Communication method, communication device, storage medium and program product

Abstract

The present disclosure relates to a communication method, a communication device, a storage medium and a program product. The communication method comprises: determining a start time and an end time of a time domain resource occupied by an AI use case, wherein the AI use case is deployed on a terminal and is used for positioning. By means of the present disclosure, the start time and the end time of the time domain resource occupied by the AI use case can be clarified, thereby improving the effectiveness of communication.

Description

Communication methods, communication equipment, storage media and software products Technical Field

[0001] This disclosure relates to the field of communication technology, and in particular to communication methods, communication devices, storage media, and program products. Background Technology

[0002] In recent years, with the rapid development of artificial intelligence (AI) and machine learning (ML) technologies, their applications have become increasingly widespread. For example, introducing AI and / or machine learning technologies into wireless air interfaces can help improve wireless transmission technology. Summary of the Invention

[0003] How to improve AI-based communication more effectively is a problem that needs to be solved.

[0004] This disclosure provides communication methods, communication devices, storage media, and program products.

[0005] According to a first aspect of the present disclosure, a communication method is proposed, the method comprising: determining the start time and end time of time-domain resources occupied by an AI use case, wherein the AI ​​use case is deployed on a terminal and used for positioning.

[0006] According to a second aspect of the present disclosure, a communication device is provided for performing the communication method described in the first aspect.

[0007] According to a third aspect of the present disclosure, a storage medium is provided that stores instructions which, when executed on a communication device, cause the communication device to perform a communication method as described in the first aspect and any one thereof.

[0008] According to a fourth aspect of the present disclosure, a program product is provided, comprising at least one of a program and instructions, wherein the program and instructions, when executed by a communication device, implement the communication method described in any one of the first aspects.

[0009] This disclosure improves the effectiveness of AI communication assistance by determining the start and end times of the time-domain resources occupied by AI use cases, thereby clarifying the resource consumption of AI processing in AI communication scenarios. Attached Figure Description

[0010] To more clearly illustrate the technical solutions in the embodiments of this disclosure, the accompanying drawings required for the description of the embodiments are introduced below. The following drawings are only some embodiments of this disclosure and do not impose specific limitations on the protection scope of this disclosure.

[0011] Figure 1A is a schematic diagram of a communication system architecture according to an embodiment of the present disclosure.

[0012] Figure 1B is a schematic diagram of a positioning process according to an embodiment of the present disclosure.

[0013] Figure 2A is a schematic diagram of a communication method interaction according to an embodiment of the present disclosure.

[0014] Figure 2B is a schematic diagram illustrating the start and end times of time-domain resources used in an AI use case according to an embodiment of the present disclosure.

[0015] Figure 2C is a schematic diagram showing the start and end times of time-domain resources occupied by an AI use case according to an embodiment of the present disclosure.

[0016] Figure 2D is a schematic diagram showing the start and end times of time-domain resources occupied by an AI use case according to an embodiment of the present disclosure.

[0017] Figure 2E is a schematic diagram illustrating the start and end times of time-domain resources used in an AI use case according to an embodiment of this disclosure.

[0018] Figure 2F is a schematic diagram showing the start and end times of time-domain resources occupied by an AI use case according to an embodiment of the present disclosure.

[0019] Figure 3 is a flowchart illustrating a communication method according to an embodiment of the present disclosure.

[0020] Figure 4 is a flowchart illustrating a communication method according to an embodiment of the present disclosure.

[0021] Figure 5 is a schematic diagram of the structure of the communication device proposed in the embodiments of this disclosure.

[0022] Figure 6A is a schematic diagram of the structure of the communication device proposed in an embodiment of this disclosure.

[0023] Figure 6B is a schematic diagram of the chip structure proposed in an embodiment of this disclosure. Detailed Implementation

[0024] This disclosure provides communication methods, communication devices, storage media, and program products.

[0025] In a first aspect, embodiments of this disclosure propose a communication method applied to a network device. The communication method includes: determining the start time and end time of time-domain resources occupied by an AI use case, wherein the AI ​​use case is deployed on a terminal and used for positioning.

[0026] In the above embodiments, by determining the start and end times of the time-domain resources occupied by AI use cases, the status of time-domain resources occupied by AI use cases can be clearly defined. This allows for corresponding communication processing based on the clearly defined time-domain resource usage of AI use cases, such as clearing the time-domain resources occupied by completed AI tasks to allow for new AI tasks. Furthermore, based on the occupied time-domain resources, the remaining available time-domain resources can be further determined, and AI functions can be allocated according to these remaining available resources. This avoids activating too many AI functions within the same timeframe, which could overload the terminal processing tasks.

[0027] In conjunction with some embodiments of the first aspect, the start time is determined based on at least one of a first time and a second time; the first time is determined based on the positioning trigger time, and the second time is determined based on the measurement time of the positioning reference signal PRS.

[0028] In the above embodiments, AI-based positioning can be determined based on the positioning trigger time and / or the PRS measurement time, so that the determination of the time domain resources occupied by AI can be based on the current execution of AI tasks, which is more in line with actual communication scenarios and improves the effectiveness of AI communication.

[0029] In conjunction with some embodiments of the first aspect, the measurement time of the positioning reference signal PRS includes: the measurement start time of the positioning reference signal; or the measurement end time of the positioning reference signal.

[0030] In the above embodiments, determining the start time of the time domain resources occupied by the AI ​​use case based on the measurement start time or measurement end time of the positioning reference signal can more clearly determine the start time of the time domain resources occupied by the AI ​​use case when the positioning reference signal occupies one or more resource units, thereby improving communication effectiveness.

[0031] In conjunction with some embodiments of the first aspect, in response to the terminal performing periodic location reporting, the start time is determined based on the second time.

[0032] In the above embodiments, when the terminal performs periodic location reporting, the location trigger time exists in the initial location configuration. Therefore, using the second time to determine the start time of the time domain resources occupied by the AI ​​use case can more effectively determine the start time of the time domain resources occupied by the AI ​​use case, even when the configuration in the initial location configuration cannot be obtained.

[0033] In conjunction with some embodiments of the first aspect, in response to the terminal performing non-periodic location reporting, the start time is determined based on the first time.

[0034] In the above embodiments, when the terminal performs non-periodic location reporting, the location trigger time exists in each location configuration. Therefore, by determining the start time of the time domain resources occupied by the AI ​​use case in the first instance, the start time of the time domain resources occupied by the AI ​​use case can be determined more effectively.

[0035] In conjunction with some embodiments of the first aspect, the end time is determined based on at least one of a third time and a fourth time; the third time is determined based on the feedback time of the positioning result, and the fourth time is determined based on a preset value.

[0036] In the above embodiments, the feedback time and / or preset value determination based on the positioning results can ensure that the end time of the time domain resources occupied by the AI ​​use case can be determined after the positioning measurement is completed, thereby ensuring the validity of the determination of the time domain resources occupied by the AI ​​use case.

[0037] In conjunction with some embodiments of the first aspect, the feedback time of the positioning result includes: the time when the terminal sends the positioning result; or the time when the positioning management function (LMF) receives the positioning coordinates fed back by the terminal.

[0038] In the above embodiments, determining the end time of the time domain resources occupied by the AI ​​use case based on the time of the terminal sending the positioning result ensures the validity of the determination of the time domain resources occupied by the AI ​​use case, provided that the terminal has completed positioning and sent the positioning result. Determining the end time of the time domain resources occupied by the AI ​​use case based on the time of the positioning coordinates received by the terminal from the LMF also ensures the validity of the determination of the time domain resources occupied by the AI ​​use case after the positioning measurement is completed.

