On-demand marking for channel classification training

Abstract

Example embodiments of the present disclosure relate to on-demand marking for channel classification training. A first device determines a classification result of a communication channel using a classification model based at least in part on channel measurement information related to the communication channel, and determines importance evaluation information indicating an importance level of the channel measurement information when updating the classification model based at least in part on a type of the classification model. And the first device sends the importance evaluation information to the second device. In accordance with a determination that the importance level of the channel measurement information exceeds an importance threshold, the second device causes a third device to perform classification marking at least for the communication channel at a location associated with the first device.

Description

Technical Field

[0001] Embodiments of the present disclosure relate generally to the field of telecommunications, and in particular, to methods, devices, apparatuses, and computer-readable storage media for on-demand labeling for channel classification training. Background Art

[0002] Location awareness is a fundamental aspect of wireless communication networks and will enable countless location-enabled services in different applications. As the accuracy of the technology evolves, the integration and utilization of location information in everyday applications will grow significantly.

[0003] Many positioning techniques that rely on techniques such as time of arrival (TOA), time difference of arrival (TDOA), and angle of arrival (AOA) require line-of-sight (LOS) propagation between a reference point (such as a network device) and the mobile device to be positioned. However, for non-line-of-sight (NLOS) propagation cases in indoor / outdoor environments, positioning accuracy can deteriorate significantly due to the ability to identify reflected multipath radio frequency (RF) propagations from different angles of arrival with different delay spreads. On the other hand, artificial intelligence (AI) algorithms are inherently superior for accuracy and efficiency in fingerprint-styled positioning inference, regardless of LOS or NLOS. Therefore, it is important to classify channel propagation, at least due to its impact on the selection of positioning methods. Summary of the invention

[0004] The scope of protection sought by various embodiments of the present invention is set out by the independent claims. Embodiments / examples and features described in this specification that do not fall within the scope of the independent claims are to be interpreted as examples that can be used to understand the various embodiments of the present invention. Please note that the term "embodiment" or "example" should be applied accordingly to the terms used in this application, i.e., if the term "example" is used, then the statement should accordingly talk about the "example", or if the term "embodiment" is used, then the statement should accordingly talk about the "embodiment".

[0005] Embodiments that do not belong to the scope of claims (if any) are to be construed as examples useful for understanding various embodiments of the present disclosure.

[0006] In a first aspect, a first device is provided. The first device includes at least one processor; and at least one memory stores instructions, which, when executed by the at least one processor, cause the first device to at least: determine a classification result of a communication channel using a classification model based at least in part on channel measurement information related to the communication channel; determine importance assessment information based at least in part on a type of the classification model, the importance assessment information indicating an importance level of the channel measurement information when updating the classification model; and send the importance assessment information to a second device.

[0007] In a second aspect, a second device is provided. The second device includes at least one processor; and at least one memory stores instructions, which, when executed by the at least one processor, cause the second device to at least: receive importance assessment information from the first device, the importance assessment information indicating the importance level of channel measurement information when updating a classification model, the classification model being used to determine a classification result of a communication channel based on the channel measurement information; determine whether the importance level of the channel measurement information exceeds an importance threshold; and, based on determining that the importance level of the channel measurement information exceeds the importance threshold, cause a third device to perform at least classification marking for the communication channel at a location associated with the first device.

[0008] In a third aspect, a method is provided. The method includes: determining, at a first device and using a classification model, a classification result of a communication channel based at least in part on channel measurement information related to the communication channel; determining, based at least in part on a type of the classification model, importance assessment information indicating an importance level of the channel measurement information when updating the classification model; and sending the importance assessment information to a second device.

[0009] In a fourth aspect, a method is provided. The method includes: receiving, at a second device, importance evaluation information from a first device, the importance evaluation information indicating an importance level of channel measurement information when updating a classification model, the classification model being used to determine a classification result of a communication channel based on the channel measurement information; determining whether the importance level of the channel measurement information exceeds an importance threshold; and causing a third device to perform classification marking at least for the communication channel based on determining that the importance level of the channel measurement information exceeds the importance threshold.

[0010] In a fifth aspect, a first device is provided. The first device includes: a component for determining a classification result of a communication channel using a classification model based at least in part on channel measurement information about the communication channel; a component for determining importance evaluation information based at least in part on a type of the classification model, the importance evaluation information indicating an importance level of the channel measurement information when updating the classification model; and a component for sending the importance evaluation information to a second device.

[0011] In a sixth aspect, a second device is provided. The second device includes: a component for receiving importance assessment information from a first device, the importance assessment information indicating an importance level of channel measurement information, a classification model being used to determine a classification result of a communication channel based on the channel measurement information; a component for determining whether the importance level of the channel measurement information exceeds an importance threshold; and a component for causing a third device to perform at least classification marking for the communication channel at a location associated with the first device based on determining that the importance level of the channel measurement information exceeds the importance threshold.

