WO2024255044A1

WO2024255044A1 - Communication method and communication apparatus

Info

Publication number: WO2024255044A1
Application number: PCT/CN2023/125053
Authority: WO
Inventors: Yiqun Ge; Hao Tang; Jianglei Ma
Original assignee: Huawei Technologies Co Ltd
Current assignee: Huawei Technologies Co Ltd
Priority date: 2023-06-13
Filing date: 2023-10-17
Publication date: 2024-12-19
Anticipated expiration: 2025-12-13
Also published as: CN121336401A

Abstract

Embodiments of the present application provide a communication method and a communication apparatus. The communication method includes sending first information according to one or more metrics, where the one or more metrics are based on distance (s) between distribution (s) of outputs of p latent layer (s) in a first AI model and distribution of inputs of the first AI model in an inference cycle, and/or distance (s) between distribution (s) of outputs of p' latent layer (s) and distribution of outputs of a first AI model in an inference cycle. According to the above technical solution, the communication quality can be improved.

Description

COMMUNICATION METHOD AND COMMUNICATION APPARATUS

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application is related to, and claims priority to, United States provisional patent application Serial No. 63/507,772, entitled "AI MODELS CROSS CONSISTENCE CHECK BY MI RATIO" , filed on June 13, 2023.

The disclosures of the aforementioned applications are hereby incorporated by reference in their entirety.

TECHNICAL FIELD

Embodiments of the present application relate to the field of communications, and more specifically, to a communication method and a communication apparatus.

BACKGROUND

Artificial intelligence (AI) -based algorithms have been introduced into wireless communications to solve some wireless problems such as channel estimation, scheduling, channel state information (CSI) compression, positioning, beam-management, and so on. AI algorithm is a data-driven method that tunes some pre-defined architectures by a set of data samples called as training data set.

Whether the AI model deployed on a device can work is crucial for communication quality. For example, in wireless communication, a user device is moving. The AI model deployed on the user device may work in some environments, but may not work in others, which can affect the communication quality.

Therefore, an urgent technical problem that needs to be solved is how to ensure communication quality.

SUMMARY

Embodiments of the present application provide a communication method and a communication apparatus. The technical solutions may ensure communication quality.

According to a first aspect, an embodiment of the present application provides a communication method, including sending first information according to one or more metrics, where the one or more metrics are based on distance (s) between distribution (s) of outputs of p latent layer (s) in a first AI model and distribution of inputs of the first AI model in an inference cycle, and/or distance (s) between distribution (s) of the outputs of p’ latent layer (s) and distribution of outputs of a first AI model in an inference cycle, and p and p’ are positive integers.

According to the above technical solution, the one or more metrics can be used to check whether the first AI model can work as expected, which is conducive to ensuring communication quality.

The p latent layer (s) of the first AI model and the p’ latent layer (s) of the first AI model may be the same or different.

In a possible design, the one or more metrics include at least one metric corresponding to a first latent layer among the p latent layer (s) or the p’ latent layer (s) , and the at least one metric includes at least one of the following: a first metric corresponding to the first latent layer, where the first metric corresponding to the first latent layer is based on a distance between the distribution of the inputs of the first AI model and distribution of outputs of the first latent layer; a second metric corresponding to the first latent layer, where the second metric corresponding to the first latent layer is based on a distance between the distribution of the outputs of the first AI model and the distribution of the outputs of the first latent layer; or a third metric corresponding to the first latent layer, where the third metric corresponding to the first latent layer is based on a ratio between the first metric and the second metric.

The one or more metric (s) can be related to the mutual information. The distance above can be calculated by HSIC, JSD, KL and so on.

In a possible design, the one or more metrics are configured to check whether the inference cycle is normal.

According to the above technical solution, the one or more metrics can be used to check whether the current inference is normal. For example, if the inference is abnormal, adjustments can be made in a timely manner, such as switching to other AI models or switching to non-AI methods, which is conducive to ensuring the quality of data processing or communication.

In a possible design, when the inference cycle is abnormal, one or more of the following are not met: first metrics in the one or more metrics decrease as indexes of corresponding latent layers increase; second metrics in the one or more metrics increase as indexes of corresponding latent layers increase; or third metrics in the one or more metrics decrease as indexes of corresponding latent layers increase.

In a possible design, the one or more metrics are configured to check whether the first AI model works with a second AI model.

According to the above technical solution, the one or more metrics can be used to check whether a plurality of AI models can work together, which is conducive to ensuring the quality of data processing or communication.

In a possible design, when the first AI model works with the second AI model, the difference (s) between the one or more metrics and one or more reference metrics is less than or equal to threshold (s) , and the one or more reference metrics are related to the p latent layer (s) and/or the p’ latent layer (s) in the second AI model.

In a possible design, the method further includes: receiving second information, where the sending first information according to one or more metrics, includes: sending the first information according to the second information.

In a possible design, the method further includes: receiving second information, where the second information is configured to indicate at least one of the following: one or more latent layers related to S metric (s) , one or more methods for measuring the S metric (s) , or one or more types of the S metric (s) , where S is a positive integer.

In a possible design, the S metric (s) includes the one or more metrics.

In a possible design, the first information indicates the one or more metrics.

According to a second aspect, an embodiment of the present application provides a communication method, including: receiving first information related to one or more metrics, where the one or more metrics are based on distance (s) between distribution (s) of outputs of p latent layer (s) in a first AI model and distribution of inputs of the first AI model in an inference cycle, and/or distance (s) between distribution (s) of outputs of p’ latent layer (s) and distribution of outputs of a first AI model in an inference cycle, and p and p’ are positive integers.

In a possible design, the method further includes: sending second information, where the second information is configured to indicate at least one of the following: one or more latent layers related to S metric (s) , one or more methods for measuring the S metric (s) , or one or more types of the S metric (s) , where S is a positive integer.

In a possible design, the first information indicates the one or more metrics.

According to a third aspect, a communication apparatus is provided. The communication apparatus includes a function or unit configured to perform the method according to the first aspect or any one of the possible designs of the first aspect.

For example, the communication apparatus may be a network device or a chip in the network device. For another example, the communication apparatus may be a terminal device or a chip in the terminal device.

According to a fourth aspect, a communication apparatus is provided. The communication apparatus includes a function or unit configured to perform the method according to the second aspect or any one of the possible designs of the second aspect.

For example, the communication apparatus may be a terminal device or a chip in the terminal device. For another example, the communication apparatus may be a network device or a chip in the network device.

According to a fifth aspect, a system is provided. The system includes: the communication apparatus according to the third aspect and the communication apparatus according to the fourth aspect.

According to a sixth aspect, a communication apparatus is provided. The communication apparatus includes at least one processor, and the at least one processor is coupled to at least one memory. The at least one memory is configured to store a computer program or one or more instructions. The at least one processor is configured to: invoke the computer program or the one or more instructions from the at least one memory and run the computer program or the one or more instructions, so that the communication apparatus performs the method in any one of the first aspect or the possible designs of the first aspect, or the communication apparatus performs the method in any one of the second aspect or the possible designs of the second aspect.

For example, the communication apparatus may be a network device or a component (for example, a chip or integrated circuit) installed in the network device. For another example, the communication apparatus may be a terminal device or a component (for example, a chip or integrated circuit) installed in the terminal device.

According to a seventh aspect, a communication apparatus is provided. The communication apparatus includes a processor and a communications interface. The processor is connected to the communications interface. The processor is configured to execute the one or more instructions, and the communications interface is configured to communicate with other network elements under the control of the processor. The processor is enabled to perform the method according to the first aspect or any one of the possible designs of the first aspect, or the second aspect or any one of the possible designs of the second aspect.

According to an eighth aspect, a computer storage medium is provided. The computer storage medium stores program code, and the program code is used to execute one or more instructions for the method according to the first aspect or any one of the possible designs of the first aspect, or the second aspect or any one of the possible designs of the second aspect.

According to a ninth aspect, the present application provides a computer program product including one or more instructions, where when the computer program product runs on a computer, the computer performs the method according to the first aspect or any one of the possible designs of the first aspect, or the second aspect or any one of the possible designs of the second aspect.

DESCRIPTION OF DRAWINGS

FIG. 1 is a schematic diagram of an application scenario according to the present application;

FIG. 2 illustrates an example communication system 100;

FIG. 3 illustrates an example device in a communication system;

FIG. 4 is a schematic diagram of a device in two cycles according to an embodiment of the present application;

FIG. 5 illustrates example local data of a device according to an embodiment of the present application;

FIG. 6 is a schematic diagram of a working situation of an AI model according to an embodiment of the present application;

FIG. 7 is a schematic diagram of an example scenario according to an embodiment of the present application;

FIG. 8 is a schematic flowchart of a communication method according to an embodiment of the present application;

FIG. 9 illustrates a schematic diagram of three example metrics according to an embodiment of the present application;

FIG. 10 is a schematic diagram of example interconnection check according to an embodiment of the present application; and

FIGS. 11-15 are schematic block diagrams of possible devices according to embodiments of the present application.

DESCRIPTION OF EMBODIMENTS

The following describes technical solutions of the present application with reference to the accompanying drawings.

The embodiments of the present invention may be applied to communication systems of next generation (e.g. sixth generation (6G) or later) , 5th Generation (5G) , new radio (NR) , long term evolution (LTE) , or the like.

FIG. 1 is a schematic structural diagram of an example communication system.

Referring to FIG. 1, as an illustrative example without limitation, a simplified schematic illustration of a communication system is provided. A communication system 100 includes a radio access network 120. The radio access network 120 may be a next generation (e.g. 6G or later) radio access network, or a legacy (e.g. 5G, 4G, 3G or 2G) radio access network. One or more communication electric device (ED) 110a-120j (generically referred to as 110) may be interconnected to one another or connected to one or more network nodes (170a, 170b, generically referred to as 170) in the radio access network 120. A core network 130 may be a part of the communication system and may be dependent or independent of the radio access technology used in the communication system 100. Also, the communication system 100 includes a public switched telephone network (PSTN) 140, the internet 150, and other networks 160.

FIG. 2 is a schematic structural diagram of another example communication system.