[0039] In conjunction with some embodiments of the first aspect, in response to the terminal sending a location result to the location management function (LMF), the end time is determined based on a third time.

[0040] In the above embodiments, when the terminal sends the positioning result to the LMF, the end time of the time domain resources occupied by the AI ​​use case is determined based on the positioning result feedback time. This can determine the end time of the time domain resources occupied by the AI ​​use case, and after the positioning measurement is completed, the validity of the determination of the time domain resources occupied by the AI ​​use case is guaranteed.

[0041] In conjunction with some embodiments of the first aspect, in response to the terminal not sending the location result to the location management function (LMF), the end time is determined based on a fourth time.

[0042] In the case where the terminal does not send the location results to the LMF, by setting preset values, it can be ensured that the end time of determining the time domain resources occupied by the AI ​​use case is after the location measurement is completed, thereby ensuring the validity of determining the time domain resources occupied by the AI ​​use case.

[0043] In conjunction with some embodiments of the first aspect, the preset value satisfies a preset time domain range that causes the end time determined based on the fourth time to be located after the start time.

[0044] In the above embodiments, by using preset values ​​to determine that the end time of the time domain resources occupied by the AI ​​use case is within a preset time domain range after the start time, it is possible to ensure that the end time of the time domain resources occupied by the AI ​​use case is determined after the location measurement is completed, thereby ensuring the validity of the determination of the time domain resources occupied by the AI ​​use case.

[0045] In conjunction with some embodiments of the first aspect, the start time is the location trigger time, and the end time is the location result feedback time.

[0046] In the above embodiments, the start time of the time domain resources occupied by the AI ​​use case is clearly defined as the location trigger time, and the end time is the location result feedback time. This can clearly define the time domain resource occupancy based on AI location and improve communication effectiveness.

[0047] In conjunction with some embodiments of the first aspect, the start time is the measurement time of the positioning reference signal PRS, and the end time is the feedback time of the positioning result.

[0048] In the above embodiments, the start time of the time domain resources occupied by the AI ​​use case is clearly defined as the measurement time of PRS, and the end time is the feedback time of the positioning result. This can clearly define the time domain resource occupancy based on AI positioning and improve communication effectiveness.

[0049] In conjunction with some embodiments of the first aspect, the start time is the measurement time of the positioning reference signal PRS, and the end time is a preset time domain range after the measurement time of the positioning reference signal PRS.

[0050] In the above embodiments, the start time of the time domain resources occupied by AI use cases is clearly defined as the measurement time of PRS, and the end time is a preset time domain range after the measurement time of PRS. This can clearly define the time domain resource occupancy based on AI positioning and improve communication effectiveness.

[0051] In conjunction with some embodiments of the first aspect, the start time is the positioning trigger time, and the end time is located within a preset time domain range after the measurement time of the positioning reference signal PRS.

[0052] In the above embodiments, the start time of the time domain resources occupied by AI use cases is clearly defined as the location trigger time, and the end time is a preset time domain range after the PRS measurement time. This can clearly define the time domain resource occupancy based on AI positioning and improve communication effectiveness.

[0053] In conjunction with some embodiments of the first aspect, the start time is the location trigger time, and the end time is a preset time range after the feedback time of the location result.

[0054] Based on some embodiments of the first aspect, it is determined whether to configure additional AI functions for the terminal based on the start and end times of the time domain resources occupied by the AI ​​use cases.

[0055] Secondly, a network device is provided, the network device including a processing module, the processing module being used to determine the start time and end time of time domain resources occupied by AI use cases, wherein the AI ​​use cases are deployed on a terminal and used for location.

[0056] In the above embodiments, by determining the start and end times of the time-domain resources occupied by AI use cases, the status of time-domain resources occupied by AI use cases can be clearly defined. This allows for corresponding communication processing based on the clearly defined time-domain resource usage of AI use cases, such as clearing the time-domain resources occupied by completed AI tasks to allow for new AI tasks. Furthermore, based on the occupied time-domain resources, the remaining available time-domain resources can be further determined, and AI functions can be allocated according to these remaining available resources. This avoids activating too many AI functions within the same timeframe, which could overload the terminal processing tasks.

[0057] In conjunction with some embodiments of the second aspect, the start time is determined based on at least one of a first time and a second time; the first time is determined based on the positioning trigger time, and the second time is determined based on the measurement time of the positioning reference signal PRS.

[0058] In the above embodiments, AI-based positioning can be determined based on the positioning trigger time and / or the PRS measurement time, so that the determination of the time domain resources occupied by AI can be based on the current execution of AI tasks, which is more in line with actual communication scenarios and improves the effectiveness of AI communication.

[0059] In conjunction with some embodiments of the second aspect, the measurement time of the positioning reference signal PRS includes: the measurement start time of the positioning reference signal; or the measurement end time of the positioning reference signal.

[0060] In the above embodiments, determining the start time of the time domain resources occupied by the AI ​​use case based on the measurement start time or measurement end time of the positioning reference signal can more clearly determine the start time of the time domain resources occupied by the AI ​​use case when the positioning reference signal occupies one or more resource units, thereby improving communication effectiveness.

[0061] In conjunction with some embodiments of the second aspect, in response to the terminal performing periodic location reporting, the start time is determined based on the second time.

[0062] In the above embodiments, when the terminal performs periodic location reporting, the location trigger time exists in the initial location configuration. Therefore, using the second time to determine the start time of the time domain resources occupied by the AI ​​use case can more effectively determine the start time of the time domain resources occupied by the AI ​​use case, even when the configuration in the initial location configuration cannot be obtained.

[0063] In conjunction with some embodiments of the second aspect, in response to the terminal performing non-periodic location reporting, the start time is determined based on the first time.

[0064] In the above embodiments, when the terminal performs non-periodic location reporting, the location trigger time exists in each location configuration. Therefore, by determining the start time of the time domain resources occupied by the AI ​​use case in the first instance, the start time of the time domain resources occupied by the AI ​​use case can be determined more effectively.

[0065] In conjunction with some embodiments of the second aspect, the end time is determined based on at least one of a third time and a fourth time; the third time is determined based on the feedback time of the positioning result, and the fourth time is determined based on a preset value.

[0066] In the above embodiments, the feedback time and / or preset value determination based on the positioning results can ensure that the end time of the time domain resources occupied by the AI ​​use case can be determined after the positioning measurement is completed, thereby ensuring the validity of the determination of the time domain resources occupied by the AI ​​use case.

[0067] In conjunction with some embodiments of the second aspect, the feedback time of the positioning result includes: the time when the terminal sends the positioning result; or the time when the positioning management function (LMF) receives the positioning coordinates fed back by the terminal.

[0068] In the above embodiments, determining the end time of the time domain resources occupied by the AI ​​use case based on the time of the terminal sending the positioning result ensures the validity of the determination of the time domain resources occupied by the AI ​​use case, provided that the terminal has completed positioning and sent the positioning result. Determining the end time of the time domain resources occupied by the AI ​​use case based on the time of the positioning coordinates received by the terminal from the LMF also ensures the validity of the determination of the time domain resources occupied by the AI ​​use case after the positioning measurement is completed.

[0069] In conjunction with some embodiments of the second aspect, in response to the terminal sending a location result to the location management function (LMF), the end time is determined based on a third time.

[0070] In the above embodiments, when the terminal sends the positioning result to the LMF, the end time of the time domain resources occupied by the AI ​​use case is determined based on the positioning result feedback time. This can determine the end time of the time domain resources occupied by the AI ​​use case, and after the positioning measurement is completed, the validity of the determination of the time domain resources occupied by the AI ​​use case is guaranteed.

