Perception processing method and apparatus, device and medium

Abstract

The present application belongs to the field of artificial intelligence. Disclosed are a perception processing method and apparatus, a device and a medium. The perception processing method in the embodiments of the present application comprises: a first node acquires one or more pieces of first data; and the first node processes the one or more pieces of first data by means of N first AI models, so as to obtain one or more pieces of second data, N being a positive integer, wherein the content of the first data or the second data comprises at least one of the following: a first signal, channel information, spectrum information calculated on the basis of the channel information or the first signal, a measurement quantity, the attribute of a perception target, the state of the perception target, and a perception result.

Description

Methods, devices, equipment and media for sensing and processing

[0001] Cross-reference to related applications

[0002] This application claims priority to Chinese Patent Application No. 202410597893.6, filed in China on May 14, 2024, the entire contents of which are incorporated herein by reference. Technical Field

[0003] This application belongs to the field of artificial intelligence technology, specifically relating to a method, apparatus, device, and medium for perception processing. Background Technology

[0004] Wireless communication network positioning methods mainly rely on the measurement results of the direct path for positioning. When a line-of-sight (LOS) path exists, it can achieve high positioning accuracy with low implementation complexity. However, it is easily affected by non-line-of-sight (NLOS) paths, especially when there is no direct path between the terminal and the positioning base station. The positioning accuracy will drop significantly, resulting in a high probability of positioning failure. Therefore, how to improve the sensing performance is an urgent problem to be solved. Summary of the Invention

[0005] This application provides a method, apparatus, device, and medium for sensing processing, which can solve the problem of how to improve sensing performance.

[0006] Firstly, a method for sensory processing is provided, including:

[0007] The first node retrieves one or more first data items;

[0008] The first node's one or more first AI models process the one or more first data to obtain one or more second data;

[0009] The content of the first data or the second data includes at least one of the following: a first signal, channel information, spectral information calculated based on the channel information or the first signal, a measurement quantity, the attribute of the perceived target, the state of the perceived target, and the perception result.

[0010] In a second aspect, a sensing processing apparatus is provided, applied to a first node, comprising: a first transceiver unit and a first processing unit;

[0011] The first transceiver unit is used to acquire one or more first data;

[0012] The first processing unit is used to process the one or more first data through N first AI models to obtain one or more second data, where N is a positive integer;

[0013] The content of the first data or the second data includes at least one of the following: a first signal, channel information, spectral information calculated based on the channel information or the first signal, a measurement quantity, the attribute of the perceived target, the state of the perceived target, and the perception result.

[0014] Thirdly, a terminal is provided, comprising 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 steps of the method described in the first aspect.

[0015] Fourthly, a terminal is provided, including a processor and a communication interface, wherein the processor is used to process the one or more first data through N first AI models to obtain one or more second data, where N is a positive integer, and the communication interface is used to acquire one or more first data; the content of the first data or the second data includes at least one of the following: a first signal, channel information, spectral information calculated based on the channel information or the first signal, a measurement quantity, the attribute of the perceived target, the state of the perceived target, and the perception result.

[0016] Fifthly, a network-side device is provided, comprising 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 steps of the sensing processing method as described in the first aspect.

[0017] In a sixth aspect, a network-side device is provided, including a processor and a communication interface, wherein the processor is used to process one or more first data through N first AI models to obtain one or more second data, where N is a positive integer, and the communication interface is used to acquire one or more first data; the content of the first data or the second data includes at least one of the following: a first signal, channel information, spectral information calculated based on the channel information or the first signal, a measurement quantity, the attribute of the perceived target, the state of the perceived target, and the perception result.

[0018] In a seventh aspect, a readable storage medium is provided, on which a program or instructions are stored, which, when executed by a processor, implement the steps of the perception processing method as described in the first aspect.

[0019] Eighthly, a wireless communication system is provided, comprising: a terminal and a network-side device, wherein the terminal is configured to perform the steps of the sensing processing method as described in the first aspect, or the network-side device is configured to perform the steps of the sensing processing method as described in the first aspect.

[0020] In a ninth aspect, a chip is provided, the chip including a processor and a communication interface coupled to the processor, the processor being configured to run a program or instructions to implement the steps of the perception processing method as described in the first aspect.

[0021] In a tenth aspect, a computer program / program product is provided, which is stored in a storage medium and is executed by at least one processor to implement the steps of the perceptual processing method as described in the first aspect.

[0022] In this embodiment, a first node acquires one or more first data; one or more first AI models of the first node process the one or more first data to obtain one or more second data; wherein, the content of the first data or second data includes at least one of the following: a first signal, original channel information, spectral information calculated based on the original channel information, a measurement quantity, the attributes of the perceived target, the state of the perceived target, and the perception result. The first node uses the first AI model to assist in perception, which can improve perception performance. On the other hand, the first AI model can replace the perception algorithm with higher complexity, reducing algorithm complexity, latency, and power consumption while obtaining comparable or better perception performance. Thirdly, it can also eliminate or mitigate non-ideal perception factors, such as clock deviation, local oscillator frequency offset, channel inconsistency, and time-domain random phase. Attached Figure Description

[0023] Figure 1 is a schematic diagram of different sensing modes of integrated communication and sensing;

[0024] Figure 2 is a schematic diagram of the neural network structure;

[0025] Figure 3 is a schematic diagram of a neuron;

[0026] Figure 4 is a schematic diagram of the architecture of a wireless communication system provided in an embodiment of this application;

[0027] Figure 5 is a flowchart of a perception processing method provided in an embodiment of this application;

[0028] Figures 6a to 6g are schematic diagrams of AI-assisted perception provided in the embodiments of this application;

[0029] Figure 7 is a schematic diagram of the extraction of a subset of time delay spectrum information provided in an embodiment of this application;

[0030] Figure 8 is a schematic diagram of time delay-Doppler spectrum information subset extraction provided in an embodiment of this application;

[0031] Figure 9 is a schematic diagram of a sensing processing apparatus provided in an embodiment of this application;

[0032] Figure 10 is a schematic diagram of a terminal provided in an embodiment of this application;

[0033] Figure 11 is a schematic diagram of a network-side device provided in an embodiment of this application. Detailed Implementation

[0034] 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.

[0035] The terms "first," "second," etc., used in 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, the first object can be one or more. Furthermore, "or" in this application indicates at least one of the connected objects. For example, the scope of protection for "A or B" covers at least three scenarios: Scenario 1: including A but not B; Scenario 2: including B but not A; Scenario 3: including both A and B. In addition, the terms "A and / or B," "at least one of A and B," and "at least one of A or B" also cover at least the above three scenarios. The character " / " generally indicates that the preceding and following objects are in an "or" relationship.

[0036] The term "instruction" in this application can be either a direct instruction (or explicit instruction) or an indirect instruction (or implicit instruction). A direct instruction can be understood as one in which the sender explicitly informs the receiver of specific information, the operation to be performed, or the requested result, etc., in the instruction sent. An indirect instruction can be understood as one in which the receiver determines the corresponding information based on the instruction sent by the sender, or makes a judgment and determines the operation to be performed or the requested result, etc., based on the judgment result.

[0037] 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), or 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 systems other than NR systems, such as 6th generation (6G) radio systems. th Generation 6G communication system.

[0038] To facilitate understanding of the embodiments of this application, the following technical points will be introduced first:

[0039] I. On the integration of communication and sensing.

[0040] Future mobile communication systems, such as Beyond 5th Generation (B5G) or Generation 6 (6G) systems, will possess sensing capabilities in addition to communication capabilities. Sensing capabilities refer to the ability of one or more devices to perceive information such as the location, distance, and speed of target objects through the transmission and reception of wireless signals, or to detect, track, identify, and image target objects, events, or environments. With the deployment of small base stations with high-frequency, high-bandwidth capabilities such as millimeter waves and terahertz waves in 6G networks, the resolution of sensing will be significantly improved compared to centimeter waves, enabling 6G networks to provide more refined sensing services. Typical sensing functions and application scenarios are shown in Table 1.

[0041] Table 1: Typical sensing functions and application scenarios.

[0042] Communication and sensing integration (referred to as communication and sensing integration) refers to the integrated design of communication and sensing functions within the same system through spectrum sharing and hardware sharing. While transmitting information, the system can sense information such as location, distance, and speed, and detect, track, and identify target devices or events. The communication system and the sensing system complement each other, thereby improving overall performance and bringing a better service experience.