[0012] In a seventh aspect, a computer readable medium is provided, wherein the computer readable medium includes instructions stored thereon for causing an apparatus to at least perform the method according to the first aspect.

[0013] In an eighth aspect, a computer readable medium is provided, wherein the computer readable medium includes instructions stored thereon for causing an apparatus to at least perform the method according to the second aspect.

[0014] It should be understood that the invention summary is not intended to identify the key or essential features of the embodiments of the present disclosure, nor is it intended to be used to limit the scope of the present disclosure. Other features of the present disclosure will become readily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Some example embodiments will now be described with reference to the accompanying drawings, in which:

[0016] Figure 1 An example communication environment is shown in which example embodiments of the present disclosure may be implemented;

[0017] Figure 2 shows a signaling flow for communication according to some example embodiments of the present disclosure;

[0018] Figure 3A and Figure 3B An example of a first type of classification model according to some example embodiments of the present disclosure is shown;

[0019] Figure 4 A flowchart illustrating a process for determining importance assessment information according to some example embodiments of the present disclosure;

[0020] Figure 5 shows an example of a second type of classification model and a reference classification model generated therefrom according to some example embodiments of the present disclosure;

[0021] Figure 6 A flowchart illustrating a process for determining importance assessment information according to some further example embodiments of the present disclosure;

[0022] Fig. 7A and Figure 7B The model performance gains of some example embodiments of the present disclosure relative to traditional model training methods are shown;

[0023] Figure 8 A flowchart showing a method implemented at a first device according to some example embodiments of the present disclosure is shown;

[0024] Fig. 9 A flowchart showing a method implemented at a second device according to some example embodiments of the present disclosure is shown;

[0025] Fig.10 shows a simplified block diagram of an apparatus suitable for implementing an example embodiment of the present disclosure; and

[0026] Fig.11 A block diagram of an example computer-readable medium is shown according to some example embodiments of the present disclosure.

[0027] Throughout the drawings, the same or similar reference numerals refer to the same or similar elements. Throughout the drawings, the same or similar reference numerals refer to the same or similar elements. DETAILED DESCRIPTION

[0028] The principles of the present disclosure will now be described with reference to some example embodiments. It should be understood that these embodiments are described only for illustrative purposes and are helpful for those skilled in the art to understand and implement the present disclosure without implying any limitation on the scope of the present disclosure. The embodiments described herein can be implemented in various ways different from the ways described below.

[0029] In the following description and claims, unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs.

[0030] References in this disclosure to "one embodiment," "an embodiment," "an example embodiment," etc. indicate that the described embodiment may include a particular feature, structure, or characteristic, but not necessarily every embodiment. Furthermore, such phrases do not necessarily refer to the same embodiment. Furthermore, when a particular feature, structure, or characteristic is described in conjunction with an embodiment, it is considered that it is within the knowledge of those skilled in the art to affect such feature, structure, or characteristic in conjunction with other embodiments, whether or not explicitly described.

[0031] It should be understood that although the terms "first", "second", etc. may be used to describe various elements in this article, these elements should not be limited by these terms. These terms are only used to distinguish one element from another element. For example, without departing from the scope of the exemplary embodiment, the first element may be referred to as the second element, and similarly, the second element may be referred to as the first element. As used herein, the term "and / or" includes any and all combinations of one or more of the listed terms.

[0032] As used herein, “at least one of: ” and “at least one of ” and similar expressions mean at least any one of the elements, or at least any two or more of the elements, or at least all of the elements, where the list of two or more elements is connected by “and” or “or”.

[0033] The terms used herein are only used for the purpose of describing specific embodiments and are not intended to limit the exemplary embodiments. As used herein, the singular forms "a", "an", and "the" are also intended to include the plural forms, unless the context clearly indicates otherwise. It should also be understood that the terms "include", "comprises", "has", "includes", and / or "comprising" when used herein specify the presence of the described features, elements, and / or components, etc., but do not exclude the presence or addition of one or more other features, elements, components, and / or combinations thereof.

[0034] As used in this application, the term "circuitry" may refer to one or more or all of the following: (a) hardware circuitry implementation only (such as implementation in analog and / or digital circuitry only) and (b) a combination of hardware circuitry and software, for example, as applicable: (i) a combination of analog circuits and digital hardware circuits and software / firmware, and (ii) hardware processor(s) with software (including digital signal processor(s), software and memory(s) that work together to enable a device (such as a mobile phone or server) Any portion that performs various functions, and (c) (multiple) hardware circuits and / or (multiple) processors, such as (multiple) microprocessors or portions of (multiple) microprocessors, that require software (e.g., firmware) for operation, but the software may not be present when not required for operation.

