Detailed Description
Technical solutions in the embodiments of the present application will be clearly described below with reference to the drawings in the embodiments of the present application, and it is apparent that the described embodiments are some embodiments of the present application, but not all embodiments. All other embodiments obtained by a person of ordinary skill in the art based on the embodiments in the present application are within the scope of the protection of the present application.
The terms first, second and the like in the description and in the claims, are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the terms so used are interchangeable under appropriate circumstances such that the embodiments of the application are capable of operation in sequences other than those illustrated or otherwise described herein, and that the terms "first" and "second" are generally intended to be used in a generic sense and not to limit the number of objects, for example, the first object may be one or more. Furthermore, in the description and claims, "and/or" means at least one of the connected objects, and the character "/" generally means a relationship in which the associated object is an "or" before and after.
It is noted that the techniques described in embodiments of the present application are not limited to long term evolution (Long Term Evolution, LTE)/LTE evolution (LTE-Advanced, LTE-a) systems, but may also be used in other wireless communication systems, such as code division multiple access (Code Division Multiple Access, CDMA), time division multiple access (Time Division Multiple Access, TDMA), frequency division multiple access (Frequency Division Multiple Access, FDMA), orthogonal frequency division multiple access (Orthogonal Frequency Division Multiple Access, OFDMA), single carrier frequency division multiple access (Single-carrier Frequency Division Multiple Access, SC-FDMA), and other systems. The terms "system" and "network" in embodiments of the present application are often used interchangeably, and the techniques described may be used for both the above-mentioned systems and radio technologies, as well as other systems and radio technologies. The following description describes a New air interface (NR) system for purposes of example and uses NR terminology in much of the description that follows, but these techniques are also applicable to applications other than NR system applications, such as generation 6 (6)th Generation, 6G) communication system.
Fig. 1 shows a block diagram of a wireless communication system to which embodiments of the present application are applicable. The wireless communication system includes a terminal 11 and a network device 12. The terminal 11 may be a mobile phone, a tablet (Tablet Personal Computer), a Laptop (Laptop Computer) or a notebook, a personal digital assistant (Personal Digital Assistant, PDA), a palm Computer, a netbook, an Ultra-mobile personal Computer (Ultra-Mobile Personal Computer, UMPC), a mobile internet appliance (Mobile Internet Device, MID), an augmented Reality (Augmented Reality, AR)/Virtual Reality (VR) Device, a robot, a Wearable Device (weather Device), a Vehicle-mounted Device (VUE), a Pedestrian terminal (petestrian UE, PUE), a smart home (home Device with a wireless communication function, such as a refrigerator, a television, a washing machine, or furniture), a game machine, a personal Computer (Personal Computer, PC), a teller machine, or a self-service machine, and the Wearable Device includes: intelligent wrist-watch, intelligent bracelet, intelligent earphone, intelligent glasses, intelligent ornament (intelligent bracelet, intelligent ring, intelligent necklace, intelligent anklet, intelligent foot chain etc.), intelligent wrist strap, intelligent clothing etc.. Note that, the specific type of the terminal 11 is not limited in the embodiment of the present application. The network-side device 12 may comprise an access network device or a core network device, wherein the access network device 12 may also be referred to as a radio access network device, a radio access network (Radio Access Network, RAN), a radio access network function or a radio access network element. The access network device 12 may include a base station, a wireless local area network (Wireless Local Area Networks, WLAN) access point, or a wireless fidelity (Wireless Fidelity, wiFi) Node, etc., where the base station may be referred to as a Node B, an Evolved Node B (eNB), an access point, a base transceiver station (Base Transceiver Station, BTS), a radio base station, a radio transceiver, a basic service set (Basic Service Set, BSS), an extended service set (Extended Service Set, ESS), a home Node B, a home Evolved Node B, a transmission and reception point (Transmitting Receiving Point, TRP), or some other suitable terminology in the field, and the base station is not limited to a specific technical vocabulary so long as the same technical effect is achieved, and it should be noted that the base station in the NR system is only described by way of example in the embodiment of the present application, and the specific type of the base station is not limited.
Artificial intelligence is currently in wide-spread use in various fields. There are various implementations of AI network models, such as neural networks, decision trees, support vector machines, bayesian classifiers, etc. The present application is described by way of example with respect to neural networks, but is not limited to a particular type of AI network model.
In general, the AI algorithm chosen and the network model employed will also vary according to the different types of problems that need to be solved. The main method for improving the performance of the 5G network by means of the AI network model is to enhance or replace the existing algorithm or processing module by using the algorithm and model based on the neural network. In certain scenarios, neural network-based algorithms and models may achieve better performance than deterministic-based algorithms. More common neural networks include deep neural networks, convolutional neural networks, recurrent neural networks, and the like.
The performance of the target AI network model for terminal positioning can be analyzed, wherein the target AI network model can position the terminal according to the wireless communication information of the terminal, and the specific function and the working principle of the target AI network model are similar to those of the positioning AI network model in the related technology, and are not described herein.
The performance monitoring method of the AI network model, the performance monitoring device of the AI network model, the terminal and the like provided by the embodiments of the present application are described in detail below with reference to the accompanying drawings through some embodiments and application scenarios thereof.
Referring to fig. 2, the performance monitoring method of the AI network model provided in the embodiment of the present application is a terminal, where the terminal may be the terminal 11 in the embodiment shown in fig. 1, or other terminals than the terminal 11 listed in the embodiment shown in fig. 1, and is not specifically limited herein, and as shown in fig. 2, the performance monitoring method of the AI network model performed by the terminal may include the following steps:
step 201, a terminal acquires first information, where the first information is used to determine performance of a target AI network model, and the target AI network model is used to locate the terminal.
In implementations, the input information for the target AI network model may include: the output information of the target AI network model may include location information of the terminal. The location information of the terminal may be used as a basis for wireless communication, for example: as a basis for beam selection, as a basis for TRP switching, as a basis for transmission power control, and the like, are not particularly limited herein.
Of course, the location information of the terminal may also serve as a data base for certain application functions, such as: the purpose of the terminal position information output by the target AI network model is not particularly limited herein, such as navigation. For convenience of explanation, in the embodiment of the present application, wireless communication is exemplified by the terminal position information output according to the target AI network model, which is not specifically limited herein.
It should be noted that, in practical applications, the change of the location of the terminal (e.g., indoor or outdoor) and the change of the wireless communication environment (e.g., whether there is a shielding between the terminal and the base station, etc.) may affect the input information of the target AI network model, thereby interfering with the output result of the target AI network model. At this time, there may be cases where the accuracy of the output result of the target AI network model is low and the positioning requirement of the terminal is not satisfied in some scenarios, for example: when the accuracy of the output result of the target AI network model is low, if beam selection is continued according to the positioning result, the selected beam may not be the optimal beam.
In this step, the terminal may obtain the first information by at least one of measuring or performing information interaction with other communication devices, where the first information may be used to determine accuracy of a positioning result obtained by the target AI network model or determine information such as a degree of matching between the target AI network model and a wireless environment of the terminal, where the lower the degree of matching between the target AI network model and the wireless environment of the terminal is, the lower the reliability of the positioning result obtained by the target AI network model is.
Step 202, the terminal sends the first information to a network side device, or the terminal determines the performance of the target AI network model according to the first information.
In one implementation, the terminal may send first information to the network side device, and the network side device determines, according to the first information, a performance of the target AI network model, for example: and determining whether the target AI network model is effective (namely, the positioning result obtained by the target AI network model can meet the positioning accuracy requirement of the terminal or the reliability of the positioning result obtained by the target AI network model is higher) or invalid (namely, the positioning result obtained by the target AI network model can not meet the positioning accuracy requirement of the terminal or the reliability of the positioning result obtained by the target AI network model is lower) under the current wireless environment of the terminal according to the first information.
If the failure of the target AI network model is determined, the network side device may take corresponding measures to reduce the probability of communication performance degradation or even communication failure caused when the terminal continues to perform wireless communication according to the positioning result obtained by the target AI network model. For example: notifying the terminal of the failure of the target AI network model; or notifying the terminal to stop the operation related to wireless communication according to the positioning result obtained by the target AI network model; or updating the target AI network model and issuing the updated target AI network model to the terminal.