[0043] The integration of communication and radar is a typical application of communication-sensing integration (communication-sensing fusion). In the past, radar systems and communication systems were strictly distinguished due to different research objects and focuses, and in most scenarios, the two systems were studied independently. In fact, radar and communication systems are both typical methods of information transmission, acquisition, processing, and exchange, and they share many similarities in terms of working principles, system architecture, and frequency bands. The design of integrated communication and radar systems is highly feasible, mainly in the following aspects: First, both communication and sensing systems are based on electromagnetic wave theory, using the transmission and reception of electromagnetic waves to complete information acquisition and transmission; second, both communication and sensing systems have structures such as antennas, transmitters, receivers, and signal processors, resulting in significant overlap in hardware resources; with technological advancements, their operating frequency bands also increasingly overlap; furthermore, they share similarities in key technologies such as signal modulation and reception detection, and waveform design. The integration of communication and radar systems can bring many advantages, such as cost savings, size reduction, power consumption reduction, improved spectral efficiency, and reduced mutual interference, thereby improving the overall system performance.

[0044] Based on the different target signal transmitting and receiving nodes, there are 6 basic sensing modes, as shown in Figure 1, which include:

[0045] (1) Base station spontaneous sensing. In this sensing mode, base station A sends a target signal and performs sensing measurements by receiving the echo of the target signal.

[0046] The aforementioned target signals include at least one of reference signals, synchronization signals, data signals, and dedicated signals. Receiving or transmitting target signals can support sensing services. For example, by receiving or transmitting target signals, sensing measurements or sensing results can be obtained. Sensing results refer to those that meet sensing requirements, such as: the shape of the sensing target, 2D or 3D environment reconstruction, spatial position, orientation, displacement, speed, and acceleration; radar-based sensing for velocity, distance, angle measurement, or imaging of target objects; the presence of people or objects; and sensing of targets such as human movements, gestures, respiratory rate, heart rate, and sleep quality.

[0047] (2) Inter-base station air interface sensing. Base station B receives the target signal sent by base station A and performs sensing measurements.

[0048] (3) Uplink air interface sensing. Base station A receives the target signal sent by terminal A and performs sensing measurements.

[0049] (4) Downlink air interface sensing. Terminal B receives the target signal sent by base station B and performs sensing measurements.

[0050] (5) Terminal spontaneous sensing. Terminal A sends a first signal and performs sensing and measurement by receiving the echo of the target signal.

[0051] (6) Sidelink (SL) sensing between terminals. Terminal B receives the target signal sent by terminal A and performs sensing measurements.

[0052] It is worth noting that each sensing mode in Figure 1 uses a target signal transmitting node and a target signal receiving node as examples. In actual systems, one or more different sensing modes can be selected according to different sensing use cases and sensing requirements, and each sensing mode can have one or more transmitting and receiving nodes. The sensing targets in Figure 1 are people and vehicles as examples, and it is assumed that neither people nor vehicles carry or install signal receiving or transmitting devices. The sensing targets in real-world scenarios are much more diverse.

[0053] Optional configuration information for the sensing signal includes at least one of the following:

[0054] 1) Signal resource identifier (IDentity, ID), used to distinguish different signal resource configurations;

[0055] 2) Signal Purpose: This indicates whether the signal is used for communication (e.g., channel measurement, channel estimation, synchronization, carrying data information, etc.), for sensing, or for both communication and sensing. Specifically, it can also specify which sensing service the signal is used for, or which type of sensing service it is used for.

[0056] 3) Waveforms, such as Orthogonal Frequency Division Multiplexing (OFDM), Single-Carrier Frequency-Division Multiple Access (SC-FDMA), Orthogonal Time-Frequency Space (OTFS), Frequency Modulated Continuous Wave (FMCW), pulse signals, etc.

[0057] 4) Subcarrier spacing, for example, the subcarrier spacing of an OFDM system is 30 kHz.

[0058] 5) Guard interval, which is the time interval from the moment the signal ends to the moment the latest echo signal of the signal is received; this parameter is proportional to the maximum sensing distance; for example, it can be expressed as c / (2R). max )Calculations show that R max For the maximum sensing distance (belonging to sensing demand information), such as for spontaneously generated and received sensing signals, R max This represents the maximum distance between the sensing signal transceiver point and the signal transmitter point; in some cases, the OFDM signal cyclic prefix (CP) can serve as a minimum guard interval; c is the speed of light.

[0059] 6) Starting frequency domain position, i.e., starting frequency point, can also be the starting resource element (RE) or resource block (RB) index;

[0060] 7) The starting time domain position, i.e. the starting time point, can also be the starting symbol index, time slot index, or frame index;

[0061] 8) The terminating frequency domain position, i.e., the terminating frequency point, can be represented by the terminating RE and RB indices;

[0062] 9) The termination time domain position, i.e. the termination time point, can be represented by the termination RE and RB indices;

[0063] 10) Frequency domain resource length, i.e. frequency domain bandwidth, which is inversely proportional to the distance resolution, wherein the frequency domain bandwidth B of each first signal is greater than or equal to c / (2ΔR), where c is the speed of light and ΔR is the distance resolution;

[0064] 11) Temporal resource length, also known as burst duration, is inversely proportional to Doppler resolution.

[0065] 12) Frequency domain resource spacing represents the spacing between adjacent signal frequency domain resource units. It can be represented by the number of REs or RBs, or by the density value Density. For example, Density = 1 means that there is one RE in each RB used to carry the signal. The frequency domain resource spacing is inversely proportional to the maximum unambiguous distance / delay. For OFDM systems, when subcarriers are continuously mapped, the frequency domain spacing is equal to the subcarrier spacing.

[0066] 13) Time-domain resource interval, which is the time interval between two adjacent signal resource units, and is associated with the maximum unambiguous Doppler frequency shift or the maximum unambiguous velocity.

[0067] 14) Time-domain resource characteristics: periodic transmission, semi-persistent transmission, and non-periodic transmission.

[0068] 15) Signal power, for example, a value taken in 2dBm increments from -20dBm to 23dBm.

[0069] 16) Sequence information, including sequence type information (ZC (Zadoff-Chu) sequence, pseudo-random sequence (PN sequence), etc.), sequence generation method, sequence length, etc.

[0070] 17) Signal direction, the angle information or beam information of the signal transmission.

[0071] 18) Quasi-co-located (QCL) relationships, for example, a sensing signal includes multiple resources, each resource is associated with a synchronization signal and PBCH block (SSB) QCL, and the QCL includes type A, B, C or D.

[0072] 19) Antenna port information, such as the maximum number of antenna ports and the antenna port index.

[0073] 20) Cyclic Prefix (CP) information, including CP type (e.g., Normal Cyclic Prefix (NCP), Extended Cyclic Prefix (ECP), or newly designed sensing measurement-specific CP), CP length, etc.

[0074] 2. Introduction to Artificial Intelligence.

[0075] Artificial intelligence (AI) has been widely applied in various fields. AI models can be implemented in various ways, such as neural networks, decision trees, support vector machines, and Bayesian classifiers. This application uses neural networks as an example, but does not limit the specific type of AI model. The structure of a neural network is shown in Figure 2.

[0076] The neural network is composed of neurons, and a schematic diagram of a neuron is shown in Figure 3. Where a1, a2, ... a K For input, w is the weight (multiplicative coefficient), b is the bias (additive coefficient), σ(.) is the activation function, and z = a1w1 + ... + a k w k +…+a K w K +b. Common activation functions include the sigmoid function, tanh function, rectified linear unit (ReLU), etc.

[0077] The parameters of a neural network can be optimized using optimization algorithms. An optimization algorithm is a class of algorithms that minimizes or maximizes an objective function (sometimes called a loss function). The objective function is often a mathematical combination of model parameters and data. For example, given data X and its corresponding label Y, we construct a neural network model f(.). With the model, we can obtain the predicted output f(x) based on the input x, and calculate the difference between the predicted value and the true value (f(x) - Y), which is the loss function. If we find suitable values ​​W and b that minimize the value of the loss function, the smaller the loss value, the closer the model is to the reality.

[0078] Most common optimization algorithms are based on the error back propagation (BP) algorithm. The basic idea of ​​the BP algorithm is that the learning process consists of two parts: forward propagation of the signal and backward propagation of the error. During forward propagation, the input sample is introduced from the input layer, processed layer by layer through the hidden layers, and then propagated to the output layer. If the actual output of the output layer does not match the expected output, the process transitions to the error back propagation stage. Error back propagation involves propagating the output error back to the input layer layer by layer through the hidden layers, distributing the error to all units in each layer, thus obtaining the error signal of each unit. This error signal serves as the basis for adjusting the weights of each unit. This process of adjusting the weights through forward and backward propagation is repeated continuously. This continuous adjustment of weights is the learning and training process of the network. This process continues until the error of the network output is reduced to an acceptable level, or until the predetermined number of learning iterations is reached.

[0079] Common optimization algorithms include gradient descent, stochastic gradient descent (SGD), mini-batch gradient descent, momentum method, momentum-driven stochastic gradient descent, adaptive gradient descent (Adagrad), Adadelta (an optimization algorithm with adaptive learning rate adjustment), root mean square propagation (RMSprop), and adaptive momentum estimation (Adam).

[0080] During error backpropagation, these optimization algorithms calculate the gradient by taking the derivative or partial derivative of the current neuron with respect to the error or loss obtained from the loss function, and then adding the effects of the learning rate, previous gradients, derivatives or partial derivatives, etc., and then pass the gradient to the previous layer.

