Information processing methods and communication equipment

Abstract

This application discloses an information processing method and a communication device, belonging to the field of communication technology. The information processing method of this application includes: the communication device acquiring first information related to the configuration information of a target AI model; the first information includes measurement-related information and / or at least one candidate data processing strategy; the communication device determines the input data or target data processing strategy of the target AI model according to the measurement-related information and / or each of the candidate data processing strategies; wherein, the target data processing strategy is used to indicate a preprocessing strategy for the measurement-related information or a preprocessing strategy for the input data of the target AI model.

Description

Technical Field

[0001] This application belongs to the field of communication technology, specifically relating to an information processing method and a communication device. Background Technology

[0002] Network-based positioning methods involve communication equipment estimating the current geographical location of a target terminal by measuring reference signals. These methods primarily rely on measurements of the direct path for positioning, achieving high accuracy with relatively low implementation complexity when a line-of-sight (LOS) path exists. However, network-based positioning methods are susceptible to non-line-of-sight (NLOS) errors, particularly when no direct path exists between the terminal and the positioning base station, leading to a significant drop in accuracy. Furthermore, these methods are easily affected by synchronization and group delays; positioning accuracy decreases substantially as synchronization and group delay errors increase.

[0003] Localization methods based on Artificial Intelligence (AI) or Machine Learning (ML) can solve the localization problems mentioned above under NLOS and synchronous deterioration conditions. However, these localization methods are computationally intensive and have low predictive performance. Summary of the Invention

[0004] This application provides an information processing method and a communication device that can solve the problems of large computational load and low prediction performance in related technologies for positioning methods.

[0005] Firstly, an information processing method is provided, the method comprising:

[0006] The communication device acquires first information related to the configuration information of the target AI model; the first information includes measurement-related information and / or at least one candidate data processing strategy;

[0007] The communication device determines the input data or target data processing strategy of the target AI model based on the measurement-related information and / or each of the candidate data processing strategies.

[0008] The target data processing strategy is used to indicate the preprocessing strategy for the measurement-related information or the preprocessing strategy for the input data of the target AI model.

[0009] Secondly, an information processing apparatus is provided, the apparatus comprising:

[0010] The first acquisition module is used to acquire first information related to the configuration information of the target AI model; the first information includes measurement-related information and / or at least one candidate data processing strategy.

[0011] A determining module is configured to determine the input data or target data processing strategy of the target AI model based on the measurement-related information and / or each of the candidate data processing strategies; wherein the target data processing strategy is used to indicate a preprocessing strategy for the measurement-related information or a preprocessing strategy for the input data of the target AI model.

[0012] Thirdly, a communication device is provided, the communication device including a processor and a memory, the memory storing a program or instructions executable on the processor, the program or instructions, when executed by the processor, implementing the method as described in the first aspect.

[0013] Fourthly, a communication device is provided, including a processor and a communication interface; wherein the processor is configured to acquire first information related to configuration information of a target AI model; the first information includes measurement-related information and / or at least one candidate data processing strategy; and determine input data or a target data processing strategy for the target AI model based on the measurement-related information and / or each of the candidate data processing strategies; wherein the target data processing strategy is used to indicate a preprocessing strategy for the measurement-related information or a preprocessing strategy for the input data of the target AI model.

[0014] Fifthly, a readable storage medium is provided, on which a program or instructions are stored, which, when executed by a processor, implement the method as described in the first aspect.

[0015] In a sixth aspect, a chip is provided, the chip including a processor and a communication interface coupled to the processor, the processor being used to run programs or instructions to implement the method as described in the first aspect.

[0016] In a seventh aspect, a computer program / program product is provided, the computer program / program product being stored in a storage medium, the computer program / program product being executed by at least one processor to implement the method as described in the first aspect.

[0017] In this embodiment, a first piece of information related to the configuration information of the target AI model is obtained through a communication device. Based on the measurement-related information and / or at least one candidate data processing strategy included in the first information, the communication device determines the input data or target data processing strategy of the target AI model. Since the first information includes measurement-related information and / or at least one candidate data processing strategy, the input data of the target AI model determined by the communication device based on the first information eliminates a large amount of redundant information. The determined target data processing strategy can be used to preprocess the measurement-related information to reduce redundant information, thereby reducing the computational load of the target AI model and improving the prediction performance of the target AI model. Attached Figure Description

[0018] Figure 1 This is a schematic diagram of a wireless communication system to which embodiments of this application can be applied;

[0019] Figure 2 This is a flowchart illustrating the information processing method provided in an embodiment of this application;

[0020] Figure 3 This is a schematic diagram of the structure of the information processing device provided in the embodiments of this application;

[0021] Figure 4 This is one of the structural schematic diagrams of the communication device provided in the embodiments of this application;

[0022] Figure 5 This is a second schematic diagram of the structure of the communication device provided in the embodiments of this application;

[0023] Figure 6 This is the third schematic diagram of the communication device provided in the embodiments of this application. Detailed Implementation

[0024] The technical solutions of the embodiments of this application will be clearly described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application are within the scope of protection of this application.

[0025] The terms "first," "second," etc., used in the specification and claims of this application are used to distinguish similar objects and not to describe a specific order or sequence. It should be understood that such terms can be used interchangeably where appropriate so that embodiments of this application can be implemented in orders other than those illustrated or described herein, and the objects distinguished by "first" and "second" are generally of the same class, not limited in number; for example, a first object can be one or more. Furthermore, in the specification and claims, "and / or" indicates at least one of the connected objects, and the character " / " generally indicates that the preceding and following objects are in an "or" relationship.

[0026] It is worth noting that the technologies described in this application are not limited to Long Term Evolution (LTE) / LTE-Advanced (LTE-A) systems, but can also be used in other wireless communication systems, such as Code Division Multiple Access (CDMA), Time Division Multiple Access (TDMA), Frequency Division Multiple Access (FDMA), Orthogonal Frequency Division Multiple Access (OFDMA), Single-carrier Frequency Division Multiple Access (SC-FDMA), and other systems. The terms "system" and "network" in this application are often used interchangeably, and the described technologies can be used with the systems and radio technologies mentioned above, as well as with other systems and radio technologies. The following description describes New Radio (NR) systems for illustrative purposes, and the term NR is used in most of the following description; however, these technologies can also be applied to communication systems other than NR system applications, such as 6th generation (6G) radio communication systems. th Generation 6G communication system.

