; wherein the first data includes channel measurement data; and to reduce the amount of the first data.

[0396] In some embodiments, the transceiver module 6102 is used to send the first data after the data volume has been reduced to the second device.

[0397] Optionally, the processing module 6101 is used to execute at least one of the other steps (such as step S2102, step S2105, but not limited thereto) executed by the first device 6100 in any of the above methods, which will not be described in detail here.

[0398] Optionally, the transceiver module 6102 is used to perform at least one of the communication steps such as sending and / or receiving performed by the first device 6100 in any of the above methods (e.g., steps S2101, S2103, S2104, S2106, but not limited thereto), which will not be elaborated here.

[0399] Figure 6B is a schematic diagram of the structure of the second device proposed in an embodiment of this disclosure. As shown in Figure 6B, the second device 6200 may include a transceiver module 6201.

[0400] In some embodiments, the transceiver module 6201 is used to receive first data after the amount of data sent by the first device has been reduced; wherein, the first data includes channel measurement data.

[0401] Optionally, the transceiver module 6201 is used to perform at least one of the communication steps such as sending and / or receiving performed by the second device 6200 in any of the above methods (e.g., step S2106, but not limited thereto), which will not be described in detail here.

[0402] In some embodiments, the processing module may be a single module or may include multiple sub-modules. Optionally, the multiple sub-modules may each perform all or part of the steps required by the processing module. Optionally, the processing module may be interchangeable with a processor.

[0403] In some embodiments, the transceiver module may include a transmitting module and / or a receiving module, which may be separate or integrated. Optionally, the transceiver module may be interchangeable with a transceiver.

[0404] Figure 7A is a schematic diagram of the structure of the communication device 7100 proposed in an embodiment of this disclosure. The communication device 7100 can be a first device or a second device. The first device or the second device can be a network device (e.g., access network device, core network device, etc.), a terminal (e.g., user equipment, vehicle, IoT device, environmental IoT device, etc.), a chip, chip system, or processor that supports the network device in implementing any of the above methods, or a chip, chip system, or processor that supports the terminal in implementing any of the above methods. The communication device 7100 can be used to implement the methods described in the above method embodiments; for details, please refer to the descriptions in the above method embodiments.

[0405] 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, such as a baseband processor or a central processing unit (CPU). The baseband processor can be used to process communication protocols and communication data, while the CPU can be used to control communication devices (e.g., base stations, baseband chips, terminal devices, terminal device chips, DUs or CUs, etc.), execute programs, and process program data. Optionally, the communication device 7100 can be used to execute any of the above methods. Optionally, one or more processors 7101 can be used to invoke instructions to cause the communication device 7100 to execute any of the above methods.

[0406] 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 (e.g., steps S2101, S2103, S2104, S2106, but not limited thereto), and the processor 7101 performs at least one of other steps (e.g., steps S2102, S2105, S2107, but not limited thereto). In optional embodiments, the transceiver may include a receiver and / or a transmitter, which may be separate or integrated. Optionally, the terms transceiver, transceiver unit, transceiver, transceiver circuit, interface circuit, interface, etc., can be used interchangeably; the terms transmitter, transmitting unit, transmitter, transmitting circuit, etc., can be used interchangeably; and the terms receiver, receiving unit, receiver, receiving circuit, etc., can be used interchangeably.

[0407] In some embodiments, the communication device 7100 further includes one or more memories 7103 for storing data. Optionally, all or part of the memories 7103 may be located outside the communication device 7100. In optional embodiments, the communication device 7100 may include one or more interface circuits 7104. Optionally, the interface circuits 7104 are connected to the memories 7102 and can be used to receive data from the memories 7102 or other devices, and to send data to the memories 7102 or other devices. For example, the interface circuits 7104 can read data stored in the memories 7102 and send the data to the processor 7101.

[0408] The communication device 7100 described in the above embodiments may be a network device or a terminal, but the scope of the communication device 7100 described in this 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 a standalone device or a part of a larger device. For example, the communication device may be: (1) a standalone integrated circuit IC, or chip, or chip system or subsystem; (2) a collection of one or more ICs, optionally, the IC collection may also include storage components for storing data and programs; (3) an ASIC, such as a modem; (4) a module that can be embedded in other devices; (5) a receiver, terminal device, smart terminal device, cellular phone, wireless device, handheld device, mobile unit, vehicle device, network device, cloud device, artificial intelligence device, etc.; (6) others, etc.

[0409] Figure 7B is a schematic diagram of the structure of the chip 7200 according to an embodiment of this disclosure. For cases where the communication device 7100 can be a chip or a chip system, the schematic diagram of the chip 7200 shown in Figure 7B can be referenced, but is not limited thereto.

[0410] Chip 7200 includes one or more processors 7201. Chip 7200 is used to perform any of the above methods.

[0411] In some embodiments, chip 7200 further includes one or more interface circuits 7202. Optionally, terms such as interface circuit, interface, and transceiver pin can be used interchangeably. In some embodiments, chip 7200 further includes one or more memories 7203 for storing data. Optionally, all or part of the memories 7203 may be located outside chip 7200. Optionally, interface circuit 7202 is connected to memory 7203, and interface circuit 7202 can be used to receive data from memory 7203 or other devices, and interface circuit 7202 can be used to send data to memory 7203 or other devices. For example, interface circuit 7202 can read data stored in memory 7203 and send the data to processor 7201.