In general, a communication system 100 enables multiple wireless or wired elements to communicate data and other content. The purpose of the communication system 100 may be to provide content, such as voice, data, video, and/or text, via broadcast, multicast and unicast, etc. The communication system 100 may operate by sharing resources, such as carrier spectrum bandwidth, between its constituent elements. The communication system 100 may include a terrestrial communication system and/or a non-terrestrial communication system. The communication system 100 may provide a wide range of communication services and applications (such as earth monitoring, remote sensing, passive sensing and positioning, navigation and tracking, autonomous delivery and mobility, etc. ) . The communication system 100 may provide a high degree of availability and robustness through a joint operation of the terrestrial communication system and the non-terrestrial communication system. For example, integrating a non-terrestrial communication system (or components thereof) into a terrestrial communication system can result in what may be considered a heterogeneous network including multiple layers. Compared to conventional communication networks, the heterogeneous network may achieve better overall performance through efficient multi-link joint operation, more flexible functionality sharing, and faster physical layer link switching between terrestrial networks and non-terrestrial networks.

The terrestrial communication system and the non-terrestrial communication system could be considered sub-systems of the communication system. In the example shown, the communication system 100 includes electronic devices (ED) 110a-110d (generically referred to as ED 110) , radio access networks (RANs) 120a-120b, non-terrestrial communication network 120c, a core network 130, a public switched telephone network (PSTN) 140, the internet 150, and other networks 160. The RANs 120a-120b include respective base stations (BSs) 170a-170b, which may be generically referred to as terrestrial transmit and receive points (T-TRPs) 170a-170b. The non-terrestrial communication network 120c includes an access node 120c, which may be generically referred to as a non-terrestrial transmit and receive point (NT-TRP) 172.

Any ED 110 may be alternatively or additionally configured to interface, access, or communicate with any other T-TRP 170a-170b and NT-TRP 172, the internet 150, the core network 130, the PSTN 140, the other networks 160, or any combination of the preceding. In some examples, ED 110a may communicate an uplink and/or downlink transmission over an interface 190a with T-TRP 170a. In some examples, the EDs 110a, 110b and 110d may also communicate directly with one another via one or more sidelink air interfaces 190b. In some examples, ED 110d may communicate an uplink and/or downlink transmission over an interface 190c with NT-TRP 172.

The air interfaces 190a and 190b may use similar communication technology, such as any suitable radio access technology. For example, the communication system 100 may implement one or more channel access methods, such as code division multiple access (CDMA) , time division multiple access (TDMA) , frequency division multiple access (FDMA) , orthogonal FDMA (OFDMA) , or single-carrier FDMA (SC-FDMA) in the air interfaces 190a and 190b. The air interfaces 190a and 190b may utilize other higher dimension signal spaces, which may involve a combination of orthogonal and/or non-orthogonal dimensions.

The air interface 190c can enable communication between the ED 110d and one or multiple NT-TRPs 172 via a wireless link or simply a link. For some examples, the link is a dedicated connection for unicast transmission, a connection for broadcast transmission, or a connection between a group of EDs and one or multiple NT-TRPs for multicast transmission.

The RANs 120a and 120b are in communication with the core network 130 to provide the EDs 110a 110b, and 110c with various services such as voice, data, and other services. The RANs 120a and 120b and/or the core network 130 may be in direct or indirect communication with one or more other RANs (not shown) , which may or may not be directly served by core network 130, and may or may not employ the same radio access technology as RAN 120a, RAN 120b or both. The core network 130 may also serve as a gateway access between (i) the RANs 120a and 120b or EDs 110a 110b, and 110c or both, and (ii) other networks (such as the PSTN 140, the internet 150, and the other networks 160) . In addition, some or all of the EDs 110a 110b, and 110c may include functionality for communicating with different wireless networks over different wireless links using different wireless technologies and/or protocols. Instead of wireless communication (or in addition thereto) , the EDs 110a 110b, and 110c may communicate via wired communication channels to a service provider or switch (not shown) , and to the internet 150. PSTN 140 may include circuit switched telephone networks for providing plain old telephone service (POTS) . Internet 150 may include a network of computers and subnets (intranets) or both, and incorporate protocols, such as Internet protocol (IP) , transmission control protocol (TCP) , and user datagram protocol (UDP) . EDs 110a 110b, and 110c may be multimode devices capable of operation according to multiple radio access technologies, and incorporate multiple transceivers necessary to support such.

The ED 110 may be widely used in various scenarios, for example, cellular communications, device-to-device (D2D) , vehicle to everything (V2X) , peer-to-peer (P2P) , machine-to-machine (M2M) , machine-type communications (MTC) , internet of things (IoT) , virtual reality (VR) , augmented reality (AR) , industrial control, self-driving, remote medical, smart grid, smart furniture, smart office, smart wearable, smart transportation, smart city, drones, robots, remote sensing, passive sensing, positioning, navigation and tracking, autonomous delivery and mobility, etc.

Each ED 110 represents any suitable end user device for wireless operation and may include such devices (or may be referred to) as a user equipment/device (UE) , a wireless transmit/receive unit (WTRU) , a mobile station, a fixed or mobile subscriber unit, a cellular telephone, a station (STA) , a machine type communication (MTC) device, a personal digital assistant (PDA) , a personal communications service (PCS) phone, a session initiation protocol phone, a wireless local loop (WLL) station, a smartphone, a laptop, a computer, a tablet, a wireless sensor, a consumer electronics device, a smart book, a vehicle, a car, a truck, a bus, a train, or an IoT device, an industrial device, or apparatus (e.g. communication module, modem, or chip) in the forgoing devices, among other possibilities. Future generation EDs 110 may be referred to using other terms. The base station 170a and 170b is a T-TRP and will hereafter be referred to as T-TRP 170. A NT-TRP will hereafter be referred to as NT-TRP 172. Each ED 110 connected to T-TRP 170 and/or NT-TRP 172 can be dynamically or semi-statically turned-on (i.e., established, activated, or enabled) , turned-off (i.e., released, deactivated, or disabled) and/or configured in response to one or more of: connection availability and connection necessity.

The T-TRP 170 may be known by other names in some implementations, such as a base station, a base transceiver station (BTS) , a radio base station, a network node, a network device, a device on the network side, a transmit/receive node, a Node B, an evolved NodeB (eNodeB or eNB) , a Home eNodeB, a next Generation NodeB (gNB) , a transmission point (TP) ) , a site controller, an access point (AP) , or a wireless router, a relay station, a remote radio head, a terrestrial node, a terrestrial network device, or a terrestrial base station, base band unit (BBU) , remote radio unit (RRU) , active antenna unit (AAU) , remote radio head (RRH) , central unit (CU) , distribute unit (DU) , positioning node, among other possibilities. The T-TRP 170 may be macro BSs, pico BSs, relay nodes, donor nodes, or the like, or combinations thereof. The T-TRP 170 may refer to the forging devices or apparatus (e.g. communication module, modem, or chip) in the forgoing devices.

In some embodiments, the parts of the T-TRP 170 may be distributed. For example, some of the modules of the T-TRP 170 may be located remote from the equipment housing the antennas of the T-TRP 170, and may be coupled to the equipment housing the antennas over a communication link (not shown) sometimes known as front haul, such as common public radio interface (CPRI) . Therefore, in some embodiments, the term T-TRP 170 may also refer to modules on the network side that perform processing operations, such as determining the location of the ED 110, resource allocation (scheduling) , message generation, and encoding/decoding, and that are not necessarily part of the equipment housing the antennas of the T-TRP 170. The modules may also be coupled to other T-TRPs. In some embodiments, the T-TRP 170 may actually be a plurality of T-TRPs that are operating together to serve the ED 110, e.g. through coordinated multipoint transmissions.

The NT-TRP 172 may be known by other names in some implementations, such as a non-terrestrial node, a non-terrestrial network device, or a non-terrestrial base station.

Artificial intelligence (AI) technologies can be applied in communication, including artificial intelligence or machine learning (AI/ML) based communication in the physical layer and/or AI/ML based communication in the higher layer, such as medium access control (MAC) layer. For example, in the physical layer, the AI/ML based communication may aim to optimize component design and/or improve the algorithm performance. For example, AI/ML may be applied in relation to the implementation of channel coding, channel modelling, channel estimation, channel decoding, modulation, demodulation, multiple-input multiple-output (MIMO) , waveform, multiple access, physical layer element parameter optimization and update, beam forming, tracking, sensing, and/or positioning, etc. For the MAC layer, the AI/ML based communication may aim to utilize the AI/ML capability for learning, prediction, and/or making decisions to solve a complicated optimization problem with possible better strategy and/or optimal solution, e.g. to optimize the functionality in the MAC layer. For example, AI/ML may be applied to implement: intelligent transmission and reception point (TRP) management, intelligent beam management, intelligent channel resource allocation, intelligent power control, intelligent spectrum utilization, intelligent modulation and coding scheme (MCS) , intelligent hybrid automatic repeat request (HARQ) strategy, intelligent transmit/receive (Tx/Rx) mode adaption, etc.

In order to facilitate understanding of the embodiments of the present application, terms related to AI/ML that may be involved in the embodiments of the present application are described below.

(1) Data collection

Data is a very important component for AI/ML techniques. Data collection is a process of collecting data by the network nodes, management entity, or UE for the purpose of AI/ML model training, data analytics, and inference.

(2) AI/ML model training

AI/ML model training is a process to train an AI/ML Model by learning the input/output relationship in a data driven manner and obtain the trained AI/ML Model for inference.

(3) AI/ML model inference

A process of using a trained AI/ML model to produce a set of outputs based on a set of inputs.

(4) AI/ML model validation

As a sub-process of training, validation is used to evaluate the quality of an AI/ML model using a dataset different from the one used for model training. Validation can help selecting model parameters that generalize beyond the dataset used for model training. The model parameter after training can be adjusted further by the validation process.

(5) AI/ML model testing

Similar to validation, testing is also a sub-process of training, and it is used to evaluate the performance of a final AI/ML model using a dataset different from the one used for model training and validation. Different from AI/ML model validation, testing does not assume subsequent tuning of the model.

(6) Online training

Online training means an AI/ML training process where the model being used for inference is typically continuously trained in (near) real-time with the arrival of new training samples.

(7) Offline training:

Offline training is an AI/ML training process where the model is trained based on the collected dataset, and where the trained model is later used or delivered for inference.

