WO2024093997A1 - Procédé et appareil de détermination d'applicabilité de modèle, et dispositif de communication - Google Patents

Procédé et appareil de détermination d'applicabilité de modèle, et dispositif de communication Download PDF

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WO2024093997A1
WO2024093997A1 PCT/CN2023/128463 CN2023128463W WO2024093997A1 WO 2024093997 A1 WO2024093997 A1 WO 2024093997A1 CN 2023128463 W CN2023128463 W CN 2023128463W WO 2024093997 A1 WO2024093997 A1 WO 2024093997A1
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target
information
model
feature information
domain
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PCT/CN2023/128463
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English (en)
Chinese (zh)
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杨昂
贾承璐
任千尧
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维沃移动通信有限公司
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Publication of WO2024093997A1 publication Critical patent/WO2024093997A1/fr

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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W24/00—Supervisory, monitoring or testing arrangements
    • H04W24/02—Arrangements for optimising operational condition

Definitions

  • the present application belongs to the field of communication technology, and specifically relates to a method, device and communication equipment for determining the applicability of a model.
  • AI Artificial Intelligence
  • the relevant technologies generally use communication equipment (such as terminals or network-side equipment, etc.) to constantly observe the output of the AI model and/or the system performance of the communication system to determine whether the AI model is applicable in the current communication environment (or wireless communication system).
  • communication equipment such as terminals or network-side equipment, etc.
  • the aforementioned AI model applicability determination scheme will cause the AI model to continue to work for a long time in an inappropriate communication environment, affecting the performance of the communication system.
  • the embodiments of the present application provide a method, apparatus, and communication device for determining the applicability of a model, which can avoid the problem that the AI model continues to work for a long time in an inappropriate communication environment and ensure the performance of the communication system.
  • a method for determining model applicability including: a communication device determines target feature information corresponding to target channel information; and determines whether the target AI model is applicable or not based on the target feature information.
  • a device for determining the applicability of a model including: a determination module for determining target feature information corresponding to target channel information; and determining whether the target AI model is applicable or not based on the target feature information.
  • a communication device comprising a processor and a memory, wherein the memory stores a program or instruction that can be executed on the processor, and when the program or instruction is executed by the processor, the steps of the method described in the first aspect are implemented.
  • a communication device comprising a processor and a communication interface, wherein the communication interface is coupled to the processor, and the processor is used to run a program or instruction to implement the steps of the method described in the first aspect.
  • a communication system comprising: at least one communication device, wherein the communication device can be used to perform Perform the steps of the method described in the first aspect.
  • a readable storage medium on which a program or instruction is stored.
  • the program or instruction is executed by a processor, the steps of the method described in the first aspect are implemented.
  • a chip comprising a processor and a communication interface, wherein the communication interface is coupled to the processor, and the processor is used to run a program or instruction to implement the steps of the method described in the first aspect.
  • a computer program product/program product is provided, wherein the computer program/program product is stored in a storage medium, and the computer program/program product is executed by at least one processor to implement the steps of the method described in the first aspect.
  • the communication device determines the target feature information corresponding to the target channel information, and then determines whether the target AI model is applicable based on the target feature information. This can improve the efficiency of determining the applicability of the target AI model, avoid the problem in the related technology that the communication device needs to constantly observe the output of the AI model and/or the system performance of the communication system to determine the applicability of the AI model, and cause the AI model to need to continue to work for a long time in an inappropriate communication environment, thereby effectively ensuring the performance of the communication system.
  • FIG1 is a schematic diagram of the structure of a wireless communication system provided by an exemplary embodiment of the present application.
  • FIG. 2 is a flowchart of a method for determining model applicability provided by an exemplary embodiment of the present application.
  • FIG. 3 is a second flowchart of a method for determining model applicability provided by an exemplary embodiment of the present application.
  • FIG. 4 is a third flowchart of a method for determining model applicability provided by an exemplary embodiment of the present application.
  • FIG5 is a schematic diagram of the structure of an apparatus for determining model applicability provided by an exemplary embodiment of the present application.
  • FIG. 6 is a schematic diagram of the structure of a communication device provided by an exemplary embodiment of the present application.
  • FIG. 7 is a schematic diagram of the structure of a terminal provided by an exemplary embodiment of the present application.
  • FIG8 is a schematic diagram of the structure of a network side device provided by an exemplary embodiment of the present application.
  • first, second, etc. in the specification and claims of the present application are used to distinguish similar objects, and are not used to describe a specific order or sequence. It should be understood that the terms used in this way are interchangeable under appropriate circumstances, so that the embodiments of the present application can be implemented in an order other than those illustrated or described here, and the objects distinguished by “first” and “second” are generally of the same type, and the number of objects is not limited.
  • the first object can be one or more.
  • “and/or” in the specification and claims represents at least one of the connected objects, and the character “/" generally represents that the objects associated with each other are in an "or” relationship.
  • LTE-Advanced Long Term Evolution
  • LTE-A LTE/LTE evolution
  • CDMA Code Division Multiple Access
  • TDMA Time Division Multiple Access
  • FDMA Frequency Division Multiple Access
  • OFDMA Orthogonal Frequency Division Multiple Access
  • SC-FDMA Single-carrier Frequency-Division Multiple Access
  • NR New Radio
  • 6G 6th Generation
  • FIG1 shows a block diagram of a wireless communication system applicable to an embodiment of the present application.
  • the wireless communication system includes a terminal 11 and a network side device 12.
  • the terminal 11 may be a mobile phone, a tablet computer (Tablet Personal Computer), a laptop computer (Laptop Computer) or a notebook computer, a personal digital assistant (Personal Digital Assistant, PDA), a handheld computer, a netbook, an ultra-mobile personal computer (ultra-mobile personal computer, UMPC), a mobile Internet device (Mobile Internet Device, MID), an augmented reality (augmented reality, AR)/virtual reality (virtual reality, VR) device
  • the terminal side devices 12 include: smart devices, robots, wearable devices (Wearable Device), vehicle-mounted equipment (VUE), pedestrian terminals (PUE), smart homes (home appliances with wireless communication functions, such as refrigerators, televisions, washing machines or furniture, etc.), game consoles, personal computers (personal computers, PCs), ATMs or self-service machines, etc., and wearable devices include: smart
  • the network side device 12 may include an access network device or a core network device, wherein the access network device 12 may also be called a wireless access network device, a wireless access network (Radio Access Network, RAN), a wireless access network function or a wireless access network unit.