[0071] In conjunction with some embodiments of the second aspect, in response to the terminal not sending the location result to the location management function (LMF), the end time is determined based on a fourth time.

[0072] In the case where the terminal does not send the location results to the LMF, by setting preset values, it can be ensured that the end time of determining the time domain resources occupied by the AI ​​use case is after the location measurement is completed, thereby ensuring the validity of determining the time domain resources occupied by the AI ​​use case.

[0073] In conjunction with some embodiments of the second aspect, the preset value satisfies a preset time domain range that causes the end time determined based on the fourth time to be located after the start time.

[0074] In the above embodiments, by using preset values ​​to determine that the end time of the time domain resources occupied by the AI ​​use case is within a preset time domain range after the start time, it is possible to ensure that the end time of the time domain resources occupied by the AI ​​use case is determined after the location measurement is completed, thereby ensuring the validity of the determination of the time domain resources occupied by the AI ​​use case.

[0075] In some embodiments of the second aspect, the start time is the location trigger time, and the end time is the location result feedback time.

[0076] In the above embodiments, the start time of the time domain resources occupied by the AI ​​use case is clearly defined as the location trigger time, and the end time is the location result feedback time. This can clearly define the time domain resource occupancy based on AI location and improve communication effectiveness.

[0077] In some embodiments of the second aspect, the start time is the measurement time of the positioning reference signal PRS, and the end time is the feedback time of the positioning result.

[0078] In the above embodiments, the start time of the time domain resources occupied by the AI ​​use case is clearly defined as the measurement time of PRS, and the end time is the feedback time of the positioning result. This can clearly define the time domain resource occupancy based on AI positioning and improve communication effectiveness.

[0079] In conjunction with some embodiments of the second aspect, the start time is the measurement time of the positioning reference signal PRS, and the end time is a preset time domain range after the measurement time of the positioning reference signal PRS.

[0080] In the above embodiments, the start time of the time domain resources occupied by AI use cases is clearly defined as the measurement time of PRS, and the end time is a preset time domain range after the measurement time of PRS. This can clearly define the time domain resource occupancy based on AI positioning and improve communication effectiveness.

[0081] In some embodiments of the second aspect, the start time is the positioning trigger time, and the end time is a preset time domain range after the measurement time of the positioning reference signal PRS.

[0082] In the above embodiments, the start time of the time domain resources occupied by AI use cases is clearly defined as the location trigger time, and the end time is a preset time domain range after the PRS measurement time. This can clearly define the time domain resource occupancy based on AI positioning and improve communication effectiveness.

[0083] In conjunction with some embodiments of the second aspect, the start time is the location trigger time, and the end time is a preset time range after the feedback time of the location result.

[0084] In conjunction with some embodiments of the second aspect, based on the start and end times of the time domain resources occupied by the AI ​​use cases, it is determined whether to configure additional AI functions for the terminal.

[0085] Thirdly, a communication device is provided for performing the communication method described in the first aspect or any one of the first aspects.

[0086] Fourthly, a communication system is provided, including a terminal and a network device, wherein the terminal and / or the network device are configured to implement the communication method of the first aspect or any one of the first aspects.

[0087] Fifthly, a storage medium is provided that stores instructions which, when executed on a communication device, cause the communication device to perform a communication method as described in the first aspect or any one of the first aspects.

[0088] In a sixth aspect, a program product is provided, comprising at least one of a program and instructions, wherein the program and instructions, when executed by a communication device, implement the communication method of the first aspect or any one of the first aspects.

[0089] In a seventh aspect, embodiments of this disclosure provide a computer program that, when run on a computer, causes the computer to perform the method as described in the first aspect or any alternative implementation thereof.

[0090] Eighthly, embodiments of this disclosure provide a chip or chip system. The chip or chip system includes processing circuitry configured to perform the method described according to the first aspect or any optional implementation thereof.

[0091] It is understood that the communication devices, communication systems, storage media, program products, computer programs, chips, or chip systems involved in the embodiments of this disclosure are all used to execute 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.

[0092] This disclosure provides communication methods, communication devices, communication systems, storage media, and program products. In some embodiments, terms such as communication method and information processing method can be used interchangeably, as can terms such as communication device and information processing device, and terms such as information processing system and communication system.

[0093] 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. In all embodiments of this disclosure, unless otherwise specified or logically conflicting, the terminology and / or descriptions between the embodiments are consistent and can be mutually referenced. Technical features in different embodiments can be combined to form new embodiments based on their inherent logical relationships.

[0094] 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.

[0095] 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.

[0096] In the embodiments disclosed herein, "multiple" refers to two or more.

[0097] In some embodiments, the terms “at least one of A or B, at least one of A and B”, “one or more”, “a plurality of”, “multiple”, etc., may be used interchangeably.

[0098] 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 whether there is a branch B); in some embodiments, B (execute B regardless of whether there is a branch A); in some embodiments, execution is selected from A and B (A and B are selectively executed); in some embodiments, both A and B are executed. The same applies when there are more branches such as A, B, C, etc.

[0099] In some embodiments, the notation "A or B" may include the following technical solutions, depending on the situation: in some embodiments, A (execute A regardless of whether a branch B exists); in some embodiments, B (execute B regardless of whether a branch A exists); 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, and C.

[0100] 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.

[0101] In some embodiments, “including A,” “containing A,” “for indicating A,” and “carrying A” can be interpreted as directly carrying A or indirectly indicating A.

[0102] In some embodiments, terms such as "time / frequency" and "time-frequency domain" refer to the time domain and / or frequency domain.

[0103] In some embodiments, terms such as “in response to…”, “in response to determining…”, “in the case of…”, “when…”, “when…”, “if…”, etc. can be used interchangeably. These descriptions all refer to the device making a corresponding action under certain objective circumstances. They do not necessarily limit the time, nor do they require the device to make a judgment action when implementing it, nor do they mean that there must be other limitations.

[0104] In some embodiments, the terms “greater than,” “greater than or equal to,” “not less than,” “more than,” “more than or equal to,” “not less than,” “higher than,” “higher than or equal to,” “not lower than,” and “above” can be used interchangeably, as can the terms “less than,” “less than or equal to,” “not greater than,” “less than,” “less than or equal to,” “not more than,” “lower than,” “lower than or equal to,” “not higher than,” and “below”.

[0105] In some embodiments, devices, etc., may be interpreted as physical or virtual, and their names are not limited to those described in the embodiments. Terms such as “device,” “equipment,” “circuit,” “network element,” “network function,” “network device,” “function,” “node,” “unit,” “section,” “system,” “network,” “chip,” “chip system,” “entity,” and “subject” are interchangeable.

[0106] In some embodiments, "network" can be interpreted as devices included in a network (e.g., access network devices, core network devices, etc.).

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] In some embodiments, the acquisition of data, information, etc., may comply with the laws and regulations of the country where the location is situated.

[0112] In some embodiments, data, information, etc., may be obtained with the user's consent.

[0113] 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.

[0114] Currently, the widespread application of 5G technology is bringing tremendous changes to all aspects of people's lives. According to the vision of the International Telecommunication Union (ITU), 5G will permeate all areas of future society, building a comprehensive information ecosystem centered on the user. Specifically, 5G user experience speeds can reach 100 Mbps to 1 Gbps, supporting ultimate service experiences such as mobile virtual reality; 5G peak speeds can reach 10 Gbps to 20 Gbps, with a traffic density of 10 Mbps per square meter (m²), capable of supporting more than a thousandfold increase in mobile traffic; 5G connection density can reach 1 million connections per square meter (m²), effectively supporting massive numbers of IoT devices; 5G transmission latency can be down to the millisecond level, meeting the stringent requirements of vehicle-to-everything (V2X) and industrial control; 5G can support mobile speeds of 500 km / h, providing a good user experience even in high-speed rail environments. It is conceivable that 5G, as a representative of new infrastructure, will reshape the future information society.