(8) AI/ML model delivery/transfer

AI/ML model delivery/transfer is a generic term referring to delivery of an AI/ML model from one entity to another entity in any manner. Delivery of an AI/ML model over the air interface includes either parameters of a model structure known at the receiving end or a new model with parameters. Delivery may contain a full model or a partial model.

(9) Life cycle management (LCM)

When the AI/ML model is trained and/or inferred at one device, it is necessary to monitor and manage the whole AI/ML process to guarantee the performance gain obtained by AI/ML technologies. For example, due to the randomness of wireless channels and the mobility of UEs, the propagation environment of wireless signals changes frequently. Nevertheless, it is difficult for an AI/ML model to maintain optimal performance in all scenarios for all the time, and the performance may even deteriorate sharply in some scenarios. Therefore, the lifecycle management (LCM) of AI/ML models is essential for the sustainable operation of AI/ML in the NR air-interface.

Life cycle management covers the whole procedure of AI/ML technologies applied on one or more nodes. In specific, it includes at least one of the following sub-process: data collection, model training, model identification, model registration, model deployment, model configuration, model inference, model selection, model activation, deactivation, model switching, model fallback, model monitoring, model update, model transfer/delivery and UE capability report.

Model monitoring can be based on inference accuracy, including metrics related to intermediate key performance indicators (KPIs) , and it can also be based on system performance, including metrics related to system performance KPIs, e.g., accuracy and relevance, overhead, complexity (computation and memory cost) , latency (timeliness of monitoring result, from model failure to action) and power consumption. Moreover, data distribution may shift after deployment due to environmental changes, and thus the model based on input or output data distribution should also be considered.

(10) Supervised learning

The goal of supervised learning algorithms is to train a model that maps feature vectors (inputs) to labels (output) , based on the training data which includes the example feature-label pairs. The supervised learning can analyze the training data and produce an inferred function, which can be used for mapping the inference data.

(11) Federated learning (FL)

Federated learning is a machine learning technique that is used to train an AI/ML model by a central node (e.g., server) and a plurality of decentralized edge nodes (e.g., UEs, next Generation NodeBs, “gNBs” ) . The central node can also be called the central device. The edge nodes can also be called worker or worker devices. The central device is connected to the worker devices.

According to the wireless FL technique, a central node may provide, to an edge node, a set of model parameters (e.g., weights, biases, gradients) that describe a global AI/ML model. The edge node may initialize a local AI/ML model with the received global AI/ML model parameters. The edge node may then train the local AI/ML model using local data samples to, thereby, produce a trained local AI/ML model. The edge node may then provide, to the central node, a set of AI/ML model parameters that describe the local AI/ML model.

Upon receiving, from a plurality of edge nodes, a plurality of sets of AI/ML model parameters that describe respective local AI/ML models at the plurality of edge nodes, the central node may aggregate the local AI/ML model parameters reported from the plurality of edge nodes and, based on such aggregation, update the global AI/ML model. A subsequent iteration progresses much like the first iteration. The central node may transmit the aggregated global model to a plurality of edge nodes. The above procedure is performed multiple iterations until the global AI/ML model is considered to be finalized, for example, the AI/ML model is converged or the training stopping conditions are satisfied.

The wireless FL technique does not involve the exchange of local data samples. Indeed, the local data samples remain at respective edge nodes.

AI-based algorithms have been introduced into wireless communications to solve a number of wireless problems such as channel estimation, scheduling, CSI compression (from UE to BS) , beamforming for MIMO, localization, and so on. AI algorithms are a data-driven approach to tuning some predefined architectures by a set of data samples called training data sets.

Neural networks are a typical way to implement AI algorithms. Deep neural network (DNN) is taken as an example, the DNN can be trained with the training data sets to obtain a model for inference. Recent AI trains DNN architectures by setting up neurons with stochastic gradient descent (SGD) algorithms. For example, DNN includes CNN, RNN, transformers, and the like.

A communication system includes a plurality of connected devices. For example, a device may be a BS or UE. For example, the communication system may be the communication system 100 in FIG. 1 or FIG. 2, and the devices can be the network elements shown in FIG. 1 or FIG. 2.

FIG. 3 is a schematic structural diagram of a device according to an embodiment of the present application. As shown in FIG. 3, the device may include at least one of sensing module, communication module, or AI module. The sensing module may be configured to sense and collect signals and/or data. The communication module may be configured to transmit and receive signals and/or data. The AI module may be configured to train and/or reason the AI implementations.

In order to facilitate understanding of the embodiment of the present application, DNN is taken as an example to illustrate an AI implementation in an embodiment of the present application.

An exemplary AI implementation is DNN-based in two cycles: a training cycle and an inference cycle. The training cycle may also be called the learning cycle. The inference cycle may also be called the reasoning circle.

FIG. 4 is a schematic diagram of a device in two cycles according to an embodiment of the present application.

As an example, during an inference cycle, the AI module of the device may perform one inference or a series of inferences with one or more DNNs to fulfill one or more tasks, where the sensing module of the device may generate signals and/or data and the communication module of the device may receive the signals and/or data from other device or devices. For example, the inputs of the one or more DNNs may be the signals and/or data generated by the sensing module of the device, and/or the signals and/or data received by the communication module of the device. After the AI module of the device finishes inferencing, the communication module of the device may transmit the inferencing results to other device or devices.

As another example, during a training cycle, the AI module of the device may train one or more DNNs, where the sensing module of the device may generate signals and/or data and the communication module of the device may receive the signals and/or data from other device or devices. For example, the training data of the one or more DNNs may be the signals and/or data generated by the sensing module of the device, and/or the signals and/or data received by the communication module of the device. During and/or after the AI module finishes training, the communication module of the device may transmit the training results to other device or devices.

The AI implementations may either switch between the two cycles or stay in the two cycles simultaneously.

For example, the AI module of the device may train a DNN during the training cycle. And at the end of the training cycle, the AI implementation switches to the inference cycle, which means the AI module performs inference on that trained DNN. At the end of the inference cycle the AI implementation switches to the training cycle again, and so on.

For another example, the AI module of the device may train a second DNN but still perform inference on a first DNN.

The device mentioned above is merely an example, and the way in which the modules are divided and the number of modules in FIG. 3 and FIG. 4 do not constitute any limitation to the embodiments of the present application. For example, a communication module may be replaced by two modules, i.e., a transmitting module and a receiving module. The transmitting module may be configured to transmit signals and/or data, and the receiving module may be configured to receive signals and/or data. For another example, the sensing module and the communication module may be integrated as one module. For another example, the device may also include a processing module. The processing module may be configured to process signals and/or data. For another example, the device may not include the AI module. For another example, the AI module may only be configured to reason the AI implementation, or the AI module only stays in the inference cycle.

Wireless systems may support AI in both learning and inferencing cycles for generalization and interconnections.

FIG. 5 shows example local data of a device. The local data of a device may include at least one of the following: local sensing data provided by the sensing module of the device, local channel data provided by the communication module of the device, local AI model data provided by the AI module of the device, or local latent output data provided by the AI module of the device. The local channel data is based on the measurement results of the channel. The local channel data can also be considered as sensing results. Thus, the local channel data can be considered as provided by the communication modules or sensing module.

For example, as shown in FIG. 5, the local sensing data may include at least one of RGB data, Lidar data, temperature, air pressure, or electric outrage.

For example, as shown in FIG. 5, the local channel data may include at least one of channel state information (CSI) , received signal strength indication (RSSI) , or delay.

The local AI model data can also be referred to as neuron data. For example, as shown in FIG. 5, the local AI model data may include at least one of the following: part or all of the neurons in the local AI model (s) deployed on the device or part or all of gradients of the local AI model (s) deployed on the device. Neurons can be considered as functions including weights.

For example, as shown in FIG. 5, the local latent output data may include one or more latent outputs of the local AI model (s) deployed on the device.

A device may receive the local data of one or more other devices. As an example, the data received by the communication module of the device may include at least one of sensing data of one or more other devices, channel data of one or more other devices, AI model data of one or more other devices, or latent output data of one or more other devices.

For example, the data received by the communication module of device #Amay include channel data of device #B and device #C, and AI model data of device #C. The channel data of device #B and device #C refer to the local channel data of device #B and the local channel data of device #C. The AI model data of device #C refers to the local AI model data of device #C. Device #A, device #B, and device #C are different devices.

For example, sensing data received by the communication module may include at least one of RGB data, Lidar data, temperature, air pressure, or electric outrage.

For example, channel data received by the communication module may include at least one of CSI, RSSI, or delay.

For example, AI model data received by the communication module may include at least one of part or all of the neurons in the AI model (s) , or part or all of gradients of the AI model (s) .

For example, latent output data received by the communication module may include one or more latent outputs of the AI model (s) .

During the training cycle, the AI module of a device may work in a single user mode or cooperative mode.

In the single user mode, the AI module of a device may train the one or more local AI models with the local data of the device.

In the cooperative mode, the AI module of a device may train the one or more local AI models with the data received from the communication module of the device.

For example, the data received from the communication module of the device may be used by the AI module to train the local AI model (s) in the following ways.

Alternative #1: the sensing data received by the communication module of the device may be accumulated into one training data set for training the local AI model (s) .

Alternative #2: the channel data received by the communication module of the device may be accumulated into one training data set for training the local AI model (s) .

Alternative #3: part or all of the neurons in the local AI model (s) may be set based on the AI model data received by the communication module of the device. For example, in a federated learning mode, neurons of an AI model on one device may be set based on the neurons or gradients of the AI model (s) on other device (s) . Or, the gradients that the communication module of the device received may be used to update the neurons in the local AI model (s) .

Alternative #4: the latent outputs received by the communication module of the device may be inputted to its local AI model (s) . For example, when device #A and device #B work together to train a DNN, the device #A trains the first part of the DNN and the device #B trains the second part of the DNN. The device #A’s communication module transmits the latent output of the first part of the DNN to the device #B. The device #B receives the latent output of the first part and inputs the latent output to the second part of the DNN.

In addition, the local data of a device and the data received by the communication module of the device can be used together to train the local AI model (s) .

For example, the local data of a device and the data received by the communication module of the device can be used by the AI module to train the local AI model (s) in the following ways.

Alternative #1: the local sensing data provided by the sensing module of the device and the sensing data received by the communication module of the device may be mixed into one training data set for training the local AI model (s) .