[0081] In this application, the AI ​​model may also be referred to as an AI unit, machine learning (ML) model, ML unit, AI structure, AI function, AI characteristic, machine learning model, neural network, neural network function, neural network functionality, etc. Alternatively, the AI ​​model may refer to a processing unit capable of implementing specific algorithms, formulas, processing flows, capabilities, etc., related to AI. Or, the AI ​​model may be a processing method, algorithm, function, module, or unit for a specific dataset. Alternatively, the AI ​​model may be a processing method, algorithm, function, module, or unit running on AI or ML-related hardware such as a graphics processing unit (GPU), neural processing unit (NPU), tensor processing unit (TPU), or application-specific integrated circuit (ASIC). This application does not impose specific limitations in this regard. Optionally, the specific dataset includes the input or output of the AI ​​model.

[0082] Optionally, the identifier of the AI ​​model may also be referred to as an AI unit identifier, an AI structure identifier, an AI algorithm identifier, or an identifier of a specific dataset associated with the AI ​​model, or an identifier of a specific scenario, environment, channel characteristics, or device related to AI or ML, or an identifier of a function, characteristic, capability, or module related to AI or ML. This application does not make any specific limitations in this regard.

[0083] The aforementioned AI functionality can be understood as an AI algorithm function. For a terminal (e.g., user equipment (UE)), the AI ​​functionality may include one or more AI models.

[0084] Figure 4 shows a block diagram of a wireless communication system applicable to an embodiment of this application. The wireless communication system includes a terminal 41 and a network-side device 42. The terminal 41 can be a mobile phone, tablet computer, laptop computer, notebook 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, flight vehicle, vehicle user equipment (VUE), shipboard equipment, pedestrian user equipment (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. Among these, in-vehicle devices can also be referred to as in-vehicle terminals, in-vehicle controllers, in-vehicle modules, in-vehicle components, in-vehicle chips, or in-vehicle units, etc. It should be noted that the specific type of terminal 41 is not limited in this application embodiment. Network-side equipment 42 may include access network equipment or core network equipment, wherein access network equipment may also be referred to as Radio Access Network (RAN) equipment, radio access network function, or radio access network unit. Access network equipment may include base stations, Wireless Local Area Network (WLAN) access points (AS), or Wireless Fidelity (WiFi) nodes, etc.The term "base station" can be referred to as Node B (NB), Evolved Node B (eNB), Next Generation Node B (gNB), New Radio Node B (NR Node B), Access Point, Relay Base Station (RBS), Serving Base Station (SBS), Base Transceiver Station (BTS), Radio Base Station, Radio Transceiver, Basic Service Set (BSS), Extended Service Set (ESS), Home Node B (HNB), Home Evolved Node B, Transmit / Receive Point (TRP), or any other suitable term in the relevant field, as long as the same technical effect is achieved. The term "base station" is not limited to any specific technical terminology. It should be noted that this application embodiment only uses a base station in an NR system as an example for description and does not limit the specific type of base station.

[0085] Core network equipment, also known as core network nodes, core network functions, or core network elements, includes, but is not limited to, at least one of the following: 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. The core network functions include: BSF (Block Network Function), Application Function (AF), Location Management Function (LMF), Gateway Mobile Location Centre (GMLC), 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 and does not limit the specific type of core network equipment. If the name of the core network equipment mentioned in this application embodiment changes in subsequent protocol versions (e.g., 6G), it will still be within the scope of protection of this application.

[0086] Optionally, the core network equipment can be implemented by one or more functional modules in a single device, or by multiple devices working together; this application does not specifically limit this. It is understood that the aforementioned functional modules can be network elements in hardware devices, software functional modules running on dedicated hardware, or virtualized functional modules instantiated on a platform (e.g., a cloud platform).

[0087] The following description, in conjunction with the accompanying drawings, details a method, apparatus, device, and medium for sensing processing provided in this application, through some embodiments and application scenarios.

[0088] Referring to Figure 5, this application embodiment provides a perception processing method applied to a first node, the specific steps of which include:

[0089] Step 51: The first node acquires one or more first data items;

[0090] Step 52: The first node processes the one or more first data through N first AI models to obtain one or more second data, where N is a positive integer;

[0091] The content of the first data or the second data includes at least one of the following:

[0092] 1) First signal;

[0093] The aforementioned first signal includes at least one of a reference signal, a synchronization signal, a data signal, and a dedicated signal. Acquiring these signals can support sensing services. For example, acquiring these signals can yield sensing measurements or sensing results. Sensing results refer to those that meet sensing requirements, such as: the shape of a sensing target, 2D or 3D environment reconstruction, spatial location, orientation, displacement, speed, and acceleration; radar-based sensing for measuring the velocity, distance, angle, or imaging of target objects; the presence of people or objects; and sensing targets such as human movements, gestures, respiratory rate, heart rate, and sleep quality.

[0094] 2) Channel information;

[0095] Optionally, the channel information includes at least one of the following: time-domain channel response, frequency-domain channel response, complex result of the channel response, amplitude or phase, I-channel or Q-channel data.

[0096] 3) Based on channel information or spectrum information obtained through reception calculation;

[0097] Optionally, the spectral information includes at least one of the following: time delay (distance) spectrum, Doppler (velocity) spectrum, angle spectrum, time delay (distance)-Doppler (velocity) spectrum, time delay (distance)-angle spectrum, time delay (distance)-Doppler (velocity)-angle spectrum, time-Doppler spectrum (micro-Doppler spectrum);

[0098] Optionally, the spectral information can refer to complex results, such as a time-delay-Doppler spectrum, which refers to the time delay, Doppler index, and corresponding complex values ​​(including phase information) in a two-dimensional spectrum; the spectral information can also refer to a power spectrum, such as a time-delay-Doppler spectrum, which refers to the time delay, Doppler index, and corresponding power values ​​(excluding phase information) in a two-dimensional spectrum.

[0099] Optionally, the spectral information mentioned above can also be spectral information calculated based on the first signal.

[0100] 4) Measured quantity;

[0101] Optionally, the measured quantities include at least one of the following: time delay, Doppler, angle, and intensity (power); wherein the time delay can be the time delay of different diameters.

[0102] Optionally, the basic measurement can be a quantified result of an actual value or soft information; wherein, soft information can be data described by mean and variance, or confidence interval and confidence level. For example, the mean and variance of a Gaussian distribution, or two values ​​and their respective probabilities.

[0103] 5) Perceive the attributes or state of the target;

[0104] Optionally, the attributes or state of the perceived target include at least one of the following: distance, velocity, orientation, spatial location, and acceleration.

[0105] 6) Perception results.

[0106] Optionally, the attributes or state of the perceived target include at least one of the following: distance, velocity, orientation, spatial location, and acceleration.

[0107] Specifically, one or more first AI models of the first node process the one or more first data to obtain one or more second data. This processing includes, but is not limited to, inference. That is, the first node uses N first AI models to perform AI inference on the one or more first data to obtain one or more second data, i.e., the inference result.

[0108] Optionally, the first or second data is data related to the perception business.

[0109] It is understood that this embodiment does not specifically limit the number of first AI models. Figure 6a illustrates a scenario where the first node deploys one first AI model, and Figure 6b illustrates a scenario where the first node deploys two first AI models.

[0110] Optionally, a first AI model can correspond to one type of intermediate data or output data. For example, the first node deploys three first AI models to perform inference, and the output data corresponding to the three first AI models are the relevant data of latency, Doppler, and angle, respectively. Alternatively, a first AI model can correspond to multiple types of intermediate data or output data. For example, the output data corresponding to a first AI model includes multiple types of data such as latency, Doppler, and angle.

[0111] Optionally, the first or second node, or the third or fourth node in this document may include, but is not limited to, at least one of the following: terminal, network-side equipment, etc. The network-side equipment may include, but is not limited to, at least one of the following: base station, core network equipment, third-party server, operation administration and maintenance (OAM), etc. The core network equipment may include, but is not limited to, at least one of the following: sensing function network element, positioning management function, etc.

[0112] Optionally, the base station may include a base station responsible for transmitting or receiving sensing signals and processing sensing data, or a base station that is not responsible for transmitting or receiving sensing signals but is only responsible for processing sensing data.

[0113] Optionally, the terminal may include a terminal responsible for transmitting or receiving sensing signals and processing sensing data, or a terminal that is not responsible for transmitting or receiving sensing signals but only for processing sensing data.

[0114] Specifically, signaling transmission between the base station and the terminal, and between terminal A and terminal B, can be via Radio Resource Control (RRC) signaling, Medium Access Control (MAC) Control Element (CE), Layer 1 signaling, or other newly defined sensing signaling; signaling transmission between the sensing function network element and the terminal can be via Non-Access Stratum (NAS) signaling (forwarded via AMF) and / or via RRC signaling, MAC CE, Layer 1 signaling, or other newly defined sensing signaling; interaction between the sensing function network element and the base station can be via AMF forwarding to the radio access network through the N2 interface; or the sensing function network element sends to the UPF, and the UPF sends to the radio access network (base station) through the N3 interface; or it can send to the radio access network through a newly defined interface; signaling transmission between base stations can be via the Xn interface.