  • the access network device 12 may include a base station, a wireless local area network (WLAN) access point or a wireless fidelity (WiFi) node, etc.
  • WLAN wireless local area network
  • WiFi wireless fidelity
  • the base station may be referred to as a node B, an evolved node B (eNB), an access point, a base transceiver station (BTS), a radio base station, a radio transceiver, a basic service set (BSS), an extended service set (ESS), a home node B, a home evolved node B, a transmitting and receiving point (TRP) or some other appropriate term in the field.
  • eNB evolved node B
  • BTS basic service set
  • ESS extended service set
  • TRP transmitting and receiving point
  • the method 200 may be, but is not limited to, executed by a communication device (such as a terminal or a network-side device), and may be specifically executed by hardware and/or software installed in the communication device.
  • the method 200 may at least include the following steps.
  • the communication device determines target characteristic information corresponding to the target channel information.
  • the communication device determines the target characteristic information corresponding to the target channel information, it can be implemented by information statistics and the like, which is not limited here.
  • the target channel information can be collected by the communication device according to the protocol agreement or network side configuration, or it can be collected according to the channel information applicable to the target AI model, and there is no limitation here.
  • the target characteristic information may be different according to different target channel information.
  • the target characteristic information corresponding to the target channel information may include at least one of the following (11)-(19).
  • the spatial beam information may include at least one of the correlation between the index distribution vector of each beam and the first distribution vector, the first quantity, and the second quantity.
  • the index of the beam may be the energy or power of the beam;
  • the correlation is an indicator that measures the degree of association or distance between two vectors, for example, the correlation may be cosine similarity or the square of cosine similarity, etc., which are not listed here one by one.
  • the first number can be the number of beams corresponding to the multiple beams when the ratio of the sum of the indicators of the multiple beams to the total indicators of the beams reaches or exceeds the first threshold. For example, assuming that there are 10 beams in total, such as beam 1, beam 2, beam 3, beam 4, beam 5, beam 6, beam 7, beam 8, beam 9, and beam 10, and the first threshold is X1, then, if the sum of the indicators (such as power or energy) of 5 beams (such as beam 1, beam 4, beam 5, beam 8, and beam 9) among the 10 beams accounts for a ratio of the sum of the indicators of the 10 beams that reaches or exceeds the first threshold X1, then the first number is 5.
  • the X1 can be implemented by protocol default, network configuration, or terminal reporting, and is not limited here.
  • all beams can be sorted according to their indicators, for example, from large to small, or from small to large.
  • the second number is the number of single beams corresponding to the ratio of the index of a single beam to the total index of the beams when it reaches or exceeds the second threshold. For example, assuming there are 10 beams in total, such as beam 1, beam 2, beam 3, beam 4, beam 5, beam 6, beam 7, beam 8, beam 9, beam 10, and the second threshold is X2, then, if the ratio of the index of beam 3, beam 7, and beam 10 among the 10 beams to the sum of the indexes of the 10 beams reaches or exceeds the second threshold X2, then the second number is 3, i.e., beam 3, beam 7, and beam 10.
  • X2 can be implemented by protocol default, network configuration, or terminal reporting, which is not limited here.
  • the first distribution vector is a beam index distribution vector adapted to the target AI model.
  • the index distribution vectors of the aforementioned beams can be understood as follows: Assuming that the index of the beam is energy or power, and there are N0 beams in total, then these N0 beams can be recorded as vectors in the form of [energy or power of the first beam, energy or power of the second beam, ..., energy of the N0th beam].
  • the index distribution vector of each beam can be obtained by shifting or cyclically shifting each beam according to the index size of each beam. For example, each time statistics are taken, the beam with the highest power is fixed to the N1th beam, and then all beam energy graphs are cyclically shifted. For example, at this time, the highest power beam is the N2th beam.
  • the index distribution vectors of the beams may also be obtained by arranging the index sizes of the beams in a descending order or a descending order.
  • the CIR includes at least one of the correlation between the indicator distribution vector of each path and the second distribution vector, the third quantity, the fourth quantity, the first path position, the first path indicator, the main path position, and the main path indicator, and the second distribution vector is a path distribution vector adapted to the target AI model.
  • the correlation can be an indicator to measure the degree of association or distance between two vectors.
  • the correlation can be cosine similarity or the square of cosine similarity, etc., which are not listed here one by one.
  • the path indicators mentioned in this embodiment may include at least one of energy, power, reference signal received power (Reference Signal Received Power, RSRP), and reference signal time difference (Reference Signal Time Difference, RSTD).
  • the third number is the number of paths corresponding to the multiple paths when the ratio of the sum of the indicators of the multiple paths to the total indicators of the paths reaches or exceeds the third threshold.
  • the third threshold is X3
  • the third number is 5.
  • the X3 can be implemented by protocol default, network configuration, or terminal reporting, and is not limited here.
  • all paths need to be sorted according to their indicators, for example, from large to small, or from small to large.
  • the fourth number is the number of paths corresponding to a single path when the ratio of the index of a single path to the total index of the paths reaches or exceeds the fourth threshold. For example, assuming there are 10 paths in total, such as path 1, path 2, path 3, path 4, path 5, path 6, path 7, path 8, path 9, and path 10, and the fourth threshold is X4, then if the ratio of the index of path 3, path 7, and path 10 among the 10 paths to the sum of the indexes of the 10 paths reaches or exceeds the fourth threshold X4, then the fourth number is 3, i.e., path 3, path 7, and path 10.
  • the X4 can be implemented by protocol default, network configuration, or terminal reporting, and is not limited here.
  • the energy or power distribution vector of each path can be understood as: assuming that there are N0 paths in total, and the index of the path is energy or power, then it can be recorded in vector form as [energy or power of the first path, energy or power of the second path, ..., energy of the N0th path].
  • the index distribution vector of each path is obtained by shifting or cyclically shifting each path according to the index size of each path. For example, each time statistics are taken, the path with the highest power is fixed as the N1th path, and then all path energy graphs are cyclically shifted. For example, at this time, the highest power path is the N2th path.
  • the index distribution vectors of the paths may be obtained by arranging the index sizes of the paths in a descending order or in a descending order.