Alternative #2: the local channel data provided by the sensing module of the device and the channel data received by the communication module of the device may be mixed into one training data set for training the local AI model (s) .

Alternative #3: part or all of the neurons in the local AI model (s) possessed by the AI module of the device and the corresponding neurons received by the communication module of the device may be averaged as the neurons in the updated local AI model (s) . Or, part or all of the gradients of the local AI model (s) possessed by the AI module of the device and the corresponding gradients received by the communication module of the device may be used to update the neurons in the local AI model (s) .

Alternative #4: the local latent outputs possessed by the AI module of the device and the latent outputs received by the communication module of the device may be averaged and inputted to its DNN (s) .

Whether the AI model deployed on a device can work is crucial for communication quality.

As data-driven method, an AI model inevitably suffers from low generalization. If a real-world sample, such as user data sample, is outlier to the training data set, the AI model wouldn’t make a good inference on the real-world sample. Moreover, even given an outlier input, the AI model may not detect it.

For example, in wireless communication, user device is moving. The AI model deployed on the user device may work in some environments, but may not work in others, which can affect the communication quality.

FIG. 6 is a schematic diagram of the working situation of an AI model.

As shown in FIG. 6, when the user data sample collected by the user device is within the zone of the training samples used to train the AI model, the AI model can work. As the user device moves, the user data sample collected by the user device may be outside the zone of the training samples, and the AI model doesn’t work.

In wireless communication, AI models deployed on different devices may need to work together. Dual sided model is taken as an example. Dual sided model may be in a form of AE, whose encoding DNN is on transmitter side and decoding DNN on receiver side. It is likely that the encoding DNN and decoding DNN are trained and provided by different providers. Moreover, it is hard for AI providers to open their DNN models. This may result in the AI models not working together.

FIG. 7 is a schematic diagram of an example scenario.

As shown in FIG. 7, an encoder deployed on UE and a decoder deployed on BS need to work together. However, the encoder and the decoder may be trained independently by different providers, e.g. provider #1 and provider #2 in FIG. 7, which may affect their interconnection.

The embodiment of the present application provides a communication method that ensures that the AI model can work by detecting the difference between at least two items in the latent layer, input layer, and output layer, thereby improving the communication performance.

FIG. 8 is a schematic flowchart of a communication method provided by the embodiments of the present application.

As shown in FIG. 8, a method 800 includes the following steps.

Step 810, a first network element receives information #1 from a second network element.

Step 820, the first network element measures one or more metrics of a first AI model according to the information #1.

The one or more metrics of the first AI model may be related to P latent layer (s) of the first AI model. The P latent layer (s) may include p latent layer (s) of the first AI model and/or p’ latent layer (s) of the first AI model. P is a positive integer.

The one or more metrics of the first AI model are based on at least one of the following: the difference (s) between the distribution (s) of output data of the p latent layer (s) of the first AI model and the distribution (s) of input data of the first AI model, or the difference (s) between the distribution (s) of output data of p’ latent layer (s) and the distribution of output data of the first AI model. p and p’ are positive integers.

The difference between the two in the embodiment of the present application can also be understood as the distance between the two. For example, the difference (s) between the distribution (s) of output data of the p latent layer (s) of the first AI model and the distribution (s) of input data of the first AI model can also be referred to as the distance (s) between the distribution (s) of output data of the p latent layer (s) of the first AI model and the distribution (s) of input data of the first AI model.

For example, the first network element may be the device in FIG. 3. The communication module of the first network element may receive the information #1. The AI module of the first network element may perform the step 820.

For example, the first network element may be a terminal device or a network device.

For example, the second network element may be the device in FIG. 3. The communication module of the second network element may transmit the information #1.

For example, the second network element may be a network device or a terminal device.

The “first AI model” is only used as an example to describe embodiments of the present application, and the “first” in the “first AI model” has no other limiting effect.

The first AI model can be a neural network model, such as a deep neural network (DNN) model.

The one or more metrics can be obtained through one or more measurements.

A metric is related to a latent layer.

For example, the metric may be in function of the latent layer.

During the inference cycle, the AI module of a device may work in a single user mode or cooperative mode. In both modes, the device may calculate the one or more metrics. The one or more metrics may be calculated during one inference cycle. During one inference cycle of an AI model, the parameters of the AI model remain unchanged.

The first AI model may include M latent layer (s) . M is a positive integer. M≥m. The m latent layer (s) belongs to the M latent layer (s) . The M latent layer (s) during one inference cycle may be denoted as T= [T₁, T₂, ... T_M] . T represents the output (s) of the M latent layer (s) . T₁ represents the output of the first latent layer among the M latent layer (s) , T₂ represents the output of the second latent layer among the M latent layer (s) , and so on. The elements in T can also be arranged in other order. The embodiments of the present application do not limit this. For the convenience of description, T mentioned above is taken as an example in the embodiments of the present application.

The one or more metric (s) can be related to the mutual information.

During one inference cycle, a latent layer in the P latent layer (s) corresponds to at least one metric.

The one or more metrics include at least one metric corresponding to a first latent layer among the P latent layer (s) . The at least one metric corresponding to the first latent layer includes at least one of a first metric corresponding to the first latent layer, a second metric corresponding to the first latent layer or a third metric corresponding to the first latent layer.

The first latent layer may belong to the p latent layer (s) . The first metric corresponding to the first latent layer is based on a distance between the distribution of the inputs of the first AI model and distribution of outputs of the first latent layer.

The first latent layer may belong to the p’ latent layer (s) . The second metric corresponding to the first latent layer is based on a distance between the distribution of the outputs of the first AI model and the distribution of the outputs of the first latent layer.

The first latent layer may belong to the p latent layer (s) and the p’ latent layer (s) . The third metric corresponding to the first latent layer is based on a ratio between the first metric and the second metric.

The “first latent layer” mentioned above is only used to describe the at least one metric corresponding to one latent layer, and does not limit the position or order of the latent layer among M latent layer (s) . The “first latent layer” can be any of the latent layer (s) . For example, the “first latent layer” mentioned above can be the m-th latent layer. 1≤m≤M. m is an integer.

Optionally, the at least one metric on one latent layer (e.g. the m-th latent layer) may include at least one of the following.

(1) metric #1 (an example of the first metric) corresponding to one latent layer may be the distance between the distribution of the inputs to the AI model and the distribution of the latent layer’s outputs.

The m-th latent layer is taken as an example, the metric #1 corresponding to the m-th latent layer may be the distance between distribution of the inputs to the AI model and the distribution of the m-th latent layer’s outputs. m is an integer. The metric #1 corresponding to the m-th latent layer can be denoted as δ₁ (T_m, X) . X is the inputs to the AI model. T_m is the m-th latent layer’s outputs.

(2) metric #2 (an example of the second metric) corresponding to one latent layer may be the distance between the distribution of the latent layer’s outputs and the distribution of the outputs from the AI model.

The m-th latent layer is taken as an example, the metric #2 corresponding to the m-th latent layer may be the distance between the distribution of the m-th latent layer’s outputs and the distribution of the outputs from the AI model. The metric #2 corresponding to the m-th latent layer can be denoted as δ₂ (T_m, Y) . Y is the outputs from the AI model.

(3) metric #3 (an example of the third metric) corresponding to one latent layer may be the ratio between metric #1 corresponding to the latent layer and metric #2 corresponding to the latent layer.

The metric #3 may also be called the metric ratio.

The m-th latent layer is taken as an example. For example, the metric #3 corresponding to the m-th latent layer may be denoted asFor another example, the metric #3 corresponding to the m-th latent layer may be denoted asFor the convenience of description, is taken as an example in the embodiments of the present application for explanation.

FIG. 9 shows a schematic diagram of three example metrics.

As shown in FIG. 9, for example, the first AI model may be an autoencoder (AE) , and the m-th latent layer’s outputs may be the outputs of the encoder in the AE, that is, the inputs of the decoder in the AE, X_latent. The AE may satisfy the following formula:
X_latent=f (X_in, γ) ;

f () represents the encoder of the AE, and γ represents the parameters of the encoderf () . g () represents the decoder of the AE, andrepresents the parameters of the decoderg () . X_in represents the inputs to the AE and the X_out represents the outputs from the AE. For ease of description, the distribution of outputs and outputs are represented by the same letter, while the distribution of inputs and inputs are represented by the same letter. For example, X_in may also represent the distribution of the inputs to the AE. The metric #1 corresponding to the m-th latent layer can be denoted as δ₁ (X_latent, X_in) . The metric #2 corresponding to the m-th latent layer can be denoted as δ₂ (X_latent, X_out) . The metric #3 corresponding to the m-th latent layer can be denoted as

The distance involved in the metric mentioned above can be calculated with methods that can be used to approximate mutual information.

For example, mutual information can be approximated by HSIC, JSD, KL, and so on. Correspondingly, the distance above can be calculated by HSIC, JSD, KL, and so on.

In this way, the one or more metrics of the first AI model can be used to determine whether an inference cycle of the first AI model is normal.

The one or more metrics of the first AI model can be used to perform checking. Performing checking may include checking whether the first AI model can work as expected, checking whether the distance meets the expectation; checking whether the distance meets the conditions, checking whether the distance is within the predefined range, checking whether the first AI model meets expectations, checking whether the inference cycle of the first AI model is normal, or checking whether the first AI model can work with the other AI model (s) . For ease of description, the other AI model (s) in the embodiments of the present application can be referred to as the second AI model.

The following are some examples of the uses of the one or more metrics in different scenarios.

If the inference cycle of the first AI model is not normal, it may be damaged, or it may not be suitable for the current data, for example, the first AI model may be outdated. The abnormal inference cycle of the AI first model may lead to incorrect inference results, which may affect the relevant data processing results or data transmission quality.

In some embodiments, the one or more metrics of the first AI model can be used to determine whether the inference cycle of the first AI model is normal.

According to information bottleneck theory, the mutual information between the distribution of the inputs to the AI model and the distribution of the latent layer’s outputs decreases over the layers, and the mutual information between the distribution of the outputs from the AI model and the distribution of the latent layer’s outputs increases over the layers. In other words, the closer the latent layer is to the input layer of the AI model, the greater the mutual information between the distribution of its outputs and the distribution of the inputs to the AI model, and the closer the latent layer is to the output layer of the AI model, the greater the mutual information between the distribution of its outputs and the distribution of the outputs from the AI model.