[0412] In some embodiments, the interface circuit 7202 performs at least one of the communication steps such as sending and / or receiving in the above-described method (e.g., steps S2101, S2103, S2104, and S2106, but not limited thereto). The interface circuit 7202 performing the communication steps such as sending and / or receiving in the above-described method refers, for example, to the interface circuit 7202 performing data interaction 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 other steps (e.g., steps S2102, S2105, and S2107, but not limited thereto).

[0413] The modules and / or devices described in the various embodiments, such as virtual devices, physical devices, and chips, can be combined or separated arbitrarily as needed. Optionally, some or all steps can also be performed collaboratively by multiple modules and / or devices, which is not limited here.

[0414] This disclosure also proposes a storage medium storing instructions that, when executed on the communication device 7100, cause the communication device 7100 to perform 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 not limited thereto; it may also be a storage medium readable by other devices. Optionally, the storage medium may be a non-transitory storage medium, but not limited thereto; it may also be a temporary storage medium.

[0415] This disclosure also provides a program product that, when executed by the communication device 7100, causes the communication device 7100 to perform any of the above methods. Optionally, the program product is a computer program product.

[0416] This disclosure also proposes a computer program that, when run on a computer, causes the computer to perform any of the above methods.

[0417] It should be understood that this disclosure is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this disclosure is limited only by the appended claims.

Claims

A data processing method, characterized by, The method is performed by a first device, and the method includes: Determine the first data; wherein the first data includes channel measurement data; Reduce the amount of data in the first set of data; Send the first data after the data volume has been reduced to the second device. The method of claim 1, wherein The reduction of the amount of the first data includes: Based on the first method, the amount of data in the first data is reduced; wherein, the first method includes at least one of the following: Aggregation method; Sampling method; Method of interception. The method according to claim 2, characterized in that The reduction of the amount of data in the first data based on the first method includes: The first method includes the aggregation method, in which m consecutive first values ​​are selected each time from a total of M first values; wherein, the first value is any value of the first data in the first dimension; wherein, M is a positive integer, m is a positive integer, and M is a positive integer multiple of m; A second value is determined based on the sum of the m consecutive first values ​​selected each time; wherein the second value is the value of the first data after the data volume is reduced in the first dimension. The method according to claim 2, characterized in that The reduction of the amount of data in the first data based on the first method includes: The first method includes the sampling method, in which a first value is selected every n first values ​​from a total of M first values; wherein, the first value is any value of the first data in the first dimension; wherein, M is a positive integer, n is a positive integer, and M is a positive integer multiple of n; A second value is determined based on one of the first values ​​selected at intervals of n; wherein the second value is the value of the first data in the first dimension after the data volume is reduced. The method according to claim 2, characterized in that The reduction of the amount of data in the first data based on the first method includes: The first method includes the truncation method, which selects p consecutive first values ​​from a total of M first values; wherein, the first value is any value of the first data in the first dimension; wherein, M is a positive integer, p is a positive integer, and p is less than M; Based on p consecutive first values, determine p second values; wherein the second value is the value of the first data after the data volume is reduced in the first dimension. The method according to any one of claims 2-5, characterized in that The method further includes: Receive first indication information sent by the second device; wherein the first indication information is used to indicate the first method. The method according to any one of claims 2-5, characterized in that The method further includes: Send a second instruction message to the second device; wherein the second instruction message is used to indicate the first method. The method according to any one of claims 1 to 7, characterized in that The determination of the first data includes: Receive a second signal; wherein the second signal is determined based on a first signal; wherein the first signal is a signal used to initiate device sensing; The first data is obtained by performing channel measurements based on the second signal. A data processing method, characterized by, The method is performed by a second device, and the method includes: The first data is received after the amount of data sent by the first device has been reduced; wherein the first data includes channel measurement data. The method of claim 9, wherein The method further includes: Based on the first data after the data volume is reduced, device perception is performed on the third device to determine the device perception result. The method according to claim 9 or 10, characterized in that The step of performing device perception on the third device based on the first data after the data volume reduction, and determining the device perception result, includes: The first data after the data volume is reduced is used as the input value of the artificial intelligence (AI) model to obtain the device perception result output by the AI ​​model; wherein, the AI ​​model is an AI model used for device perception. The method according to any one of claims 9-11, characterized in that The method further includes: Send a first indication message to the first device; wherein the first indication message is used to indicate a first method; wherein the first method is a processing method for reducing the amount of data in the first data. The method according to any one of claims 9-11, characterized in that The method further includes: Receive second indication information sent by the first device; wherein the second indication information is used to indicate a first method; wherein the first method is a processing method for reducing the amount of data in the first data. The method according to claim 12 or 13, characterized in that The first method includes at least one of the following: Aggregation method; Sampling method; Method of interception. A first device, characterized in that include: The processing module is configured to determine first data; wherein the first data includes channel measurement data; The processing module is also configured to reduce the amount of data in the first data; The transceiver module is configured to send the first data, after the data volume has been reduced, to the second device. A second device, characterized in that, include: The transceiver module is configured to receive first data after the amount of data sent by the first device has been reduced; wherein the first data includes channel measurement data. A first device, characterized in that, include: One or more processors; The processor is used to execute the data processing method according to any one of claims 1-8. A second device, characterized in that, include: One or more processors; The processor is used to execute the data processing method according to any one of claims 9-14. A communication system, characterized in that, include: A first device, configured to implement the data processing method according to any one of claims 1-8; A second device is configured to implement the data processing method according to any one of claims 9-14. A storage medium storing instructions, characterized in that, When the instruction is executed on the communication device, it causes the communication device to perform the data processing method as described in any one of claims 1-8 or 9-14. A computer program, characterized in that, When it is run on a computer, it causes the computer to perform the data processing method as described in any one of claims 1-8 or 9-14.

Images