  • the PDP information includes at least one of the correlation between the indicator distribution vector of each path and the second distribution vector, the third quantity, the fourth quantity, the first path position, the first path indicator, the main path position, and the main path indicator; wherein the third quantity is the number of paths corresponding to the multiple paths when the ratio of the sum of the indicators of multiple paths to the total path indicator reaches or exceeds the third threshold, the fourth quantity is the number of paths corresponding to the single path when the ratio of the indicator of a single path to the total path indicator reaches or exceeds the fourth threshold, the second distribution vector is the path distribution vector adapted to the target AI model, and the path indicator includes at least one of energy, power, RSRP, and RSTD. It can be understood that the PDP information can refer to the relevant description in the aforementioned CIR, which will not be repeated here.
  • Time of Arrival (TOA) information (16) Time of Arrival (TOA) information.
  • NLOS Non-Line-of-Sight
  • the rank-related information can be understood as the distribution of the feature vectors of each data stream or the gap between them, so as to characterize the concentration of indicators such as energy of the data stream.
  • the target channel information and/or target feature information mentioned above may be related to the scope of application of the target AI model in S220.
  • the target feature information may be PDP and TOA, and this embodiment does not limit this.
  • the rank-related information may include at least one of the correlation between the index distribution vector of each data stream and the third distribution vector, the fifth quantity, and the sixth quantity.
  • the third distribution vector is a data stream distribution vector adapted to the target AI model, and the index of the data stream includes at least one of energy, power, eigenvalue, and singular value.
  • the fifth quantity is the number of data streams corresponding to the multiple data streams when the ratio of the sum of the indicators of the multiple data streams to the total indicators of the data streams reaches or exceeds the fifth threshold. For example, taking the indicator of the data stream as total energy as an example, assuming that there are 5 data streams in total, such as data stream 1, data stream 2, data stream 3, data stream 4, and data stream 5, and the fifth threshold is X5, then, if the sum of the total energy of 2 data streams (such as data stream 1 and data stream 4) among the 5 data streams accounts for the sum of the total energy of the 5 data streams. The ratio reaches or exceeds the fifth threshold X5, then the third quantity is 2.
  • the X5 can be implemented by protocol default, network configuration or terminal reporting.
  • all data streams can be sorted according to their indicators, or all data streams need to be sorted according to their data stream identifiers, such as sorting from large to small, or from small to large.
  • the sixth quantity is the number of data streams corresponding to a single data stream when the ratio of the index of a single data stream to the total index of the data stream reaches or exceeds the sixth threshold. For example, taking the index of the data stream as total energy, assuming that there are 5 data streams in total, such as data stream 1, data stream 2, data stream 3, data stream 4, and data stream 5, and the sixth threshold is X6, then, if the ratio of the energy of data stream 3 and data stream 4 in the 5 data streams to the sum of the indexes of the 5 data streams reaches or exceeds the sixth threshold X6, then the fourth quantity is 3, that is, data stream 3 and data stream 4.
  • the X6 can be The protocol default, network configuration or terminal reporting implementation is not restricted here.
  • the aforementioned data stream can also be understood as a data block or layer (Layer), and this embodiment does not limit this.
  • the target AI model mentioned in the context of this application has multiple implementation methods, such as the target AI model can be a neural network, a decision tree, a support vector machine, a Bayesian classifier, etc.
  • this embodiment directly determines whether the target AI model is suitable based on the target feature information corresponding to the target channel information. This can more efficiently determine whether the AI model is suitable, and avoids the problem that the AI model needs to work for a long time in an inapplicable environment, thereby effectively ensuring the performance of the communication system.
  • the applicable scope of the target AI model is determined by the feature information corresponding to the training data (i.e., the data used for training the target AI model).
  • the corresponding feature information of the training data is determined as the applicable scope of the target AI model.
  • the target AI model is used for positioning, the target feature information is the LOS information mean, and the corresponding LOS information mean of the training data is Y1, then the applicable scope of the target AI model is near the LOS information mean Y1.
  • the training data used for training the target AI model is determined according to the purpose of the target AI model. For example, assuming that the target AI model is used for signal processing, then the training data is related to signal processing. For another example, assuming that the target AI model is used for channel prediction, then the training data is related to channel prediction, etc., and there is no limitation here.
  • the purpose of the target AI model in this embodiment may include at least one of the following.
  • the signal processing includes signal detection, signal filtering, signal equalization, etc.
  • the signal can be a demodulation reference signal (Demodulation Reference Signal, DMRS), a sounding reference signal (Sounding Reference Signal, SRS), a synchronization signal (Synchronization Signal Block, SSB), a phase reference signal (Tracking Reference Signal, TRS), a phase tracking reference signal (Phase-tracking reference signal, PTRS), a channel state information reference signal (Channel State Information Reference Signal, CSI-RS), etc.
  • the signal demodulation may be the demodulation of signals such as the physical downlink control channel (PDCCH), the physical downlink shared channel (PDSCH), the physical uplink control channel (PUCCH), the physical uplink shared channel (PUSCH), the physical random access channel (PRACH), and the physical broadcast channel (PBCH).
  • PDCCH physical downlink control channel
  • PDSCH physical downlink shared channel
  • PUCCH physical uplink control channel
  • PUSCH physical uplink shared channel
  • PRACH physical random access channel
  • PBCH physical broadcast channel
  • the signal transmission and reception may be transmission and reception of PDCCH, PDSCH, PUCCH, PUSCH, PRACH, PBCH and other signals.
  • the channel state information acquisition includes signal state information feedback and frequency division multiplexing (FDD) uplink and downlink partial reciprocity acquisition, etc.
  • the signal state information feedback may include channel related information, channel matrix related information, channel characteristic information, channel matrix characteristic information, precoding matrix indicator (Precoding matrix indicator, PMI), rank indicator (Rank indicator, RI), CSI-RS resource indicator (CSI-RS Resource Indicator, CRI), channel quality indicator (CQI), layer indicator (Layer Indicator, LI) and other information feedback.
  • the FDD uplink and downlink partial reciprocity can be understood as: for the FDD system, according to the partial reciprocity, the base station and other network side devices obtain the angle and delay information according to the uplink channel, and can notify the terminal of the angle information and delay information through CSI-RS precoding or direct indication.
  • the terminal reports according to the indication of the base station or selects and reports within the indication range of the base station, thereby reducing the calculation amount of the terminal and the overhead of CSI reporting.