For a normal inference cycle of an AI model, the metric #1 decreases over the layers: δ₁ (T_m+1, X) ≤δ₁ (T_m, X) . δ₁ (T_m+1, X) represents metric #1 corresponding to the (m+1) -th latent layer.

For a normal inference cycle, the metric #2 increases over the layers: δ₂ (T_m+1, Y) ≥δ₂ (T_m, Y) . δ₂ (T_m+1, Y) represents metric #2 corresponding to the (m+1) -th latent layer.

Therefore, if the inference cycle is normal, the metric #3 may be decreasing over the layers, such as ρ₁＜ρ₂＜... ＜ρ_M. ρ₁ represents the metric #3 corresponding to the first latent layer, ρ₂ represents the metric #3 corresponding to the second latent layer, and so on.

The conditions for determining whether an inference cycle is normal can be set according to the above trends.

If the one or more metrics don’t match one or more of the above trends, it is possible that the inference cycle is abnormal.

For example, as long as the one or more metrics do not meet one of the trends mentioned above, the inference cycle can be considered abnormal.

For another example, if the one or more metrics do not meet all the above trends, the inference cycle can be considered abnormal.

Correspondingly, for the first AI model, when the inference cycle of the first AI model is abnormal, one or more of the following may not be met:

first metrics in the one or more metrics of the first AI model decrease as indexes of corresponding latent layers increase;

second metrics in the one or more metrics of the first AI model increase as indexes of corresponding latent layers increase; or

third metrics in the one or more metrics of the first AI model decrease as indexes of corresponding latent layers increase.

Basically, if a method for approximating mutual information doesn’t change the tendencies above, it can be used as the method for computing the distance involved in the metric (s) .

In the embodiments of the present application, the one or more metrics can be used to check whether the current inference is normal. For example, if the inference is abnormal, adjustments can be made in a timely manner, such as switching to other AI models or switching to non-AI methods, which is conducive to ensuring the quality of data processing or communication.

In some scenarios, a plurality of AI models that need to work together.

In some embodiments, the one or more metrics can be used to check whether a plurality of AI models that need to work together can work together. In other words, the one or more metrics can be used to check the interconnection or cross consistency of the AI models.

Optionally, the one or more metrics of the first AI model can be used to check whether the first AI model can work with a second AI model. The second AI model may be an AI model that needs to work with the first AI model.

The closer the metrics corresponding to the same latent layers of two AI models are, the higher the likelihood that the two AI models can work together.

For example, for two AI models with the same structure, the closer the metric corresponding to the m-th latent layer of one AI model is to the metric corresponding to the m-th latent layer of the other AI model, the higher the possibility that the two AI models can work together, that is, the output of the m-th latent layer of one AI model can be used as the input of the (m+1) -th latent layer of the other AI model.

The conditions based on the one or more metrics for determining whether the AI models can work together can be set as needed.

The difference (s) between the one or more metrics of the first AI model and corresponding one or more reference metrics may be used to check interconnection. The one or more reference metrics may be the same or similar to one or more metrics of the second AI model deployed on other device (s) .

Optionally, if the difference (s) between the one or more metrics of the first AI model and corresponding one or more reference metrics are less than or equal to one or more thresholds, the first AI model deployed on the first network element may be able to work with the second AI model deployed on other device (s) .

One metric corresponding to one latent layer is taken as an example, if the difference between the metrics of that latent layer in the first AI model and the second AI model is less than or equal to a threshold, the two AI models can be considered to be able to work together. For example, if the difference between the metric#3 corresponding to m-th latent layer in the first AI model and the reference metric#3 is less than or equal to a threshold, the first AI model and the second AI model may be able to work together. The reference metric#3 may be the metric#3 corresponding to m-th latent layer in the second AI model.

Each metric in the one or more metrics of the first AI model may correspond to one threshold. The thresholds corresponding to different metrics can be the same or different.

When there are a plurality of metrics, for example, if the differences between the metrics of the first AI model and the corresponding reference metrics are less than or equal to the corresponding thresholds, respectively, the first AI model and the second AI model may be able to work together. The corresponding reference metrics may be the same or similar to one or more metrics corresponding to the P latent layer (s) of the second AI model.

Two metrics corresponding to one latent layer are taken as examples, for example, if the difference between the metric#3 corresponding to m-th latent layer in the first AI model and the corresponding reference metric #3 is less than or equal to threshold #1, and the difference between the metric#2 corresponding to m-th latent layer in the first AI model and the corresponding reference metric #2 is less than or equal to threshold #2, the first AI model and the second AI model may be able to work together. The threshold #1 and the threshold #2 can be the same or different. The corresponding reference metric #3 may be the metric #3 corresponding to m-th latent layer in the second AI model. The corresponding reference metric #2 may be the metric #2 corresponding to m-th latent layer in the second AI model.

When there are a plurality of metrics, for example, if the statistical value of the differences between the metrics of the first AI model and the corresponding reference metrics is less than or equal to the threshold, the first AI model and the second AI model may be able to work together. The corresponding reference metrics may be the metrics corresponding to P latent layer (s) of the second AI model.

The statistical value of the differences may be the average of the differences, the sum of the differences, the maximum or minimum value of the differences, etc.

Two metrics corresponding to one latent layer are taken as examples. For example, if the average of the difference between the metric #3 corresponding to m-th latent layer in the first AI model and the corresponding reference metric #3 and the difference between the metrics #2 corresponding to m-th latent layer in the first AI model and the corresponding reference metric #2 is less than or equal to a threshold, the first AI model and the second AI model may be able to work together. The corresponding reference metric #3 may be the metric #3 corresponding to m-th latent layer in the second AI model. The corresponding reference metric #2 may be the metric #2 corresponding to m-th latent layer in the second AI model.

Whether the one or more metrics of the first AI model are within one or more ranges may be used to check interconnection.

The one or more ranges may be related to the one or more metrics of the second AI model deployed on other device (s) . For example, the one or more ranges may include range #Awhich is related to metric #1 of the second AI model deployed on other device (s) . The metric #1 may be a, and the range #Amay be (a-b, a+b) . a and b are positive numbers.

Optionally, if the one or more metrics of the first AI model are within the corresponding range, the first AI model deployed on the first network element may be able to work with the second AI model deployed on other device (s) .

The conditions for checking interconnection based on the relationship between the one or more metrics of the first AI model and the one or more ranges can refer to the previous text. That is, the relationship between the difference (s) between the one or more metrics of the first AI model and the corresponding reference metric and the threshold (s) in the previous text can be replaced by the relationship between the one or more metrics of the first AI model and the range (s) to set the judgment conditions. To avoid repetition, it will not be repeated here.

The above conditions are only examples, and the embodiments of the present application do not limit the conditions for performing interconnection checks based on the one or more metrics of the first AI model.

In the embodiments of the present application, the one or more metrics can be used to check whether a plurality of AI models can work together, which is conducive to ensuring the quality of data processing or communication.

In some scenarios, as the first network element moves, the local data collected by the first network element may be outside the zone of the training samples, statistically outliers, and the AI model doesn’t work.

In some embodiments, the one or more metrics of the first AI model can be used to check whether the first AI model can work. In other words, the one or more metrics of the first AI model can be used to check generalization of the first AI model.

Checking generalization and interconnection can be done in a similar way. The conditions for checking generalization can refer to the conditions for checking interconnection mentioned above.

The difference (s) between the one or more metrics of the first AI model and corresponding one or more reference metrics may be used to check generalization.

Optionally, if the difference (s) between the one or more metrics of the first AI model and corresponding one or more reference metrics are less than or equal to one or more thresholds, the first AI model deployed on the first network element may be able to work.

Whether the one or more metrics of the first AI model are within one or more ranges may be used to check generalization.

Optionally, if the one or more metrics are within the corresponding range, the first AI model deployed on the first network element may be able to work.

In the embodiments of the present application, the one or more metrics of the first AI model can be used to check whether the first AI model can work, which is conducive to ensuring the quality of data processing or communication.

In step 810, the second network element may send information #1 in broadcast, multicast, or unicast way.

The step 810 can be an optional step. For example, the first network element itself may determine to perform step 820.

Further, optionally, the first network element may store the one or more metrics measured by the first network element in step 820.

For example, the first network element may store the one or more metrics as a function of the latent layers.

Further, optionally, the method 800 may also include step 830.

S830, the first network element sends information #2 (an example of the first information) to the second network element according to the one or more metrics of the first AI model.

Optionally, the information #2 may be used to indicate the one or more metrics.

For example, the communication module of the first network element may send the information #2.

The first network element may send information #2 to other devices.

The following describes an exemplary explanation of method 800 based on three application scenarios (Scenario-1 to Scenario-3) .

Scenario-1

Optionally, the method 800 can be used to detect whether the inference cycle of the first AI model is normal.

In some embodiments, the information #1 may be used to trigger the measurement.

The first network element receives the information #1, and then measures the one or more metrics of the first AI model.

For example, the information #1 may be used to indicate the first network element to measure the one or more metrics.

For another example, the information #1 may be used to indicate the first network element to send the one or more metrics.

For another example, the information #1 may be used to indicate the first network element to check whether the inference cycle of the first AI model is normal.

The information#1 may indicate checking whether the inference cycle of the first AI model is normal with the one or more metrics of the first AI model. Alternatively, it may be predefined to use the one or more metrics of the first AI model to check whether the inference cycle of the first AI model is normal. Alternatively, the first network element may decide to use the one or more metrics of the first AI model to check whether the inference cycle of the first AI model is normal.

The first network element receives the information #1. Then the first network element measures the one or more metrics of the first AI model, and checks whether the inference cycle of the first AI model is normal according to the one or more metrics.

For another example, the information #1 may be used to indicate the first network element to send the check result of the inference cycle of the first AI model.

In some embodiments, the information #1 (an example of the second information) may be used to indicate at least one of the following: one or more latent layers related to S metric (s) , the one or more methods for measuring the S metric (s) , one or more types of the S metric (s) . S is a positive integer.

The method for measuring a metric may include the method for calculating the distance mentioned above, such as HSIC, JSD, KL, and so on.