[0115] In recent years, artificial intelligence (AI) technology has made continuous breakthroughs in multiple fields. The ongoing development of fields such as intelligent voice and computer vision has not only brought a wide variety of applications to smart terminals, but has also found widespread use in education, transportation, home, healthcare, retail, security, and many other sectors, bringing convenience to people's lives while promoting industrial upgrading across various industries. AI technology is also accelerating its cross-disciplinary integration with other disciplines, combining knowledge from different fields while providing new directions and methods for the development of various disciplines.

[0116] Recent research has introduced AI technology into wireless air interfaces and investigated how AI can assist in improving wireless air interface transmission technology. This includes, for example, the following three aspects (A through C):

[0117] A. AI-enabled connectivity. This refers to using AI to improve communication performance, such as using AI for beam management.

[0118] B. Computing power services. This means that the network side can provide computing power to the terminal side, such as helping the terminal to perform model training and model inference.

[0119] C. Ultimate AI Service. This involves enhancing the network transmission pipeline to improve the user experience of AI application services.

[0120] Figure 1A is a schematic diagram of a communication system architecture according to an embodiment of the present disclosure.

[0121] As shown in Figure 1A, the communication system 100 includes a terminal 101 and a network device 102.

[0122] In some embodiments, terminal 101 includes, for example, 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, but is not limited thereto.

[0123] In some embodiments, network device 102 may include at least one of access network device and core network device.

[0124] 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 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, but is not limited thereto.

[0125] 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.

[0126] 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.

[0127] In some embodiments, a core network device may be a single device comprising one or more network elements, or it may be multiple devices or a group of devices, each comprising all or part of the aforementioned one or more network elements. Network elements may be virtual or physical. The core network may include, for example, at least one of an Evolved Packet Core (EPC), a 5G Core Network (5GCN), or a Next Generation Core (NGC).

[0128] In some embodiments, the core network device is, for example, a Location Management Function (LMF).

[0129] 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.

[0130] 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.

[0131] 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), 6th generation mobile communication system (6G), 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).

[0132] In some embodiments, AI or ML-based positioning in the wireless air interface can assist in improving the transmission technology of the wireless air interface. The principle of AI or ML-based positioning is to train an AI or ML model and deploy the AI ​​or ML model on the terminal 101 or network device 102.

[0133] In some embodiments, the AI ​​or ML model can be deployed on terminal 101. Terminal 101 obtains channel measurement results by measuring the Positioning Reference Signal (PRS) sent by network device 102. Terminal 101 inputs the relevant channel measurement results into the AI ​​model, and the AI ​​model outputs positioning coordinates or intermediate parameters for positioning coordinate calculation. These intermediate parameters may include, for example, time information, angle information, etc.

[0134] In some embodiments, AI or ML-based localization over a wireless air interface mainly involves three steps (also referred to as time steps) as shown in Figure 1B. Referring to Figure 1B, the three time steps involved in the localization process mainly include:

[0135] - Location process triggering (shown in the diagram): Network device 102 configures terminal 101 to perform AI- or ML-based location. In non-periodic location, all steps triggering this location process will be present. In periodic location, these steps triggering the location process are present in the initial configuration.

[0136] -PRS Measurement: Terminal 101 measures the PRS sent by network device 102. The PRS may occupy multiple Orthogonal Frequency Division Multiplexing (OFDM) symbols.

[0137] -Location result feedback: This step is not mandatory. When location is triggered externally, the terminal will provide location information. However, when the location coordinates are used for local applications on the terminal, the terminal will not provide location results.

[0138] In Figure 1B, t represents time, indicating the time-domain resources occupied by the positioning execution. The positioning process shown in Figure 1B involves the positioning trigger time, PRS measurement time, and positioning result feedback time, which can be understood as occupying one or more time-domain resources. These time-domain resources can be understood as time units, moments, points in time, or time periods, such as symbols, time slots, sub-time slots, etc.

[0139] With the popularization of AI, more and more AI function use cases will be deployed on terminal 101. These AI function use cases require terminal 101 to support the software and hardware environment for AI processing. When running these AI functions, they will also occupy the computing power unit of terminal 101.

[0140] Since the storage of the AI ​​processor in terminal 101 is limited, each AI function use case needs to be cleared from the processing unit after completing its task so that the next task can proceed. On the network device 102 side, network device 102 also needs to be aware of the AI ​​processor's usage to avoid activating too many AI functions at the same time, which could overload the processing tasks of terminal 101.

[0141] In view of this, the embodiments of this disclosure determine the time domain resources occupied by the AI ​​use case for AI-based positioning use cases, so that the terminal 101 and the network device 102 can clearly understand the time domain resources occupied by the AI ​​use case.

[0142] The time-domain resources occupied by AI use cases can also be understood as the processing resources required to execute AI-related tasks, or as the occupancy of AI processing units.

[0143] Figure 2A is a schematic diagram illustrating a communication method according to an embodiment of the present disclosure. As shown in Figure 2A, this embodiment of the present disclosure relates to a communication method for a communication system 100, the method comprising:

[0144] In step S2101, network device 102 sends first information to terminal 101.

[0145] In some embodiments, the first information is used to configure the terminal 101 to perform AI- or ML-based positioning. Alternatively, the first information can be understood as being used to trigger positioning. Or, the first information can be used to determine the trigger time of the positioning process.

[0146] In some embodiments, terminal 101 receives first information sent by network device 102.

[0147] In some embodiments, network device 102 sends a PRS signal to terminal 101.

[0148] In some embodiments, terminal 101 receives a PRS signal sent by network device 102.

[0149] In step S2102, terminal 101 performs positioning.

[0150] In some embodiments, terminal 101 measures the PRS signal sent by network device 102 for location purposes. There may be one or more PRS signals.

[0151] In some embodiments, the PRS signal occupies one or more time-domain resources. These time-domain resources can also be represented by time units. These time units can be, for example, one or more of the following: symbols, time slots, sub-time slots, etc.

[0152] In some embodiments, the measurement time for the terminal 101 to measure the PRS signal occupies one or more time units.

[0153] In some embodiments, the measurement time for the terminal 101 to measure the PRS signal includes the measurement start time of the positioning reference signal and / or the measurement end time of the positioning reference signal.

[0154] In step S2103, terminal 101 returns the positioning result.

[0155] In some embodiments, step S2103 is optional.

[0156] In some embodiments, when location is triggered externally, the terminal will provide location information.

[0157] In some embodiments, the terminal 101 feeds back the positioning result to the LMF.

[0158] In some embodiments, the terminal 101 periodically reports its location.

[0159] In some embodiments, terminal 101 performs non-periodic location reporting.

[0160] In step S2104a, terminal 101 determines the time domain resources occupied by the AI ​​use case.

[0161] In step S2104b, network device 102 determines the time domain resources occupied by the AI ​​use case.

[0162] In some embodiments, AI use cases are deployed on the endpoint and used for location.

[0163] In some embodiments, the time-domain resources occupied by the AI ​​use case are determined by determining the start and end times of the time-domain resources occupied by the AI ​​use case.

[0164] In some embodiments, the time involved in this disclosure can be understood as a time unit, moment, point in time, or time period, such as one or more of the following: symbol, time slot, sub-time slot, etc.