  • the beam management may include beam measurement, beam reporting, beam prediction, beam failure detection, beam failure recovery, new beam indication in beam failure recovery, etc.
  • the channel prediction may include prediction of channel state information, beam prediction, etc.
  • the interference suppression may include suppression of intra-cell interference, inter-cell interference, out-of-band interference, intermodulation interference, etc.
  • Terminal positioning includes estimating the specific position (including horizontal position and/or vertical position) or possible future trajectory of the terminal through a reference signal (such as SRS), or information to assist position estimation or trajectory estimation.
  • a reference signal such as SRS
  • the prediction and management of high-level services and parameters may include throughput, required data packet size, service requirements, mobile speed, noise information, etc.
  • the analysis of control signaling may include analysis of power control related signaling, beam management related signaling, etc.
  • the efficiency of determining the applicability of the target AI model can be improved, thereby avoiding the problem in related technologies that the communication equipment needs to constantly observe the output of the AI model and/or the system performance of the communication system to determine the applicability of the AI model, resulting in the AI model needing to continue to work for a long time in an inappropriate communication environment, thereby effectively ensuring the performance of the communication system.
  • the target feature information corresponding to the target channel information is periodically determined through the present application, and the applicability of the target AI model is determined based on the target feature information to flexibly select the most matching AI model, thereby greatly improving the generalization performance of the AI model in different communication environments and ensuring the flexibility and stability of the communication system.
  • the method 300 may be, but is not limited to, executed by a communication device (such as a terminal or a network-side device), and may be specifically executed by hardware and/or software installed in the communication device.
  • the method 300 may at least include the following steps.
  • the communication device determines target characteristic information corresponding to the target channel information.
  • the implementation process of S310 can refer to the relevant description in the method embodiment 200.
  • the communication device when determining the target characteristic information corresponding to the target channel information, can determine the target characteristic information of the target channel information in the target domain; wherein the target domain may include but is not limited to at least one of the delay domain, beam domain, Doppler domain, and space domain.
  • the communication device determines the target characteristic information of the target channel information, and the determined target domain is different from the domain corresponding to the acquired target channel information
  • conversion between different domains can be performed. For example, when the target domain includes the delay domain, the target channel information in the frequency domain is converted to the delay domain; and/or, when the target domain includes the beam domain, the target channel information in the antenna domain is converted to the beam domain; and/or, when the target domain includes the Doppler domain, the target channel information in the time domain is converted to the Doppler domain.
  • the target domain determined by the communication device may be one or more. Then, when there are multiple target domains, the communication device may combine the characteristic information of multiple target domains for comparison to determine whether the target AI model is applicable (or whether the target AI model is invalid). For example, when the target domain includes the delay domain, the Doppler domain, and the beam domain, it is possible to simultaneously determine whether the target AI model is applicable based on the characteristic information of the delay domain, the characteristic information of the Doppler domain, and the characteristic information of the beam domain. For example, when the characteristic information of the delay domain, the characteristic information of the Doppler domain, and the characteristic information of the beam domain are all within the applicable scope of the target AI model, it is determined that the target AI model is applicable. Otherwise, it is determined that the target AI model is not applicable.
  • the communication device may determine the target feature information corresponding to the target channel information in any one of the following methods 1-3.
  • Mode 1 determining the statistical value of the target feature information according to the statistical value of the previous target feature information and the currently collected target feature information.
  • the statistical value of the last target feature information is X
  • the currently collected target feature information is Y
  • the statistical value of the target feature information is alpha*X+beta*Y, where alpha and beta are weights.
  • alpha and beta can be implemented by protocol agreement, high-level configuration, etc.
  • the values of alpha and beta can both be 1.
  • Mode 2 determining the statistical value of the target feature information according to the average value of all target feature information collected within the first time.
  • the value of the first time may be implemented by protocol default or network configuration or terminal reporting.
  • the average value may be a geometric average, an arithmetic average, a weighted average, etc.
  • the weighted value of the weighted average may be implemented by protocol default or network configuration or terminal reporting, which is not limited here.
  • Mode 3 determining the statistical value of the target feature information based on a Gaussian mixture model (GMM).
  • the Gaussian mixture model is to accurately quantify things using a Gaussian probability density function (normal distribution curve). It is a model that decomposes things into several Gaussian probability density functions.
  • the GMM is a method for obtaining statistical information.
  • several model parameters formed based on Gaussian probability density functions are used as the statistical value of the target feature information, such as the mean, variance and ratio/probability/contribution of each decomposed Gaussian probability density function to the total model as the statistical value of the target feature information.
  • the mean and variance of T Gaussian distributions can be obtained through the Gaussian mixture model to serve as the statistical value of the target feature information.
  • the terminal determines the target feature information
  • which of the aforementioned methods 1-3 is adopted can be determined by protocol agreement, high-level configuration or terminal autonomy, etc., and is not restricted here.
  • the type of the target domain and/or the target feature information mentioned in this embodiment can be determined by at least one of the following (21)-(24).
  • S320 Determine whether the target AI model is applicable or not based on the target feature information.
  • determining whether the target AI model is applicable or not based on the target feature information may include the following S321 and/or S322.
  • the target feature information described in S321-S322 can be determined based on the target domain, and/or, the target feature information can be determined based on any of the aforementioned methods 1-3, which is not limited here.
  • the communication device reports (or triggers) first information, or does not report any information, and the first information is used to indicate that the target AI model is available or can work normally.
  • the first information can be implemented by protocol agreement or network side configuration, and is not limited here.
  • second information is reported (or triggered), and the second information is used to indicate or request at least one of model switching, model deactivation, and enabling a non-AI algorithm.
  • the target feature information is within the applicable scope of the AI model or algorithm to be switched or enabled.
  • the method 400 may be, but is not limited to, executed by a communication device (such as a terminal or a network side device), and may be specifically executed by a communication device installed in The hardware and/or software in the communication device executes.
  • the method 400 may at least include the following steps.
  • the predetermined method includes at least one of the following methods 1 to 6.
  • Mode 1 collecting or counting the target channel information or target feature information in real time.
  • Mode 2 based on the observation period, collects or counts the target channel information or target characteristic information at a second time interval; wherein the second time can be understood as the observation period. It can be understood that the observation period and the value of the second time can be implemented by protocol agreement, high-level configuration or network-side configuration, and is not limited here.