The type of the metric may include the metric #1, metric #2, and/or metric #3.

Exemplarily, the first network element may receive a message that asks for measuring S metric (s) , which specifies on which layer (s) in which time period (s) to measure which metric (s) in which method (s) .

For example, the message may ask for measuring metric #3 corresponding to m-th latent layer, where the distance involved in measuring the metric #3 is calculated using KL. Further, the first network element may receive a message that asks for measuring S metric (s) in which time period (s) .

The above items that are not indicated by information #1 can be indicated by other information sent by the second network element, pre-configured, determined by the first network element itself, and/or predefined. Alternatively, all of the above items can be determined by the first network element itself, and/or predefined.

Exemplarily, the AI module of the first network element may follow the information#1 to perform the measurement and computations on its first AI model.

In this case, the one or more metrics measured by the first network element are the S metrics indicated by the information #1.

In addition, the first network element can also perform measurement without following the items requested by the information#1. In other words, the one or more metrics measured by the first network element in step 820 may differ from the S metric (s) indicated by the information #1.

For example, the information #1 may ask for measuring metric (s) #3 corresponding to P’ latent layer (s) , where the distance (s) involved in measuring the metric (s) #3 is calculated using KL. The first network element may measure metric (s) #3 corresponding to the P latent layer (s) , where the distance (s) involved in measuring the metric (s) #3 is calculated using KL. P’is a positive integer. The P latent layer (s) may be some of the P’ latent layer (s) .

The forms of information#1 mentioned above are examples, and do not limit the form of information #1.

The step 810 can be an optional step in Scenario-1. For example, the first network element may determine to perform the step 820 by the first network element itself.

Further, optionally, the method 800 may also include: checking whether the inference cycle of the first AI model is normal with the one or more metrics of the first AI model measured by the first network element in step 820.

The AI module of the first network element may do the statistics on the accumulated metrics to check if the metrics satisfy the decreasing or increasing properties above. If the AI module of the first network element suspects an abnormal decrease or increase of the metrics, it may decide that the inference cycle of the first AI model is abnormal. The AI module of the first network element may raise an alarming message.

For example, the first network element may measure metrics #3 corresponding to a plurality of latent layers in the first AI model. If the metrics #3 don’t satisfyρ₁＜ρ₂＜... ＜ρ_M, the first network element may decide that the inference cycle is abnormal.

The above condition is only an example. The conditions for determining whether inference cycle of the first AI model is normal can be set as needed.

Further, optionally, the method 800 may also include step 830.

S830, the first network element sends information #2 indicating the one or more metrics of the first AI model to the second network element.

In some embodiments, the information #2 may include the one or more metrics of the first AI model measured by the first network element.

Optionally, the first network element may report the one or more metrics of the first AI model when the measurement is completed.

The first network element may keep reporting the one or more metrics of the first AI model to the second network element.

For example, the first network element may report the one or more metrics of the first AI model in the requested periods.

For example, the first network element may periodically report information #2. Or the first network element may report information #2 in response to the information #1.

Alternatively, the first network element may report the one or more metrics of the first AI model if the inference cycle of the first AI model is abnormal.

In some cases, the second network element may not be aware of the items related to the one or more metrics of the first AI model, such as the latent layer (s) corresponding to the one or more metrics of the first AI model.

For example, the first network element may not follow the items indicated by the information #1 to perform the measurements. Or, the information #1 may be used to trigger the first network element to perform the measurements.

In the above cases, the first network element may send information indicating the items related to the one or more metrics of the first AI model.

For example, the first network element may send information indicating on which layer (s) , and in which method (s) which metric (s) is measured.

The information #2 may include some or all of the one or more metrics of the first AI model.

For example, the first network element may report the metrics that the AI module judges as abnormal.

In some embodiments, the information #2 may indicate other content related to the one or more metrics of the first AI model.

For another example, there may be multiple ranges. Each range corresponds to a level. The information #2 may indicate the level (s) corresponding to the range (s) to which the one or more metrics of the first AI model belong.

For another example, if the first network element detects whether the inference cycle of the first AI model is normal, the information #2 may indicate whether the inference cycle of the first AI model is normal.

If the first network element reports the one or more metrics of the first AI model to the second network element, the second network element can determine whether the inference cycle of the first AI model is normal with the one or more metrics of the first AI model.

Further, if the inference cycle of the current first AI model deployed on the first network element is abnormal, the current first AI model may be replaced. For example, the current first AI model may be switched to other AI models. Alternatively, the current first AI model may be replaced by a non-AI model.

The switched model can be configured by the second network element.

Alternatively, the switched model can also be determined by the first network element and notified to the second network element.

The following describes an example scenario.

In some scenarios, a plurality of AI models deployed on different devices may need to work together.

If one of these AI models cannot perform an inference normally, these AI models cannot work together.

For example, an encoder and a decoder deployed on different devices may need to work together. The encoder can be deployed on the transmitter side and the decoder can be deployed on the receiver side. The transmitter side is an encoding device. The receiver side is a decoding device. The encoder of the encoding device may output to the decoder of the decoding device.

Optionally, method 800 may be applied to check whether the inference cycle of encoder or the decoder deployed on the first network element is normal.

The following takes a DNN-based autoencoder as an example. The encoder can be an encoding DNN and the decoder can be a decoding DNN.

There are two devices, i.e. device #1 and device #2. For example, the device #1 may include the modules shown in FIG. 3, where the sensing module may be used to collect the local data, the AI module may be used to perform inference on its local data with encoding DNN #1 in the AE #1, and the communication module may be used to receive signals and/or data and transmit signals and/or data. The device #2 may include the modules shown in FIG. 3, where the sensing module may be used to collect the local data, the AI module may be used to perform inference on the data received from the encoding DNN on other device with decoding DNN #2 in the AE #2, and the communication module may be used to receive signals and/or data and transmit signals and/or data.

The encoding DNN on the device #1 need to work with the decoding DNN on the device #2. The metric can be used to determine whether the inference cycle of encoding DNN is normal. Or the metric can be used to determine whether the inference cycle of decoding DNN is normal.

Exemplarily, the device #1 can be the first network element, and the device #2 can be the second network element. Alternatively, the device #1 can be the second network element, and the device #2 can be the first network element.

Taking device #1 as the first network element as an example.

The AI module of the device #1 may measure the one or more metrics of AE #1.

Further, the AI module of the device #1 may memorize the one or more metrics of AE #1.

The above process corresponds to step 820, and the specific description can refer to step 820, which will not be repeated here.

In some implementations, the device #2 may send information #1 to the device #1. The device #1 may measure the one or more metrics of AE #1 according to the information #1. In this case, the device #2 can be considered as the second network element.

For example, the communication module of the device #2 may send information #1 to the device #1 to ask the device #1 to perform the measurement and feedback the metrics.

The above process corresponds to step 810, and the specific description can refer to step 810, which will not be repeated here.

Further, the AI module of the device #1 may check whether the inference cycle of AE #1 is normal with the one or more metrics of AE #1.

Further, the communication module of the device #1 may transmit the one or more metrics of AE #1 to the device #2.

Alternatively, the communication module of the device #1 may transmit the metric (s) that the AI module of the device #1 judges as abnormal to the device #2.

The above process corresponds to step 830, and the specific description can refer to step 830, which will not be repeated here.

The above is only an example process of the application of the technical solutions in the present application embodiments to inference cycle check. The technical solutions in the present application embodiments can also be implemented in other ways when it is applied to inference cycle check, and the related description can refer to method 800, which will not be repeated here.

Scenario-2

Optionally, the method 800 can be used to check AI model generalization. In other words, the method 800 can be used to check whether the first AI model can work.

In some embodiments, the information #1 may be used to trigger the measurement.

For another example, the information #1 may be used to indicate the first network element to check whether the first AI model can work.

The information#1 may indicate checking whether the first AI model can work with the one or more metrics of the first AI model. Alternatively, it may be predefined to use the one or more metrics of the first AI model to check whether the first AI model can work. Alternatively, the first network element may decide to use the one or more metrics of the first AI model to check whether the first AI model can work.

The first network element receives the information #1. Then the first network element measures the one or more metrics of the first AI model, and check whether the first AI model can work according to the one or more metrics of the first AI model.

For another example, the information #1 may be used to indicate the first network element to send the check result.

In some embodiments, the information #1 may be used to indicate at least one of the following: one or more latent layers related to S metric (s) , the one or more methods for measuring the S metric (s) , one or more types of the S metric (s) . S is a positive integer.

In addition, the first network element can also measure without following the items requested by the information#1. In other words, the one or more metrics of the first AI model measured by the first network element in step 820 may be differ from the S metric (s) indicated by the information #1.

The relevant description of the information #1 can be referred to Scenario-1, and will not be repeated here.

The step 810 can be an optional step in Scenario-2. For example, the first network element may determine to perform the step 820 by the first network element itself.

Further, optionally, the method 800 may also include: checking whether the first AI model can work with the one or more metrics of the first AI model measured by the first network element in step 820.

The AI module of the first network element may check if the metrics satisfy the conditions above. If the AI module of the first network element suspects the one or more metrics of the first AI model do not meet the conditions above, it may decide that the first AI model cannot work.

For example, the first network element may measure metrics #3 corresponding to the m-th latent layer in the first AI model. If the metrics #3 is outside the range, the first network element may decide that the first AI model cannot work.

The above condition is only an example. The conditions for determining whether the first AI model can work can be set as needed.

Further, optionally, the method 800 may also include step 830.

Alternatively, the first network element may report the one or more metrics of the first AI model if the first AI model cannot work.

Other examples of the information #2 include the one or more metrics of the first AI model can also refer to Scenario-1, which will not be repeated here.

For example, if the first network element detects whether the first AI model cannot work, the information #2 may indicate that the first AI model cannot work.

If the first network element reports the one or more metrics of the first AI model to the second network element, it can also be performed by the second network element to determine whether the first AI model can work with the one or more metrics of the first AI model.

Further, if the current first AI model deployed on the first network element cannot work, the current first AI model may be replaced. For example, the current first AI model may be switched to other AI models. Alternatively, the current first AI model may be replaced by a non-AI model.

The following describes an example scenario.