[0115] Optionally, the sensing function network element, also known as the sensing network element or sensing network function, can be located on the access network side or the core network side. It refers to a network node in the core network or access network that is responsible for at least one of the following functions: sensing request processing, sensing resource scheduling, sensing information interaction, and sensing data processing. It can be an upgrade based on the AMF or LMF in the 5G network, or it can be other network nodes or newly defined network nodes. Specifically, the functional characteristics of the sensing function network element can include at least one of the following:

[0116] 1) Interact with wireless signal transmitting equipment or wireless signal measuring equipment (including the target terminal or the serving base station of the target terminal or the base station associated with the target area) to exchange target information. The target information includes sensing processing requests, sensing capabilities, sensing auxiliary data, sensing measurement types, sensing resource configuration information, etc., in order to obtain the value of the target sensing result or sensing measurement (uplink measurement or downlink measurement) sent by the wireless signal measuring equipment. The wireless signal can also be referred to as the sensing signal.

[0117] 2) The sensing method to be used is determined based on factors such as the type of sensing service, the information of sensing service consumers, the required Quality of Service (QoS) requirements, the sensing capabilities of the wireless signal transmitting equipment, and the sensing capabilities of the wireless signal measuring equipment. The sensing method may include: base station A transmitting and base station B receiving, or base station transmitting and terminal receiving, or base station A transmitting and receiving, or terminal transmitting and base station receiving, or terminal transmitting and receiving, or terminal A transmitting and terminal B receiving, etc.

[0118] 3) The sensing equipment serving the sensing service is determined based on factors such as the type of sensing service, the information of the sensing service consumers, the required sensing QoS requirements, the sensing capabilities of the wireless signal transmitting equipment, and the sensing capabilities of the wireless signal measuring equipment. The sensing equipment includes wireless signal transmitting equipment or wireless signal measuring equipment.

[0119] 4) Manage the overall coordination and scheduling of resources required for sensing services, such as configuring sensing resources for base stations or terminals accordingly;

[0120] 5) Process the values ​​of the sensed measurements or perform calculations to obtain the sensing results. Further, verify the sensing results and estimate the sensing accuracy.

[0121] In one embodiment of this application, the first node processes the one or more first data through N first AI models to obtain one or more second data, including at least one of the following:

[0122] 1) The first node processes the one or more first data as input data for each of the N first AI models to obtain N second data, where N is greater than or equal to 2;

[0123] For example, if N equals 2, there are two first AI models. The first node uses one or two first data points as input data for each first AI model to perform AI inference and obtain two second data points.

[0124] 2) The first node processes the one or more first data as input data of the first first AI model among the N first AI models to obtain the first intermediate data, uses the m-th intermediate data as input data of the (m+1)-th first AI model to obtain the (m+1)-th intermediate data, and uses the (m+1)-th intermediate data as input data of the N-th first AI model to obtain one or more second data; where m = N-1, M is a positive integer, and N is greater than or equal to 2.

[0125] It is understandable that the above N first AI models can be arranged sequentially according to the order in which the output data of the previous first AI model is used as the input data of the next first AI model.

[0126] It is understandable that in the scenario involving multiple first AI models, including two first AI models, the third intermediate data and the second intermediate data can be the same intermediate data. Referring to Figure 6b, two first AI models are deployed on the first node. The first node processes the first data as input data for the first first AI model to obtain the first intermediate data, and then processes the first intermediate data as input data for the second first AI model to obtain the second data.

[0127] It is understandable that after step 51, the first node can also use one or more second data as input data for an AI model or non-AI model deployed on the first node or other nodes, and the AI ​​model or non-AI model can perform inference to obtain the final output data, as shown in Figure 6c.

[0128] In one embodiment of this application, the first data includes at least one of the following:

[0129] 1) Data generated by the first node;

[0130] 2) Data obtained by the first node from the second node;

[0131] 3) The data obtained by the first node through processing one or more third data using X second AI models, where X is a positive integer;

[0132] That is, the aforementioned second AI model can be deployed on the first or third node.

[0133] 4) The data obtained by the first node through processing one or more fourth data using Y first models, where the first model is a non-AI model and Y is a positive integer;

[0134] That is, the first model mentioned above can be deployed on the first node or the fourth node.

[0135] The content of the third or fourth data includes at least one of the following: a first signal, channel information, spectral information calculated based on the channel information or the first signal, a measurement quantity, the attributes of the perceived target, the state of the perceived target, and the perception result.

[0136] Optionally, in this embodiment, the first data may be the output data of an AI model or a non-AI model deployed on the first node or other nodes. Referring to Figure 6d, the non-AI model is deployed on the first node or the fourth node, the input data of the non-AI model is the fourth data, and the intermediate output data of the non-AI model is the first data.

[0137] Optionally, in this embodiment, the first data can also be the input data or intermediate data of multiple AI models or non-AI models deployed on the first node or other nodes. For at least one AI model in Figures 6e to 6g, it can be the first AI model, the input data can be the first data, and the intermediate data can be the second data, or the intermediate data can be the first data and the output data can be the second data. The process by which the first AI model in Figures 6e to 6g processes the first data to obtain the second data can be referred to the description in Figures 6a to 6d above, and will not be repeated here.

[0138] In one embodiment of this application, the method further includes:

[0139] The first node sends first information to the second, third, or fourth node, the first information being used to indicate the content or format of the first data.

[0140] That is, in this embodiment, before the first node obtains the first data from the second node, the third node, or the fourth node, the first node may send relevant information about the content or format of the first data to the second node, the third node, or the fourth node. This relevant information is used to indicate what content needs to be included in the first data, or to indicate the format of each content in the first data.

[0141] In one embodiment of this application, the data obtained by the first node through processing one or more third data using X second AI models includes at least one of the following:

[0142] 1) The data obtained by the first node by processing one or more third data as input data for each of the X second AI models;

[0143] 2) The first node processes one or more third data as input data of the first second AI model among X second AI models to obtain the first intermediate data, uses the nth intermediate data as input data of the (n+1)th second AI model to obtain the (n+1)th intermediate data, and uses the (n+1)th intermediate data as input data of the Xth second AI model to obtain the data; where n = X-1, and n is a positive integer, and X is greater than or equal to 2.

[0144] In one embodiment of this application, the data obtained by processing one or more fourth data through Y first models via the first node includes at least one of the following:

[0145] 1) The data obtained by the first node by processing one or more fourth data as input data for each of the Y first models;

[0146] 2) The first node processes one or more fourth data as input data of the first first model in Y first models to obtain the first intermediate data, uses the qth intermediate data as input data of the (q+1)th first model to obtain the (q+1)th intermediate data, and uses the (q+1)th intermediate data as input data of the Yth first model to obtain the data; where q = Y-1, and q is a positive integer, and Y is greater than or equal to 2.

[0147] In one embodiment of this application, the method further includes:

[0148] The first node processes the one or more second data through Q third AI models to obtain one or more fifth data, where Q is a positive integer;

[0149] The fifth data includes at least one of the following: a first signal, channel information, spectral information calculated based on the channel information or the first signal, a measurement quantity, the attributes of the perceived target, the state of the perceived target, and the perception result.

[0150] That is, after step 52, the first node can send the second data to one or more third AI models, process the second data as input data for Q third AI models, and obtain one or more fifth data.

[0151] In another embodiment of this application, the first node sends one or more second data to other nodes, which serve as input data for AI or non-AI models on the other nodes.

[0152] In one embodiment of this application, the Q third AI models of the first node process the one or more second data to obtain one or more fifth data, including:

[0153] The first node processes the one or more second data as input data for each of the Q third AI models to obtain one or more fifth data.

[0154] The first node processes the one or more second data as input data to the first third AI model among the Q third AI models to obtain the first intermediate data, uses the z-th intermediate data as input data to the (z+1)-th third AI model to obtain the (z+1)-th intermediate data, and processes the (z+1)-th intermediate data as input data to the Q-th third AI model to obtain one or more fifth data; where z = Q-1, and z is a positive integer, and Q is greater than or equal to 2.

[0155] In one embodiment of this application, the first AI model or the third AI model is determined by at least one of the following methods: determined by the first node based on second information configured by the network-side device, determined autonomously by the first node, agreed upon by the protocol, determined by the first node based on higher-layer signaling, or selected by the first node from the first AI model pool based on third information.

[0156] In one embodiment of this application, the second AI model is determined by at least one of the following methods: determined by the first node or the third node based on fourth information configured by the network-side device; determined autonomously by the first node or the third node; agreed upon by the protocol; determined by the first node or the third node based on higher-layer signaling; or selected from the second AI model pool by the first node or the third node based on fifth information.