  • Mode 3 collect or count the target channel information or target feature information within the observation window. For example, within 200ms, only 1ms-10ms is within the observation window, then the communication device can only collect or count the target channel information or target feature information within 1ms-10ms to determine the applicability of the target AI model. It can be understood that the size of the observation window can be achieved by protocol agreement, high-level configuration or network-side configuration, and is not limited here.
  • the target channel information or target feature information is collected or counted when the communication device moves beyond a predetermined distance.
  • a predetermined distance For example, assuming that the first observation position is point A, then when the distance of the communication device from point A exceeds a predetermined distance, the communication device collects or counts the target channel information or target feature information.
  • the values of the first observation position and the predetermined distance can be implemented by protocol predetermination, network side configuration or high-level configuration, and are not limited here.
  • Mode 5 Based on the second observation position, the target channel information or target feature information is collected or counted when the communication device leaves the designated area, and the designated area is the area where the target channel information or target feature information was collected last time.
  • the second observation position can be implemented by protocol agreement, high-level configuration, network side configuration, etc., which is not limited here.
  • Mode 6 Based on the third observation position, when the change in the physical position of the communication device exceeds a predetermined value, the target channel information or target feature information is collected or counted.
  • the third observation position and the predetermined value can be implemented by protocol agreement, high-level configuration, network side configuration, etc., and are not limited here.
  • the target AI model is used for purposes such as terminal positioning, then when the communication device moves a predetermined distance or leaves a designated area or changes in physical position, it may cause the spatial statistical information to change, that is, the communication environment has changed. Therefore, the communication device verifies the applicability of the target AI model by collecting or counting target channel information or target feature information, thereby ensuring the performance of the communication system.
  • the communication device can execute S430 based on the collected or counted target feature information to determine whether the target AI model is applicable.
  • the determination process can refer to the relevant description in the aforementioned method embodiments 200-300, which will not be repeated here.
  • the communication device directly collects or counts the target channel information based on method 1-method 6, then the communication device needs to execute S420 based on the collected or counted target channel information to obtain the target feature information corresponding to the target channel information, and then determine whether the target AI model is applicable based on the target feature information.
  • the communication device determines target characteristic information corresponding to the target channel information.
  • the implementation process of S430 can, as a possible implementation method, when the communication device determines whether the target AI model is applicable or not based on the target feature information, it can also calculate the target statistic corresponding to the target feature information corresponding to the target channel information, and determine whether the target AI model is applicable or not based on the target statistic (for example, whether the distance between the target statistic and a preset statistic is less than a threshold, and the preset statistic is determined based on the applicable scope of the AI model); wherein the target statistic includes at least one of the following (31)-(33).
  • the mean and variance in the above (31)-(32) can be first-order statistics, second-order statistics or higher-order statistics, and are not limited here.
  • the communication device can calculate the mean and/or variance of the target feature information, and determine whether the target AI model is applicable or not based on the mean and/or variance.
  • CDF Cumulative Distribution Function
  • PDF Probability Density Function
  • PMF Probability Mass Function
  • the communication device can calculate the target statistics corresponding to the target feature information based on CDF, PDF or PMF, and determine whether the target AI model is applicable or not based on the target statistics.
  • the generalization performance of the AI model in complex environments can be effectively improved, and the flexibility and stability of the communication system can be enhanced.
  • the method 200-400 for determining model applicability provided in the embodiments of the present application may be performed by a device for determining model applicability.
  • the device for determining model applicability is described by taking the method for determining model applicability performed by the device for determining model applicability as an example.
  • the apparatus 500 includes a first determination module 510 for determining target feature information corresponding to target channel information; and a second determination module 520 for determining whether the target AI model is applicable or not based on the target feature information.
  • the first determination module 510 determines the target characteristic information corresponding to the target channel information, including: determining the target characteristic information of the target channel information in the target domain; wherein the target domain includes at least one of a delay domain, a beam domain, and a Doppler domain.
  • the first determination module 510 determines the target characteristic information of the target channel information, and further includes at least one of the following: when the target domain includes the delay domain, converting the target channel information in the frequency domain to the delay domain; when the target domain includes the beam domain, converting the target channel information in the antenna domain to the beam domain; When the target domain includes the Doppler domain, the target channel information in the time domain is converted into the Doppler domain.
  • the second determination module 510 determines the target feature information corresponding to the target channel information, including any one of the following: determining the statistical value of the target feature information based on the statistical value of the previous target feature information and the currently collected target feature information; determining the statistical value of the target feature information based on the average value of all target feature information collected within the first time; determining the statistical value of the target feature information based on a Gaussian mixture model GMM.
  • the second determination module 520 determines whether the target AI model is applicable or not based on the target feature information, including any one of the following: when the target feature information on the target domain is within the scope of application of the target AI model, determining that the target AI model is applicable; when the target feature information on the target domain is not within the scope of application of the target AI model, determining that the target AI model is not applicable.
  • the second determination module 520 determines whether the target AI model is applicable or not based on the target feature information, including: calculating the target statistic corresponding to the target feature information; determining whether the target AI model is applicable or not based on the target statistic; wherein the target statistic includes at least one of the following: mean; variance; a statistic determined based on at least one of the cumulative distribution function CDF, the probability density function PDF, and the probability mass function PMF.
  • the scope of application of the target AI model is determined by feature information corresponding to the training data.
  • the device also includes a reporting module, used for any of the following: when the target AI model is applicable, reporting first information, or not reporting any information, the first information being used to indicate that the target AI model is available or can work normally; when the target AI model is not applicable, reporting second information, the second information being used to indicate or request at least one of model switching, model deactivation, and enabling of a non-AI algorithm.
  • a reporting module used for any of the following: when the target AI model is applicable, reporting first information, or not reporting any information, the first information being used to indicate that the target AI model is available or can work normally; when the target AI model is not applicable, reporting second information, the second information being used to indicate or request at least one of model switching, model deactivation, and enabling of a non-AI algorithm.
  • the first determination module 510 is also used to: collect or count the target channel information or target feature information in a predetermined manner; wherein the predetermined manner includes at least one of the following: real-time collection or counting of the target channel information or target feature information; based on an observation period, collecting or counting the target channel information or target feature information at second intervals; collecting or counting the target channel information or target feature information located in an observation window; based on a first observation position, collecting or counting the target channel information or target feature information when the communication device moves more than a predetermined distance; based on a second observation position, collecting or counting the target channel information or target feature information when the communication device leaves a designated area, the designated area being the area where the target channel information or target feature information was last collected; based on a third observation position, collecting or counting the target channel information or target feature information when the change in the physical position of the communication device exceeds a predetermined value.