In one implementation, the terminal may determine the performance of the target AI network model from the first information. The implementation manner is similar to the implementation manner that the network side device determines the performance of the target AI network model according to the first information, and is different in that, in the implementation manner, a main body for executing the performance of the target AI network model according to the first information is a terminal, which is not described herein.
As an alternative embodiment, the first information includes at least one of:
in M continuous time units, based on the position information and the motion state information of the terminal determined by the target AI network model, M is a positive integer;
the position information of the terminal is determined by adopting a first mode, wherein the first mode does not comprise a positioning mode corresponding to the target AI network model;
an error or confidence of the location information of the terminal determined based on the target AI network model;
the change amplitude or the change rate of the channel measurement information of the terminal;
determining the distance information between the terminal and a positioning reference unit PRU or a transmission receiving node TRP by adopting a second mode, wherein the second mode does not comprise a mode corresponding to the target AI network model;
The distance information between the terminal and other terminals determined by adopting the second mode;
determining position information of the other terminals based on the target AI network model;
line of sight propagation LOS or non line of sight propagation NLOS indication information;
identification information of the other terminals;
identification information of the PRU or TRP.
In an option one, the terminal may determine the location information and the motion state information of the terminal based on the target AI network model in M continuous time units, where the location information of the terminal may be determined periodically by using the target AI network model, or the first information may be detected periodically, and in this case, the time units may include time in one period. The time units may include: orthogonal frequency division multiplexing (Orthogonal Frequency Division Multiplex, OFDM) symbols, slots, subframes, reference signal periods, milliseconds, seconds, minutes, hours, days, etc., are not particularly limited herein.
In implementation, the location information of the terminal may be determined when each of the M continuous time units is determined based on the target AI network model, and the motion state information of the terminal in the M continuous time units may also be determined based on other manners (such as detecting the motion state information of the terminal using a motion sensor), for example: speed of movement, distance of movement, etc.
And the position information of the terminal in M continuous time units, which is determined based on the target AI network model, and the motion state information of the terminal in M continuous time units can be checked mutually, and if the position information and the motion state information are not matched, the target AI network model can be determined to be invalid.
Optionally, the determining, by the terminal, the performance of the target AI network model according to the first information includes: and determining that the target AI network model fails when a difference between a first distance and a second distance is greater than or equal to a first threshold, wherein the first distance is a distance between a position of the terminal at a 1 st time unit and a position of the terminal at an M time unit, which is determined based on the motion state information, and the second distance is a distance between the position of the terminal at the 1 st time unit and the position of the terminal at the M time unit, which is determined based on the target AI network model, and M is greater than 1.
For example: assuming that the motion state information is the motion speed of the terminal in the M continuous time units, if the formula v|t is satisfied1 -tM |＞T1 +|P1 -PM And (3) determining that the target AI network model fails, wherein v represents the movement speed of the terminal in the M continuous time units, and t1 Representing a first time unit, t, of the M consecutive time unitsM Represents the Mth time unit, T, of the M continuous time units1 Represents a first threshold, P1 Representing the position of the terminal at a first time unit of the M continuous time units, P, determined based on the target AI network modelM Representing the location of the terminal at an mth time unit of the M continuous time units determined based on the target AI network model. P1 -PM I represents solving for P1 And PM Such as euclidean distance.
It should be noted that, in implementation, it may also be determined that the target AI network model is valid if the difference between the first distance and the second distance is less than the first threshold.
And secondly, the position information of the terminal determined by a first mode except the target AI network model is adopted, the position information of the terminal determined based on the target AI network model can be checked, and when the difference between the position information and the position information is large, the target AI network model can be determined to be invalid.
The first aspect may include: the present location information of the terminal, the location information of the terminal estimated based on the sensing information of the terminal by the sensor, etc. are analyzed based on the historical location information of the terminal, which is not meant to be exhaustive.
Optionally, the target AI network model is determined to be invalid if a distance between the location of the terminal determined in the first manner and the location of the terminal determined based on the target AI network model is greater than or equal to a second threshold.
Wherein a distance between the location of the terminal determined using the first manner and the location of the terminal determined based on the target AI network model may represent: a spatial distance between the location of the terminal determined in the first manner and the location of the terminal determined based on the target AI network model, or a degree of difference between the location information of the terminal determined in the first manner and the location information of the terminal determined based on the target AI network model.
It should be noted that, in implementation, the target AI network model may also be determined to be valid when a distance between the location of the terminal determined in the first manner and the location of the terminal determined based on the target AI network model is less than the second threshold.
In addition, the above-described first means may include one or at least two means, and each means may determine one or at least two positions of the terminal, at which time the accuracy of the position of the terminal determined based on the target AI network model may be verified based on the average or weighted average of at least two positions determined in all the first means, so that the performance of the target AI network model may be determined based on the accuracy of the position of the terminal determined by the target AI network model.
Optionally, determining that the target AI network model fails if a distance between a first location and the location of the terminal determined based on the target AI network model is greater than or equal to a third threshold, where the first location is a mean or weighted mean of N locations of the terminal determined using the first manner, and N is an integer greater than 1;
for example: satisfying the formulaDetermining that the target AI network model fails, where P(n) Represents the nth of the N positions of the terminal determined in the first mode, N is a positive integer less than or equal to N, P represents the position of the terminal determined based on the target AI network model, T2 Representing the third threshold.
It should be noted that, in implementation, the target AI network model may also be determined to be valid if the distance between the first location and the location of the terminal determined based on the target AI network model is less than the third threshold.
And thirdly, based on the error or the confidence coefficient of the position information of the terminal determined by the target AI network model, the reliability degree of the position information of the terminal determined by the target AI network model can be intuitively reflected, so that the failure of the target AI network model can be determined under the condition that the error or the confidence coefficient of the position information of the terminal determined by the target AI network model is larger. In contrast, in the case where the error of the location information of the terminal determined based on the target AI network model is small or the confidence is high, it may be determined that the target AI network model is valid.
Wherein the location of the terminal is typically continuously variable, the location of the terminal may be divided into at least two intervals to determine the degree of error or confidence of the location of the terminal within the at least two intervals, respectively.
Optionally, determining that the target AI network model fails if the minimum error of the location information of the terminal determined based on the target AI network model is greater than or equal to a ninth threshold.
The target AI network model may determine that the position information of the terminal is located in at least two possible positions or position intervals, and if the minimum errors of the terminal determined based on the target AI network model in at least two possible positions or position intervals are all greater than or equal to a ninth threshold, it may determine that the errors of the target AI network model are too large, thereby determining that the target AI network model fails.
Of course, when at least one error of the terminal determined based on the target AI network model in at least two possible positions or position intervals is less than the ninth threshold, it may be determined that the error of the target AI network model is small, thereby determining that the target AI network model is valid.
Optionally, determining that the target AI network model fails if the maximum confidence of the location information of the terminal determined based on the target AI network model is less than or equal to a tenth threshold.
The confidence of the position information of the terminal is inversely related to the error of the position information of the terminal in the above alternative embodiment, that is, the higher the confidence of the position information of the terminal is, the smaller the error of the position information of the terminal is.
For example: based on the intermediate result of the target AI network model (e.g., estimating the position of the terminal according to the channel state information), and quantizing the position label, if the quantization interval is 2m, the position interval of 0-2m is quantized to 1m, the position interval of 2-4m is quantized to 3m, and so on, at this time, the problem of estimating the position of the terminal can be converted into the problem of classifying the position of the terminal, that is, determining the confidence that the position of the terminal is located in the position interval corresponding to each position label, so that the result output by the last layer softmax of the target AI network model can be used as the confidence. At this time, if the maximum value of the confidence levels corresponding to all the position tags output by the last layer of softmax of the target AI network model is smaller than a certain threshold, it may be determined that the target AI network model fails.
And in the fourth option, the change amplitude or the change rate of the channel measurement information of the terminal is changed.
The channel measurement information of the terminal can reflect the change condition of the wireless communication environment where the terminal is located, and the target AI network model can determine the position information of the terminal according to the wireless communication information of the terminal, so that the change condition of the wireless communication environment where the terminal is located directly relates to the accuracy of the position information of the terminal output by the target AI network model.