[0165] In some embodiments, the start time of the time-domain resources used by the AI ​​use case is determined based on at least one of a first time and a second time. The first time is determined based on the trigger time of the localization process, and the second time is determined based on the measurement time of the PRS.

[0166] In some embodiments, the start time of the time domain resources used by the AI ​​use case is determined based on the trigger time of the localization process.

[0167] The trigger time for the positioning process can be understood as the time when the terminal is triggered to perform positioning. The trigger time for the positioning process can also be referred to as the positioning trigger time.

[0168] In some embodiments, the measurement time of the PRS includes either the measurement start time of the PRS or the measurement end time of the PRS. This can also be understood as the start time of the time-domain resources occupied by the AI ​​use case being determined based on the measurement start time of the PRS, or based on the measurement end time of the PRS. In one example, where one or more PRSs are configured to occupy one or more time units, the start time of the time-domain resources occupied by the AI ​​use case includes either the start time of all PRSs configured or the end time of all PRS configurations.

[0169] In some embodiments, the start time of determining the time domain resources occupied by the AI ​​use case is determined based on whether the terminal 101 performs periodic location reporting, or based on a second time.

[0170] In some embodiments, in response to the terminal 101 performing non-periodic location reporting, the start time of the time domain resources occupied by the AI ​​use case is determined based on a first time. As shown by the dashed lines in Figures 2B, 2E, and 2F, the start time of the time domain resources occupied by the AI ​​use case is within the time period occupied by the location trigger. In one example, the start time of the time domain resources occupied by the AI ​​use case is the location trigger time.

[0171] In some embodiments, in response to the periodic location reporting by terminal 101, the start time of the time domain resources occupied by the AI ​​use case is determined based on a second time. As shown by the dashed lines in Figures 2C and 2D, the start time of the time domain resources occupied by the AI ​​use case is within the time period occupied by the PRS measurement. In one example, the start time of the time domain resources occupied by the AI ​​use case is the start time of the PRS measurement. In another example, the start time of the time domain resources occupied by the AI ​​use case is the end time of the PRS measurement.

[0172] In some embodiments, the end time of the time-domain resources consumed by the AI ​​use case is determined based on at least one of a third time and a fourth time. The third time is determined based on the feedback time of the positioning result. The fourth time is determined based on a preset value.

[0173] In some embodiments, the feedback time of the positioning result includes: the time when the terminal 101 sends the positioning result; or the time when the LMF receives the positioning result fed back by the terminal 101.

[0174] The positioning result fed back by terminal 101 can be positioning coordinates. Alternatively, the positioning result fed back by terminal 101 can be intermediate parameters such as time information and angle information used for positioning coordinate calculation.

[0175] The location result fed back by terminal 101 can also be understood as terminal 101 sending the location result to LMF.

[0176] In some embodiments, the end time of the time domain resources occupied by the AI ​​use case is determined based on whether the terminal 101 sends the location result to the LMF, whether it is determined based on a third time or a fourth time.

[0177] In some embodiments, in response to the terminal sending the location result to the LMF, the end time of the time domain resources occupied by the AI ​​use case is determined based on a third time.

[0178] In some embodiments, in response to the terminal sending the location result to the LMF, the end time of the time domain resources occupied by the AI ​​use case can also be determined based on a fourth time.

[0179] In some embodiments, in response to the terminal not sending the location result to the LMF, the end time of the time domain resources occupied by the AI ​​use case is determined based on a fourth time.

[0180] In some embodiments, in response to the terminal not sending the location result to the LMF, the end time of the time domain resources occupied by the AI ​​use case can also be determined based on a third time.

[0181] In some embodiments, the fourth time determined based on a preset value can be understood as the fourth time being preset. For example, it can be predefined by the protocol, reported by terminal 101, or configured by network device 102.

[0182] In some embodiments, the preset value satisfies a preset time domain range that ensures the end time determined based on the fourth time is located after the start time. In one example, the end time of the time domain resources occupied by the AI ​​use case determined based on the fourth time includes the time within a preset time period after the start time of the time domain resources occupied by the AI ​​use case. For example, if the start time of the time domain resources occupied by the AI ​​use case is T0 and the preset time is k, then the end time of the time domain resources occupied by the AI ​​use case is T0+k. This preset time can be predefined by the protocol or reported by terminal 101.

[0183] In some embodiments, the end time of the time-domain resources used by the AI ​​use case is determined based on the feedback time of the location result. For example, in one example, the end time of the time-domain resources used by the AI ​​use case is determined based on the latest response time of the location result configured in the LMF.

[0184] In some embodiments, the start and end times of the time-domain resources used by AI use cases can be determined using one or more methods described in the above embodiments.

[0185] In some embodiments, Figure 2B is a schematic diagram illustrating the start and end times of time-domain resources occupied by an AI use case according to an embodiment of the present disclosure. Referring to Figure 2B, the dashed lines in the figure represent the start and end times of time-domain resources occupied by the AI ​​use case. Specifically, in Figure 2B, the start time of the time-domain resources occupied by the AI ​​use case is the location trigger time, and the end time of the time-domain resources occupied by the AI ​​use case is the feedback time of the location result.

[0186] In some embodiments, FIG2C is a schematic diagram showing the start and end times of time-domain resources occupied by an AI use case according to an embodiment of the present disclosure. Referring to FIG2C, the dashed lines in the figure represent the start and end times of time-domain resources occupied by the AI ​​use case. In FIG2C, the start time of the time-domain resources occupied by the AI ​​use case is the measurement time of the positioning reference signal (PRS), and the end time of the time-domain resources occupied by the AI ​​use case is the feedback time of the positioning result.

[0187] In some embodiments, FIG2D is a schematic diagram illustrating the start and end times of time-domain resources occupied by an AI use case according to an embodiment of the present disclosure. Referring to FIG2D, the dashed lines in the figure represent the start and end times of time-domain resources occupied by the AI ​​use case. In FIG2D, the start time of the time-domain resources occupied by the AI ​​use case is the measurement time of the positioning reference signal (PRS), and the end time of the time-domain resources occupied by the AI ​​use case is within a preset time-domain range after the measurement time of the PRS.

[0188] In some embodiments, FIG2E is a schematic diagram illustrating the start and end times of time-domain resources occupied by an AI use case according to an embodiment of the present disclosure. Referring to FIG2E, the dashed lines in the figure represent the start and end times of time-domain resources occupied by the AI ​​use case. In FIG2E, the start time of the time-domain resources occupied by the AI ​​use case is the trigger time of the positioning process, and the end time of the time-domain resources occupied by the AI ​​use case is within a preset time-domain range after the measurement time of the positioning reference signal PRS.

[0189] In some embodiments, Figure 2F is a schematic diagram illustrating the start and end times of time-domain resources occupied by an AI use case according to an embodiment of this disclosure. Referring to Figure 2F, the dashed lines in the figure represent the start and end times of time-domain resources occupied by the AI ​​use case. In Figure 2F, the start time of the time-domain resources occupied by the AI ​​use case is the trigger time of the localization process, and the end time of the time-domain resources occupied by the AI ​​use case is within a preset time-domain range after the feedback time of the localization result.

[0190] Step S2105a: Terminal 101 clears the time domain resources occupied by AI use cases.

[0191] In some embodiments, if the terminal 101 determines the start and end times of the time domain resources occupied by the AI ​​use case, and the AI ​​use case has finished executing the current AI function (e.g., completed the positioning task), the time domain resources occupied by the AI ​​use case can be cleared to reduce the occupation of terminal AI processing resources so as to perform other AI functions.