[0027] Figure 1 This is a schematic diagram of a wireless communication system to which the embodiments of this application can be applied. Figure 1 The wireless communication system shown includes a terminal 11 and a network-side device 12; wherein:

[0028] Terminal 11 can be a mobile phone, tablet computer, laptop computer, personal digital assistant (PDA), handheld computer, netbook, ultra-mobile personal computer (UMPC), mobile internet device (MID), augmented reality (AR) / virtual reality (VR) device, robot, wearable device, vehicle-mounted device (VUE), pedestrian terminal (PUE), smart home (home devices with wireless communication capabilities, such as refrigerators, televisions, washing machines, or furniture), game console, personal computer (PC), ATM, or self-service machine, etc. Wearable devices include: smartwatches, smart bracelets, smart headphones, smart glasses, smart jewelry (smart bracelets, smart chains, smart rings, smart necklaces, smart anklets, smart anklets, etc.), smart wristbands, smart clothing, etc. It should be noted that the specific type of terminal 11 is not limited in this application embodiment.

[0029] Network-side equipment 12 may include access network equipment or core network equipment. Access network equipment may also be referred to as radio access network equipment, radio access network (RAN), radio access network function, or radio access network unit. Access network equipment may include base stations, WLAN access points, or WiFi nodes, etc. Base stations may be referred to as Node B, evolved Node B (eNB), access point, base transceiver station (BTS), radio base station, radio transceiver, Basic Service Set (BSS), Extended Service Set (ESS), home B node, home evolved B node, Transmitting Receiving Point (TRP), or any other suitable term in the field, as long as the same technical effect is achieved. The base station is not limited to specific technical terms. It should be noted that in this embodiment, only a base station in an NR system is used as an example for description, and the specific type of base station is not limited.Core network equipment may include, but is not limited to, at least one of the following: core network node, core network function, Mobility Management Entity (MME), Access and Mobility Management Function (AMF), Session Management Function (SMF), User Plane Function (UPF), Policy Control Function (PCF), Policy and Charging Rules Function (PCRF), Edge Application Server Discovery Function (EASDF), Unified Data Management (UDM), Unified Data Repository (UDR), Home Subscriber Server (HSS), Centralized network configuration (CNC), Network Repository Function (NRF), Network Exposure Function (NEF), Local NEF (or L-NEF), and Binding Support. Functions include BSF, Application Function (AF), Location Management Function (LMF), Enhanced Serving Mobile Location Centre (E-SMLC), and Network Data Analytics Function (NWDAF). It should be noted that this application embodiment only uses core network equipment in the NR system as an example for description, and does not limit the specific type of core network equipment.

[0030] The information processing method provided in this application will be described in detail below with reference to the accompanying drawings, through some embodiments and application scenarios.

[0031] Figure 2 This is a flowchart illustrating the information processing method provided in an embodiment of this application, such as... Figure 2 As shown, the method includes steps 201-202; wherein:

[0032] Step 201: The communication device acquires first information related to the configuration information of the target AI model; the first information includes measurement-related information and / or at least one candidate data processing strategy.

[0033] Step 202: The communication device determines the input data or target data processing strategy of the target AI model based on the measurement-related information and / or each of the candidate data processing strategies;

[0034] The target data processing strategy is used to indicate the preprocessing strategy for the measurement-related information or the preprocessing strategy for the input data of the target AI model.

[0035] It should be noted that the embodiments of this application can be applied to scenarios such as terminal positioning and Channel State Information (CSI) estimation. Optionally, the target task performed by the target AI model may include tasks such as positioning and / or CSI estimation.

[0036] Taking the target task performed by the target AI model as an example, the first information includes: input data such as measurement-related information used for AI positioning and / or at least one candidate data processing strategy used for AI positioning.

[0037] In practice, the communication equipment includes at least one of the following: a terminal; a network-side device; a positioning server; a monitoring device (Actor); an NWADF; an LMF or an LMF evolution device. For example, the terminal may include, but is not limited to, the type of terminal 11 listed above; the network-side device may include, but is not limited to, the type of network-side device 12 listed above; the positioning server may include an E-SMLC, an LMF, or an LMF evolution device.

[0038] Optionally, the target AI model can be at least one AI network architecture obtained through deep learning or machine learning. The communication device can pre-acquire the configuration information of the target AI model. For example, the communication device determines the configuration information of the target AI model itself; or, the communication device receives the configuration information of the target AI model sent by other devices.

[0039] After acquiring the first information related to the configuration information of the target AI model, the communication device can determine at least one of the following based on the measurement-related information and / or each of the candidate data processing strategies:

[0040] a) Input data of the target AI model; for example, the input data of the target AI model may include at least one of the input data format, input information and measurement information of the target AI model.

[0041] b) Preprocessing strategies for the measurement-related information; for example, preprocessing strategies for the measurement-related information may include: strategies for instructing how to perform feature extraction, and / or strategies for instructing how to select measurement quantities.

[0042] c) Preprocessing strategy for the input data of the target AI model.

[0043] Optionally, the communication device can receive updates to the configuration information of the target AI model and / or updates to the first information. Specifically, both the configuration information of the target AI model and the first information can be divided into variable parameters and fixed parameters; the updates to the configuration information of the target AI model can be specific to the variable parameters and the model within the configuration information; the updates to the first information can be specific to the variable parameters and the model within the first information.

[0044] In the information processing method provided in this application embodiment, first information related to the configuration information of the target AI model is obtained through a communication device. Based on the measurement-related information and / or at least one candidate data processing strategy included in the first information, the communication device determines the input data or target data processing strategy of the target AI model. Since the first information includes measurement-related information and / or at least one candidate data processing strategy, the input data of the target AI model determined by the communication device based on the first information eliminates a large amount of redundant information. The determined target data processing strategy can be used to preprocess the measurement-related information to reduce redundant information, thereby reducing the computational load of the target AI model and improving the prediction performance of the target AI model.