Wherein, the channel measurement information of the terminal may include at least one of the following:
channel state information, the channel state information comprising: at least one of time domain channel state information, frequency domain channel state information, spatial domain channel state information, delay-doppler domain channel state information, and power delay profile;
channel characteristic information, the channel characteristic information comprising: at least one of Doppler frequency domain, first path delay, first path power, first path phase, first path angle, maximum H path delay, maximum H path power, maximum H path phase and maximum H path angle, H being an integer greater than or equal to 1;
channel quality information, the channel quality information comprising: at least one of reference signal received power RSRP, reference signal received quality RSRQ, signal-to-noise ratio SNR, and signal-to-interference-plus-noise ratio SINR.
The more drastic the change amplitude or change rate of the channel measurement information of the terminal, the larger the position change of the terminal in adjacent time units or M consecutive unit times can be indicated. Specifically, the change amplitude or change rate of the channel measurement information of the terminal may reflect the change situation of the wireless network environment where the terminal is located, while the channel measurement information of the terminal in two adjacent times or continuous periods M should be similar due to the continuity of the movement of the terminal, if the change of the channel measurement information measured by the terminal in two adjacent times or continuous periods M is too severe, it may indicate that there may be other factors interfering with the wireless communication environment where the terminal is located in this period, for example: the more the vehicle obstructs the wireless link of the terminal, the more the amplitude or rate of change of the channel measurement information of the terminal is drastic. Similarly, the confidence of the information input into the target AI network model in the period of time is low, and the confidence of the position information of the terminal output by the target AI network model is low, so that the failure of the target AI network model can be determined.
In implementation, the confidence of the location information of the terminal determined based on the target AI network model may also be set as a function related to a channel measurement information change rate, so that the confidence of the location information of the terminal determined based on the target AI network model may be calculated based on the channel measurement information change rate, and thus the target AI network model may be determined to be valid based on the higher confidence or may be determined to be invalid based on the lower confidence.
For example: at the satisfaction of |RSRP1 -RSRPM |＞T4 Determining that the target AI network model fails, wherein RSRP1 Representing RSRP detected by the terminal in a first time unit of the consecutive M time units; RSRPM Representing RSRP, T detected by the terminal in an mth time unit of the consecutive M time units4 Representing the fourth threshold.
Option five, determining the distance information between the terminal and the positioning reference unit (Positioning Reference Unit, PRU) or the transmitting and receiving node (Transmission Reception Point, TRP) in the second manner, where the second manner may include distance detection based on sidelink (sidelink) communication, distance detection based on bluetooth (bluetooth) communication, and other manners of distance detection (ranging), which are not specifically limited herein.
The true Ground-position (Ground-position) of the PRU and the TRP are known, and the accuracy of estimating the current position of the terminal can be assisted by establishing the association relation between the terminal and the TRP/PRU.
In addition, the number of PRUs/TRP may be one or at least two, and the real location (e.g., ground-truth) of each PRU/TRP may be distinguished by associating the identification information of the PRU/TRP with the location information of each PRU/TRP in the first information.
Optionally, determining that the target AI network model fails if a difference between a third distance, which is a distance between the terminal determined in the second manner and a positioning reference unit PRU or a transmission receiving node TRP, and a fourth distance, which is a distance between a location of the terminal determined based on the target AI network model and a location of the PRU or TRP, is greater than or equal to a fifth threshold
For example: detecting a third distance between the terminal and the PRU/TRP by a distance detection mode based on side link (sidelink) communication or Bluetooth (blue) communication, determining position information of the terminal based on a target AI network model, determining a fourth distance between a position corresponding to the position information and the PRU/TRP, and verifying accuracy of the fourth distance by using the third distance, wherein if |P is satisfied1 -P0 |＞D+T5 It can be determined that the error of the fourth distance is large, thereby determining that the target AI network model fails, wherein P1 Representing the position of the terminal determined based on the target AI network model, P0 Represents the true position of PRU/TRP, D represents the third distance, T5 Representing the fifth threshold described above.
Of course, in the case where the difference between the third distance and the fourth distance is smaller than the fifth threshold described above, it may be determined that the target AI network model is valid.
And step six, adopting the distance information between the terminal and other terminals determined in the second mode.
Similar to option five described above, in the present embodiment, the accuracy of the target AI network model may be verified based on the relative positional relationship between the two terminals.
The location of the other terminal may be determined based on the target AI network model, or may be determined in other manners, for example: for the purpose of illustration, in the embodiment of the present application, the location of the other terminal is also determined by using the target AI network model for illustration.
In addition, the number of the other terminals may be one or at least two, and the location information of each other terminal may be distinguished by associating the identification information of the other terminal with the location information of each other terminal in the first information.
Optionally, determining that the target AI network model fails if a difference between a fifth distance, which is the distance between the terminal determined in the second manner and the other terminal, and a sixth distance, which is the distance between the location of the terminal determined based on the target AI network model and the location of the other terminal determined based on the target AI network model, is greater than or equal to a sixth threshold
The second mode has the same meaning as the second mode in the fifth option, and is not described herein.
The positions of the terminal and other terminals are determined by using the target AI network model, so that the sixth distance can be calculated according to the two positions after the positions of the two terminals are respectively determined based on the target AI network model. For example: if meeting |P1 -P2 |＞D+T6 And if the difference between the sixth distance and the fifth distance is too large, namely, the error of determining the positions of the terminal and other terminals determined by the target AI network model is large, so that the failure of the target AI network model can be judged, wherein P is as follows1 Representing the position of the terminal determined based on the target AI network model, P2 Represents the position of the other terminal determined based on the target AI network model, D represents the fifth distance, T6 Representing the sixth threshold described above.
Option seven, the location information of the other terminal determined based on the target AI network model may be used to verify the positioning accuracy of the target AI network model, for example: for other terminals with known positions, the known positions of the other terminals can be compared with the positions of the other terminals determined based on the target AI network model, and if the distance between the known positions and the positions of the other terminals is large, the positioning accuracy of the target AI network model can be determined to be low, so that the failure of the target AI network model can be judged.
Option eight, line of Sight (LOS) or non-Line of Sight (Non Line of Sight, NLOS) indicates information.
In practice, there may be AI network models that can only achieve higher positioning accuracy in LOS scenarios, and AI network models that can only achieve higher positioning accuracy in NLOS scenarios. In this way, the AI network model capable of obtaining higher positioning accuracy in the LOS scene can be selected according to the wireless communication environment in which the terminal is located as the LOS scene, and the AI network model capable of obtaining higher positioning accuracy in the NLOS scene can be selected according to the wireless communication environment in which the terminal is located as the NLOS scene.
Optionally, when the target AI network model is a network model used in an LOS scenario and the LOS or NLOS indication information indicates that the LOS duty ratio of the terminal is less than or equal to a seventh threshold, determining that the target AI network model fails; and/or determining that the target AI network model fails when the target AI network model is a network model used in an NLOS scene and the LOS or NLOS indication information indicates that the NLOS duty ratio of the terminal is less than or equal to an eighth threshold.
The LOS indication information may indicate a duty ratio of an LOS path in a wireless communication environment where the terminal is located, and the NLOS indication information may indicate a duty ratio of an NLOS path in the wireless communication environment where the terminal is located. If the duty ratio of the LOS path is smaller than or equal to the seventh threshold, the wireless communication environment where the terminal is located is more prone to the NLOS scene, and at this time, a better positioning effect can be obtained by adopting a positioning AI network model suitable for the NLOS scene, and the positioning effect is poor when adopting the positioning AI network model suitable for the LOS scene; if the duty ratio of the NLOS path is less than or equal to the eighth threshold, the wireless communication environment where the terminal is located is more prone to the LOS scene, and at this time, a better positioning effect can be obtained by adopting the positioning AI network model suitable for the LOS scene, and the positioning effect is poor when adopting the positioning AI network model suitable for the NLOS scene.
In this embodiment, the performance of the target AI network model may be determined according to whether the LOS/NLOS scene applied by the target AI network model adopted by the terminal is matched with the LOS/NLOS scene in which the wireless communication environment in which the terminal is located.
At least one of the first threshold, the second threshold, the third threshold, the fourth threshold, the fifth threshold, the sixth threshold, the seventh threshold, the eighth threshold, and the ninth threshold may be an error threshold determined by the terminal according to a positioning accuracy requirement, a traffic scenario, or the like, or an error threshold agreed in a protocol, or an error threshold indicated by the network side device, which is not specifically limited herein.