[0192] In step S2105b, network device 102 determines whether to configure additional AI functions for the terminal based on the start and end times of the time domain resources occupied by the AI ​​use case.

[0193] In some embodiments, network device 102, upon determining the start and end times of the time-domain resources used by AI use cases, can determine the usage of time-domain resources in terminal 101. In one example, if network device 102 can determine the total number of time-domain resources currently being used by terminal 101 within a certain time unit and the total number of time-domain resources that the terminal can provide for AI, it can determine the remaining time-domain resources available for AI to perform its corresponding functions. Based on the remaining time-domain resources available for AI to perform its corresponding functions, network device 102 can further determine whether to configure additional AI functions for the terminal.

[0194] In some embodiments, the network device predefines the number of time-domain resources occupied by each AI processing task. If two AI tasks run simultaneously, the total number of time-domain resources occupied is the sum of the time-domain resources occupied by the two AI tasks. For example, in one example, once the start and end times of the time-domain resources occupied by the AI ​​use case are determined, the network device 102 can clearly determine whether an AI use case occupies processing resources and how many processing resource units it occupies at a certain moment or time period. If multiple AI use cases exist at a certain moment, such as AI task 1 and AI task 2 both occupying processing resources at a certain moment, with resource units occupied by 3 and 5 respectively, then the network device 102 knows that terminal 101 has a total of 8 resource units occupied at that moment.

[0195] The resource units mentioned above can also be understood as one or more of the symbols, time slots, sub-time slots, etc. mentioned above. Processing resources can be understood as the time-domain resources mentioned above.

[0196] The communication method involved in the embodiments of this disclosure may include at least one of steps S2101 to S2105b. For example, step S2104b may be implemented as a standalone embodiment, but is not limited thereto.

[0197] In some embodiments, steps S2101, S2102, S2103, S2104a, S2105a, and S2105b are optional, and one or more of these steps may be omitted or substituted in different embodiments.

[0198] In some embodiments, if an arrow in the interaction diagram representing the sending of information, signaling, etc. from one subject to another passes through other subjects, it can be interpreted as the information being forwarded from one subject to another via other subjects, or it can be interpreted as the information being sent from one subject to another without passing through other subjects.

[0199] In some embodiments, the steps and their optional implementations in other embodiments described before or after this embodiment, as well as other related parts in the specification, can be referred to, and will not be repeated here.

[0200] Figure 3 is a flowchart illustrating a communication method according to an embodiment of the present disclosure. As shown in Figure 3, the embodiment of the present disclosure relates to a communication method executed by a communication device, which may be, for example, a network device 102 or a terminal 101. The method includes:

[0201] Step S3101: Determine the start and end times of the time domain resources occupied by the AI ​​use case.

[0202] In some embodiments, optional implementations of step S3101 can be found in the optional implementations of steps S2104a and S2104b in FIG2A, as well as other related parts in the embodiments involved in FIG2A, which will not be repeated here.

[0203] In some embodiments, AI use cases are deployed on the endpoint and used for location.

[0204] In some embodiments, the start time of the time-domain resources used by AI use cases is determined based on at least one of a first time and a second time;

[0205] The first time is determined based on the location trigger time, and the second time is determined based on the PRS measurement time.

[0206] In some embodiments, the measurement time of PRS includes: the start time of PRS measurement; or the end time of PRS measurement.

[0207] In some embodiments, in response to the terminal periodically reporting its location, the start time of the time domain resources used by the AI ​​use case is determined based on a second time.

[0208] In some embodiments, in response to the terminal performing non-periodic location reporting, the start time of the time domain resources occupied by the AI ​​use case is determined based on a first time.

[0209] In some embodiments, the end time of the time-domain resources consumed by the AI ​​use case is determined based on at least one of a third time and a fourth time.

[0210] The third time is determined based on the feedback time of the positioning results, and the fourth time is determined based on the preset value.

[0211] In some embodiments, the feedback time of the positioning result includes: the time when the terminal sends the positioning result; or the time when the LMF receives the positioning coordinates fed back by the terminal.

[0212] In some embodiments, in response to the terminal sending a location result to the location management function (LMF), the end time of the time domain resources occupied by the AI ​​use case is determined based on a third time.

[0213] In some embodiments, in response to the terminal not sending location results to the location management function (LMF), the end time of the time domain resources occupied by the AI ​​use case is determined based on a fourth time.

[0214] In some embodiments, the preset value satisfies a preset time domain range that places the end time determined based on the fourth time after the start time of the time domain resources occupied by the AI ​​use case.

[0215] In some embodiments, the start time of the time domain resources occupied by the AI ​​use case is the location trigger time, and the end time of the time domain resources occupied by the AI ​​use case is the feedback time of the location result.

[0216] In some embodiments, the start time of the time domain resources occupied by the AI ​​use case is the measurement time of the localization reference signal (PRS), and the end time of the time domain resources occupied by the AI ​​use case is the feedback time of the localization result.

[0217] In some embodiments, the start time of the time domain resources occupied by the AI ​​use case is the measurement time of the positioning reference signal (PRS), and the end time of the time domain resources occupied by the AI ​​use case is within a preset time domain range after the measurement time of the positioning reference signal (PRS).

[0218] In some embodiments, the start time of the time domain resources occupied by the AI ​​use case is the location trigger time, and the end time of the time domain resources occupied by the AI ​​use case is within a preset time domain range after the measurement time of the location reference signal PRS.

[0219] In some embodiments, the start time of the time domain resources occupied by the AI ​​use case is the location trigger time, and the end time of the time domain resources occupied by the AI ​​use case is within a preset time domain range after the feedback time of the location result.

[0220] In some embodiments, when the communication device is a network device, the network device can configure other AI functions different from the positioning based on the time domain resources occupied by the AI ​​use case. This optional implementation can be found in the optional implementation of step S2105b in Figure 2A, and other related parts in the embodiments involved in Figure 2A, which will not be repeated here.

[0221] In some embodiments, when the communication device is a terminal, the terminal can clear the time domain resources occupied by the AI ​​use case based on the time domain resources occupied by the AI ​​use case. This optional implementation can be found in the optional implementation of step S2105a in Figure 2A, and other related parts in the embodiments involved in Figure 2A, which will not be repeated here.

[0222] Figure 4 is a flowchart illustrating a communication method according to an embodiment of the present disclosure. As shown in Figure 4, the communication method according to an embodiment of the present disclosure includes the following steps:

[0223] Step S4101: Configure the network device with first information. The first information is used to configure the terminal for AI positioning.

[0224] Step S4102: The terminal performs positioning.

[0225] In some embodiments, terminal 101 determines the start and end times of the time-domain resources occupied by the AI ​​use case, wherein the AI ​​use case is deployed on the terminal and used for location.

[0226] In some embodiments, after the terminal 101 completes AI-based positioning, it clears the start and end times of the time domain resources occupied by the AI ​​use case.

[0227] In step S4103, the network device determines the start and end times of the time domain resources occupied by the AI ​​use case, wherein the AI ​​use case is deployed on the terminal and used for positioning.

[0228] Step S4104: The network device determines whether to configure additional AI functions for the terminal based on the start and end times of the time domain resources occupied by the AI ​​use case.

[0229] The communication method involved in the embodiments of this disclosure may include at least one of steps S4101 to S4104. For example, step S4103 may be implemented as a separate embodiment, but is not limited thereto.

[0230] In some embodiments, steps S4101 and S4102 are optional, and one or more of these steps may be omitted or substituted in different embodiments.

[0231] In some embodiments, the steps and their optional implementations in other embodiments described before or after this embodiment, as well as other related parts in the specification, can be referred to, and will not be repeated here.