[0157] It is understandable that the first AI model, the second AI model, and the third AI model can be the same AI model or different AI models.

[0158] In one embodiment of this application, the second or fourth information includes at least one of the following:

[0159] 1) Model structure information;

[0160] Optionally, the model structure information may include at least one of the following: the type of AI model (such as Gaussian process, support vector machine, various neural networks (fully connected neural network, convolutional neural network, recurrent neural network or residual network, combination of multiple small networks, such as fully connected + convolution, convolution + residual) etc.) and the structure of the model (such as the number of layers of the neural network, the number of neurons in each layer, activation function etc.).

[0161] 2) Supported AI model hyperparameter configuration;

[0162] Optionally, the supported hyperparameter configurations for AI models include at least one of the following: relevant parameters in the kernel function, relevant parameters in the activation function, relevant parameters in the normalization layer, etc.

[0163] 3) Supported AI model data processing methods, i.e., the preprocessing methods for data before it is input into the AI ​​model. Optionally, the model data processing methods may include, but are not limited to, at least one of the following: normalization, upsampling, downsampling, etc.

[0164] 4) Supported AI model execution cycles, i.e., how often the AI ​​model is executed;

[0165] 5) Supported AI model update cycle, i.e. how often the AI ​​model is updated;

[0166] 6) Supported AI model update information;

[0167] Optionally, the AI ​​model update information includes at least one of the following: kernel function update information, hyperparameter update information, prediction mode update information, and computation mode update information.

[0168] 7) Complexity information of supported AI models, such as the number of floating point operations (FLOPs) for model inference, such as 100 iterations, hardware conditions, and computing conditions;

[0169] 8) Available AI resources, including computing or storage resources available for AI use;

[0170] 9) Supported AI frameworks or algorithms;

[0171] 10) Information on the number of AI models;

[0172] 11) AI model category information;

[0173] 12) AI model identification information;

[0174] 13) Priority information of AI models;

[0175] 14) AI model attribute information;

[0176] 15) AI model accuracy information;

[0177] 16) AI model error information;

[0178] 17) AI model feature information;

[0179] 18) Adapt to environmental information;

[0180] 19) Process delay information;

[0181] 20) Information on the fusion method of the AI ​​model output results;

[0182] 21) AI model lifecycle information;

[0183] 22) Information about the input data of the AI ​​model;

[0184] 23) Information about the AI ​​model's output data.

[0185] In one embodiment of this application, the third or fifth information includes at least one of the following: model error information, network-side device mobility information, terminal mobility information, network-side device environmental information, terminal environmental information, perception accuracy requirement information, perception service information, and model priority information.

[0186] In one embodiment of this application, the intermediate data includes at least one of the following: 1) a first signal; 2) channel information; 3) spectral information calculated based on the channel information or the first signal; 4) a measurement quantity; 5) the attributes or state of the perceived target; and 6) the perception result.

[0187] Optionally, the first, second, third, fourth, fifth, or intermediate data may include different contents from the six contents mentioned in 1) to 6). For example, the first, second, third, fourth, or fifth data as input data may include channel information, and the first, second, third, fourth, or fifth data as output data may include basic measurement quantities. Another example is that the first, second, third, fourth, or fifth data as input data may include channel information, and the first, second, third, fourth, or fifth data as output data may include sensing results. Yet another example is that the first, second, third, fourth, or fifth data as input data may include channel information, the intermediate data may include spectral information calculated based on the channel information or the first signal, and the first, second, third, fourth, or fifth data as output data may include sensing results.

[0188] Optionally, the first, second, third, fourth, fifth, or intermediate data may include the same content among the six types of content mentioned above (1) to (6); for example, the input data and output data may include the first signal or channel information, in which case the AI ​​model in the sensing node can overcome channel estimation errors and noise to obtain more accurate data.

[0189] In one embodiment of this application, the content of the first data, second data, third data, fourth data, fifth data, or intermediate data further includes at least one of the following:

[0190] 1) Perceive signal identification information;

[0191] Optionally, the sensed signal identification information may include the index of the reference signal, etc.

[0192] 2) Sensing and measurement configuration identification information;

[0193] 3) Perceive business information;

[0194] Optionally, the perceived service information includes the perceived service identifier.

[0195] 4) Data subscription identifier;

[0196] 5) Purpose of the measurement;

[0197] Optionally, the measurement application includes at least one of the following: communication, sensing, wireless sensing.

[0198] 6) Time information;

[0199] 7) Sensing node information;

[0200] Optionally, the sensing node information includes at least one of the following: sensing node identifier, sensing node location, and sensing direction of the sensing node.

[0201] 8) Sensing link information;

[0202] Optionally, the sensing link information includes at least one of the following: sensing link sequence number and transceiver node identifier.

[0203] 9) Description of measured quantities;

[0204] Optionally, the measurement description information includes at least one of the following: a) form, such as amplitude value, phase value, or a complex value combining amplitude and phase; b) resource type, such as time-domain measurement result or frequency-domain resource measurement result.

[0205] 10) Measurement index information.

[0206] Optionally, the measurement metrics include at least one of the following: signal-to-noise ratio (SNR), perceived SNR, and reference signal receiving power (RSRP). In one embodiment of this application, the sensing service includes at least one of the following: detecting the presence of a target, detecting the number of targets, positioning, trajectory tracking, velocity detection, distance detection, angle detection, acceleration detection, material analysis, composition analysis, shape detection, category classification, and radar cross section (RCS). Section (RCS) detection, polarization scattering characteristic detection, fall detection, intrusion detection, indoor positioning, gesture recognition, lip reading, gait recognition, facial expression recognition, face recognition, respiration monitoring, heart rate monitoring, pulse monitoring, humidity, brightness, temperature, or atmospheric pressure monitoring, air quality monitoring, weather condition monitoring, environmental reconstruction, terrain, building, or vegetation distribution detection, pedestrian or vehicle flow detection, crowd density, vehicle density detection, etc.; or, sensing services can also refer to a category of sensing services, that is, classifying multiple different sensing services according to certain characteristics, such as dividing them according to function into detection-type sensing services (e.g., including intrusion detection, fall detection), parameter estimation-type sensing services (distance, angle, speed calculation), recognition-type sensing services (action recognition, identity recognition), etc.; they can also be divided according to the sensing range (near-range sensing, medium-range sensing, long-range sensing), according to the level of sensing fineness (coarse-grained sensing, fine-grained sensing, etc.), according to power consumption or energy consumption, according to resource consumption, etc.

[0207] In one embodiment of this application, the format of the first data, the second data, the third data, the fourth data, the fifth data, or the intermediate data includes at least one of the following:

[0208] 1) Dimensions of channel information;

[0209] For example, frequency domain channel response information on a single symbol (one-dimensional channel information), time-frequency domain channel response information on multiple symbols (two-dimensional channel information), time-frequency spatial domain channel response information of multiple symbols and multiple antennas (three-dimensional channel information), and different dimensions of scale, i.e., the number of sampling points or sampling interval (e.g., time-frequency domain density).

[0210] 2) The range of spectral information;

[0211] It can be a limitation on the range of different spectral information (or a truncation window for different dimensions of spectral information). That is, the spectral information can be the complete spectral information calculated based on the channel information, or it can be a subset of the complete spectral information, such as a subset of spectral information corresponding to a specific time delay or Doppler range in the time-delay-Doppler spectrum, or information on paths or sampling points in the time-delay-Doppler spectrum whose power or amplitude exceeds a preset threshold; it can also indicate the upper limit of the number of paths or sampling points of a specific input spectrum that the AI ​​model supports; or it can indicate the minimum granularity of the specific input spectrum that the AI ​​model supports, that is, the interval between two adjacent sampling points (corresponding to the sensing resolution).

[0212] For example, the spectral information includes partial spectral information, such as the information of the N2-N3 sampling points or the N2-N3 paths in the time delay spectrum (as shown in the boxed part in Figure 7).

[0213] For example, the spectral information subset is the portion of the time-delay-Doppler spectrum where the absolute Doppler value is less than X1 and the time delay value is less than X2 (as shown in the boxed portion in Figure 8).

[0214] 3) Supports the number of measurements that can be input into the AI ​​model at one time;

[0215] 4) Supported quantization methods for measured values;

[0216] Optionally, quantization methods include quantization granularity.

[0217] 5) Soft information type.

[0218] Soft information can be data described by mean and variance, or confidence interval and confidence level.

[0219] In one embodiment of this application, the first data, the third data, or the fourth data is obtained through at least one of the following methods:

[0220] 1) Sensing data processing based on a device; wherein the sensing data includes at least one of the following: a first signal, channel information, spectral information calculated based on the channel information or the first signal, a measurement quantity, the attribute of the sensing target, the state of the sensing target, and the sensing result;

[0221] 2) Joint processing of sensing data from multiple different devices;

[0222] 3) Processing of sensory data acquired based on a single sensing mode;

[0223] Optionally, the sensing mode includes at least one of the following: base station self-transmission and self-reception, base station A transmits and base station B receives, base station transmits and terminal receives, terminal transmits and base station receives, terminal self-transmission and self-reception, terminal A transmits and terminal B receives; or the sensing mode may also refer to single-base sensing or dual-base sensing.