[0045] The configuration information of the target AI model provided in this application embodiment may include at least one of the following:

[0046] 1) Model identifier ID information;

[0047] 2) Model structure information; specifically, the model structure information may include at least one of the following:

[0048] a) Any one or a combination of fully connected neural networks, convolutional neural networks, recurrent neural networks, and residual networks;

[0049] b) The number of hidden layers;

[0050] c) The connection method between the input layer and the hidden layer;

[0051] d) Connection methods between multiple hidden layers;

[0052] e) The connection method between the hidden layer and the output layer;

[0053] f) The number of neurons in each layer.

[0054] 3) Model type information; for example, the model type information may include at least one of the following: fully connected model; hybrid model; unsupervised model; supervised model.

[0055] 4) Model parameter information; specifically, the model parameter information includes at least one of the following:

[0056] a) Model application documentation;

[0057] b) Model description parameter information; for example, model description parameter information may include the input format of model parameters and / or the output format of model parameters, etc.

[0058] c) Hyperparameter information of the model; for example, the hyperparameter information of the model may include at least one of the following: the activation model used by the target AI model, the number of iterations, and the batch size.

[0059] d) Initial parameter information of the model; for example, the initial parameter information of the model may include at least one of the following: initial parameters for meta-learning; initial parameters for training.

[0060] e) The weights of the model; for example, the weights of the model may include the weights and biases of the neurons in a neural network.

[0061] 5) Model input information; wherein, the model input information may include at least one of the following: the model input data format; data format description; data type of the model input; data size.

[0062] 6) Model output information; wherein, the model output information may include at least one of the following: the output data format of the model; a description of the data format; the data output by the model.

[0063] 7) Model inference process (AI inference); For example, the model inference process can be: the process of obtaining output information based on the configuration information of the target AI model and the measurement-related information used for AI localization.

[0064] 8) Optimizer status information.

[0065] Optionally, the configuration information of the target AI model may further include: a model usage instruction; wherein the model usage instruction may be used to indicate that: (a) the communication device independently executes the target task based on the target AI model; or, (b) the communication device assists in executing part of the target task based on the target AI model;

[0066] For example: A communication device performs the first part or all of the target task based on the target AI model, obtains a first measurement result, and sends it to another communication device; the other communication device performs the second part or all of the target task, obtains a second measurement result; then, the other communication device determines the prediction result of the target task based on the first and second measurement results. It is understood that the first and second parts of the target task can be the same, overlap, or be completely different.

[0067] To illustrate further: A communication device performs part or all of the target task based on the target AI model, obtains a first measurement result, and sends it to another communication device; the other communication device then determines the prediction result of the target task based on the first measurement result.

[0068] Optionally, the configuration information of the target AI model provided in this application embodiment may include at least one of the following:

[0069] 1) List information of neural networks, the list information including at least one of the following: neuron type of each neural network; neuron weights and / or biases of each neural network;

[0070] 2) The type and / or location of the activated network element;

[0071] 3) Hyperparameter information; where hyperparameter information may include at least one of the following: activation model used by the target AI model, number of iterations, and batch size.

[0072] 4) Loss function information.

[0073] Optionally, the input data of the target AI model may include at least one of the following:

[0074] 1) First Channel Impulse Response (CIR) information, wherein the length of the first CIR information is N1, where N1 is a positive integer;

[0075] 2) The first CIR matrix is ​​N2×N3 in dimension and has a translation parameter of M; N2, N3 and M are all positive integers;

[0076] 3) Path-related information for N4 paths, where N4 is a positive integer.

[0077] 4) Long-term CIR information. For example, long-term information is the smoothed information of K1 CIRs.

[0078] Optionally, the input data of the target AI model may further include at least one of the following: positioning signal measurement information of the terminal; location information of the terminal; error information; power of the first path; delay of the first path; time of arrival (TOA) of the first path; reference signal time difference (RSTD) of the first path; angle of arrival of the first path; antenna subcarrier phase difference of the first path; power of the multipath; delay of the multipath; TOA of the multipath; RSTD of the multipath; angle of arrival of the multipath; antenna subcarrier phase difference of the multipath; average excess delay; root mean square delay spread; coherence bandwidth; channel impulse response of multiple antennas; number of antennas; expected AoA, expected AoD; LOS / NLOS indication information; estimation error; measurement error.

[0079] The target data processing strategy provided in this application embodiment may include at least one of the following:

[0080] 1) AI model input format; for example, the AI ​​model input format may include at least one of the following: model input data format or format description, length of CIR information, dimension of CIR matrix and / or translation parameters.

[0081] 2) CIR information truncation length;

[0082] Specifically, CIR-based AI positioning achieves high accuracy. However, transmitting a high-dimensional CIR matrix (e.g., 4096×18) from the terminal to the core network incurs significant feedback overhead. Furthermore, the CIR contains a large amount of redundant information, such as numerous zero elements in the middle and end, which not only increases the CIR feedback overhead but also makes it more difficult for the AI ​​model to learn CIR features. This application's embodiment improves the prediction accuracy of the AI ​​model by truncating the CIR to infer location.

[0083] 3) The number of rows and / or columns of the CIR matrix;

[0084] 4) CIR information translation;

[0085] Specifically, CIR dimension extension transforms an N1×1 dimensional CIR into a (N1-M1)×M2 dimensional CIR matrix. For example, the toplitz matrix of a CIR is a 4096×19 dimensional matrix, where columns 2-19 are obtained by shifting the first column of the channel CIR one position to the right.

[0086] Let the CIR matrix T = [t] ij ]∈C m×n If t ij =t j-i (i,j=1,2,...,n), then:

[0087]

[0088] 5) Path-related information for N5 paths, where N5 is a positive integer;

[0089] 6) Normalization strategy;

[0090] 7) Long-term smoothing method; one embodiment can be the measurement information corresponding to K (K>1) samples, or the smoothed result of the measurement information of the upper layer L3.

[0091] 8) A short-term smoothing method, one embodiment of which can be the smoothing result of measurement information corresponding to a sample or the measurement information of physical layer L1.

[0092] The candidate data processing strategy provided in this application embodiment may include at least one of the following: path-related information; features of CIR information; normalization strategy; long term indication; short term indication; CIR information averaged from L measurement results, where L is a positive integer.