[0232] In some embodiments, if an arrow in the interaction diagram representing the sending of information, signaling, etc. from one subject to another passes through other subjects, it can be interpreted as the information being forwarded from one subject to another via other subjects, or it can be interpreted as the information being sent from one subject to another without passing through other subjects.

[0233] In some embodiments, the steps and their optional implementations in other embodiments described before or after this embodiment, as well as other related parts in the specification, can be referred to, and will not be repeated here.

[0234] The communication method provided in this disclosure is applicable to AI-based positioning. The principle of AI-based positioning is that the model can be deployed on the terminal side. The terminal side obtains channel measurement results by measuring the PRS transmitted by the base station. Then, the terminal inputs the relevant channel measurement results into the AI ​​model. The AI ​​model outputs positioning coordinates or intermediate parameters (such as time information, angle information, etc.) for calculating permanent positioning coordinates.

[0235] AI-based positioning involves AI processing units, which can be understood as processing resources for executing AI-related tasks. With the increasing prevalence of AI, more and more AI function use cases will be deployed on the terminal side. These AI function use cases require a hardware and software environment on the terminal that supports AI processing, and running these AI functions also requires the use of computing power units on the terminal side.

[0236] Since the storage of the terminal AI processor is limited, each AI function needs to be cleared from the processing unit after completing its task to allow for the next task. On the network side, it also needs to be aware of the AI ​​processor's occupancy to avoid activating too many AI functions simultaneously, which could overload the terminal's processing capacity. Specifically, for AI-based positioning use cases, how to define the occupancy of the AI ​​processing unit still needs to be determined in the proposed solution.

[0237] In some embodiments, in AI-based positioning, this disclosure proposes a method for determining the time occupied by the terminal-side AI processing unit.

[0238] The entire positioning process may involve three time points, as shown in Figure 1B:

[0239] -Location process trigger: The network configures the terminal to perform AI / ML-based location. In non-periodic location, this step will occur at all times; in periodic location, this step will only occur in the initial configuration.

[0240] -PRS Measurement: The terminal measures the PRS transmitted by the network. Note that the PRS may occupy multiple OFDM symbols.

[0241] -Location result feedback: This step is not mandatory. When location is triggered externally, the terminal will provide location information. However, when the location coordinates are used for local applications on the terminal, the terminal will not provide feedback.

[0242] In some embodiments, the communication method provided in this disclosure can be understood as a method for determining the time occupied by an AI processing unit.

[0243] The methods for determining the time occupied by the AI ​​processing unit include the following:

[0244] (1) Determine the start time and end time of the occupied time. The start time may be determined by a first time or a second time, wherein the first time is determined based on the positioning process trigger time and the second time is determined based on the PRS measurement time. The end time of the occupied time may be determined by a third time or a fourth time, wherein the third time is determined by the positioning result feedback time and the fourth time is determined based on a preset value.

[0245] The determination of the time occupied by the AI ​​processing unit can be found in the various methods of determining the occupancy symbol shown in Figures 2B to 2F.

[0246] (2) Based on (1), the second time includes the start time of all configurations assigned to the PRS or the end time of all PRS configurations.

[0247] (3) The end time of the occupied time determined based on the fourth time as described in (1) includes the time within a preset time period of the start time. For example, if the start time is T0 and the preset time is k, then the end time is T0+k. The preset time can be predefined by the protocol or reported by the terminal.

[0248] (4) The third time includes the time when the terminal sends the positioning result or the time when the LMF receives the positioning coordinates.

[0249] (5) Based on (1)-(4), in response to the terminal's positioning result being fed back to the LMF, the end time of the occupied time is determined based on the third time.

[0250] (6) Based on (1)-(4), in response to the terminal's feedback result not being fed back to LMF, the end time of the occupied time is determined based on the fourth time.

[0251] (7) Based on (1)-(4), in response to the terminal performing periodic location reporting, the start time of the occupied time is determined based on the second time.

[0252] (8) Based on (1)-(4), in response to the terminal performing non-periodic location reporting, the start time of the occupied time is determined based on the first time.

[0253] (9) On the network side, the network determines the usage status of the AI ​​processing units on the terminal side based on the occupancy time of the AI ​​processing units corresponding to the AI ​​functions. For example, the network side can determine how much remaining processing capacity the terminal has by judging the total number of AI processing units currently occupied by the terminal and the total amount of AI processing that the terminal can provide in a certain time unit. Based on this information, the network can further determine whether to configure additional AI functions for the terminal.

[0254] This disclosure also proposes an apparatus (also referred to as a communication device, etc.) for implementing any of the above methods. For example, an apparatus is proposed that includes units or modules for implementing the steps performed by the terminal in any of the above methods. Furthermore, another apparatus is proposed that includes units or modules for implementing the steps performed by a network device (e.g., an access network device, a core network functional node, a core network device, etc.) in any of the above methods.

[0255] 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.

[0256] 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).

[0257] Figure 5 is a schematic diagram of the structure of the communication device proposed in an embodiment of this disclosure. The communication device 5100 is used to perform any of the above methods. The communication device 5100 can be a network device, or it can be a terminal.

[0258] In some embodiments, as shown in FIG5, the communication device 5100 may include at least one of a transceiver module 5101, a processing module 5102, etc.

[0259] In some embodiments, the transceiver module 5101 is used to interact with other devices.

[0260] In some embodiments, the processing module 5102 is used to determine the start time and end time of the time domain resources occupied by the AI ​​use case, wherein the AI ​​use case is deployed on a terminal and used for location.

[0261] In some embodiments, the start time of the time-domain resources occupied by the AI ​​use case is determined based on at least one of a first time and a second time; the first time is determined based on the location trigger time, and the second time is determined based on the measurement time of the location reference signal PRS.

[0262] In some embodiments, the measurement time of the PRS includes: the measurement start time of the positioning reference signal; or the measurement end time of the positioning reference signal.

[0263] In some embodiments, in response to the terminal periodically reporting its location, the start time of the time domain resources used by the AI ​​use case is determined based on a second time.

[0264] In some embodiments, in response to the terminal performing non-periodic location reporting, the start time of the time domain resources occupied by the AI ​​use case is determined based on a first time.

[0265] In some embodiments, the end time of the time domain resources occupied by the AI ​​use case is determined based on at least one of a third time and a fourth time; the third time is determined based on the feedback time of the positioning result, and the fourth time is determined based on a preset value.

[0266] In some embodiments, the feedback time of the positioning result includes: the time when the terminal sends the positioning result; or the time when the LMF receives the positioning coordinates fed back by the terminal.

[0267] In some embodiments, in response to the terminal sending a location result to the location management function (LMF), the end time of the time domain resources occupied by the AI ​​use case is determined based on a third time.

[0268] In some embodiments, in response to the terminal not sending location results to the location management function (LMF), the end time of the time domain resources occupied by the AI ​​use case is determined based on a fourth time.

[0269] In some embodiments, the preset value satisfies a preset time domain range that places the end time determined based on the fourth time after the start time of the time domain resources occupied by the AI ​​use case.

[0270] In some embodiments, the start time of the time domain resources occupied by the AI ​​use case is the location trigger time, and the end time of the time domain resources occupied by the AI ​​use case is the feedback time of the location result.

[0271] In some embodiments, the start time of the time domain resources occupied by the AI ​​use case is the measurement time of PRS, and the end time of the time domain resources occupied by the AI ​​use case is the feedback time of the localization result.

[0272] In some embodiments, the start time of the time domain resources occupied by the AI ​​use case is the measurement time of the PRS, and the end time of the time domain resources occupied by the AI ​​use case is within a preset time domain range after the measurement time of the positioning reference signal PRS.