[0035] This definition of circuitry applies to all uses of the term in this application, including in any claims. As another example, as used in this application, the term circuitry also covers an implementation of only a hardware circuit or processor (or multiple processors) or portions of a hardware circuit or processor and its (or their) accompanying software and / or firmware. For example, if applicable to a particular claim element, the term circuitry also covers a baseband integrated circuit or processor integrated circuit for a mobile device, or a similar integrated circuit in a server, cellular network device, or other computing or network device.

[0036] As used herein, the term "communication network" refers to a network that complies with any suitable communication standard, such as New Radio (NR), Long Term Evolution (LTE), Advanced LTE (LTE-A), Wideband Code Division Multiple Access (WCDMA), High Speed ​​Packet Access (HSPA), Narrowband Internet of Things (NB-IoT), etc. In addition, the communication between the terminal device and the network equipment in the communication network can be performed according to any suitable generation of communication protocols, including but not limited to the first generation (1G), second generation (2G), 2.5G, 2.75G, third generation (3G), fourth generation (4G), 4.5G, fifth generation (5G) communication protocols and / or any other protocols currently known or developed in the future. The embodiments of the present disclosure can be applied to various communication systems. In view of the rapid development of communication, there are of course communication technologies and systems of future types that can implement the present disclosure. The scope of the present disclosure should not be considered to be limited to the above-mentioned systems.

[0037] As used herein, the term "network device" refers to a node in a communication network, via which a terminal device accesses the network and receives services from it. A network device may refer to a base station (BS) or an access point (AP), for example, a Node B (Node B or NB), an evolved NodeB (e Node B or eNB), a NR NB (also referred to as a gNB), a remote radio unit (RRU), a radio head (RH), a remote radio head (RRH), a relay, an integrated access and backhaul (IAB) node, a low-power node such as a femto, a micro, a non-terrestrial network (NTN) or a non-terrestrial network device (such as a satellite network device), a low earth orbit (LEO) satellite and a synchronous earth orbit (GEO) satellite, an aircraft network device, etc., depending on the terminology and technology applied. In some example embodiments, a radio access network (RAN) separation architecture includes a centralized unit (CU) and a distributed unit (DU) at an IAB donor node. The IAB node mobile terminal (IAB-MT) portion, which behaves like a UE to a parent node, and the DU portion of the IAB node behaves like a base station to a next-hop IAB node.

[0038] The term "terminal device" refers to any terminal device capable of wireless communication. As an example and not limitation, the terminal device may also be referred to as a communication device, a user equipment (UE), a user station (SS), a portable user station, a mobile station (MS) or an access terminal (AT). The terminal device may include, but is not limited to, a mobile phone, a cellular phone, a smart phone, a voice over IP (VoIP) phone, a wireless local loop phone, a tablet computer, a wearable terminal device, a personal digital assistant (PDA), a portable computer, a desktop computer, an image capture terminal device such as a digital camera, a game terminal device, a music storage and playback facility, a vehicle-mounted wireless terminal device, a wireless endpoint, a mobile station, a laptop embedded device (LEE), a laptop device (LME), a USB dongle, a smart device, a wireless customer premises equipment (CPE), an Internet of Things (IoT) device, a watch or other wearable device, a head-mounted display (HMD), a vehicle, a drone, medical equipment and applications (e.g., remote surgery), industrial equipment and applications (e.g., robots and / or other wireless devices operating in industrial and / or automated processing chain environments), consumer electronic devices, equipment operating on commercial and / or industrial wireless networks, etc. The terminal device may also correspond to the mobile terminal (MT) part of the IAB node (eg, a relay node).In the following description, the terms "terminal device", "communication device", "terminal", "user equipment" and "UE" may be used interchangeably.

[0039] As used herein, the term "resource", "transmission resource", "resource block", "physical resource block" (PRB), "uplink resource" or "downlink resource" may refer to any resource used to perform communication (e.g., communication between a terminal device and a network device), such as a resource in the time domain, a resource in the frequency domain, a resource in the spatial domain, a resource in the code domain, or any other resource that enables communication. In the following, unless explicitly stated, resources in the frequency domain and the time domain will be used as examples of transmission resources to describe some example embodiments of the present disclosure. Note that the example embodiments of the present disclosure are also applicable to other resources in other domains.

[0040] As used herein, the term "model" is referred to as the association between input and output learned from training data, and therefore the corresponding output for a given input can be generated after training. The generation of the model can be based on machine learning (ML) technology. Machine learning technology can also be referred to as artificial intelligence (AI) technology. Typically, a machine learning model that receives input information and makes predictions based on the input information can be constructed. For example, a classification model can predict the category of input information in a predetermined number of categories. As used herein, a "model" can also be referred to as a "machine learning model", "learning model", "machine learning network" or "learning network", which can be used interchangeably herein.