[0224] 4) Joint processing of sensing data acquired from multiple different sensing modes;

[0225] 5) Sensor-based sensing data processing;

[0226] Optionally, the sensor includes at least one of the following: a visible light camera, an infrared camera, a Global Navigation Satellite System (GNSS), a lidar, a millimeter-wave radar, a thermometer, a hygrometer, a barometer, a gyroscope, an accelerometer, a magnetometer, a gravity sensor, a sonar, a rain gauge, etc.

[0227] 6) Obtained by joint processing of sensor data from both sensors and wireless sensing;

[0228] 7) Obtained by joint processing of sensing data from different frequency bands;

[0229] Optionally, sensing data from different frequency bands may include, but are not limited to: sensing data from the sub-6GHz band, sensing data from the millimeter-wave band, and sensing data from the THz band.

[0230] 8) Obtained by joint processing of sensing data based on different Radio Access Technologies (RATs).

[0231] Optional, RAT includes, but is not limited to, 4G, 5G, 6G, Wireless Fidelity (Wifi), Ultra Wide Band (UWB), Bluetooth, etc.

[0232] The perception processing method provided in the embodiments of this application is applicable to the following scenarios and has the following effects:

[0233] 1) Improving Sensing Performance. Wireless communication network positioning methods primarily rely on measurements of the direct path for localization. While achieving high positioning accuracy with low implementation complexity when a line-of-sight (LOS) path exists, they are susceptible to non-line-of-sight (NLOS) conditions. In particular, when no direct path exists between the terminal and the positioning base station, positioning accuracy drops significantly, leading to a high probability of positioning failure. Existing research indicates that positioning methods based on Artificial Intelligence (AI) or Machine Learning (ML) can solve positioning problems in NLOS scenarios. Similar to positioning, in multi-target sensing scenarios within integrated sensing environments, and in scenarios with complex signal propagation environments (similar to NLOS), AI-based sensing methods are expected to improve sensing performance compared to non-AI methods.

[0234] 2) Replacement of highly complex perception algorithms. Some high-resolution perception algorithms, such as Music, are highly complex, with significant latency and power consumption. Compared to high-resolution perception algorithms, AI-based perception methods can achieve comparable or better perception performance while reducing algorithm complexity, latency, and power consumption compared to non-AI methods.

[0235] 3) Perceiving Non-Ideal Factors. Perceiving non-ideal factors arise from the non-ideals of components within modules such as transceiver antennas, RF modules, frequency source modules, and signal processing modules. Although non-ideal factors are widespread in communication systems, due to differences in signal processing mechanisms, some non-ideal factors have a far greater impact on sensing performance than on communication performance. Perceiving non-ideal factors mainly include clock skew, local oscillator frequency offset, channel inconsistency, and time-domain random phase. AI-based methods, compared to non-AI methods, have the potential to eliminate or mitigate perceived non-ideal factors.

[0236] The optional implementation methods of this application are described below with reference to Embodiment 1 and Embodiment 2.

[0237] Example 1

[0238] Step 1: The first node acquires the input data (i.e., the first data) of the first AI model; wherein, the first AI model is deployed on the first node; the first data includes at least one of the data generated by the first node and the data acquired by the first node from other nodes;

[0239] Optionally, if the first data is data obtained by the first node (e.g., a terminal) from other nodes (e.g., network-side devices), then before other nodes send the first data to the first node, the other nodes need to receive a first signaling, which indicates at least one of the following: the content of the first data, and the format of the first data.

[0240] Optionally, prior to step 1, the first node determines one or more first AI models, specifically in any of the following ways:

[0241] 1) The first node receives second information configured by the network-side device, the second information including configuration information of one or more first AI models; the first node determines one or more first AI models based on the second information;

[0242] 2) The first node sends a request message to the network side, the request message being used to request the configuration of one or more first AI models; the first node receives second information sent by the network side; wherein, the second information includes configuration information of one or more first AI models; the first node determines one or more first AI models based on the second information;

[0243] 3) The first node is based on autonomously determining one or more first AI models;

[0244] 4) The first node determines one or more first AI models based on the protocol;

[0245] 5) The first node determines one or more first AI models based on higher-level signaling;

[0246] 6) Based on the third information, the first node selects one or more first AI models from the first AI model pool; wherein the first AI model pool includes K AI models, and K is a positive integer.

[0247] Step 2: The first AI model of the first node processes the first data (e.g., inference based on the first AI model) to obtain the second data (i.e., the output data);

[0248] Optionally, the first node sends the second data to other nodes.

[0249] Example 2

[0250] Step 1: The first node acquires the input data (i.e., the first data) of the first AI model; wherein, the first AI model is deployed on the first node; the first data includes at least one of the data generated by the first node and the data acquired by the first node from other nodes;

[0251] Optionally, if the first data is data obtained by the first node (e.g., a terminal) from other nodes (e.g., network-side devices), then before other nodes send the first data to the first node, the other nodes need to receive a first signaling, which indicates at least one of the following: relevant information about the content of the first data, and relevant information about the format of the first data.

[0252] Optionally, prior to step 1, the first node determines one or more first AI models, specifically in any of the following ways:

[0253] 1) The first node receives second information configured by the network-side device, the second information including configuration information of one or more first AI models; the first node determines one or more first AI models based on the second information;

[0254] 2) The first node sends a request message to the network side, the request message being used to request the configuration of one or more first AI models; the first node receives second information sent by the network side; wherein, the second information includes configuration information of one or more first AI models; the first node determines one or more first AI models based on the second information;

[0255] 3) The first node is based on autonomously determining one or more first AI models;

[0256] 4) The first node determines one or more first AI models based on the protocol;

[0257] 5) The first node determines one or more first AI models based on higher-level signaling;

[0258] 6) Based on the third information, the first node selects one or more first AI models from the first AI model pool; wherein the first AI model pool includes K AI models, and K is a positive integer.

[0259] Step 2: The first AI model of the first node processes the first data (e.g., inference based on the first AI model) to obtain intermediate data;

[0260] Step 3: The second AI model of the first node processes the intermediate data (e.g., inference based on the first AI model) to obtain the second data.

[0261] Optionally, the first node sends the second data to other nodes.

[0262] The sensing processing method provided in this application can be executed by a sensing processing device. This application uses an example of a sensing processing device executing the sensing processing method to illustrate the sensing processing device provided in this application.

[0263] Referring to Figure 9, this application embodiment provides a sensing processing device applied to a first node. The device 90 includes: a first transceiver unit 91 and a first processing unit 92.

[0264] The first transceiver unit 91 is used to acquire one or more first data;

[0265] The first processing unit 92 is used to process the one or more first data through N first AI models to obtain one or more second data, where N is a positive integer;

[0266] The content of the first data or the second data includes at least one of the following: a first signal, channel information, spectral information calculated based on the channel information or the first signal, a measurement quantity, the attribute of the perceived target, the state of the perceived target, and the perception result.

[0267] In one embodiment of this application, the first processing unit 92 is further used for at least one of the following:

[0268] The one or more first data are processed as input data for each of the N first AI models to obtain N second data;

[0269] The first data is processed as the input data of the first AI model among the N first AI models to obtain the first intermediate data. The m-th intermediate data is used as the input data of the (m+1)-th first AI model to obtain the (m+1)-th intermediate data. The (m+1)-th intermediate data is used as the input data of the N-th first AI model to obtain one or more second data. Wherein, m = N-1, and m is a positive integer, and N is greater than or equal to 2.

[0270] In one embodiment of this application, the first data includes at least one of the following:

[0271] 1) Data generated by the first node;

[0272] 2) Data obtained by the first node from the second node;

[0273] 3) The data obtained by the first node through processing one or more third data using X second AI models;

[0274] 4) The data obtained by the first node through processing one or more fourth data using Y first models, wherein the first model is a non-AI model;

[0275] The content of the third or fourth data includes at least one of the following: a first signal, channel information, spectral information calculated based on the channel information or the first signal, a measurement quantity, the attributes of the perceived target, the state of the perceived target, and the perception result.

[0276] In one embodiment of this application, the first transceiver unit 91 is further configured to send first information to a second node, a third node, or a fourth node, wherein the first information is used to indicate the content of the first data or the format of the first data.

[0277] In one embodiment of this application, the data obtained by processing one or more third data through X second AI models includes at least one of the following:

[0278] 1) Data obtained by processing one or more third-party data as input data for each of the X second AI models;

[0279] 2) Process one or more third data as input data of the first second AI model among X second AI models to obtain the first intermediate data, use the nth intermediate data as input data of the (n+1)th second AI model to obtain the (n+1)th intermediate data, and use the (n+1)th intermediate data as input data of the Xth second AI model to obtain the data.