As an optional implementation manner, before the terminal obtains the first information, the method further includes:
the terminal receives second indication information or third indication information from the network side equipment, wherein the second indication information is used for indicating the terminal to measure and/or report the first information, and the third indication information is used for indicating the terminal to monitor the performance of the target AI network model.
In one possible implementation manner, when the terminal receives the second indication information from the network side device, the terminal may measure the first information according to the indication of the second indication information and/or report the first information to the network side device. For example: the terminal obtains the first information according to the second indication information; or the terminal sends the pre-stored or received first information to the network side equipment according to the second indication information; or the terminal measures the first information according to the second indication information and sends the first information to the network side equipment; or the terminal measures the first information according to the second indication information, and determines the performance of the target AI network model judged by the terminal according to the first information based on other indication information or triggering of the condition. When the terminal reports the first information to the network side device, the network side device may determine the performance of the target AI network model based on the received first information.
In one possible implementation manner, when the terminal receives the third indication information from the network side device, the terminal may determine, according to the indication of the third indication information, the performance of the target AI network model according to the first information.
In this embodiment, the performance monitoring process for the target AI network model may be triggered based on the indication information of the network side device.
Optionally, the third indication information includes identification information of a first performance monitoring method, and the third indication information is used for indicating the terminal to perform performance monitoring on the target AI network model according to the first performance monitoring method.
In this way, the network side device may recommend the performance supervision method to the terminal through the third indication information, for example: and supervising the performance of the target AI network model according to the first threshold value, the second threshold value, the third threshold value and the like.
Optionally, the third indication information further includes second information, where the second information is used to assist in performance supervision of the target AI network model.
Wherein the second information may include a threshold value for assisting performance supervision of the target AI network model, for example: at least one of the first threshold, the second threshold, the third threshold, the fourth threshold, the fifth threshold, the sixth threshold, the seventh threshold, the eighth threshold, and the ninth threshold, and/or the second information may further include location information for assisting in performance supervision of the target AI network model, such as: location information of PRU/TRP, or location information of other terminals.
In this way, the network side device may dynamically configure the allowable error degree of the target AI network model through the third indication information, and/or the network side device may provide data support for the terminal to determine the performance of the target AI network model through the third indication information.
As an optional implementation manner, before the terminal receives the second indication information or the third indication information from the network side device, the method further includes:
and the terminal sends first request information to the network side equipment, wherein the first request information is used for requesting performance supervision of the target AI network model.
The first request information may request the network side device to perform performance supervision on the target AI network model, so that the network side device may send second indication information to the terminal based on the received first request information, where the second indication information may indicate the terminal to measure and report the first information to the network side device; or, the first request information may request the network side device to allow the terminal to perform performance supervision on the target AI network model, so that the network side device may send third indication information to the terminal based on the received first request information, where the third indication information may instruct the terminal to determine performance of the target AI network model according to the measured first information; or, the first request information may request performance supervision of the target AI network model, and the network side device decides that the network side device or the terminal performs performance supervision of the target AI network model, so that the network side device may send the second indication information or the third indication information to the terminal based on the received first request information.
In this embodiment, the terminal may trigger a performance monitoring procedure for the target AI network model.
Optionally, the first request information includes identification information of a second performance monitoring method, and the first request information is used for requesting performance monitoring of the target AI network model according to the second performance monitoring method.
In this embodiment, the terminal may recommend a performance supervision method to the network side device through the first request information, for example: and supervising the performance of the target AI network model according to the first threshold value, the second threshold value, the third threshold value and the like.
As an optional implementation manner, after the terminal sends the first information to the network side device, the method further includes:
and the terminal receives fourth indication information from the network side equipment, wherein the fourth indication information is used for indicating the performance of the target AI network model.
In this embodiment, after determining the performance of the target AI network model, the terminal may report the performance result to the network side device, so that the network side device learns the performance of the target AI network model, and accordingly takes corresponding measures, for example: when the target AI network model fails, the target AI network model can be retrained, and the retrained target AI network model is issued to the terminal.
Optionally, in a case where the fifth indication information indicates that the target AI network model fails, the fifth indication information also indicates a cause of the failure of the target AI network model.
Wherein the cause of the failure of the target AI network model may correspond to a method of determining a performance of the target AI network model from the first information, e.g.: the reasons for the failure of the target AI network model may include: the difference between the first distance and the second distance is greater than or equal to a first threshold, the distance between the location of the terminal determined using the first manner and the location of the terminal determined based on the target AI network model is greater than or equal to a second threshold, the distance between the first location and the location of the terminal determined based on the target AI network model is greater than or equal to a third threshold, and so on, which are not described herein.
In this embodiment, the terminal may inform the network side device of the cause of the failure of the target AI network model through the fifth indication information, and at this time, the network side device may take corresponding measures according to the cause, or when adjusting the target AI network model, it may be able to determine how to adjust the target AI network model according to the cause.
In the embodiment of the application, a terminal acquires first information, wherein the first information is used for determining the performance of a target AI network model, and the target AI network model is used for positioning the terminal; and the terminal sends the first information to network side equipment, or the terminal determines the performance of the target AI network model according to the first information. The terminal can acquire first information for assisting in determining the performance of the target AI network model, and report the first information to the network side device, so that the network side device can judge the performance of the target AI network model according to the first information, or the terminal can judge the performance of the target AI network model directly according to the first information. In this way, the situation that the performance of the target AI network model cannot meet the positioning requirement can be timely found, and then appropriate measures can be taken according to the result that the performance of the target AI network model cannot meet the positioning requirement, for example: updating a target AI network model, adopting other positioning modes to position the terminal, and the like. The probability of low wireless communication performance and even errors caused by the fact that the positioning performance of the target AI network model can not meet the positioning requirement and related wireless communication is continuously executed according to the positioning result of the target AI network model is reduced.
Referring to fig. 3, another performance monitoring method for an AI network model provided in this embodiment of the present application is a network side device, where the network side device may be the network side device 12 in the embodiment shown in fig. 1, or other network side devices other than the network side device 12 listed in the embodiment shown in fig. 1, and not limited herein, and as shown in fig. 3, the performance monitoring method for an AI network model performed by the network side device may include the following steps:
step 301, a network side device receives first information from a terminal, and determines performance of a target AI network model according to the first information, where the first information is used to determine performance of the target AI network model, and the target AI network model is used to locate the terminal.
Step 302, the network side device receives fifth indication information from the terminal, where the fifth indication information is used to indicate the performance of the target AI network model.
It should be noted that, in the embodiment shown in fig. 3, the performance monitoring method of the AI network model performed by the network side device includes the above steps 301 and 302 as an example for illustration, and in implementation, the performance monitoring method of the AI network model performed by the network side device may include only one of the above steps 301 and 302, and the specific process may refer to the description in the embodiment of the terminal side method shown in fig. 2, which is not limited herein.
Optionally, the first information includes at least one of:
in M continuous time units, based on the position information and the motion state information of the terminal determined by the target AI network model, M is a positive integer;
the position information of the terminal is determined by adopting a first mode, wherein the first mode does not comprise a positioning mode corresponding to the target AI network model;
an error or confidence of the location information of the terminal determined based on the target AI network model;
the change amplitude or the change rate of the channel measurement information of the terminal;
determining the distance information between the terminal and a positioning reference unit PRU or a transmission receiving node TRP by adopting a second mode, wherein the second mode does not comprise a mode corresponding to the target AI network model;
the distance information between the terminal and other terminals determined by adopting the second mode;
determining position information of the other terminals based on the target AI network model;
line of sight propagation LOS or non line of sight propagation NLOS indication information;
identification information of the other terminals;
identification information of the PRU or TRP.