[0093] It should be noted that the features of the CIR information include at least one of the following: the truncation length of the CIR information; the number of rows in the CIR matrix; the number of columns in the CIR matrix; and the CIR translation parameters.

[0094] Optionally, path-related information includes at least one of the following: number of paths; path characteristic information; path selection criteria. Specifically, path characteristic information may include at least one of the following: time information; energy information; angle information.

[0095] For example, time information may include at least one of the following: multipath delay; multipath time of arrival (TOA); multipath reference signal time difference (RSTD).

[0096] For example, energy information may include at least one of the following: multipath reference signal received power (RSRP).

[0097] For example, angle information may include at least one of the following: Angle of Arrival (AOA) measurement results; Angle of Departure (AoD) measurement results.

[0098] The path selection criteria include at least one of the following: 1) Paths in the multipath with energy greater than a first threshold, where the first threshold is the product of the energy of the path with the highest energy and the first value; 2) Paths in the multipath with the highest energy ranking among the top N6, where N6 is a positive integer.

[0099] Optionally, the normalization strategy includes at least one of the following: a time normalization strategy; an energy normalization strategy; indication information for indicating whether normalization is required; and normalization coefficients. Specific details are as follows:

[0100] 1. Energy normalization strategies may include at least one of the following:

[0101] 1) Normalize the values ​​of multiple CIRs received by the terminal; for example, multiple CIRs may include CIRs from multiple base stations and / or CIRs measured multiple times.

[0102] 2) Normalize the data based on the maximum value of the measurement information received by the terminal from multiple TRPs or base stations;

[0103] 3) Normalize the value of a CIR received by the terminal;

[0104] 4) Normalize the data based on the maximum value of a measurement information received by the terminal from a TRP or base station;

[0105] 5) Normalize the data based on the maximum path received by the terminal;

[0106] 6) Amplify the CIR received by the terminal; for example, amplify the CIR received by the terminal to N times, or amplify the CIR received by the terminal to the maximum value K.

[0107] 2. Time normalization strategies may include at least one of the following:

[0108] 1) CIR relative to the maximum path of multiple TRPs or base stations;

[0109] 2) CIR corresponding to the time of arrival (TOA) relative to the reference TRP or base station;

[0110] 3) The CIR corresponding to the reference TRP or the RSTD of the base station; for example, change the CIR of base station a to the CIR of the RSTD, that is, shift the CIR pattern to the right of the TOA of the reference base station.

[0111] 4) CIR corresponding to the round-trip time (RTT) relative to the Sounding Reference Signal (SRS);

[0112] 5) The CIR corresponding to the received and transmitted (Rx-Tx) measurements between the terminal and the TRP or base station; for example, the CIR pattern shifted right by TX corresponds to the TS unit time.

[0113] 3. Time normalization strategies may include at least one of the following:

[0114] 1) Shift the measured CIR of other TRPs or base stations relative to the time corresponding to the maximum path of the reference TRP or base station;

[0115] 2) Shift the measured CIR relative to the reference TRP or the time of arrival (TOA) of the base station;

[0116] 3) Shift the measured CIR relative to the reference TRP or the base station's RSTD;

[0117] 4) Shift the measured CIR relative to the reference TRP or the base station's Rx-Tx;

[0118] 5) Shift the measured CIR relative to the TOA of the SRS transmission time;

[0119] 6) Shift the measured CIR relative to the Rx-Tx of the terminal and TRP or base station; for example, shift the CIR pattern to the right by the TS unit time corresponding to TX.

[0120] It is worth noting that the measured CIR can be the CIR of a reference cell or a neighboring cell; the measured CIR can be the CIR of a single antenna or multiple antennas; optionally, the CIR includes at least one of the following:

[0121] Time-domain channel impulse response;

[0122] Time-domain cross-correlation vectors or matrices;

[0123] Time-domain autocorrelation vector or matrix;

[0124] Frequency domain channel response;

[0125] Frequency domain cross-correlation vector or matrix;

[0126] Frequency domain autocorrelation vector or matrix;

[0127] Frequency domain subcarrier phase vector or matrix;

[0128] Frequency domain subcarrier phase difference vector or matrix.

[0129] Optionally, the measurement-related information includes at least one of the following: signal measurement information; location information; error value; CIR information; and power delay spectrum (PDP) information. Specific details are as follows:

[0130] 1) Signal measurement information may include at least one of the following: RSTD measurement results; RTT measurement results; AOA measurement results; AOD measurement results; RSRP; multipath measurement information; LOS indication information. Specifically, multipath measurement information may include at least one of the following: power of the first path / multipath; time delay of the first path / multipath; TOA of the first path / multipath; RSTD of the first path / multipath; antenna subcarrier phase difference of the first path / multipath; antenna subcarrier phase of the first path / multipath.

[0131] 2) Location information may include at least one of the following: absolute location information (such as latitude and longitude information); relative location information.

[0132] 3) Error values ​​may include at least one of the following: position error value; measurement error value.

[0133] 4) CIR information may include at least one of the following: time-domain or frequency-domain CIR information; processing information of time-domain or frequency-domain CIR information (such as truncation information).

[0134] 5) CIR information may include at least one of the following: CIR information for a single antenna; CIR information for multiple antennas.

[0135] Optionally, the communication device may acquire the measurement-related information by means of a target method or target device.

[0136] The target method includes at least one of the following: Observed Time Difference of Arrival (OTDOA); Global Navigation Satellite System (GNSS); Downlink Time Difference of Arrival (TDOA); Uplink Time Difference of Arrival (TDOA); Bluetooth AoA; Bluetooth AoD; RTT.

[0137] In practice, the target device may include at least one of the following: Bluetooth; sensor; wireless high fidelity (WiFi).

[0138] The information processing method provided in this application can be executed by an information processing device. This application uses an information processing device executing the information processing method as an example to illustrate the information processing device provided in this application.

[0139] Figure 3 This is a schematic diagram of the structure of the information processing device provided in the embodiments of this application, such as... Figure 3 As shown, the information processing device 300 can be applied to communication equipment, and the information processing device 300 includes:

[0140] The first acquisition module 301 is used to acquire first information related to the configuration information of the target AI model; the first information includes measurement-related information and / or at least one candidate data processing strategy.