[0273] In some embodiments, the start time of the time-domain resources occupied by the AI ​​use case is the location trigger time, and the end time of the time-domain resources occupied by the AI ​​use case is within a preset time-domain range after the measurement time of the location reference signal (PRS).

[0274] In some embodiments, the start time of the time domain resources occupied by the AI ​​use case is the location trigger time, and the end time of the time domain resources occupied by the AI ​​use case is within a preset time domain range after the feedback time of the location result.

[0275] Figure 6A is a schematic diagram of the structure of the communication device proposed in an embodiment of this disclosure. The communication device 6100 can be a network device (e.g., access network device, core network device, etc.), a terminal (e.g., user equipment, 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 6100 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.

[0276] As shown in Figure 6A, the communication device 6100 is used to execute any of the above methods. In some embodiments, the communication device 6100 includes one or more processors 6101. The processor 6101 may be a general-purpose processor or a special-purpose processor, such as a baseband processor or a central processing unit. The baseband processor may be used to process communication protocols and communication data, and the central processing unit may 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 6100 is used to execute any of the above methods. Optionally, one or more processors 6101 are used to invoke instructions to cause the communication device 6100 to execute any of the above methods.

[0277] In some embodiments, the communication device 6100 further includes one or more transceivers 6102. When the communication device 6100 includes one or more transceivers 6102, the transceiver 6102 performs at least one of the communication steps such as sending and / or receiving in the above method (e.g., step S2101, but not limited thereto), and the processor 6101 performs other steps (e.g., step S2104a or step S2104b, but not limited thereto). In optional embodiments, the transceiver may include a receiver and / or a transmitter, which may be separate or integrated together. 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; the terms receiver, receiving unit, receiver, receiving circuit, etc., can be used interchangeably.

[0278] In some embodiments, the communication device 6100 further includes one or more memories 6103 for storing data and / or instructions. Optionally, one or more processors 6101 are used to invoke instructions stored in the memory 6103 to cause the communication device 6100 to perform any of the above methods. Optionally, all or part of the memory 6103 may also be located outside the communication device 6100. In an optional embodiment, the communication device 6100 may include one or more interface circuits 6104. Optionally, the interface circuit 6104 is connected to the memory 6103 and can be used to receive data and / or instructions from the memory 6103 or other devices, and can be used to send data and / or instructions to the memory 6103 or other devices. For example, the interface circuit 6104 can read data and / or instructions stored in the memory 6103 and send the data and / or instructions to the processor 6101.

[0279] The communication device 6100 described in the above embodiments may be a network device or a terminal, but the scope of the communication device 6100 described in this disclosure is not limited thereto, and the structure of the communication device 6100 may not be limited by FIG. 6A. 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, programs and / or instructions; (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.

[0280] Figure 6B is a schematic diagram of the chip structure proposed in an embodiment of this disclosure. For cases where the communication device 6100 can be a chip or a chip system, please refer to the schematic diagram of the chip 6200 shown in Figure 6B, but it is not limited thereto.

[0281] Chip 6200 includes one or more processors 6201. Chip 6200 is used to perform any of the methods described above.

[0282] In some embodiments, chip 6200 further includes one or more interface circuits 6202. Optionally, terms such as interface circuit, interface, and transceiver pin can be used interchangeably. In some embodiments, chip 6200 further includes one or more memories 6203 for storing data and / or instructions. Optionally, all or part of the memories 6203 may be located outside of chip 6200. Optionally, interface circuit 6202 is connected to memory 6203, and interface circuit 6202 can be used to receive data and / or instructions from memory 6203 or other devices, and interface circuit 6202 can be used to send data and / or instructions to memory 6203 or other devices. For example, interface circuit 6202 can read data and / or instructions stored in memory 6203 and send the data and / or instructions to processor 6201.

[0283] In some embodiments, the interface circuit 6202 performs at least one of the communication steps such as sending and / or receiving in the above-described method (e.g., step S2101, but not limited thereto). For example, the interface circuit 6202 performing the communication steps such as sending and / or receiving in the above-described method means that the interface circuit 6202 performs data and / or instruction interaction between the processor 6201, the chip 6200, the memory 6203, or the transceiver device. In some embodiments, the processor 6201 performs other steps (e.g., step S2104a or step S2104b, but not limited thereto).

[0284] 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.

[0285] This disclosure also proposes a storage medium storing instructions that, when executed on a communication device, cause the communication device 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.

[0286] This disclosure also proposes a program product, including a program and / or instructions, which, when executed by a communication device, cause the communication device to perform any of the above methods. Optionally, the program product is a computer program product. Optionally, the program product is stored on the storage medium.

[0287] This disclosure also proposes a computer program that, when run on a computer, causes the computer to perform any of the above methods.

Claims

1. A communication method applied to a network device, characterized in that, include: Determine the start and end times of the time-domain resources occupied by the AI ​​use case, wherein the AI ​​use case is deployed on the terminal and used for positioning.

2. The method according to claim 1, characterized in that, The start time is determined based on at least one of a first time and a second time; The first time is determined based on the positioning trigger time, and the second time is determined based on the measurement time of the positioning reference signal PRS.

3. The method according to claim 2, characterized in that, The measurement time of the positioning reference signal PRS includes: The measurement start time of the positioning reference signal; or The measurement end time of the positioning reference signal.

4. The method according to claim 2 or 3, characterized in that, In response to the terminal performing periodic location reporting, the start time is determined based on the second time; or, in response to the terminal performing non-periodic location reporting, the start time is determined based on the first time.

5. The method according to any one of claims 1 to 4, characterized in that, The end time is determined based on at least one of a third time and a fourth time. The third time is determined based on the feedback time of the positioning result, and the fourth time is determined based on a preset value.

6. The method according to claim 5, characterized in that, The feedback time of the positioning result includes: the time when the terminal sends the positioning result; or the time when the positioning management function (LMF) receives the positioning coordinates fed back by the terminal.

7. The method according to claim 5 or 6, characterized in that, In response to the terminal sending a location result to the Location Management Function (LMF), the end time is determined based on a third time; or, in response to the terminal not sending a location result to the LMF, the end time is determined based on a fourth time.

8. The method according to any one of claims 5 to 7, characterized in that, The preset value satisfies a preset time domain range that makes the end time determined based on the fourth time fall after the start time.

9. The method according to any one of claims 1 to 8, characterized in that, The start time is the positioning trigger time, and the end time is the feedback time of the positioning result; or, the start time is the measurement time of the positioning reference signal (PRS), and the end time is the feedback time of the positioning result; or, the start time is the measurement time of the positioning reference signal (PRS), and the end time is within a preset time domain range after the measurement time of the positioning reference signal (PRS); or, the start time is the positioning trigger time, and the end time is within a preset time domain range after the measurement time of the positioning reference signal (PRS); or, the start time is the positioning trigger time, and the end time is within a preset time domain range after the feedback time of the positioning result.

10. The method according to any one of claims 1 to 9, characterized in that, The method further includes: Based on the start and end times of the time domain resources used by AI use cases, determine whether to configure additional AI functions for the terminal.

11. A communication device, characterized in that, The communication device is used to perform the communication method according to any one of claims 1-10.

12. A storage medium, characterized in that, The storage medium stores instructions that, when executed on a communication device, cause the communication device to perform the communication method as described in any one of claims 1-10.

13. A program product, characterized in that, It includes at least one of a program and instructions, wherein when the program and instructions are executed by a communication device, they implement the communication method according to any one of claims 1-10.

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