Optionally, the network side device determines, according to the first information, performance of the target AI network model, including at least one of:
Determining that the target AI network model fails if a difference between a first distance and a second distance is greater than or equal to a first threshold, wherein the first distance is a distance between a position of the terminal at a 1 st time unit and a position of the terminal at an M-th time unit determined based on the motion state information, and the second distance is a distance between the position of the terminal at the 1 st time unit and the position of the terminal at the M-th time unit determined based on the target AI network model, and M is greater than 1;
determining that the target AI network model fails if a distance between the location of the terminal determined in the first manner and the location of the terminal determined based on the target AI network model is greater than or equal to a second threshold;
determining that the target AI network model fails when a distance between a first position and the position of the terminal determined based on the target AI network model is greater than or equal to a third threshold, wherein the first position is a mean or weighted mean of N positions of the terminal determined by adopting the first mode, and N is an integer greater than 1;
Determining that the target AI network model fails under the condition that the change amplitude or the change rate of the channel state information measured by the terminal is larger than or equal to a fourth threshold value;
determining that the target AI network model fails if a difference between a third distance, which is a distance between the terminal determined in the second manner and a positioning reference unit PRU or a transmission receiving node TRP, and a fourth distance, which is a distance between a location of the terminal determined based on the target AI network model and a location of the PRU or TRP, is greater than or equal to a fifth threshold;
determining that the target AI network model fails if a difference between a fifth distance, which is a distance between the terminal determined in the second manner and other terminals, and a sixth distance, which is a distance between the location of the terminal determined based on the target AI network model and the location of the other terminals determined based on the target AI network model, is greater than or equal to a sixth threshold;
determining that the target AI network model fails when the target AI network model is a network model used in an LOS scene and the LOS or NLOS indication information indicates that the LOS duty ratio of the terminal is smaller than or equal to a seventh threshold;
Determining that the target AI network model fails when the target AI network model is a network model used in an NLOS scene and the LOS or NLOS indication information indicates that the NLOS duty ratio of the terminal is smaller than or equal to an eighth threshold;
determining that the target AI network model fails under the condition that the minimum error of the position information of the terminal determined based on the target AI network model is larger than or equal to a ninth threshold value;
and determining that the target AI network model fails under the condition that the maximum confidence of the position information of the terminal determined based on the target AI network model is smaller than or equal to a tenth threshold value.
Optionally, the channel measurement information of the terminal includes at least one of:
channel state information, the channel state information comprising: at least one of time domain channel state information, frequency domain channel state information, spatial domain channel state information, delay-doppler domain channel state information, and power delay profile;
channel characteristic information, the channel characteristic information comprising: at least one of Doppler frequency domain, first path delay, first path power, first path phase, first path angle, maximum H path delay, maximum H path power, maximum H path phase and maximum H path angle, H being an integer greater than or equal to 1;
Channel quality information, the channel quality information comprising: at least one of reference signal received power RSRP, reference signal received quality RSRQ, signal-to-noise ratio SNR, and signal-to-interference-plus-noise ratio SINR.
Optionally, before the network side device receives the first information from the terminal or receives the fifth indication information from the terminal, the method further includes:
the network side equipment sends second indication information or third indication information to the terminal, wherein the second indication information is used for indicating the terminal to measure and/or report the first information, and the third indication information is used for indicating the terminal to monitor the performance of the target AI network model.
Optionally, the third indication information includes identification information of a first performance monitoring method, and the third indication information is used for indicating the terminal to perform performance monitoring on the target AI network model according to the first performance monitoring method.
Optionally, the third indication information further includes second information, where the second information is used to assist in performance supervision of the target AI network model.
Optionally, before the network side device sends the second indication information or the third indication information to the terminal, the method further includes:
The network side equipment receives first request information from the terminal, wherein the first request information is used for requesting performance supervision of the target AI network model.
Optionally, the first request information includes identification information of a second performance monitoring method, and the first request information is used for requesting performance monitoring of the target AI network model according to the second performance monitoring method.
Optionally, after the network side device receives the first information from the terminal, the method further includes:
and the network side equipment sends fourth indication information to the terminal, wherein the fourth indication information is used for indicating the performance of the target AI network model.
Optionally, in a case where the fourth indication information indicates that the target AI network model fails, the fourth indication information further indicates a cause of the failure of the target AI network model.
Optionally, in a case where the fifth indication information indicates that the target AI network model fails, the fifth indication information also indicates a cause of the failure of the target AI network model.
The performance monitoring method of the AI network model executed by the network side equipment corresponds to the performance monitoring method of the AI network model executed by the terminal, and the terminal and the network side equipment execute the steps in the performance monitoring method of the respective AI network model respectively, so that the situation that the positioning performance of the target AI network model is low can be found in time, and proper measures are taken to reduce the probability that the wireless communication performance and the application experience are low due to the fact that the positioning performance of the target AI network model is low.
According to the performance monitoring method for the AI network model, the execution main body can be a performance monitoring device for the AI network model. In the embodiment of the application, taking the performance monitoring device of the AI network model as an example, the performance monitoring device of the AI network model provided in the embodiment of the application is described.
Referring to fig. 4, a performance monitoring apparatus for an AI network model provided in an embodiment of the present application may be an apparatus in a terminal, and as shown in fig. 4, a performance monitoring apparatus 400 for an AI network model may include the following modules:
an obtaining module 401, configured to obtain first information, where the first information is used to determine performance of a target AI network model, and the target AI network model is used to locate the terminal;
the first sending module 402 or the first determining module 403 is configured to send the first information to a network side device, and the first determining module 403 is configured to determine, according to the first information, a performance of the target AI network model.
It should be noted that, in the embodiment shown in fig. 4, the performance monitoring apparatus 400 of the AI network model includes the first transmitting module 402 and the first determining module 403 as an example, and in implementation, the performance monitoring apparatus 400 of the AI network model may include only one of the first transmitting module 402 and the first determining module 403, which is not limited herein.
Optionally, the first information includes at least one of:
in M continuous time units, based on the position information and the motion state information of the terminal determined by the target AI network model, M is a positive integer;
the position information of the terminal is determined by adopting a first mode, wherein the first mode does not comprise a positioning mode corresponding to the target AI network model;
an error or confidence of the location information of the terminal determined based on the target AI network model;
the change amplitude or the change rate of the channel measurement information of the terminal;
determining the distance information between the terminal and a positioning reference unit PRU or a transmission receiving node TRP by adopting a second mode, wherein the second mode does not comprise a mode corresponding to the target AI network model;
the distance information between the terminal and other terminals determined by adopting the second mode;
determining position information of the other terminals based on the target AI network model;
line of sight propagation LOS or non line of sight propagation NLOS indication information;
identification information of the other terminals;
identification information of the PRU or TRP.
Optionally, the first determining module 403 is configured to perform at least one of:
determining that the target AI network model fails if a difference between a first distance and a second distance is greater than or equal to a first threshold, wherein the first distance is a distance between a position of the terminal at a 1 st time unit and a position of the terminal at an M-th time unit determined based on the motion state information, and the second distance is a distance between the position of the terminal at the 1 st time unit and the position of the terminal at the M-th time unit determined based on the target AI network model, and M is greater than 1;
Determining that the target AI network model fails if a distance between the location of the terminal determined in the first manner and the location of the terminal determined based on the target AI network model is greater than or equal to a second threshold;
determining that the target AI network model fails when a distance between a first position and the position of the terminal determined based on the target AI network model is greater than or equal to a third threshold, wherein the first position is a mean or weighted mean of N positions of the terminal determined by adopting the first mode, and N is an integer greater than 1;
determining that the target AI network model fails under the condition that the change amplitude or the change rate of the channel state information measured by the terminal is larger than or equal to a fourth threshold value;
determining that the target AI network model fails if a difference between a third distance, which is a distance between the terminal determined in the second manner and a positioning reference unit PRU or a transmission receiving node TRP, and a fourth distance, which is a distance between a location of the terminal determined based on the target AI network model and a location of the PRU or TRP, is greater than or equal to a fifth threshold;
Determining that the target AI network model fails if a difference between a fifth distance, which is a distance between the terminal determined in the second manner and other terminals, and a sixth distance, which is a distance between the location of the terminal determined based on the target AI network model and the location of the other terminals determined based on the target AI network model, is greater than or equal to a sixth threshold;
determining that the target AI network model fails when the target AI network model is a network model used in an LOS scene and the LOS or NLOS indication information indicates that the LOS duty ratio of the terminal is smaller than or equal to a seventh threshold;
determining that the target AI network model fails when the target AI network model is a network model used in an NLOS scene and the LOS or NLOS indication information indicates that the NLOS duty ratio of the terminal is smaller than or equal to an eighth threshold;
determining that the target AI network model fails under the condition that the minimum error of the position information of the terminal determined based on the target AI network model is larger than or equal to a ninth threshold value;
and determining that the target AI network model fails under the condition that the maximum confidence of the position information of the terminal determined based on the target AI network model is smaller than or equal to a tenth threshold value.