[0141] The determining module 302 is configured to determine the input data or target data processing strategy of the target AI model based on the measurement-related information and / or each of the candidate data processing strategies; wherein the target data processing strategy is used to indicate the preprocessing strategy for the measurement-related information or the preprocessing strategy for the input data of the target AI model.

[0142] In the information processing apparatus provided in this application embodiment, by acquiring first information related to the configuration information of the target AI model, the communication device determines the input data or target data processing strategy of the target AI model based on the measurement-related information and / or at least one candidate data processing strategy included in the first information. Since the first information includes measurement-related information and / or at least one candidate data processing strategy, the input data of the target AI model determined by the communication device based on the first information eliminates a large amount of redundant information. The determined target data processing strategy can be used to preprocess the measurement-related information to reduce redundant information, thereby reducing the computational load of the target AI model and improving the prediction performance of the target AI model.

[0143] Optionally, the information processing device 300 further includes:

[0144] The second acquisition module is used by the communication device to acquire the configuration information of the target AI model.

[0145] Optionally, the configuration information of the target AI model includes at least one of the following:

[0146] Model ID information; model structure information; model type information; model parameter information; model input information; model output information; model inference process; optimizer state information.

[0147] Optionally, the input data of the target AI model includes at least one of the following:

[0148] First channel impulse response (CIR) information, wherein the length of the first CIR information is N1, and N1 is a positive integer;

[0149] The first CIR matrix is ​​N2×N3 in dimension, and the translation parameter is M; N2, N3 and M are all positive integers.

[0150] The path-related information for N4 paths, where N4 is a positive integer;

[0151] Long-term CIR information.

[0152] Optionally, the target data processing strategy includes at least one of the following:

[0153] AI model input format;

[0154] CIR information truncation length;

[0155] The number of rows and / or columns of the CIR matrix;

[0156] CIR information translation;

[0157] The path-related information for N5 paths, where N5 is a positive integer;

[0158] Normalization strategy;

[0159] Long-term smoothing methods;

[0160] Short-term smoothing method.

[0161] Optionally, the candidate data processing strategy includes at least one of the following: path-related information; features of CIR information; normalization strategy; long-term indication; short-term indication; CIR information averaged from L measurement results, where L is a positive integer.

[0162] Optionally, the path-related information includes at least one of the following: number of paths; path feature information; path selection conditions.

[0163] Optionally, the path feature information includes at least one of the following: time information; energy information; angle information.

[0164] Optionally, the path selection condition includes at least one of the following: paths in the multipath with energy greater than a first threshold, where the first threshold is the product of the energy of the path with the highest energy and a first value; and the top N6 paths in the multipath with the highest energy, where N6 is a positive integer.

[0165] Optionally, the CIR information includes at least one of the following features:

[0166] The truncation length of the CIR information; the number of rows in the CIR matrix; the number of columns in the CIR matrix; and the CIR shift parameters.

[0167] Optionally, the normalization strategy includes at least one of the following: a time normalization strategy; an energy normalization strategy; indication information for indicating whether normalization is required; and a normalization coefficient.

[0168] Optionally, the energy normalization strategy includes at least one of the following:

[0169] Normalization is performed based on the maximum value of multiple CIRs received by the terminal;

[0170] The maximum value of the measurement information received by the terminal from multiple Transmitter Points (TRPs) or base stations is normalized.

[0171] Normalization is performed based on the maximum value of a CIR received by the terminal;

[0172] Normalization is performed based on the maximum value of a measurement information received by the terminal from a TRP or base station;

[0173] Normalization is performed based on the maximum path received by the terminal;

[0174] The signal is amplified based on the CIR received by the terminal.

[0175] Optionally, the time normalization strategy includes at least one of the following:

[0176] CIR relative to the maximum path of multiple TRPs or base stations;

[0177] CIR relative to the reference TRP or the time of arrival (TOA) of the base station;

[0178] CIR relative to the reference TRP or the reference signal time difference RSTD of the base station;

[0179] CIR relative to the round-trip time (RTT) of the sounding reference signal (SRS);

[0180] The CIR corresponding to the received and transmitted Rx-Tx measurements between the terminal and the TRP or base station.

[0181] Optionally, the time normalization strategy includes at least one of the following:

[0182] The CIR of measurements for other TRPs or base stations is shifted relative to the time corresponding to the maximum path of the reference TRP or base station.

[0183] The measured CIR is shifted relative to the reference TRP or the time of arrival (TOA) of the base station;

[0184] The measured CIR is shifted relative to the reference TRP or the base station's RSTD;

[0185] The measured CIR is shifted relative to the reference TRP or the Rx-Tx of the base station;

[0186] The measured CIR is shifted relative to the TOA of the SRS transmission time;

[0187] The measured CIR is shifted relative to the received and transmitted Rx-Tx of the terminal and TRP or base station.

[0188] Optionally, the measurement-related information includes at least one of the following: signal measurement information; location information; error value; CIR information; and power delay spectrum (PDP) information.

[0189] Optionally, the signal measurement information includes at least one of the following: Reference Signal Time Difference (RSTD) measurement result; Round-Trip Time Delay (RTT) measurement result; Angle of Arrival (AOA) measurement result; Angle of Departure (AOD) measurement result; Reference Information Received Power (RSRP); Multipath measurement information; Line-of-Sight (LOS) indication information.

[0190] Optionally, the first acquisition module 301 is specifically used to acquire the measurement-related information based on a target method or a target device; wherein the target method includes at least one of the following: Time Difference of Arrival (OTDOA); Global Navigation Satellite System (GNSS); Downlink Time Difference of Arrival (TDOA); Uplink Time Difference of Arrival (TDOA); Bluetooth AoA; Bluetooth AoD; RTT;

[0191] The target device includes at least one of the following: Bluetooth; sensor; wireless high-fidelity WiFi.

[0192] Optionally, the model structure information includes at least one of the following:

[0193] Any one or a combination of fully connected neural networks, convolutional neural networks, recurrent neural networks, and residual networks;

[0194] The number of hidden layers;

[0195] The connection method between the input layer and the hidden layer;

[0196] Connection methods between multiple hidden layers;

[0197] How the hidden layer connects to the output layer;

[0198] The number of neurons in each layer.