A dual sided model including encoder and decoder deployed on different devices is taken as an example. The encoder or decoder may be deployed on the first network element and work together with the opposing decoder or encoder. The following is an example of deploying an encoder on the first network element for explanation. The encoder deployed on the first network element is a part of an AE. The output of the encoder can be considered as the output of a latent layer of the AE.As the first network element moves, the encoder on the first network element may not work. The method 800 can be used to check whether the current encoder on the first network element can work.

The first network element may be a terminal device and the second network element may be a network device. Due to limited AI/ML capability supporting a large AI model on the first network element side, it may not be possible to deploy a large encoder. There may be multiple candidate encoders, and the optimal encoder may depend on the location of the first network element.

For example, the second network element may configure the candidate AI models to the first network element. The second network element may send the candidate AI models by broadcast.

Further, the second network element may also configure latent layer (s) associated to a candidate AI model, such as {model index, latent layer (s) } . The latent layer (s) can be used to calculate metric (s) .

The first network element may measure the one or more metrics of the candidate AI models. The candidate AI models can be considered as the first AI model.

The first network element may report the metrics to the second network element. The second network element may determine the optimal AI model from the candidate AI models according to the metrics. Then the second network element may configure the optimal AI model to the first network element. For example, the second network element may indicate the index of the optimal AI model to the first network element.

Alternatively, the optimal AI model can be determined by the first network element. The first network element may inform the optimal AI model to the second network element.

The first network element may communicate with the second network element based on the optimal AI model.

The first network element may keep reporting its metric (s) on selected latent layer (s) .

Alternatively, the first network element may report its metric (s) when the metric (s) is outside the corresponding range.

The second network element may indicate switching the first AI model on the first network element when the metric (s) is outside the corresponding range.

Alternatively, the first network element may determine switching the first AI model when the metric (s) is outside the corresponding range and inform the second network element the AI model after switching.

Scenario-3

Optionally, the method 800 can be used to check the interconnection of a plurality of AI models. In other words, the method 800 can be used to check whether the first AI model can work with the second AI model.

In some embodiments, the information #1 may be used to trigger the measurement.

For another example, the information #1 may be used to indicate the first network element to check whether the first AI model can work with the second AI model.

The information#1 may indicate checking whether the first AI model can work with the second AI model by the one or more metrics of the first AI model. Alternatively, it may be predefined to use the one or more metrics of the first AI model to check whether the first AI model can work with the second AI model. Alternatively, the first network element may decide to use the one or more metrics of the first AI model to check whether the first AI model can work with the second AI model.

The first network element receives the information #1. Then the first network element measures the one or more metrics of the first AI model, and check whether the first AI model can work with the second AI model according to the one or more metrics of the first AI model.

In this case, the one or more metrics of the first AI model measured by the first network element are the S metrics indicated by the information #1.

Further, optionally, the method 800 may also include: checking whether the first AI model can work with the second AI model by the one or more metrics of the first AI model measured by the first network element in step 820.

The AI module of the first network element may check if the metrics satisfy the conditions above. If the AI module of the first network element suspects the one or more metrics of the first AI model do not meet the conditions above, it may decide that the first AI model cannot work with the second AI model.

For example, the first network element may measure metrics #3 corresponding to the m-th latent layer of the first AI model. If the difference between the metrics #3 corresponding to the m-th latent layer of the first AI model and the metrics #3 corresponding to the m-th latent layer of the second AI model is greater than the threshold, the first network element may decide that the first AI model cannot work the second AI model.

The above condition is only an example. The conditions for determining whether the first AI model can work with the second AI model can be set as needed.

Further, optionally, the method 800 may also include step 830.

Alternatively, the first network element may report the one or more metrics of the first AI model if the first AI model cannot work with the second AI model.

For example, if the first network element detects whether the first AI model cannot work with the second AI model, the information #2 may indicate that the first AI model cannot work with the second AI model.

If the first network element reports the one or more metrics of the first AI model to the second network element, it can also be performed by the second network element to determine whether the first AI model can work with the second AI model by the one or more metrics of the first AI model.

The following describes an example scenario.

In some scenarios, a plurality of AI models deployed on different devices may need to work together. These AI models may be trained independently by different providers.

Optionally, method 800 may be applied to check whether the encoder and the decoder deployed on different devices can work together.

There are two devices, i.e. device #1 and device #2. For example, the device #1 may include the modules shown in FIG. 3, where the sensing module may be used to collect the local data, AI module may be used to perform inference on an its local data with encoding DNN #1 in the AE #1, and communication module may be used to receive signals and/or data and transmit signals and/or data. The device #2 may include the modules shown in FIG. 3, where the sensing module may be used to collect the local data, AI module may be used to perform inference on the data received from the encoding DNN on other device with decoding DNN #2 in the AE #2, and communication module may be used to receive signals and/or data and transmit signals and/or data.

The encoding DNN on the device #1 need to work with the decoding DNN on the device #2. The metric can be used to determine whether the AI models on two devices can work together.

The AI module of the device #1 may measure the one or more metrics corresponding to the P latent layer (s) of AE #1 (an example of the first AI model) .

The AI module of the device #2 may measure the one or more metrics corresponding to the P latent layer (s) of AE #2 (an example of the second AI model) .

Further, the communication module of the device #2 may send the one or more metrics corresponding to the P latent layer (s) of AE #2 to the device #1.

Further, the communication module of the device #1 may transmit the one or more metrics corresponding to the P latent layer (s) of AE #1 to the device #2.

Optionally, the communication module of the device #1 may transmit the check result indicating whether the encoder #1 in the AE #1 can work with decoder #2 in the AE #2.

If the communication module of the device #1 transmits the one or more metrics corresponding to the P latent layer (s) of AE #1 to the device #2, device #2 may check whether the encoder #1 in the AE #1 can work with decoder #2 in the AE #2.

FIG. 10 shows a schematic diagram of example interconnection check.

f₁ () represents the encoder of the AE #1 in device #1. γ₁ represents parameters of the encoder f₁ () . g₁ () represents the decoder of the AE #1, andrepresents parameters of the decoder g₁ () . The output of the encoder is the input of the decoder.

X_in1 represents the inputs to the AE #1 and the X_out1 represents the outputs from the AE #1. The metric #1 corresponding to a latent layer latent1 can be denoted as δ₁ (X_latent1, X_in1) . The metric #2 corresponding to the latent layer latent1 can be denoted as δ₂ (X_latent1, X_out1) . The metric #3 corresponding to the latent layer latent1 can be denoted as

f₂ () represents the encoder of the autoencoder #2 in device #2. γ₂ represents parameters of the encoder f₂ () . g₂ () represents the decoder of the autoencoder #2, andrepresents parameters of the decoder g₂ () . The output of the encoder is the input of the decoder.

X_in2 represents the inputs to the AE #2 and the X_out2 represents the outputs from the AE #2. The metric #1 corresponding to a latent layer latent2 can be denoted as δ₁ (X_latent2, X_in2) . The metric #2 corresponding to the latent layer latent2 can be denoted as δ₂ (X_latent2, X_out2) . The metric #3 corresponding to the latent layer latent2 can be denoted as

The AI module of the device #1 measuresThe AI module of the device #2 measures

The device #1 may receive theand check whether the encoder #1 can work with decoder #2 according to the difference between theand

Alternatively, the device #2 may receive theand check whether the encoder #1 can work with decoder #2 according to the difference between theand

The above is only an example process of the application of the technical solutions in the present application embodiments to interconnection check. The technical solutions in the present application embodiments can also be implemented in other ways when it is applied to interconnection check, and the related description can refer to method 800, which will not be repeated here.

The transmission process in scenario-1, scenario-2 and example scenario-3 are merely examples. For other implementation methods, please refer to method 800.

The communication method according to the embodiments of the present application is described in detail above, and the communication apparatus according to the embodiments of the present application will be described in detail below with reference to FIGS. 11-15.

FIG. 11 is a schematic block diagram of a communication apparatus 10 according to an embodiment of the present application. As shown in FIG. 11, the communication apparatus 10 includes:

a transceiver module 12, configured to send first information according to one or more metrics, where the one or more metrics are based on distance (s) between distribution (s) of outputs of p latent layer (s) in a first AI model and distribution of inputs of the first AI model in an inference cycle, and/or distance (s) between distribution (s) of outputs of p’ latent layer (s) and distribution of outputs of a first AI model in an inference cycle, and p and p’ are positive integers.

The communication apparatus 10 in this embodiment of the present application may correspond to the first network element in the communication method in the embodiments of the present application described above, and the foregoing management operations and/or functions and other management operations and/or functions of modules of the communication apparatus 10 are intended to implement corresponding steps of the foregoing methods. For brevity, details are not described herein again.

The transceiver module 12 in this embodiment of the present application may be implemented by a transceiver.

As shown in FIG. 12, a communication apparatus 20 may include a transceiver 21. Optionally, the communication apparatus 20 may further include a processor 22 and/or a memory 23. The memory 23 may be configured to store indication information, or may be configured to store code, instructions, and the like that is to be executed by the processor 22.

FIG. 13 is a schematic block diagram of a communication apparatus 30 according to an embodiment of the present application. As shown in FIG. 13, the communication apparatus 30 includes:

a transceiver module 31, configured to receive first information related to one or more metrics, where the one or more metrics are based on distance (s) between distribution (s) of outputs of p latent layer (s) in a first AI model and distribution of inputs of the first AI model in an inference cycle, and/or distance (s) between distribution (s) of outputs of p’ latent layer (s) and distribution of outputs of a first AI model in an inference cycle, and p and p’ are positive integers.

The communication apparatus 30 in this embodiment of the present application may correspond to the second network element in the communication method in the embodiments of the present application described above, and the management operations and/or functions and other management operations and/or functions of modules of the communication apparatus 30 are intended to implement corresponding steps of the foregoing methods. For brevity, details are not described herein again.

The transceiver module 31 in this embodiment of the present application may be implemented by a transceiver.

As shown in FIG. 14, a communication apparatus 40 may include a transceiver 41. Optionally, the communication apparatus 40 may further include a processor 42 and/or a memory 43. The memory 43 may be configured to store indication information, or may be configured to store code, instructions, and the like that is to be executed by the processor 42.