Optionally, the channel measurement information of the terminal includes at least one of:
channel state information, the channel state information comprising: at least one of time domain channel state information, frequency domain channel state information, spatial domain channel state information, delay-doppler domain channel state information, and power delay profile;
channel characteristic information, the channel characteristic information comprising: at least one of Doppler frequency domain, first path delay, first path power, first path phase, first path angle, maximum H path delay, maximum H path power, maximum H path phase and maximum H path angle, H being an integer greater than or equal to 1;
channel quality information, the channel quality information comprising: at least one of reference signal received power RSRP, reference signal received quality RSRQ, signal-to-noise ratio SNR, and signal-to-interference-plus-noise ratio SINR.
Optionally, the performance monitoring apparatus 400 of the AI network model further includes:
the first receiving module is configured to receive second indication information or third indication information from the network side device, where the second indication information is used to instruct the terminal to measure and/or report the first information, and the third indication information is used to instruct the terminal to perform performance supervision on the target AI network model.
Optionally, the third indication information includes identification information of a first performance monitoring method, and the third indication information is used for indicating the terminal to perform performance monitoring on the target AI network model according to the first performance monitoring method.
Optionally, the third indication information further includes second information, where the second information is used to assist in performance supervision of the target AI network model.
Optionally, the performance monitoring apparatus 400 of the AI network model further includes:
and the second sending module is used for sending first request information to the network side equipment, wherein the first request information is used for requesting performance supervision of the target AI network model.
Optionally, the first request information includes identification information of a second performance monitoring method, and the first request information is used for requesting performance monitoring of the target AI network model according to the second performance monitoring method.
Optionally, the performance monitoring apparatus 400 of the AI network model further includes:
and the second receiving module is used for receiving fourth indication information from the network side equipment, wherein the fourth indication information is used for indicating the performance of the target AI network model.
Optionally, in a case where the fourth indication information indicates that the target AI network model fails, the fourth indication information further indicates a cause of the failure of the target AI network model.
Optionally, the performance monitoring apparatus 400 of the AI network model further includes:
and the third sending module is used for sending fifth indicating information to the network side equipment, wherein the fifth indicating information is used for indicating the performance of the target AI network model.
Optionally, in a case where the fifth indication information indicates that the target AI network model fails, the fifth indication information also indicates a cause of the failure of the target AI network model.
The performance monitoring device 400 of the AI network model provided in this embodiment of the present application can implement each process implemented by the terminal in the method embodiment shown in fig. 2, and can obtain the same beneficial effects, so that repetition is avoided, and details are not repeated here.
Referring to fig. 5 or fig. 6, another performance monitoring apparatus for an AI network model provided in an embodiment of the present application may be an apparatus in a network side device.
As shown in fig. 5, a performance monitoring apparatus 500 of an AI network model may include the following modules:
a third receiving module 501, configured to receive first information from a terminal, where the first information is used to determine a performance of a target AI network model, and the target AI network model is used to locate the terminal;
A second determining module 502, configured to determine a performance of the target AI network model according to the first information.
As shown in fig. 6, another performance monitoring apparatus 600 of an AI network model may include the following modules:
a fourth receiving module 601, configured to receive fifth indication information from a terminal, where the fifth indication information is used to indicate performance of the target AI network model.
It should be noted that, in implementation, the network side device may further include the third receiving module 501, the second determining module 502, and the fourth receiving module 601, as shown in fig. 5 or fig. 6, which are only examples of two possible performance monitoring apparatuses of the AI network model.
Optionally, the first information includes at least one of:
in M continuous time units, based on the position information and the motion state information of the terminal determined by the target AI network model, M is a positive integer;
the position information of the terminal is determined by adopting a first mode, wherein the first mode does not comprise a positioning mode corresponding to the target AI network model;
an error or confidence of the location information of the terminal determined based on the target AI network model;
the change amplitude or the change rate of the channel measurement information of the terminal;
Determining the distance information between the terminal and a positioning reference unit PRU or a transmission receiving node TRP by adopting a second mode, wherein the second mode does not comprise a mode corresponding to the target AI network model;
the distance information between the terminal and other terminals determined by adopting the second mode;
determining position information of the other terminals based on the target AI network model;
line of sight propagation LOS or non line of sight propagation NLOS indication information;
identification information of the other terminals;
identification information of the PRU or TRP.
Optionally, the second determining module 502 is configured to perform at least one of:
determining that the target AI network model fails if a difference between a first distance and a second distance is greater than or equal to a first threshold, wherein the first distance is a distance between a position of the terminal at a 1 st time unit and a position of the terminal at an M-th time unit determined based on the motion state information, and the second distance is a distance between the position of the terminal at the 1 st time unit and the position of the terminal at the M-th time unit determined based on the target AI network model, and M is greater than 1;
determining that the target AI network model fails if a distance between the location of the terminal determined in the first manner and the location of the terminal determined based on the target AI network model is greater than or equal to a second threshold;
Determining that the target AI network model fails when a distance between a first position and the position of the terminal determined based on the target AI network model is greater than or equal to a third threshold, wherein the first position is a mean or weighted mean of N positions of the terminal determined by adopting the first mode, and N is an integer greater than 1;
determining that the target AI network model fails under the condition that the change amplitude or the change rate of the channel state information measured by the terminal is larger than or equal to a fourth threshold value;
determining that the target AI network model fails if a difference between a third distance, which is a distance between the terminal determined in the second manner and a positioning reference unit PRU or a transmission receiving node TRP, and a fourth distance, which is a distance between a location of the terminal determined based on the target AI network model and a location of the PRU or TRP, is greater than or equal to a fifth threshold;
determining that the target AI network model fails if a difference between a fifth distance, which is a distance between the terminal determined in the second manner and other terminals, and a sixth distance, which is a distance between the location of the terminal determined based on the target AI network model and the location of the other terminals determined based on the target AI network model, is greater than or equal to a sixth threshold;
Determining that the target AI network model fails when the target AI network model is a network model used in an LOS scene and the LOS or NLOS indication information indicates that the LOS duty ratio of the terminal is smaller than or equal to a seventh threshold;
determining that the target AI network model fails when the target AI network model is a network model used in an NLOS scene and the LOS or NLOS indication information indicates that the NLOS duty ratio of the terminal is smaller than or equal to an eighth threshold;
determining that the target AI network model fails under the condition that the minimum error of the position information of the terminal determined based on the target AI network model is larger than or equal to a ninth threshold value;
and determining that the target AI network model fails under the condition that the maximum confidence of the position information of the terminal determined based on the target AI network model is smaller than or equal to a tenth threshold value.
Optionally, the channel measurement information of the terminal includes at least one of:
channel state information, the channel state information comprising: at least one of time domain channel state information, frequency domain channel state information, spatial domain channel state information, delay-doppler domain channel state information, and power delay profile;
Channel characteristic information, the channel characteristic information comprising: at least one of Doppler frequency domain, first path delay, first path power, first path phase, first path angle, maximum H path delay, maximum H path power, maximum H path phase and maximum H path angle, H being an integer greater than or equal to 1;
channel quality information, the channel quality information comprising: at least one of reference signal received power RSRP, reference signal received quality RSRQ, signal-to-noise ratio SNR, and signal-to-interference-plus-noise ratio SINR.
Optionally, the performance monitoring apparatus 500 of the AI network model or the performance monitoring apparatus 600 of the AI network model further includes:
and the fourth sending module is used for sending second indication information or third indication information to the terminal, wherein the second indication information is used for indicating the terminal to measure and/or report the first information, and the third indication information is used for indicating the terminal to perform performance supervision on the target AI network model.
Optionally, the third indication information includes identification information of a first performance monitoring method, and the third indication information is used for indicating the terminal to perform performance monitoring on the target AI network model according to the first performance monitoring method.
Optionally, the third indication information further includes second information, where the second information is used to assist in performance supervision of the target AI network model.