[0199] Optionally, the model type information includes at least one of the following:

[0200] Fully connected model; hybrid model; unsupervised model; supervised model.

[0201] Optionally, the model parameter information includes at least one of the following:

[0202] Model application documentation;

[0203] Descriptive parameters of the model;

[0204] Hyperparameter information of the model;

[0205] Initial parameter information of the model;

[0206] The weights of the model.

[0207] Optionally, the configuration information of the target AI model includes at least one of the following:

[0208] List information of neural networks, the list information including at least one of the following: neuron type of each neural network; neuron weights and / or biases of each neural network;

[0209] The type and / or location of the activated network element;

[0210] Hyperparameter information;

[0211] Loss function information.

[0212] Optionally, the information processing device 300 further includes:

[0213] The receiving module is used to receive update information of the configuration information of the target AI model, and / or update information of the first information.

[0214] Optionally, the communication device includes at least one of the following:

[0215] terminal;

[0216] Network-side equipment;

[0217] Location server;

[0218] Actor (monitoring equipment);

[0219] Network data analysis function NWADF;

[0220] Location management function LMF or LMF evolution device.

[0221] The information processing apparatus in this application embodiment can be a communication device, such as a communication device with an operating system, or a component in a communication device, such as an integrated circuit or a chip. The communication device can include at least one of the following: a terminal; a network-side device; a positioning server; an actor; an NWADF; an LMF or an LMF evolution device. For example, the terminal can include, but is not limited to, the type of terminal 11 listed above; the network-side device can include, but is not limited to, the type of network-side device 12 listed above; the positioning server can include an E-SMLC, an LMF, or an LMF evolution device.

[0222] The information processing apparatus provided in this application embodiment can implement the various processes implemented in the above information processing method embodiment and achieve the same technical effect. To avoid repetition, it will not be described again here.

[0223] Figure 4 This is one of the structural schematic diagrams of the communication device provided in the embodiments of this application, such as... Figure 4As shown, the communication device 400 includes a processor 401 and a memory 402. The memory 402 stores a program or instructions that can run on the processor 401. When the program or instructions are executed by the processor 401, they implement the various steps of the above-described information processing method embodiments and achieve the same technical effect. To avoid repetition, they will not be described again here.

[0224] This application embodiment also provides a communication device, including a processor and a communication interface; wherein, the processor is configured to acquire first information related to the configuration information of a target AI model; the first information includes measurement-related information and / or at least one candidate data processing strategy; and determine the input data or target data processing strategy of the target AI model based on the measurement-related information and / or each of the candidate data processing strategies; wherein, the target data processing strategy is used to indicate a preprocessing strategy for the measurement-related information or a preprocessing strategy for the input data of the target AI model.

[0225] This communication device embodiment corresponds to the above-described communication device-side method embodiment. All implementation processes and methods of the above-described method embodiment can be applied to this communication device embodiment and can achieve the same technical effect.

[0226] Optionally, the communication device may include a terminal. Figure 5 This is a second schematic diagram of the communication device provided in the embodiments of this application, as shown below. Figure 5 As shown, the communication device 500 includes, but is not limited to, at least some of the following components: radio frequency unit 501, network module 502, audio output unit 503, input unit 504, sensor 505, display unit 506, user input unit 507, interface unit 508, memory 509, and processor 510.

[0227] Those skilled in the art will understand that the communication device 500 may also include a power supply (such as a battery) for supplying power to various components. The power supply may be logically connected to the processor 510 through a power management system, thereby enabling functions such as managing charging, discharging, and power consumption through the power management system. Figure 5 The communication device structure shown does not constitute a limitation on the communication device. The communication device may include more or fewer components than shown, or combine certain components, or have different component arrangements, which will not be elaborated here.

[0228] It should be understood that, in this embodiment, the input unit 504 may include a graphics processing unit (GPU) 5041 and a microphone 5042. The GPU 5041 processes image data of still images or videos obtained by an image capture device (such as a camera) in video capture mode or image capture mode. The display unit 506 may include a display panel 5061, which may be configured in the form of a liquid crystal display, an organic light-emitting diode, or the like. The user input unit 507 includes at least one of a touch panel 5071 and other input devices 5072. The touch panel 5071 is also called a touch screen. The touch panel 5071 may include a touch detection device and a touch controller. Other input devices 5072 may include, but are not limited to, physical keyboards, function keys (such as volume control buttons, power buttons, etc.), trackballs, mice, and joysticks, which will not be described in detail here.

[0229] In this embodiment, after receiving downlink data from the network-side device, the radio frequency unit 501 can transmit it to the processor 510 for processing; in addition, the radio frequency unit 501 can send uplink data to the network-side device. Typically, the radio frequency unit 501 includes, but is not limited to, antennas, amplifiers, transceivers, couplers, low-noise amplifiers, duplexers, etc.

[0230] The memory 509 can be used to store software programs or instructions, as well as various data. The memory 509 may primarily include a first storage area for storing programs or instructions and a second storage area for storing data. The first storage area may store the operating system, application programs or instructions required for at least one function (such as sound playback, image playback, etc.). Furthermore, the memory 509 may include volatile memory or non-volatile memory, or both. The non-volatile memory may be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. Volatile memory can be random access memory (RAM), static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDRSDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link dynamic random access memory (SLDRAM), and direct memory bus RAM (DRRAM). The memory 509 in this embodiment includes, but is not limited to, these and any other suitable types of memory.

[0231] Processor 510 may include one or more processing units; optionally, processor 510 integrates an application processor and a modem processor, wherein the application processor mainly handles operations involving the operating system, user interface, and applications, and the modem processor mainly handles wireless communication signals, such as a baseband processor. It is understood that the aforementioned modem processor may also not be integrated into processor 510.

[0232] The processor 510 is configured to acquire first information related to the configuration information of the target AI model; the first information includes measurement-related information and / or at least one candidate data processing strategy; and determine the input data or target data processing strategy of the target AI model based on the measurement-related information and / or each of the candidate data processing strategies; wherein the target data processing strategy is used to indicate a preprocessing strategy for the measurement-related information or a preprocessing strategy for the input data of the target AI model.