[0041] Deep learning (DL) is one of the machine learning algorithms that uses multiple layers of processing units to process inputs and provide corresponding outputs. A neural network (NN) model is an example of a model based on deep learning. A neural network can process inputs to provide corresponding outputs, and typically includes an input layer, an output layer, and one or more hidden layers between the input layer and the output layer. The neural network used in deep learning typically includes a large number of hidden layers to increase the depth of the network. The layers of the neural network are connected in sequence so that the output of the previous layer is provided as the input of the next layer, wherein the input layer receives the input of the neural network, and the output of the output layer serves as the final output of the neural network. Each layer of the neural network includes one or more nodes (also called processing nodes or neurons), each of which processes the input from the previous layer.

[0042] Typically, model lifecycle management can generally include three stages, namely the training stage, the verification stage, and the application stage (also known as the reasoning stage). In the training stage, a given machine learning model can be iteratively trained (or optimized) using a large amount of training data until the model can obtain consistent reasoning similar to that which human intelligence can make from the training data. During training, the parameter value set of the model is iteratively updated until the training goal is reached. Through the training process, the machine learning model can be regarded as being able to learn the association between input and output (also known as input-output mapping) from the training data. In the verification stage, the verification input is applied to the trained machine learning model to test whether the model can provide the correct output, thereby determining the performance of the model. Typically, the verification stage can be considered as a step in the training process, or sometimes it can be omitted. In the reasoning stage, the resulting machine learning model can be used to process the real-world model input and determine the corresponding model output based on the parameter value set obtained from the training process. In some cases, model lifecycle management may include a retraining or update stage so that the model can be evolved to have better performance. Example Environment

[0043] Figure 1 An example communication environment 100 in which example embodiments of the present disclosure can be implemented is shown. In the communication environment 100, multiple communication devices are involved, including one or more first devices 110-1, 110-2, and 110-3, a second device 120, a third device 130, and a fourth device 140. For the purpose of discussion, the first devices 110-1, 110-2, and 110-3 are collectively or individually referred to as first devices 110.

[0044] It should be understood that Figure 1The number of devices and their connections shown in the figure is for illustrative purposes only and does not imply any limitation. The communication environment 100 may include any suitable number of devices suitable for implementing the embodiments of the present disclosure. Although not shown, it should be understood that one or more additional devices may be involved in the communication environment 100.

[0045] Communications in the communication environment 100 may be implemented according to any suitable communication protocol, including but not limited to cellular communication protocols of the first generation (1G), second generation (2G), third generation (3G), fourth generation (4G) and fifth generation (5G), wireless local area network communication protocols such as Institute of Electrical and Electronics Engineers (IEEE) 802.11, and / or any other currently known or later developed protocol. In addition, communications may utilize any appropriate wireless communication technology, including but not limited to: code division multiple access (CDMA), frequency division multiple access (FDMA), time division multiple access (TDMA), frequency division duplex (FDD), time division duplex (TDD), multiple input multiple output (MIMO), orthogonal frequency division multiple access (OFDM), discrete Fourier transform spread OFDM (DFT-s-OFDM) and / or any other currently known or later developed technology.

[0046] In the communication environment 100, the first device 110 and the fourth device 140 can communicate with each other. Figure 1 In the example of , the first device 110 is shown as a terminal device, and the fourth device 140 is shown as a network device, such as a transmission reception point (TRP). In some example embodiments, if the first device 110 is a terminal device and the fourth device 140 is a network device, the link from the fourth device 140 to the first device 110 is referred to as a downlink (DL), and the link from the first device 110 to the fourth device 140 is referred to as an uplink (UL).

[0047] Positioning techniques may be applied to obtain location information of the first device 110. In some example embodiments, the positioning techniques may be based on DL and DL plus UL positioning measurements performed at the first device 110 for UE-assisted positioning, or based on UL and DL plus UL measurements at the fourth device 140 for network-assisted positioning. In some cases, depending on the category of the communication channel between the first device 110 and the fourth device 140, different positioning techniques may be applied to ensure positioning accuracy. For example, different positioning methods may be applied to identify whether the communication channel has line-of-sight (LOS) propagation or does not have non-line-of-sight (NLOS) propagation. Therefore, it is important to identify the category of the communication channel, at least with respect to its impact on the accuracy of the positioning estimate.

[0048] It has been proposed to deploy one or more classification models at the first device 110 to predict the classification result of the communication channel between the first device 110 and the fourth device 140. The classification model can be constructed based on AI technology. The processing of the classification model can be represented as where f AI () represents the classification model, x represents the channel measurement information related to the communication channel, Represents the classification result predicted by the classification model to indicate which category the communication channel is classified into.