[0280] Where n = X - 1, and n is a positive integer, and X is greater than or equal to 2.

[0281] In one embodiment of this application, the data obtained by processing one or more fourth data through Y first models includes at least one of the following:

[0282] 1) Data obtained by processing one or more fourth data points as input data for each of the multiple first models;

[0283] 2) Process one or more fourth data as input data of the first first model in Y first models to obtain the first intermediate data, use the qth intermediate data as input data of the (q+1)th first model to obtain the (q+1)th intermediate data, and use the (q+1)th intermediate data as input data of the Yth first model to obtain the data; where q = Y-1, and q is a positive integer, and Y is greater than or equal to 2.

[0284] In one embodiment of this application, the first processing unit 92 is further configured to process the one or more second data through Q third AI models to obtain one or more fifth data, where Q is a positive integer;

[0285] The fifth data includes at least one of the following: a first signal, channel information, spectral information calculated based on the channel information or the first signal, a measurement quantity, the attributes of the perceived target, the state of the perceived target, and the perception result.

[0286] In one embodiment of this application, the first processing unit 92 is further used for at least one of the following:

[0287] The one or more second data are processed as input data for each of the Q third AI models to obtain one or more fifth data;

[0288] The first intermediate data is obtained by processing the one or more second data as input data of the first third AI model among the Q third AI models. The z-th intermediate data is obtained by processing the (z+1)-th third AI model as input data. The (z+1)-th intermediate data is obtained by processing the (z+1)-th intermediate data as input data of the Q-th third AI model. One or more fifth data are obtained by processing the (z+1)-th intermediate data as input data of the Q-th third AI model. Where z = Q-1, z is a positive integer, and Q is greater than or equal to 2.

[0289] In one embodiment of this application, the first AI model or the third AI model is determined by at least one of the following methods: determined by the first node based on second information configured by the network-side device, determined autonomously by the first node, agreed upon by the protocol, determined by the first node based on higher-layer signaling, or selected by the first node from the first AI model pool based on third information.

[0290] In one embodiment of this application, the second AI model is determined by at least one of the following methods: determined by the first node or the third node based on fourth information configured by the network-side device; determined autonomously by the first node or the third node; agreed upon by the protocol; determined by the first node or the third node based on higher-layer signaling; or selected from the second AI model pool by the first node or the third node based on fifth information.

[0291] In one embodiment of this application, the second or fourth information includes at least one of the following: model structure information, hyperparameter configuration of supported AI models, supported model data processing methods, supported model allowed cycles, supported model update cycles, supported model update information, supported model complexity information, available AI resources, supported AI frameworks or algorithms, number of models, model category information, model identification information, model priority information, model attribute information, model accuracy information, model error information, model feature information, adaptation environment information, processing latency information, fusion method information of model output results, model lifecycle information, information on model input data, and information on model output data.

[0292] In one embodiment of this application, the third or fifth information includes at least one of the following: model error information, network-side device mobility information, terminal mobility information, network-side device environmental information, terminal environmental information, perception accuracy requirement information, perception service information, and model priority information.

[0293] In one embodiment of this application, the intermediate data includes at least one of the following: a first signal, channel information, spectral information calculated based on the channel information or the first signal, a measurement quantity, the attribute of the perceived target, the state of the perceived target, and the perception result.

[0294] In one embodiment of this application, the content of the first data, second data, third data, fourth data, fifth data, or intermediate data may further include at least one of the following: sensing signal identification information, sensing measurement configuration identification information, sensing service information, data subscription identification, measurement purpose, time information, sensing node information, sensing link information, measurement description information, and measurement index information.

[0295] In one embodiment of this application, the format of the first data, second data, third data, fourth data, fifth data, or intermediate data includes at least one of the following: the dimension of channel information, the range of spectral information, the number of measurements that can be input into the AI ​​model at one time, the quantization method of the values ​​of the supported measurements, and the type of soft information.

[0296] In one embodiment of this application, the first data, the third data, or the fourth data is obtained through at least one of the following methods:

[0297] 1) Sensing data processing based on a single device;

[0298] 2) Joint processing of sensing data from multiple different devices;

[0299] 3) Processing of sensory data acquired based on a single sensing mode;

[0300] 4) Joint processing of sensing data acquired from multiple different sensing modes;

[0301] 5) Sensor-based sensing data processing;

[0302] 6) Joint processing of sensor data based on sensors and wireless sensing;

[0303] 7) Joint processing of sensing data based on different frequency bands;

[0304] 8) Joint processing of perception data based on different RATs.

[0305] The apparatus provided in this application embodiment can implement the various processes implemented in the method embodiment of FIG5 and achieve the same technical effect. To avoid repetition, it will not be described again here.

[0306] This application also provides a terminal, including a processor and a communication interface, wherein the communication interface is coupled to the processor, and the processor is used to run programs or instructions to implement the steps in the method embodiment shown in FIG5. This terminal embodiment corresponds to the above-described terminal-side method embodiment, and all implementation processes and methods of the above-described method embodiments can be applied to this terminal embodiment and can achieve the same technical effect. The terminal can be the sensing processing device shown in FIG5. Specifically, FIG10 is a schematic diagram of the hardware structure of a terminal implementing an embodiment of this application.

[0307] The terminal 1000 includes, but is not limited to, at least some of the following components: radio frequency unit 1001, network module 1002, audio output unit 1003, input unit 1004, sensor 1005, display unit 1006, user input unit 1007, interface unit 1008, memory 1009, and processor 1010.

[0308] Those skilled in the art will understand that the terminal 1000 may also include a power supply (such as a battery) for powering various components. The power supply can be logically connected to the processor 1010 through a power management system, thereby enabling functions such as charging, discharging, and power consumption management through the power management system. The terminal structure shown in Figure 10 does not constitute a limitation on the terminal. The terminal may include more or fewer components than shown, or combine certain components, or have different component arrangements, which will not be elaborated here.

[0309] It should be understood that, in this embodiment, the input unit 1004 may include a graphics processor 10041 and a microphone 10042. The graphics processor 10041 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 1006 may include a display panel 10061, which may be configured in the form of a liquid crystal display, an organic light-emitting diode, or the like. The user input unit 1007 includes a touch panel 10071 and at least one of other input devices 10072. The touch panel 10071 is also called a touch screen. The touch panel 10071 may include a touch detection device and a touch controller. Other input devices 10072 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.

[0310] In this embodiment, after receiving downlink data from the network-side device, the radio frequency unit 1001 can transmit it to the processor 1010 for processing; in addition, the radio frequency unit 1001 can send uplink data to the network-side device. Typically, the radio frequency unit 1001 includes, but is not limited to, antennas, amplifiers, transceivers, couplers, low-noise amplifiers, duplexers, etc.

[0311] The memory 1009 can be used to store software programs or instructions, as well as various data. The memory 1009 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 1009 may include volatile memory or non-volatile memory. 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 1009 in this embodiment includes, but is not limited to, these and any other suitable types of memory.

[0312] The processor 1010 may include one or more processing units; optionally, the processor 1010 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 the processor 1010.

[0313] The radio frequency unit 1001 is used to acquire one or more first data; the processor 1010 is used to process the one or more first data through N first AI models to obtain one or more second data, where N is a positive integer; wherein the content of the first data or the second data includes at least one of the following: a first signal, channel information, spectral information calculated based on the channel information or the first signal, a measurement quantity, the attribute of the perceived target, the state of the perceived target, and the perception result.

[0314] It is understood that the implementation process of each implementation method mentioned in this embodiment can refer to the relevant description in Figure 5 of the method embodiment and achieve the same or corresponding technical effects. To avoid repetition, it will not be described again here.

[0315] This application also provides a network-side device. As shown in FIG11, the network-side device 1100 includes a processor 1101, a network interface 1102, and a memory 1103. The network-side device may be the sensing processing device shown in FIG5. The network interface 1102 is, for example, a Common Public Radio Interface (CPRI).

[0316] Specifically, the network-side device 1100 in this application embodiment further includes: instructions or programs stored in memory 1103 and executable on processor 1101. Processor 1101 calls the instructions or programs in memory 1103 to execute the methods executed by each module shown in FIG5 and achieve the same technical effect. To avoid repetition, it will not be described in detail here.

[0317] This application also provides a readable storage medium storing a program or instructions. When the program or instructions are executed by a processor, they implement the various processes of the method embodiment in Figure 5 above and achieve the same technical effect. To avoid repetition, they will not be described again here.

[0318] The processor mentioned above 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. In some examples, the readable storage medium may be a non-transient readable storage medium.

[0319] 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 method embodiment in Figure 5 above, and can achieve the same technical effect. To avoid repetition, it will not be described again here.

[0320] 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.