[0233] The communication device provided in this application embodiment obtains first information related to the configuration information of the target AI model. Based on the measurement-related information and / or at least one candidate data processing strategy included in the first information, the communication device determines the input data or target data processing strategy of the target AI model. Since the first information includes measurement-related information and / or at least one candidate data processing strategy, the input data of the target AI model determined by the communication device based on the first information eliminates a large amount of redundant information. The determined target data processing strategy can be used to preprocess the measurement-related information to reduce redundant information, thereby reducing the computational load of the target AI model and improving the prediction performance of the target AI model.

[0234] Optionally, the communication equipment may include network-side equipment. Figure 6 This is the third schematic diagram of the communication device provided in the embodiments of this application, as shown below. Figure 6 As shown, the communication device 600 includes: an antenna 601, a radio frequency (RF) device 602, a baseband device 603, a processor 604, and a memory 605. The antenna 601 is connected to the RF device 602. In the uplink direction, the RF device 602 receives information through the antenna 601 and transmits the received information to the baseband device 603 for processing. In the downlink direction, the baseband device 603 processes the information to be transmitted and sends it to the RF device 602. The RF device 602 processes the received information and transmits it through the antenna 601.

[0235] The method executed by the communication device in the above embodiments can be implemented in the baseband device 603, which includes a baseband processor.

[0236] The baseband device 603 may include, for example, at least one baseband board on which multiple chips are disposed, such as... Figure 6 As shown, one of the chips is, for example, a baseband processor, which is connected to the memory 605 via a bus interface to call the program in the memory 605 and execute the network device operations shown in the above method embodiment.

[0237] The communication device may also include a network interface 606, such as a common public radio interface (CPRI).

[0238] Specifically, the communication device 600 in this application embodiment further includes: instructions or programs stored in memory 605 and executable on processor 604. Processor 604 calls the instructions or programs in memory 605 to execute the steps of the information processing method described above and achieve the same technical effect. To avoid repetition, it will not be described in detail here.

[0239] This application also provides a readable storage medium, which can be volatile or non-volatile. The readable storage medium stores a program or instructions. When the program or instructions are executed by a processor, they implement the various processes of the above-described information processing method embodiments and achieve the same technical effect. To avoid repetition, they will not be described again here.

[0240] The processor is the processor in the terminal described in the above embodiments. The readable storage medium includes computer-readable storage media, such as computer read-only memory (ROM), random access memory (RAM), magnetic disk, or optical disk.

[0241] This application embodiment also provides a chip, which includes a processor and a communication interface. The communication interface is coupled to the processor. The processor is used to run programs or instructions to implement the various processes of the above information processing method embodiments and can achieve the same technical effect. To avoid repetition, it will not be described again here.

[0242] It should be understood that the chip mentioned in the embodiments of this application may also be referred to as a system-on-a-chip, system chip, chip system, or system-on-a-chip, etc.

[0243] This application also provides a computer program / program product, which is stored in a storage medium and executed by at least one processor to implement the various processes of the above-described information processing method embodiments, and can achieve the same technical effect. To avoid repetition, it will not be described again here.

[0244] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element. Furthermore, it should be noted that the scope of the methods and apparatuses in the embodiments of this application is not limited to performing functions in the order shown or discussed, but may also include performing functions substantially simultaneously or in the reverse order, depending on the functions involved. For example, the described methods may be performed in a different order than described, and various steps may be added, omitted, or combined. Additionally, features described with reference to certain examples may be combined in other examples.

[0245] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a computer software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal (which may be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0246] The embodiments of this application have been described above with reference to the accompanying drawings. However, this application is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of this application without departing from the spirit and scope of the claims, and all of these forms are within the protection scope of this application.

Claims

1. An information processing method, characterized in that, include: The communication device acquires first information related to the configuration information of the target AI model; the first information includes measurement-related information and / or at least one candidate data processing strategy; the measurement-related information includes processing information of time-domain or frequency-domain CIR information; The communication device determines the target data processing strategy for the target AI model based on the measurement-related information and / or each of the candidate data processing strategies. The target data processing strategy is used to indicate a preprocessing strategy for the measurement-related information or a preprocessing strategy for the input data of the target AI model. The target data processing strategy includes: CIR information truncation length; The path-related information for N5 paths, where N5 is a positive integer; The path-related information includes: the number of paths, path feature information, and path selection conditions; The path feature information includes time information, and the path selection criteria include the N6 paths with the highest energy among the multipaths, where N6 is a positive integer.

2. The information processing method according to claim 1, characterized in that, The method further includes: The communication device acquires the configuration information of the target AI model.

3. The information processing method according to claim 1 or 2, characterized in that, The configuration information of the target AI model includes at least one of the following: Model ID information; model structure information; model type information; model parameter information; model input information; model output information; model inference process; optimizer state information.

4. The information processing method according to any one of claims 1 to 3, characterized in that, The input data for the target AI model includes at least one of the following: First channel impulse response (CIR) information, wherein the length of the first CIR information is N1, and N1 is a positive integer; The first CIR matrix is ​​N2×N3 in dimension, and the translation parameter is M; N2, N3 and M are all positive integers. The path-related information for N4 paths, where N4 is a positive integer; Long-term CIR information.

5. The information processing method according to any one of claims 1 to 3, characterized in that, The target data processing strategy also includes at least one of the following: AI model input format; The number of rows and / or columns of the CIR matrix; CIR information translation; Normalization strategy; Long-term smoothing methods; Short-term smoothing method.

6. The information processing method according to any one of claims 1 to 3, characterized in that, The candidate data processing strategy includes at least one of the following: Path-related information; characteristics of CIR information; Normalization strategy; Long term indicator; Short term indicator; CIR information averaged from L measurements, where L is a positive integer.

7. The information processing method according to claim 1, characterized in that, The path feature information also includes at least one of the following: Energy information; angular information.

8. The information processing method according to claim 1, characterized in that, The path selection criteria also include: Among the multipath paths, the path with energy greater than a first threshold is defined as the product of the energy of the path with the highest energy and a first value.