  • the predetermined manner includes at least one of the following: real-time collection or counting of the target channel information or target feature information; based on an
  • the target characteristic information corresponding to the target channel information includes at least one of the following: spatial beam information; channel impulse response CIR; power delay spectrum PDP information; delay spread Delay spread information; Doppler information; arrival time TOA information; line-of-sight transmission LOS information; non-line-of-sight transmission NLOS information; and rank-related information.
  • the spatial beam information includes at least one of a correlation between an index distribution vector of each beam and a first distribution vector, a first quantity, and a second quantity; wherein the first quantity is the number of beams corresponding to the multiple beams when the ratio of the sum of the indexes of the multiple beams to the total index of the beams reaches or exceeds a first threshold, and the second quantity is the number of beams corresponding to the single beam when the ratio of the index of the single beam to the total index of the beam reaches or exceeds a second threshold, and the The first distribution vector is a beam index distribution vector adapted to the target AI model, and the index of the beam includes the energy or power of the beam.
  • the index distribution vector of each beam is obtained by shifting or cyclically shifting each beam according to the index size of each beam.
  • the CIR or PDP includes at least one of the correlation between the indicator distribution vector of each path and the second distribution vector, a third quantity, a fourth quantity, the first path position, the first path indicator, the main path position, and the main path indicator; wherein, the third quantity is the number of path corresponding to the multiple path when the ratio of the sum of the indicators of multiple path to the total path indicator reaches or exceeds the third threshold, the fourth quantity is the number of path corresponding to the single path when the ratio of the indicator of a single path to the total path indicator reaches or exceeds the fourth threshold, the second distribution vector is a path distribution vector adapted to the target AI model, and the path indicators include at least one of energy, power, reference signal received power RSRP, and reference signal time difference RSTD.
  • the rank-related information includes at least one of the correlation between the indicator distribution vector of each data stream and the third distribution vector, a fifth quantity, and a sixth quantity; wherein, the fifth quantity is the number of data streams corresponding to the multiple data streams when the proportion of the sum of the indicators of multiple data streams to the total indicators of the data streams reaches or exceeds the fifth threshold, and the sixth quantity is the number of data streams corresponding to the single data stream when the proportion of the indicators of a single data stream to the total indicators of the data stream reaches or exceeds the sixth threshold.
  • the third distribution vector is a data stream distribution vector adapted to the target AI model, and the indicators of the data stream include at least one of energy, power, eigenvalues, and singular values.
  • the type of the target domain and the target feature information is determined by at least one of the following: network side indication; determined according to configuration information of the target AI model; determined according to description information of the target AI model; or obtained interactively during the training process of the target AI model.
  • the uses of the target AI model include at least one of the following: signal processing; signal demodulation; signal reception and transmission; channel state information acquisition; beam management; channel prediction; interference suppression; terminal positioning; prediction and management of high-level services and parameters; and analysis of control signaling.
  • the device 500 for determining the applicability of the model in the embodiment of the present application may be a terminal or a network-side device.
  • the terminal may include but is not limited to the types of the terminal 11 listed above
  • the network-side device may include but is not limited to the types of the network-side device 12 listed above, etc., which are not specifically limited in the embodiment of the present application.
  • the device 500 for determining model applicability provided in the embodiment of the present application can implement the various processes implemented in the method embodiments of Figures 2 to 4 and achieve the same technical effects. To avoid repetition, they will not be described here.
  • the embodiment of the present application further provides a communication device 600, including a processor 601 and a memory 602, wherein the memory 602 stores a program or instruction that can be run on the processor 601.
  • the communication device 600 is a terminal
  • the program or instruction is executed by the processor 601 to implement the various steps of the above method embodiments 200-400, and can achieve the same technical effect.
  • the communication device 600 is a network side device
  • the program or instruction is executed by the processor 601 to implement the various steps of the above method embodiments 200-400, and can achieve the same technical effect. To avoid repetition, it will not be repeated here.
  • the communication device may be a terminal, which may include a processor and a communication interface.
  • the communication interface is coupled to the processor, and the processor is used to run a program or instruction to implement the steps of the method described in method embodiments 200-400.
  • This terminal embodiment corresponds to the above-mentioned communication device side method embodiment, and each implementation process and implementation method of the above-mentioned method embodiment can be applied to the terminal embodiment and can achieve the same technical effect.
  • Figure 7 is a schematic diagram of the hardware structure of a terminal implementing an embodiment of the present application.
  • the terminal 700 includes but is not limited to: a radio frequency unit 701, a network module 702, an audio output unit 703, an input unit 704, a sensor 705, a display unit 706, a user input unit 707, an interface unit 708, a memory 709, and at least some of the components of a processor 710.
  • the terminal 700 may also include a power source (such as a battery) for supplying power to each component, and the power source may be logically connected to the processor 710 through a power management system, so as to implement functions such as managing charging, discharging, and power consumption management through the power management system.
  • a power source such as a battery
  • the terminal structure shown in FIG7 does not constitute a limitation on the terminal, and the terminal may include more or fewer components than shown in the figure, or combine certain components, or arrange components differently, which will not be described in detail here.
  • the input unit 704 may include a graphics processing unit (GPU) 1041 and a microphone 7042, and the graphics processor 7041 processes the image data of a static picture or video obtained by an image capture device (such as a camera) in a video capture mode or an image capture mode.
  • the display unit 706 may include a display panel 7061, and the display panel 7061 may be configured in the form of a liquid crystal display, an organic light emitting diode, etc.
  • the user input unit 707 includes a touch panel 7071 and at least one of other input devices 7072.
  • the touch panel 7071 is also called a touch screen.
  • the touch panel 7071 may include two parts: a touch detection device and a touch controller.
  • Other input devices 7072 may include, but are not limited to, a physical keyboard, function keys (such as a volume control key, a switch key, etc.), a trackball, a mouse, and a joystick, which will not be repeated here.
  • the RF unit 701 can transmit the data to the processor 710 for processing; in addition, the RF unit 701 can send uplink data to the network side device.