[0321] 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 method embodiment in FIG5 above, and can achieve the same technical effect. To avoid repetition, it will not be described again here.

[0322] This application also provides a communication system, including: a terminal and a network-side device, wherein the terminal can be used to perform the steps of the method shown in Figure 5 above, or the network-side device can be used to perform the steps of the method shown in Figure 5 above.

[0323] 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.

[0324] From 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 computer software products plus necessary general-purpose hardware platforms, and of course, they can also be implemented by hardware. The computer software product is stored in a storage medium (such as ROM, RAM, magnetic disk, optical disk, etc.) and includes several instructions to cause the terminal or network-side device to execute the methods described in the various embodiments of this application.

[0325] 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 implementations under the guidance of this application without departing from the spirit and scope of the claims. All of these implementations are within the protection scope of this application.

Claims

1. A method for sensory processing, comprising: The first node retrieves one or more first data items; The first node processes the one or more first data through N first artificial intelligence (AI) models to obtain one or more second data, where N is a positive integer; The content of the first data or the second data includes at least one of the following: a first signal, channel information, spectral information calculated based on the channel information or the first signal, a measurement quantity, the attribute of the perceived target, the state of the perceived target, and the perception result.

2. The method of claim 1, wherein, The first node processes the one or more first data through N first AI models to obtain one or more second data, including at least one of the following: The first node processes the one or more first data as input data for each of the N first AI models to obtain N second data; The first node processes the one or more first data as input data to the first first AI model among the N first AI models to obtain the first intermediate data, uses the mth intermediate data as input data to the (m+1)th first AI model to obtain the (m+1)th intermediate data, and uses the (m+1)th intermediate data as input data to the Nth first AI model to obtain one or more second data. Where m = N-1, and m is a positive integer, and N is greater than or equal to 2.

3. The method of claim 1 or 2, wherein, The first data includes at least one of the following: The data generated by the first node; The data that the first node obtains from the second node; The data obtained by the first node through processing one or more third data using X second AI models, where X is a positive integer; The data obtained by the first node through processing one or more fourth data through Y first models, where the first model is a non-AI model and Y is a positive integer; The content of the third or fourth data includes at least one of the following: a first signal, channel information, spectral information calculated based on the channel information or the first signal, a measurement quantity, the attributes of the perceived target, the state of the perceived target, and the perception result.

4. The method according to claim 3, further comprising: The first node sends first information to the second, third, or fourth node, the first information being used to indicate the content or format of the first data.

5. The method according to claim 3, wherein, The data obtained by the first node through processing one or more third data using X second AI models includes at least one of the following: The first node processes one or more third data as input data for each of the X second AI models to obtain the data; The first node processes one or more third data as input data to the first second AI model among X second AI models to obtain the first intermediate data, uses the nth intermediate data as input data to the (n+1)th second AI model to obtain the (n+1)th intermediate data, and uses the (n+1)th intermediate data as input data to the Xth second AI model to obtain the data. Where n = X - 1, and n is a positive integer, and X is greater than or equal to 2.

6. The method of claim 3, wherein, The data obtained by the first node through processing one or more fourth data using Y first models includes at least one of the following: The first node processes one or more fourth data points as input data for each of the Y first models to obtain the data; The first node processes one or more fourth data as input data to the first first model among Y first models to obtain the first intermediate data, uses the qth intermediate data as input data to the (q+1)th first model to obtain the (q+1)th intermediate data, and uses the (q+1)th intermediate data as input data to the Yth first model to obtain the data; where q = Y-1, and q is a positive integer, and Y is greater than or equal to 2.

7. The method according to claim 1, further comprising: The first node processes the one or more second data through Q third AI models to obtain one or more fifth data, where Q is a positive integer; The fifth data includes at least one of the following: a first signal, channel information, spectral information calculated based on the channel information or the first signal, a measurement quantity, the attributes of the perceived target, the state of the perceived target, and the perception result.

8. The method of claim 7, wherein, The first node processes the one or more second data through Q third AI models to obtain one or more fifth data, including: The first node processes the one or more second data as input data for each of the Q third AI models to obtain one or more fifth data. The first node processes the one or more second data as input data to the first third AI model among the Q third AI models to obtain the first intermediate data, uses the z-th intermediate data as input data to the (z+1)-th third AI model to obtain the (z+1)-th intermediate data, and processes the (z+1)-th intermediate data as input data to the Q-th third AI model to obtain one or more fifth data; where z = Q-1, and z is a positive integer, and Q is greater than or equal to 2.

9. The method of claim 1 or 8, wherein, The first AI model or the third AI model is determined by at least one of the following methods: determined by the first node based on second information configured by the network-side device; determined autonomously by the first node; agreed upon by the protocol; determined by the first node based on higher-layer signaling; or selected by the first node from the first AI model pool based on third information.

10. The method of claim 3, wherein, The second AI model is determined by at least one of the following methods: determined by the first node or the third node based on the fourth information configured by the network-side device; determined autonomously by the first node or the third node; agreed upon by the protocol; determined by the first node or the third node based on higher-layer signaling; or selected from the second AI model pool by the first node or the third node based on the fifth information.

11. The method of claim 9 or 10, wherein, The second or fourth information includes at least one of the following: model structure information, hyperparameter configuration of supported AI models, supported model data processing methods, allowed model cycles, supported model update cycles, supported model update information, supported model complexity information, available AI resources, supported AI frameworks or algorithms, number of models, model category information, model identification information, model priority information, model attribute information, model accuracy information, model error information, model feature information, adaptation environment information, processing latency information, fusion method information of model output results, model lifecycle information, information on model input data, and information on model output data.

12. The method of claim 9 or 10, wherein, The third or fifth information includes at least one of the following: model error information, network-side device mobility information, terminal mobility information, network-side device environmental information, terminal environmental information, perception accuracy requirement information, perception service information, and model priority information.

13. The method of claim 2 or 5 or 6 or 8, wherein, The intermediate data includes at least one of the following: a first signal, channel information, spectral information calculated based on the channel information or the first signal, a measurement quantity, the attributes of the sensed target, the state of the sensed target, and the sensed result.

14. The method of claim 1 or 2 or 3 or 5 or 6 or 7 or 8 or 13, wherein, The content of the first data, second data, third data, fourth data, fifth data, or intermediate data may also include at least one of the following: sensing signal identification information, sensing measurement configuration identification information, sensing service information, data subscription identification, measurement purpose, time information, sensing node information, sensing link information, measurement description information, and measurement index information.

15. The method of claim 1 or 2 or 3 or 5 or 6 or 7 or 8, wherein, The format of the first, second, third, fourth, fifth, or intermediate data includes at least one of the following: the dimension of channel information, the range of spectral information, the number of measurements that can be input into the AI ​​model at one time, the quantization method of the values ​​of the supported measurements, and the type of soft information.

16. The method of claim 1 or 2 or 3 or 5 or 6, wherein, The first data, or the third data, or the fourth data, is obtained through at least one of the following methods: Based on the processing of sensing data from a single device; Joint processing of sensing data from multiple different devices; Processing of sensory data acquired based on a sensing mode; Joint processing of sensing data acquired from multiple different sensing modes; Sensor-based sensing data processing; Joint processing of sensor data based on sensor and wireless sensing; Joint processing of sensing data from different frequency bands; Joint processing of perception data based on different RATs.

17. An apparatus for perception processing, applied to a first node, comprising: First transceiver unit and first processing unit; The first transceiver unit is used to acquire one or more first data; The first processing unit is used to process the one or more first data through N first AI models to obtain one or more second data, where N is a positive integer; The content of the first data or the second data includes at least one of the following: a first signal, channel information, spectral information calculated based on the channel information or the first signal, a measurement quantity, the attribute of the perceived target, the state of the perceived target, and the perception result.

18. The apparatus of claim 17, wherein, The format of the first or second data includes at least one of the following: the dimension of channel information, the range of spectral information, the number of measurements that can be input into the AI ​​model at one time, the quantization method of the values ​​of the supported measurements, and the type of soft information.

19. The apparatus of claim 17, wherein, The first data was obtained through at least one of the following methods: Based on the processing of sensing data from a single device; Joint processing of sensing data from multiple different devices; Processing of sensory data acquired based on a sensing mode; Joint processing of sensing data acquired from multiple different sensing modes; Sensor-based sensing data processing; Joint processing of sensor data based on sensor and wireless sensing; Joint processing of sensing data from different frequency bands; Joint processing of sensed data based on different wireless access technologies (RATs).

20. A terminal comprising 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 steps of the perceptual processing method as claimed in any one of claims 1 to 16.

21. A network-side device, comprising 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 steps of the sensing processing method as claimed in any one of claims 1 to 16.

22. A readable storage medium storing a program or instructions that, when executed by a processor, implement the steps of the perceptual processing method as described in any one of claims 1 to 16.

23. A computer program product comprising computer instructions that, when executed by a processor, implement the steps of the method as described in any one of claims 1 to 16.

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