9. The information processing method according to claim 6, characterized in that, The CIR information includes at least one of the following characteristics: The truncation length of the CIR information; the number of rows in the CIR matrix; the number of columns in the CIR matrix; and the CIR shift parameters.

10. The information processing method according to claim 5 or 6, characterized in that, The normalization strategy includes at least one of the following: Time normalization strategy; energy normalization strategy; indication information for whether normalization is required; normalization coefficient.

11. The information processing method according to claim 10, characterized in that, The energy normalization strategy includes at least one of the following: Normalization is performed based on the maximum value of multiple CIRs received by the terminal; The maximum value of the measurement information received by the terminal from multiple Transmitter Points (TRPs) or base stations is normalized. Normalization is performed based on the maximum value of a CIR received by the terminal; Normalization is performed based on the maximum value of a measurement information received by the terminal from a TRP or base station; Normalization is performed based on the maximum path received by the terminal; The signal is amplified based on the CIR received by the terminal.

12. The information processing method according to claim 10, characterized in that, The time normalization strategy includes at least one of the following: CIR relative to the maximum path of multiple TRPs or base stations; CIR relative to the reference TRP or the time of arrival (TOA) of the base station; CIR relative to the reference TRP or the reference signal time difference RSTD of the base station; CIR relative to the round-trip time (RTT) of the sounding reference signal (SRS); The CIR corresponding to the received and transmitted Rx-Tx measurements between the terminal and the TRP or base station.

13. The information processing method according to claim 10, characterized in that, The time normalization strategy includes at least one of the following: The CIR of measurements for other TRPs or base stations is shifted relative to the time corresponding to the maximum path of the reference TRP or base station. The measured CIR is shifted relative to the reference TRP or the time of arrival (TOA) of the base station; The measured CIR is shifted relative to the reference TRP or the base station's RSTD; The measured CIR is shifted relative to the reference TRP or the Rx-Tx of the base station; The measured CIR is shifted relative to the TOA of the SRS transmission time; The measured CIR is shifted relative to the received and transmitted Rx-Tx of the terminal and TRP or base station.

14. The information processing method according to any one of claims 1 to 13, characterized in that, The measurement-related information includes at least one of the following: Signal measurement information; location information; error value; CIR information; power delay spectrum (PDP) information.

15. The information processing method according to claim 14, characterized in that, The signal measurement information includes at least one of the following: Reference signal time difference (RSTD) measurement results; round-trip time delay (RTT) measurement results; angle of arrival (AOA) measurement results; angle of departure (AOD) measurement results; reference information received power (RSRP); multipath measurement information; line-of-sight (LOS) indication information.

16. The information processing method according to any one of claims 4 to 15, characterized in that, The CIR includes at least one of the following: Time-domain channel impulse response; Time-domain cross-correlation vectors or matrices; Time-domain autocorrelation vector or matrix; Frequency domain channel response; Frequency domain cross-correlation vector or matrix; Frequency domain autocorrelation vector or matrix; Frequency domain subcarrier phase vector or matrix; Frequency domain subcarrier phase difference vector or matrix.

17. The information processing method according to any one of claims 1 to 16, characterized in that, The communication device acquires the measurement-related information including: The communication device acquires the measurement-related information based on the target method or target device; The target method includes at least one of the following: Time Difference of Arrival (OTDOA); Global Navigation Satellite System (GNSS); Downlink Time Difference of Arrival (TDOA); Uplink Time Difference of Arrival (TDOA); Bluetooth AoA; Bluetooth AoD; RTT; The target device includes at least one of the following: Bluetooth; sensor; wireless high-fidelity WiFi.

18. The information processing method according to claim 3, characterized in that, The model structure information includes at least one of the following: Any one or a combination of fully connected neural networks, convolutional neural networks, recurrent neural networks, and residual networks; The number of hidden layers; The connection method between the input layer and the hidden layer; Connection methods between multiple hidden layers; How the hidden layer connects to the output layer; The number of neurons in each layer.

19. The information processing method according to claim 3, characterized in that, The model type information includes at least one of the following: Fully connected model; hybrid model; unsupervised model; supervised model.

20. The information processing method according to claim 3, characterized in that, The model parameter information includes at least one of the following: Model application documentation; Descriptive parameters of the model; Hyperparameter information of the model; Initial parameter information of the model; The weights of the model.

21. The information processing method according to claim 1 or 2, characterized in that, The configuration information of the target AI model includes at least one of the following: List information of neural networks, the list information including at least one of the following: neuron type of each neural network; neuron weights and / or biases of each neural network; The type and / or location of the activated network element; Hyperparameter information; Loss function information.

22. The information processing method according to any one of claims 1 to 21, characterized in that, The method further includes: The communication device receives updated configuration information of the target AI model and / or updated information of the first information.

23. The information processing method according to any one of claims 1 to 22, characterized in that, The communication device includes at least one of the following: terminal; Network-side equipment; Location server; Actor (monitoring equipment); Network data analysis function NWADF; Location management function LMF or LMF evolution device.

24. An information processing device, characterized in that, include: The first acquisition module is used to acquire first information related to the configuration information of the target AI model; the first information includes measurement-related information and / or at least one candidate data processing strategy; the measurement-related information includes processing information of time-domain or frequency-domain CIR information; The determination module is used to determine the target data processing strategy of the target AI model based on the measurement-related information and / or each of the candidate data processing strategies; The target data processing strategy is used to indicate a preprocessing strategy for the measurement-related information or a preprocessing strategy for the input data of the target AI model. The target data processing strategy includes: CIR information truncation length; The path-related information for N5 paths, where N5 is a positive integer; The path-related information includes: the number of paths, path feature information, and path selection conditions; The path feature information includes time information, and the path selection criteria include the N6 paths with the highest energy among the multipaths, where N6 is a positive integer.

25. A communication device, characterized in that, It includes a processor and a memory, the memory storing a program or instructions that can run on the processor, the program or instructions being executed by the processor to implement the information processing method as described in any one of claims 1 to 23.

26. A readable storage medium, characterized in that, The readable storage medium stores a program or instructions that, when executed by a processor, implement the information processing method as described in any one of claims 1 to 23.

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