  • the RF unit 701 includes but is not limited to an antenna, an amplifier, a transceiver, a coupler, a low noise amplifier, a duplexer, etc.
  • the memory 709 can be used to store software programs or instructions and various data.
  • the memory 709 may mainly include a first storage area for storing programs or instructions and a second storage area for storing data, wherein the first storage area may store an operating system, an application program or instruction required for at least one function (such as a sound playback function, an image playback function, etc.), etc.
  • the memory 709 may include a volatile memory or a non-volatile memory, or the memory 709 may include both volatile and non-volatile memories.
  • the non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory.
  • the volatile memory may be a random access memory (RAM), a static random access memory (SRAM), a dynamic random access memory (DRAM), a synchronous dynamic random access memory (SDRAM), a double data rate synchronous dynamic random access memory (DDRSDRAM), an enhanced synchronous dynamic random access memory (ESDRAM), a synchronous link dynamic random access memory (SLDRAM) and a direct RAM bus random access memory (DRRAM).
  • the memory in the embodiments of the present application 709 includes, but is not limited to, these and any other suitable types of memory.
  • the processor 710 may include one or more processing units; optionally, the processor 710 integrates an application processor and a modem processor, wherein the application processor mainly processes operations related to an operating system, a user interface, and application programs, and the modem processor mainly processes wireless communication signals, such as a baseband processor. It is understandable that the modem processor may not be integrated into the processor 710.
  • the processor 710 is used to determine the target feature information corresponding to the target channel information, and determine whether the target AI model is applicable or not based on the target feature information.
  • the processor 710 determines the target characteristic information corresponding to the target channel information, including: determining the target characteristic information of the target channel information in a target domain; wherein the target domain includes at least one of a delay domain, a beam domain, and a Doppler domain.
  • the processor 710 determines the target characteristic information of the target channel information, and also includes at least one of the following: when the target domain includes the delay domain, converting the target channel information in the frequency domain to the delay domain; when the target domain includes the beam domain, converting the target channel information in the antenna domain to the beam domain; when the target domain includes the Doppler domain, converting the target channel information in the time domain to the Doppler domain.
  • the processor 710 determines the target feature information corresponding to the target channel information, including any one of the following: determining the statistical value of the target feature information based on the last calculated value of the target feature information and the currently collected target feature information; determining the statistical value of the target feature information based on the average value of all target feature information collected within the first time; determining the statistical value of the target feature information based on a Gaussian mixture model GMM.
  • the processor 710 determines whether the target AI model is applicable or not based on the target feature information, including any one of the following: when the target feature information on the target domain is within the applicable scope of the target AI model, determining that the target AI model is applicable; when the target feature information on the target domain is not within the applicable scope of the target AI model, determining that the target AI model is not applicable.
  • the processor 710 determines whether the target AI model is applicable or not based on the target feature information, including: calculating a target statistic corresponding to the target feature information; determining whether the target AI model is applicable or not based on the target statistic; wherein the target statistic includes at least one of the following: mean; variance; a statistic determined based on at least one of the cumulative distribution function CDF, the probability density function PDF, and the probability mass function PMF.
  • the scope of application of the target AI model is determined by feature information corresponding to the training data.
  • the radio frequency unit 701 is used for any of the following: when the target AI model is applicable, reporting first information, or not reporting any information, the first information being used to indicate that the target AI model is available or can work normally; when the target AI model is not applicable, reporting second information, the second information being used to indicate or request at least one of model switching, model deactivation, and enabling of a non-AI algorithm.
  • the processor 710 is further configured to: collect or count the target channel information or target feature information in a predetermined manner; wherein the predetermined manner includes at least one of the following: real-time collection or counting of the target channel information or target feature information; based on an observation period, collecting or counting the target channel information or target feature information at second intervals; collecting or counting the target channel information or target feature information located within an observation window; based on a first observation position, The target channel information or target characteristic information is collected or counted when the communication device moves more than a predetermined distance; based on a second observation position, the target channel information or target characteristic information is collected or counted when the communication device leaves a designated area, and the designated area is the area where the target channel information or target characteristic information was collected last time; based on a third observation position, the target channel information or target characteristic information is collected or counted when the change in the physical position of the communication device exceeds a predetermined value.
  • the predetermined manner includes at least one of the following: real-time collection or counting of the target channel information
  • the target characteristic information corresponding to the target channel information includes at least one of the following: spatial beam information; channel impulse response CIR; power delay spectrum PDP information; delay spread Delay spread information; Doppler information; arrival time TOA information; line-of-sight transmission LOS information; non-line-of-sight transmission NLOS information; and rank-related information.
  • the spatial beam information includes at least one of the correlation between the index distribution vector of each beam and the first distribution vector, a first quantity, and a second quantity; wherein, the first quantity is the number of beams corresponding to the multiple beams when the ratio of the sum of the indicators of multiple beams to the total beam indicators reaches or exceeds the first threshold, and the second quantity is the number corresponding to the single beam when the ratio of the indicators of a single beam to the total beam indicators reaches or exceeds the second threshold, and the first distribution vector is a beam indicator distribution vector adapted to the target AI model, and the indicators of the beam include the energy or power of the beam.
  • the index distribution vector of each beam is obtained by shifting or cyclically shifting each beam according to the index size of each beam.
  • the CIR or PDP includes at least one of the correlation between the indicator distribution vector of each path and the second distribution vector, a third quantity, a fourth quantity, the first path position, the first path indicator, the main path position, and the main path indicator; wherein, the third quantity is the number of path corresponding to the multiple path when the ratio of the sum of the indicators of multiple path to the total path indicator reaches or exceeds the third threshold, the fourth quantity is the number of path corresponding to the single path when the ratio of the indicator of a single path to the total path indicator reaches or exceeds the fourth threshold, the second distribution vector is a path distribution vector adapted to the target AI model, and the path indicators include at least one of energy, power, reference signal received power RSRP, and reference signal time difference RSTD.
  • the rank-related information includes at least one of the correlation between the indicator distribution vector of each data stream and the third distribution vector, a fifth quantity, and a sixth quantity; wherein, the fifth quantity is the number of data streams corresponding to the multiple data streams when the proportion of the sum of the indicators of multiple data streams to the total indicators of the data streams reaches or exceeds the fifth threshold, and the sixth quantity is the number of data streams corresponding to the single data stream when the proportion of the indicators of a single data stream to the total indicators of the data stream reaches or exceeds the sixth threshold.