The processor 22 or the processor 42 may be an integrated circuit chip and have a signal processing capability. In an embodiment process, steps in the foregoing method embodiments can be implemented by using a hardware-integrated logical circuit in the processor, or by using instructions in the form of software. The processing module 21 may be a general-purpose processor, a digital signal processor (Digital Signal Processor, DSP) , an application-specific integrated circuit (Application Specific Integrated Circuit, ASIC) , a field programmable gate array (Field Programmable Gate Array, FPGA) , or another programmable logic device, a discrete gate or a transistor logic device, or a discrete hardware component. All methods, steps, and logical block diagrams disclosed in this embodiment of the present application may be implemented or performed. The general-purpose processor may be a microprocessor, or the processor may be any conventional processor or the like. Steps of the methods disclosed in the embodiments of the present invention may be directly performed and completed by a hardware decoding processor, or may be performed and completed by using a combination of hardware and software modules in the decoding processor. The software module may be located in a storage medium known in the art, such as a random-access memory, a flash memory, a read-only memory, a programmable read-only memory, an electrically erasable programmable memory, or a register. The storage medium is located in the memory, and the processor reads the information in the memory and completes the steps in the foregoing methods in combination with the hardware of the processor.

It may be understood that the memory 23 or the memory 43 in the embodiments of the present invention may be a volatile memory or a non-volatile memory, or may include a volatile memory and a non-volatile memory. The non-volatile memory may be a read-only memory (Read-Only Memory, ROM) , a programmable read-only memory (Programmable ROM, PROM) , an erasable programmable read-only memory (Erasable PROM, EPROM) , an electrically erasable programmable read-only memory (Electrically EPROM, EEPROM) , or a flash memory. The volatile memory may be a random access memory (Random Access Memory, RAM) , and be used as an external cache. Through example but not limitative description, many forms of RAMs may be used, for example, a static random access memory (Static RAM, SRAM) , a dynamic random access memory (Dynamic RAM, DRAM) , a synchronous dynamic random access memory (Synchronous DRAM, SDRAM) , a double data rate synchronous dynamic random access memory (Double Data Rate SDRAM, DDR SDRAM) , an enhanced synchronous dynamic random access memory (Enhanced SDRAM, ESDRAM) , a synchronous link dynamic random access memory (Synch Link DRAM, SLDRAM) , and a direct rambus dynamic random access memory (Direct Rambus RAM, DR RAM) . The storage of the system and the method described in this specification aim to include, but are not limited to, these and any other proper storage.

An embodiment of the present application further provides a system. As shown in FIG. 15, a system 50 includes:

the communication apparatus 10 according to the embodiments of the present application and the communication apparatus 20 according to the embodiments of the present application.

An embodiment of the present application further provides a computer storage medium, and the computer storage medium may store a program instruction for executing any of the foregoing methods.

Optionally, the storage medium may be specifically the memory 23 or 43.

A person of ordinary skill in the art will be aware that, in combination with the examples described in the embodiments disclosed in this specification, units and algorithm steps may be implemented by using electronic hardware or a combination of computer software and electronic hardware. Whether the functions are performed by using hardware or software depends on particular applications and design constraint conditions of the technical solutions. A person skilled in the art may use different methods to implement the described functions for each particular application, but it should not be considered that the embodiment goes beyond the scope of the present application.

It would be understood by a person skilled in the art that, for the purpose of convenience and brevity, in a detailed working process of the foregoing system, apparatus, and unit, reference may be made to a corresponding process in the foregoing method embodiments, and details are not described herein again.

In the several embodiments provided in the present application, the disclosed system, apparatus, and method may be implemented in other manners. For example, the described apparatus embodiment is merely an example. For example, the unit division is a logical function division and other methods of division may be used in an actual embodiment. For example, a plurality of units or components may be combined or integrated into another system, or some features may be ignored or not performed. In addition, the displayed or discussed mutual couplings or direct couplings or communication connections may be implemented using various communication interfaces. The indirect couplings or communication connections between the apparatuses or units may be implemented in electronic, mechanical, or other forms.

The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, that is, the parts may be located in one unit, or may be distributed among a plurality of network units. Some or all of the units may be selected based on actual requirements to achieve the objectives of the embodiments.

In addition, function units in the embodiments of the present application may be integrated into one processing unit, each of the units may exist alone physically, or two or more units may be integrated into one unit.

When the functions are implemented in the form of a software functional unit and sold or used as an independent product, the functions may be stored in a computer-readable storage medium. The technical solutions of the present application may be implemented in the form of a software product. The software product is stored in a storage medium, and includes several instructions for instructing a computer device (which may be a personal computer, a server, a network device, or the like) to perform all or some of the steps of the methods described in the embodiments of the present application. The foregoing storage medium includes any medium that can store program code, such as a USB flash drive, a removable hard disk, a read-only memory (Read-Only Memory, ROM) , a random access memory (Random Access Memory, RAM) , a magnetic disk, an optical disc or the like.

The foregoing descriptions are merely specific embodiments of the present application, but are not intended to limit the protection scope of the present application. Any variation or replacement readily figured out by a person skilled in the art within the technical scope disclosed in the present application shall fall within the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.

Claims

A communication method, comprising:

sending first information according to one or more metrics, wherein the one or more metrics are based on distance (s) between distribution (s) of outputs of p latent layer (s) in a first AI model and distribution of inputs of the first AI model in an inference cycle, and/or distance (s) between distribution (s) of outputs of p’ latent layer (s) and distribution of outputs of a first AI model in an inference cycle, and p and p’ are positive integers.
The communication method according to claim 1, wherein the one or more metrics comprise at least one metric corresponding to a first latent layer among the p latent layer (s) or the p’ latent layer (s) , and the at least one metric comprises at least one of the following:

a first metric corresponding to the first latent layer, wherein the first metric corresponding to the first latent layer is based on a distance between the distribution of the inputs of the first AI model and distribution of outputs of the first latent layer;

a second metric corresponding to the first latent layer, wherein the second metric corresponding to the first latent layer is based on a distance between the distribution of the outputs of the first AI model and the distribution of the outputs of the first latent layer; or

a third metric corresponding to the first latent layer, wherein the third metric corresponding to the first latent layer is based on a ratio between the first metric and the second metric.
The communication method according to claim 1 or 2, wherein the one or more metrics are configured to check whether the inference cycle is normal.
The communication method according to claim 3, wherein when the inference cycle is abnormal, one or more of the following are not met:

first metrics in the one or more metrics decrease as indexes of corresponding latent layers increase;

second metrics in the one or more metrics increase as indexes of corresponding latent layers increase; or

third metrics in the one or more metrics decrease as indexes of corresponding latent layers increase.
The communication method according to claim 1 or 2, wherein the one or more metrics are configured to check whether the first AI model works with a second AI model.
The communication method according to claim 5, wherein when the first AI model works with the second AI model, the difference (s) between the one or more metrics and one or more reference metrics is less than or equal to threshold (s) , and the one or more reference metrics are related to p latent layer (s) and/or p’ latent layer (s) in the second AI model.
The communication method according to any one of claims 1 to 6, further comprising:

receiving second information, wherein the second information is configured to indicate at least one of the following: one or more latent layers related to S metric (s) , one or more methods for measuring the S metric (s) , or one or more types of the S metric (s) , wherein S is a positive integer.
The communication method according to any one of claims 1 to 7, wherein the first information indicates the one or more metrics.
A communication method, comprising:

receiving first information related to one or more metrics, wherein the one or more metrics are based on distance (s) between distribution (s) of outputs of p latent layer (s) in a first AI model and distribution of inputs of the first AI model in an inference cycle, and/or distance (s) between distribution (s) of outputs of p’ latent layer (s) and distribution of outputs of a first AI model in an inference cycle, and p and p’ are positive integers.
The communication method according to claim 9, wherein the one or more metrics comprise at least one metric corresponding to a first latent layer among the p latent layer (s) or the p’ latent layer (s) , and the at least one metric comprises at least one of the following:

a first metric corresponding to the first latent layer, wherein the first metric corresponding to the first latent layer is based on a distance between the distribution of the inputs of the first AI model and distribution of outputs of the first latent layer;

a second metric corresponding to the first latent layer, wherein the second metric corresponding to the first latent layer is based on a distance between the distribution of the outputs of the first AI model and the distribution of the outputs of the first latent layer; or

a third metric corresponding to the first latent layer, wherein the third metric corresponding to the first latent layer is based on a ratio between the first metric and the second metric.
The communication method according to claim 9 or 10, wherein the one or more metrics are configured to check whether the inference cycle is normal.
The communication method according to claim 11, wherein when the inference cycle is abnormal, one or more of the following are not met:

first metrics in the one or more metrics decrease as indexes of corresponding latent layers increase;

second metrics in the one or more metrics increase as indexes of corresponding latent layers increase; or

third metrics in the one or more metrics decrease as indexes of corresponding latent layers increase.
The communication method according to claim 9 or 10, wherein the one or more metrics are configured to check whether the first AI model works with a second AI model.
The communication method according to claim 13, wherein when the first AI model works with the second AI model, the difference (s) between the one or more metrics and one or more reference metrics is less than or equal to threshold (s) , and the one or more reference metrics are related to p latent layer (s) and/or p’ latent layer (s) in the second AI model.
The communication method according to any one of claims 9 to 14, further comprising:

sending second information, wherein the second information is configured to indicate at least one of the following: one or more latent layers related to S metric (s) , one or more methods for measuring the S metric (s) , or one or more types of the S metric (s) , wherein S is a positive integer.
The communication method according to any one of claims 9 to 15, wherein the first information indicates the one or more metrics.
An apparatus, wherein the apparatus comprises a processor and a memory storing one or more instructions that are capable of being run on the processor, and when the one or more instructions are run, the apparatus is enabled to perform the method according to any one of claims 1 to 8 or perform the method according to any one of claims 9 to 16.
An apparatus, wherein the apparatus comprises a unit to perform the method according to any one of claims 1 to 8 or perform the method according to any one of claims 9 to 16.
A communication system, comprising a first communication apparatus and a second communication apparatus, wherein the first communication apparatus is configured to perform the method according to any one of claims 1 to 8, and the second communication apparatus is configured to perform the method according to any one of claims 9 to 16.
A computer-readable storage medium, comprising one or more instructions, wherein when the one or more instructions are run on a computer, the computer is configured to perform the method according to any one of claims 1 to 8, or the method according to any one of claims 9 to 16.