Optionally, the performance monitoring apparatus 500 of the AI network model or the performance monitoring apparatus 600 of the AI network model further includes:
and a fifth receiving module, configured to receive first request information from the terminal, where the first request information is used to request performance supervision on the target AI network model.
Optionally, the first request information includes identification information of a second performance monitoring method, and the first request information is used for requesting performance monitoring of the target AI network model according to the second performance monitoring method.
Optionally, the performance monitoring apparatus 500 of the AI network model or the performance monitoring apparatus 600 of the AI network model further includes:
and the fifth sending module is used for sending fourth indication information to the terminal, wherein the fourth indication information is used for indicating the performance of the target AI network model.
Optionally, in a case where the fourth indication information indicates that the target AI network model fails, the fourth indication information further indicates a cause of the failure of the target AI network model.
Optionally, in a case where the fifth indication information indicates that the target AI network model fails, the fifth indication information also indicates a cause of the failure of the target AI network model.
The performance monitoring apparatus 500 of the AI network model or the performance monitoring apparatus 600 of the AI network model provided in this embodiment of the present application can implement each process implemented by the network side device in the method embodiment shown in fig. 3, and can obtain the same beneficial effects, so that repetition is avoided, and no description is repeated here.
The performance monitoring device of the AI network model in the embodiment of the application may be an electronic device, for example, an electronic device with an operating system, or may be a component in the electronic device, for example, an integrated circuit or a chip. The electronic device may be a terminal, or may be other devices than a terminal. By way of example, terminals may include, but are not limited to, the types of terminals 11 listed above, other devices may be servers, network attached storage (Network Attached Storage, NAS), etc., and embodiments of the application are not specifically limited.
The performance monitoring device of the AI network model provided in the embodiment of the present application can implement each process implemented by the method embodiment shown in fig. 2 or fig. 3, and achieve the same technical effects, so that repetition is avoided, and no further description is provided here.
Optionally, as shown in fig. 7, the embodiment of the present application further provides a communication device 700, including a processor 701 and a memory 702, where the memory 702 stores a program or instructions that can be executed on the processor 701, for example, when the communication device 700 is a terminal, the program or instructions implement, when executed by the processor 701, the steps of the method embodiment shown in fig. 2, and achieve the same technical effects. When the communication device 700 is a network side device, the program or the instructions, when executed by the processor 701, implement the steps of the method embodiment shown in fig. 3, and achieve the same technical effects, and for avoiding repetition, will not be described herein.
The embodiment of the application also provides a terminal, which comprises a processor and a communication interface, wherein the communication interface is used for acquiring first information, the first information is used for determining the performance of a target AI network model, and the target AI network model is used for positioning the terminal; the communication interface is further configured to send the first information to a network side device or the processor is configured to determine, according to the first information, performance of the target AI network model.
The terminal embodiment corresponds to the terminal-side method embodiment, and each implementation process and implementation manner of the method embodiment can be applied to the terminal embodiment, and the same technical effects can be achieved. Specifically, fig. 8 is a schematic hardware structure of a terminal for implementing an embodiment of the present application.
The terminal 800 includes, but is not limited to: at least part of the components of the radio frequency unit 801, the network module 802, the audio output unit 803, the input unit 804, the sensor 805, the display unit 806, the user input unit 807, the interface unit 808, the memory 809, and the processor 810, etc.
Those skilled in the art will appreciate that the terminal 800 may further include a power source (e.g., a battery) for powering the various components, and that the power source may be logically coupled to the processor 810 by a power management system for performing functions such as managing charging, discharging, and power consumption by the power management system. The terminal structure shown in fig. 8 does not constitute a limitation of the terminal, and the terminal may include more or less components than shown, or may combine certain components, or may be arranged in different components, which will not be described in detail herein.
It should be appreciated that in embodiments of the present application, the input unit 804 may include a graphics processing unit (Graphics Processing Unit, GPU) 8041 and a microphone 8042, with the graphics processor 8041 processing image data of still pictures or video obtained by an image capturing device (e.g., a camera) in a video capturing mode or an image capturing mode. The display unit 806 may include a display panel 8061, and the display panel 8061 may be configured in the form of a liquid crystal display, an organic light emitting diode, or the like. The user input unit 807 includes at least one of a touch panel 8071 and other input devices 8072. Touch panel 8071, also referred to as a touch screen. The touch panel 8071 may include two parts, a touch detection device and a touch controller. Other input devices 8072 may include, but are not limited to, a physical keyboard, function keys (e.g., volume control keys, switch keys, etc.), a trackball, a mouse, a joystick, and so forth, which are not described in detail herein.
In this embodiment, after receiving downlink data from the network side device, the radio frequency unit 801 may transmit the downlink data to the processor 810 for processing; in addition, the radio frequency unit 801 may send uplink data to the network side device. In general, the radio frequency unit 801 includes, but is not limited to, an antenna, an amplifier, a transceiver, a coupler, a low noise amplifier, a duplexer, and the like.
The memory 809 may be used to store software programs or instructions and various data. The memory 809 may mainly include a first storage area storing programs or instructions and a second storage area storing data, wherein the first storage area may store an operating system, application programs or instructions (such as a sound playing function, an image playing function, etc.) required for at least one function, and the like. Further, the memory 809 may include volatile memory or nonvolatile memory, or the memory 809 may include both volatile and nonvolatile memory. The nonvolatile Memory may be a Read-Only Memory (ROM), a Programmable ROM (PROM), an Erasable PROM (EPROM), an Electrically Erasable EPROM (EEPROM), or a flash Memory. The volatile memory may be random access memory (Random Access Memory, RAM), static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (ddr SDRAM), enhanced SDRAM (Enhanced SDRAM), synchronous DRAM (SLDRAM), and Direct RAM (DRRAM). Memory 809 in embodiments of the present application includes, but is not limited to, these and any other suitable types of memory.
The processor 810 may include one or more processing units; optionally, the processor 810 integrates an application processor that primarily processes operations involving an operating system, user interface, application programs, etc., and a modem processor that primarily processes wireless communication signals, such as a baseband processor. It will be appreciated that the modem processor described above may not be integrated into the processor 810.
The radio frequency unit 801 is configured to obtain first information, where the first information is used to determine performance of a target AI network model, and the target AI network model is used to locate the terminal;
the radio frequency unit 801 is further configured to send the first information to a network side device, or the processor 810 is configured to determine, according to the first information, performance of the target AI network model.
Optionally, the first information includes at least one of:
in M continuous time units, based on the position information and the motion state information of the terminal determined by the target AI network model, M is a positive integer;
the position information of the terminal is determined by adopting a first mode, wherein the first mode does not comprise a positioning mode corresponding to the target AI network model;
An error or confidence of the location information of the terminal determined based on the target AI network model;
the change amplitude or the change rate of the channel measurement information of the terminal;
determining the distance information between the terminal and a positioning reference unit PRU or a transmission receiving node TRP by adopting a second mode, wherein the second mode does not comprise a mode corresponding to the target AI network model;
the distance information between the terminal and other terminals determined by adopting the second mode;
determining position information of the other terminals based on the target AI network model;
line of sight propagation LOS or non line of sight propagation NLOS indication information;
identification information of the other terminals;
identification information of the PRU or TRP.