[0049] The first device 110 may detect a reference signal propagated from the fourth device 140 through the corresponding communication channel to obtain channel measurement information. In some example embodiments, the fourth device 140 may send the reference signal based on a configuration or signaling from the second device 120. In some example embodiments, the communication channel may be classified by the classification model as a LOS channel (with LOS propagation) or a NLOS channel (with NLOS propagation).

[0050] In some example embodiments, the second device 120 may maintain and manage the classification model(s) used by the first device 110. The second device 120 may include a location server or controller. In some example embodiments, the second device 120 may include a network element in a core network (CN) that is configured for location management. In some example embodiments, the second device 120 may include a location management function (LMF), but other terms may be used.

[0051] The accuracy of the classification model depends on the training data. A pre-requisite in model supervised learning is that the training data needs to be pre-labeled. For a classification model configured for channel classification, the labeled training data may include sample channel measurement information as sample model input, and the true classification result as the true model label. It usually requires external devices / equipment to support field measurement and labeling.

[0052] For example, the third device 130 in the communication environment 100 may be configured to support field measurements and classification marking. The third device 130 is generally capable of determining its location. In some example embodiments, the third device 130 may include a positioning reference unit (PRU), but other terms may also be used. The third device 130 may be requested by the second device 120 to perform field measurements and determine a true classification result for the channel measurement information measured at the location. Note that although one third device is shown, there may be multiple third devices that may be requested to perform classification marking.

[0053] In some cases, the classification model can be evolved or fine-tuned to have better performance (e.g., higher accuracy) even if the model has been deployed to the first device. Such model evolution may require additional labeled training data. However, there is a lack of knowledge to appropriately decide and request a third device to perform field measurements and marking. If one or more third devices are requested to perform marking, the collected training data may not always be useful in improving model performance. Requiring a third device to mark areas where the current classification model may have provided good estimates or miss blind spots where more training samples are needed for model training can result in a waste of resources.

[0054] For the purpose of efficient training data updating and retraining models, it is desirable to have a minimum amount of well-labeled training data that is considered to be informative and can help improve model performance. Working Principle and Example Signaling Flow

[0055] According to some example embodiments of the present disclosure, a solution for on-demand marking for channel classification training is provided. In this solution, a first device determines importance assessment information based on the type of classification model to indicate the importance level of channel measurement information when updating the classification model. The importance assessment information is sent to a second device. The second device compares the importance level with an importance threshold. If the importance level of the channel measurement information exceeds the importance threshold, the second device causes a third device to perform classification marking for the communication channel of the first device. In this way, on-demand marking can be performed in a coordinated manner by evaluating the importance of the channel measurement information to the improvement of the classification model. The marking overhead of the third device is reduced, and efficient marking of training data updates for model improvement can be achieved.

[0056] In some example embodiments, more training data can be obtained from the classification labels to update or train the classification model. In this case, efficient model training to achieve optimal training performance is feasible with a small amount of well-labeled training data.

[0057] The embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0058] Reference now Figure 2 , which shows a signaling flow 200 for communication according to some example embodiments of the present disclosure. Figure 2 As shown, signaling flow 200 involves first device 110, second device 120, and third device 130. For discussion purposes, Figure 1 The signaling flow 200 will be described with reference to FIG. Figure 2, but it should be understood that there may be multiple first devices that perform similar operations as described below with respect to the first device 110 and multiple third devices that perform similar operations as described below with respect to the third device 130.

[0059] The first device 110 determines 205 a classification result for the communication channel based at least in part on channel measurement information related to the communication channel. A classification model is applied to determine a classification result for the communication channel.

[0060] In some example embodiments, fourth device 140 may transmit a reference signal, and first device 110 may measure the reference signal to obtain channel measurement information, the reference signal being propagated on a communication channel between first device 110 and fourth device 140. In some example embodiments, first device 110 may include a terminal device, and fourth device 140 may include a network device.

[0061] The channel measurement information may include one or more types of information that are useful for characterizing the communication channel. In some example embodiments, the channel measurement information may include a channel impulse response (CIR), channel state information (CSI), a received signal strength indicator (RSSI), a reference signal received power (RSRP), and / or other information that may be measured.

[0062] The classification model may be constructed to extract representative features of channel measurement information in a high-dimensional feature space via machine learning, and use the features to classify communication channels. The classification model may be configured with multiple potential channel categories, into which the communication channels may be classified. In some example embodiments, the classification model may perform binary classification to classify the communication channel into a first channel category or a second channel category. In some example embodiments, the multiple channel categories may include LOS channels and NLOS channels. The classification result may indicate the predicted probability that the communication channel is classified as a LOS channel or a NLOS channel. Depending on the actual application, other channel categories may also be defined, which is not limited in the scope of the present disclosure.