  • the third distribution vector is a data stream distribution vector adapted to the target AI model, and the indicators of the data stream include at least one of energy, power, eigenvalues, and singular values.
  • the type of the target domain and the target feature information is determined by at least one of the following: network side indication; determined according to configuration information of the target AI model; determined according to description information of the target AI model; or obtained interactively during the training process of the target AI model.
  • the uses of the target AI model include at least one of the following: signal processing; signal demodulation; signal reception and transmission; channel state information acquisition; beam management; channel prediction; interference suppression; terminal positioning; prediction and management of high-level services and parameters; and analysis of control signaling.
  • the communication device 600 may also be a network side device, which may include a processor and a communication interface, the communication interface is coupled to the processor, and the processor is used to run a program or instruction to implement the steps of the method described in embodiments 200-400.
  • the network side device embodiment corresponds to the above communication device side method embodiment, and each implementation process and implementation method of the above method embodiment can be applied to the network side device embodiment, and can achieve the same technical effect.
  • the embodiment of the present application also provides a network side device.
  • the network side device 800 includes: an antenna 801, a radio frequency device 802, a baseband device 803, a processor 804 and a memory 805.
  • the antenna 801 is connected to the radio frequency device 802.
  • the radio frequency device 802 receives information through the antenna 801 and sends the received information to the baseband device 803 for processing.
  • the baseband device 803 processes the information to be sent and sends it to the radio frequency device 802.
  • the radio frequency device 802 processes the received information and sends it out through the antenna 801.
  • the method executed by the network-side device in the above embodiment may be implemented in the baseband device 803, which includes a baseband processor.
  • the baseband device 803 may include, for example, at least one baseband board, on which multiple chips are arranged, as shown in Figure 8, one of which is, for example, a baseband processor, which is connected to the memory 805 through a bus interface to call the program in the memory 805 and execute the network device operations shown in the above method embodiment.
  • the network side device may also include a network interface 806, which is, for example, a common public radio interface (CPRI).
  • a network interface 806, which is, for example, a common public radio interface (CPRI).
  • CPRI common public radio interface
  • the network side device 800 of the embodiment of the present disclosure also includes: instructions or programs stored in the memory 805 and executable on the processor 804.
  • the processor 804 calls the instructions or programs in the memory 805 to execute the methods executed by the modules shown in Figure 5 and achieve the same technical effect. To avoid repetition, it will not be repeated here.
  • An embodiment of the present application also provides a readable storage medium, on which a program or instruction is stored.
  • a program or instruction is stored.
  • the various processes of the above-mentioned method embodiments 200-400 are implemented and the same technical effect can be achieved. To avoid repetition, it will not be repeated here.
  • the processor is the processor in the terminal described in the above embodiment.
  • the readable storage medium includes a computer readable storage medium, such as a computer read-only memory ROM, a random access memory RAM, a magnetic disk or an optical disk.
  • An embodiment of the present application further provides a chip, which includes a processor and a communication interface, wherein the communication interface is coupled to the processor, and the processor is used to run network-side device programs or instructions to implement the various processes of the above-mentioned method embodiments 200-400, and can achieve the same technical effect. To avoid repetition, it will not be repeated here.
  • the chip mentioned in the embodiments of the present application can also be called a system-level chip, a system chip, a chip system or a system-on-chip chip, etc.
  • An embodiment of the present application also provides a computer program product, which includes a processor, a memory, and a program or instruction stored in the memory and executable on the processor.
  • a computer program product which includes a processor, a memory, and a program or instruction stored in the memory and executable on the processor.
  • An embodiment of the present application also provides a communication system, including: at least one communication device, which can be used to execute the various processes of the method embodiments 200-400 as described above, and can achieve the same technical effect. To avoid repetition, it will not be repeated here.
  • the technical solution of the present application can be embodied in the form of a computer software product, which is stored in a storage medium (such as ROM/RAM, a magnetic disk, or an optical disk), and includes a number of instructions for enabling a terminal (which can be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to execute the methods described in each embodiment of the present application.
  • a storage medium such as ROM/RAM, a magnetic disk, or an optical disk
  • a terminal which can be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.

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  • Computer Networks & Wireless Communication (AREA)
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Abstract

La présente demande appartient au domaine technique des communications. Sont divulgués un procédé et un appareil pour déterminer une applicabilité de modèle, ainsi qu'un dispositif de communication. Le procédé de détermination d'applicabilité de modèle dans les modes de réalisation de la présente demande comprend les étapes suivantes : un dispositif de communication détermine des informations de caractéristique cible correspondant à des informations de canal cible ; et selon les informations de caractéristique cible, détermine qu'un modèle d'IA cible est applicable ou n'est pas applicable.
PCT/CN2023/128463 2022-11-04 2023-10-31 Procédé et appareil de détermination d'applicabilité de modèle, et dispositif de communication WO2024093997A1 (fr)

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US20210321221A1 (en) * 2020-04-14 2021-10-14 Qualcomm Incorporated Neural network based line of sight detection for positioning
CN113938232A (zh) * 2020-07-13 2022-01-14 华为技术有限公司 通信的方法及通信装置
WO2022033456A1 (fr) * 2020-08-13 2022-02-17 华为技术有限公司 Procédé de retour de mesure d'informations d'état de canal et appareil associé
WO2022205438A1 (fr) * 2021-04-02 2022-10-06 Zte Corporation Systèmes et procédés d'établissement de rapport et de gestion de faisceau à l'aide d'une intelligence artificielle

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CN107422358A (zh) * 2017-05-19 2017-12-01 上海斐讯数据通信技术有限公司 一种全场景定位方法及系统
US20210321221A1 (en) * 2020-04-14 2021-10-14 Qualcomm Incorporated Neural network based line of sight detection for positioning
CN113938232A (zh) * 2020-07-13 2022-01-14 华为技术有限公司 通信的方法及通信装置
WO2022033456A1 (fr) * 2020-08-13 2022-02-17 华为技术有限公司 Procédé de retour de mesure d'informations d'état de canal et appareil associé
WO2022205438A1 (fr) * 2021-04-02 2022-10-06 Zte Corporation Systèmes et procédés d'établissement de rapport et de gestion de faisceau à l'aide d'une intelligence artificielle

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