Optionally, the determining, by the processor 810, the performance of the target AI network model according to the first information includes at least one of:
determining that the target AI network model fails if a difference between a first distance and a second distance is greater than or equal to a first threshold, wherein the first distance is a distance between a position of the terminal at a 1 st time unit and a position of the terminal at an M-th time unit determined based on the motion state information, and the second distance is a distance between the position of the terminal at the 1 st time unit and the position of the terminal at the M-th time unit determined based on the target AI network model, and M is greater than 1;
Determining that the target AI network model fails if a distance between the location of the terminal determined in the first manner and the location of the terminal determined based on the target AI network model is greater than or equal to a second threshold;
determining that the target AI network model fails when a distance between a first position and the position of the terminal determined based on the target AI network model is greater than or equal to a third threshold, wherein the first position is a mean or weighted mean of N positions of the terminal determined by adopting the first mode, and N is an integer greater than 1;
determining that the target AI network model fails under the condition that the change amplitude or the change rate of the channel state information measured by the terminal is larger than or equal to a fourth threshold value;
determining that the target AI network model fails if a difference between a third distance, which is a distance between the terminal determined in the second manner and a positioning reference unit PRU or a transmission receiving node TRP, and a fourth distance, which is a distance between a location of the terminal determined based on the target AI network model and a location of the PRU or TRP, is greater than or equal to a fifth threshold;
Determining that the target AI network model fails if a difference between a fifth distance, which is a distance between the terminal determined in the second manner and other terminals, and a sixth distance, which is a distance between the location of the terminal determined based on the target AI network model and the location of the other terminals determined based on the target AI network model, is greater than or equal to a sixth threshold;
determining that the target AI network model fails when the target AI network model is a network model used in an LOS scene and the LOS or NLOS indication information indicates that the LOS duty ratio of the terminal is smaller than or equal to a seventh threshold;
determining that the target AI network model fails when the target AI network model is a network model used in an NLOS scene and the LOS or NLOS indication information indicates that the NLOS duty ratio of the terminal is smaller than or equal to an eighth threshold;
determining that the target AI network model fails under the condition that the minimum error of the position information of the terminal determined based on the target AI network model is larger than or equal to a ninth threshold value;
and determining that the target AI network model fails under the condition that the maximum confidence of the position information of the terminal determined based on the target AI network model is smaller than or equal to a tenth threshold value.
Optionally, the channel measurement information of the terminal includes at least one of:
channel state information, the channel state information comprising: at least one of time domain channel state information, frequency domain channel state information, spatial domain channel state information, delay-doppler domain channel state information, and power delay profile;
channel characteristic information, the channel characteristic information comprising: at least one of Doppler frequency domain, first path delay, first path power, first path phase, first path angle, maximum H path delay, maximum H path power, maximum H path phase and maximum H path angle, H being an integer greater than or equal to 1;
channel quality information, the channel quality information comprising: at least one of reference signal received power RSRP, reference signal received quality RSRQ, signal-to-noise ratio SNR, and signal-to-interference-plus-noise ratio SINR.
Optionally, before executing the acquiring the first information, the radio frequency unit 801 is further configured to receive second indication information or third indication information from the network side device, where the second indication information is used to instruct the terminal to measure and/or report the first information, and the third indication information is used to instruct the terminal to perform performance supervision on the target AI network model.
Optionally, the third indication information includes identification information of a first performance monitoring method, and the third indication information is used for indicating the terminal to perform performance monitoring on the target AI network model according to the first performance monitoring method.
Optionally, the third indication information further includes second information, where the second information is used to assist in performance supervision of the target AI network model.
Optionally, before executing the receiving the second indication information or the third indication information from the network side device, the radio frequency unit 801 is further configured to send first request information to the network side device, where the first request information is used to request performance supervision on the target AI network model.
Optionally, the first request information includes identification information of a second performance monitoring method, and the first request information is used for requesting performance monitoring of the target AI network model according to the second performance monitoring method.
Optionally, after the sending the first information to the network side device, the radio frequency unit 801 is further configured to receive fourth indication information from the network side device, where the fourth indication information is used to indicate performance of the target AI network model.
Optionally, in a case where the fourth indication information indicates that the target AI network model fails, the fourth indication information further indicates a cause of the failure of the target AI network model.
Optionally, after the processor 810 executes the determining, according to the first information, the performance of the target AI network model, the radio frequency unit 801 is further configured to send fifth indication information to the network side device, where the fifth indication information is used to indicate the performance of the target AI network model.
Optionally, in a case where the fifth indication information indicates that the target AI network model fails, the fifth indication information also indicates a cause of the failure of the target AI network model.
The terminal 800 provided in this embodiment of the present application can execute each process executed by each module in the performance monitoring apparatus 400 of the AI network model shown in fig. 4, and can obtain the same beneficial effects, and for avoiding repetition, the description is omitted here.
The embodiment of the application also provides network side equipment, which comprises a processor and a communication interface, wherein the communication interface is used for receiving first information from a terminal, the processor is used for determining the performance of a target AI network model according to the first information, the first information is used for determining the performance of the target AI network model, and the target AI network model is used for positioning the terminal; or the communication interface is configured to receive fifth indication information from the terminal, where the fifth indication information is used to indicate the performance of the target AI network model.
The network side device embodiment corresponds to the network side device method embodiment, and each implementation process and implementation manner of the method embodiment can be applied to the network side device embodiment, and the same technical effects can be achieved.
Specifically, the embodiment of the application also provides network side equipment. As shown in fig. 9, the network side device 900 includes: an antenna 901, a radio frequency device 902, a baseband device 903, a processor 904, and a memory 905. The antenna 901 is connected to a radio frequency device 902. In the uplink direction, the radio frequency device 902 receives information via the antenna 901, and transmits the received information to the baseband device 903 for processing. In the downlink direction, the baseband device 903 processes information to be transmitted, and transmits the processed information to the radio frequency device 902, and the radio frequency device 902 processes the received information and transmits the processed information through the antenna 901.
The method performed by the network side device in the above embodiment may be implemented in the baseband apparatus 903, where the baseband apparatus 903 includes a baseband processor.
The baseband apparatus 903 may, for example, include at least one baseband board, where a plurality of chips are disposed, as shown in fig. 9, where one chip, for example, a baseband processor, is connected to the memory 905 through a bus interface, so as to call a program in the memory 905 to perform the network device operation shown in the above method embodiment.
The network-side device may also include a network interface 906, such as a common public radio interface (Common Public Radio Interface, CPRI).
Specifically, the network side device 900 of the embodiment of the present invention further includes: instructions or programs stored in the memory 905 and executable on the processor 904, the processor 904 calls the instructions or programs in the memory 905 to perform the methods performed by the modules shown in fig. 5 or fig. 6, and achieve the same technical effects, and are not repeated here.
The embodiment of the present application further provides a readable storage medium, where a program or an instruction is stored, and when the program or the instruction is executed by a processor, the program or the instruction implement each process of the method embodiment shown in fig. 2 or fig. 3, and the same technical effects can be achieved, so that repetition is avoided, and no further description is given here.
Wherein the processor is a processor in the terminal described in the above embodiment. The readable storage medium includes computer readable storage medium such as computer readable memory ROM, random access memory RAM, magnetic or optical disk, etc.
The embodiment of the application further provides a chip, where the chip includes a processor and a communication interface, where the communication interface is coupled to the processor, and the processor is configured to run a program or instructions, to implement each process of the method embodiment shown in fig. 2 or fig. 3, and to achieve the same technical effect, so that repetition is avoided, and no further description is given here.
It should be understood that the chips referred to in the embodiments of the present application may also be referred to as system-on-chip chips, or the like.
The embodiments of the present application further provide a computer program/program product, which is stored in a storage medium, and executed by at least one processor to implement the respective processes of the method embodiments shown in fig. 2 or fig. 3, and achieve the same technical effects, and are not repeated herein.
The embodiment of the application also provides a communication system, which comprises: a terminal and a network side device, wherein the terminal can be used for executing the steps of the performance supervision method of the AI network model shown in fig. 2, and the network side device can be used for executing the steps of the performance supervision method of the AI network model shown in fig. 3.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element. Furthermore, it should be noted that the scope of the methods and apparatus in the embodiments of the present application is not limited to performing the functions in the order shown or discussed, but may also include performing the functions in a substantially simultaneous manner or in an opposite order depending on the functions involved, e.g., the described methods may be performed in an order different from that described, and various steps may also be added, omitted, or combined. Additionally, features described with reference to certain examples may be combined in other examples.
From the above description of the embodiments, it will be clear to those skilled in the art that the above-described embodiment method may be implemented by means of software plus a necessary general hardware platform, but of course may also be implemented by means of hardware, but in many cases the former is a preferred embodiment. Based on such understanding, the technical solutions of the present application may be embodied essentially or in a part contributing to the prior art in the form of a computer software product stored in a storage medium (such as ROM/RAM, magnetic disk, optical disk), comprising several instructions for causing a terminal (which may be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to perform the method described in the embodiments of the present application.
The embodiments of the present application have been described above with reference to the accompanying drawings, but the present application is not limited to the above-described embodiments, which are merely illustrative and not restrictive, and many forms may be made by those of ordinary skill in the art without departing from the spirit of the present application and the scope of the claims, which are also within the protection of the present application.