[0063] In addition to classifying the communication channel, the first device 110 also determines whether and / or how to report auxiliary information to support on-demand classification tagging. Specifically, the first device 110 determines 210 importance assessment information based at least in part on the type of classification model, the importance assessment information indicating the importance level of the channel measurement information in updating the classification model. The first device 110 sends 215 the importance assessment information to the second device 120.

[0064] In terms of lifecycle management of at least the classification model applied by the first device 110, it is beneficial to employ on-demand classification tagging in training. In an example embodiment of the present disclosure, the first device 110 determines importance assessment information related to channel measurement information, which may enable the second device 120 to trigger the third device 130 to perform classification tagging in the event that important channel measurement information is discovered in updating the classification model. As used herein, updating important channel measurement information in the classification model may involve a situation where the channel measurement information is informative and provides new features that are not currently captured by the classification model. In such a case, the classification tagging of the corresponding communication channel may provide new informative training data to help fine-tune the classification model, for example, to correctly classify communication channels with similar characteristics.

[0065] Different importance assessment information may be determined for different types of classification models. In order to measure whether the channel measurement information is important in updating the classification model, in some example embodiments, the importance assessment information may be determined based on the uncertainty or credibility of the classification result determined by the model based on the channel measurement information. In some example embodiments, the uncertainty level of the classification result may be determined, wherein the uncertainty level may represent the degree of certainty or suspicion of the classification model regarding its classification result. The uncertainty level may also be referred to as the suspicion level. Alternatively, the certainty level, reliability level, or confidence level of the classification result may be measured in comparison to the "uncertainty level".

[0066] Generally, the root cause of the classification error between "ambiguity of the channel itself" and "estimated immaturity of the classification model" cannot be directly identified. However, the immaturity of the classification model has not been taken into account, which may mislead the second device 120 to take inappropriate subsequent actions. For example, if the classification ambiguity is caused by the ambiguity of the channel itself, and the classification model is considered to be confident in its classification results, this may affect the selection of a subsequent positioning method, which is selected between a geometric type (e.g., TDOA, AOA, AOD) or a fingerprint type scheme. On the other hand, if the classification ambiguity is caused by the immaturity of the classified model before it is fully trained or fine-tuned, this can affect subsequent training data enhancement and model updates in terms of model lifecycle management, that is, more labeled training data needs to be collected for model fine-tuning.

[0067] For example, in Figure 1 , it is assumed that the first device 110 - 1 can obtain i The channel measurement information is represented by f AI-s () classification model to generate classification results To indicate that the communication channel is a LOS channel. The first device 110-3 may obtain jThe channel measurement information is represented by f AI-s () classification model to generate classification results To indicate that the communication channel is a NLOS channel. The first device 110-3 may obtain k The channel measurement information is represented by f AI-h () different types of classification models to generate classification results To indicate that the communication channel is a LOS channel. By considering the actual propagation conditions of the communication channel, the classification result at the first device 110-1 may be determined to be credible, while the classification results at the first device 110-2 and the first device 110-3 are suspicious.

[0068] In view of the above, it is beneficial to evaluate the uncertainty or credibility of the classification result given by the current classification model. In some example embodiments, the importance level of the channel measurement information can be determined based on the uncertainty level of the classification result. Importance assessment information can be generated to include at least the uncertainty level of the classification result. In some example embodiments, a higher uncertainty level of the classification result can correspond to a higher importance level of the channel measurement information, which means that the communication channel or the channel measurement information may be important, so that a classification mark is required.

[0069] In some example embodiments, the uncertainty levels of different types of classification models may be determined by different methods by considering the classification scheme implemented by the classification model. Some types of classification models may require relatively high overhead for calculating the uncertainty level, while some other types of classification models may require relatively low overhead.

[0070] For some types of classification models (e.g., the first type of classification model) that require high overhead for calculating the uncertainty level, the first device 110 can determine the uncertainty level of the classification and generate importance assessment information to include at least the uncertainty level. In some example embodiments, the uncertainty level of the classification result output from the first type can be determined based on the intermediate output information obtainable from the model, and therefore there is no need to reconstruct the classification model. In this case, the calculation of the uncertainty level may not introduce high overhead, and therefore can be implemented at the first device 110.

[0071] For some other types of classification models (e.g., the second type of classification model) that require low overhead, the first device 110 may request other devices (such as the second device 120) to assist in determining the degree of certainty. In some example embodiments, the uncertainty level of the classification result output from the second type of classification model may be determined by reconstructing the classification model, which may result in high overhead and resource consumption. In those embodiments, the first device 110 may provide at least channel measurement information to the second device 120. In this way, the importance assessment information may include at least the channel measurement information. The first device 110 may send an auxiliary request including the importance assessment information to the second device 120. Upon receiving the auxiliary request, the second device 120 may determine the uncertainty level based on the channel measurement information.

[0072] Some example embodiments of calculating uncertainty levels for different types of classification models are described below with reference to FIGS. Figure 6 Described in detail.

[0073] In some example embodiments, in addition to or instead of factors affecting the level of uncertainty of the classification result, the level of importance of the channel measurement information may be determined based on other factors that may indicate whether the current communication channel of the first device 110 is informative or important for improving the classification model.

[0074] The above describes that importance assessment information is reported to the second device from the perspective of the first device 110, and the importance assessment information is related to channel measurement information related to a certain communication channel. In some example embodiments, the fourth device 140 may obtain the channel measurement information, for example, by receiving the channel measurement information from the first device 110 or by measuring the reference signal sent from the first device 110 to obtain the channel measurement information. In this case, if the classification model is deployed at the fourth device 140, the fourth device 140 may perform similar operations as described herein with respect to the first device 110. In other words, the network device may also send importance assessment information to facilitate classification tags for updating the classification model.

[0075] At the side of the second device 120, it receives 220 the importance evaluation information and may therefore determine the importance level of the channel measurement information acquired at the first device 110. The second device 120 determines 225 whether the importance level of the channel measurement information exceeds an importance threshold. The importance threshold may be any predetermined threshold level.

[0076] In some example embodiments, if the importance assessment information from the first device 110 includes an uncertainty level of the classification result, the second device 120 may determine the importance level of the corresponding channel measurement information based on the uncertainty level, and compare the importance level with the importance threshold. For example, a higher uncertainty level may correspond to a higher importance level of the corresponding channel measurement information. In some examples, the uncertainty level may be considered to be the importance level of the corresponding channel measurement information. In some examples, the importance level of the corresponding channel measurement information may be determined based on one or more other factors in addition to the uncertainty level. In some example embodiments, if the importance assessment information from the first device 110 includes the channel measurement information itself, as described above, the second device 120 may first determine the uncertainty level based on the channel measurement information. The determination of the uncertainty level will be described in detail below.

[0077] In the case where the importance level of the channel measurement information exceeds the importance threshold, the second device 120 enables the third device 130 to perform classification marking, which is at least for the communication channel at the location associated with the first device 110. Using the importance evaluation information reported from the first device 110, the second device 120 is able to evaluate the importance of certain channel measurement information in the improvement of the classification model. The second device 120 may be requested by the third device 130 to perform classification marking in the area where the important channel measurement information is found.

[0078] In some cases, if the importance level of the channel measurement information is determined to be below the importance threshold, the second device 120 may discard the importance evaluation information. In this way, the classification mark is not triggered for channel measurement information that is not important when updating the model. Both the marking efficiency and the model update efficiency are improved.

[0079] The third device 130 performs 235 classification marking in response to a request from the second device 120. The location where the third device 130 performs classification marking may be any location within the area where the first device 110 is located. In some example embodiments, the second device 120 may select an appropriate third device 130 located near the first device 110 to perform classification marking. In some example embodiments, the third device 130 may be movable and may be requested by the second device 120 to move to the area where the first device 110 is located. The third device 130 has the ability to determine the true classification result of the communication channel of the fourth device 140 in the area. The true classification result may mark the communication channel as a first channel category (e.g., a LOS channel) or a second channel category (e.g., a NLOS channel).

[0080] In some example embodiments, the third device 130 may perform further measurements on the communication channel between the first device 110 and the fourth device 140, and mark the communication channel with the true classification result. For example, the third device 130 may obtain sample channel measurement information (denoted as "x") related to the communication channel, and determine a true classification result (denoted as "y") for the sample channel measurement information. The third device 130 may send 240 the classification marking result to the second device 120, the classification marking result including a pair of sample channel measurement information and a corresponding true classification result {x, y}.

[0081] In some example embodiments, within the area where the third device 130 is moved, the third device 130 may perform classification marking for other communication channels. The third device 130 may obtain one or more additional pairs of sample channel measurement information and corresponding true classification results by changing its position within the geographic area where the first device 110 is located and / or its antenna orientation. The classification marking results are sent to the second device 120, and the classification marking results may include more than one pair of sample channel measurement information and corresponding true classification results.

[0082] The second device 120 receives 245 the classification labeling result from the third device 130, and updates 250 at least the classification model used by the first device 110 based on the classification labeling result. In some example embodiments, the second device 120 may update the training data set using at least one pair of sample channel measurement information and the corresponding ground truth classification result. After sufficient training data is collected from the third device 130 and other data sources, the second device 120 may trigger an update of the classification model. For example, the second device 120 may determine whether the size of the newly collected training data exceeds a threshold size. If the size exceeds the threshold size, an update of the classification model may be triggered. Since the training data is evaluated as important and informative, the updated classification model may be improved to have a higher accuracy.

[0083] In some cases, in addition to the classification model used in the first device 110 reporting the importance assessment information, the second device 120 may maintain one or more other classification models. The sample channel measurement information collected by the third device 130 and the corresponding (multiple) true value classification results may be shared between the classification models. In other words, the second device 120 may update one or more other classification models based on the sample channel measurement information and the corresponding true value classification results. Those classification models maintained by the second device 120 may be of different types and / or different model configurations, but may all be configured to classify communication channels. Channel measurement information that is considered important when updating one classification model may also be important and useful when updating other classification models.

[0084] In some example embodiments, if the inputs of different classification models are different (e.g., different channel measurement information inputs are required), the third device 130 may be requested by the second device 120 to collect different sample channel measurement information and true classification results related to the same communication channel.

[0085] The second device 120 can apply any suitable updating technique for the classification model, which is not limited within the scope of the present disclosure.

[0086] In some example embodiments, where one or more classification models are updated, the second device 120 may send (255) updates to the classification model(s) to the first device 110. The first device 110 receives 260 the updates to the classification model(s) and may apply the updated classification model(s) for subsequent channel classification. In an example embodiment, the second device 120 may provide an updated classification model previously used by the first device 110. In an example embodiment, other updated classification models may also be provided to the first device 110. The first device 110, which is configured with a plurality of (updated) classification models, may select one of the models for use, e.g., depending on circumstances associated with the communication channel. Classification model of the first type and calculation of uncertainty levels

[0087] As described above, importance evaluation information or uncertainty level of channel measurement information may be determined, depending on the type of classification model.

[0088] In some example embodiments, for a first type of classification model, the uncertainty level of the classification result output from the model may be determined based on intermediate output information obtainable from the model. As an example, in the case of binary classification, the classification model may determine a first number of model votes for a first channel category (denoted as "N1") and a second number of model votes for a second channel category (denoted as "N2") based on input channel measurement information.

[0089] The classification result may be determined based on a ratio of the first number to the second number (eg, N1 / N2), wherein a higher ratio may indicate a higher probability that the communication channel is classified into the first channel category.

[0090] For this type of classification model, the classification result is a "soft" indicator related to the channel class into which the communication channel is classified. This type of classification model can be represented as f AI-s ().exist Figure 1The first devices 110-1 and 110-2 shown in the example of use this type of classification model to perform channel classification. Some examples of this type of classification model may include, but are not limited to, a k-nearest neighbor (KNN) model and a support vector machine (SVM) model.

[0091] Figure 3A and Figure 3B An example of a first type of classification model according to some example embodiments of the present disclosure is shown. Figure 3A and Figure 3B A feature space 300 is shown, which includes a plurality of features 302 associated with a first channel category (denoted as "category 1") and a plurality of features 304 associated with a second channel category (denoted as "category 2"). A classification scheme applied by the classification model is configured to measure corresponding distances between features extracted from channel measurement information and features in the feature space 300, and the classification scheme selects a predetermined number (e.g., K features) of features having low distances (e.g., K features having the lowest distances).

[0092] Of the total K selected features, the classification model may count a first number of features associated with a first channel category (i.e., a first number of model votes for the first channel category, N1) and a second number of features associated with a second channel category (i.e., a second number of model votes for the second channel category, N2). The ratio of the first number to the second number may be used to determine the probability that the communication channel belongs to the first channel category.

[0093] exist Figure 3A In the example of FIG. 1 , the channel measurement information x obtained by the first device 110 - 1 i The feature 312 of is close to six features associated with category 1 and one feature associated with category 2, which means that the probability of the communication channel of the first device 110-1 is 6 / 7. Figure 3B In the example of FIG. 1 , the channel measurement information x obtained by the first device 110 - 2 j The feature 314 is close to four features associated with category 1 and three features associated with category 2, which means that the probability that the communication channel of the first device 110 - 1 belongs to the first channel category is 4 / 7.

[0094] It should be understood that for the purpose of illustration, Figure 3A and Figure 3B An example is provided without suggesting a classification method of the first type of classification model. Some classification models may operate in other ways to determine the model votes for the two channel categories and then output a classification result.

[0095] In some example embodiments, the uncertainty level of the classification result output by this type of classification model can be determined based on intermediate output information (e.g., a first number of model votes for a first channel category, N1, and a second number of model votes for a second channel category).

[0096] Figure 4 A flowchart of a process 400 for determining importance assessment information according to some example embodiments of the present disclosure is shown. The process 400 may be implemented by the first device 110, for example.

[0097] At block 410, the first device 110 counts a first number N1 of model votes for the first channel category N1 and a second number N2 of model votes for the second channel category. These two numbers may be obtained from the classification model.

[0098] At block 420 , the first device 110 determines a degree of difference between the first number N1 and the second number N2 , and at block 430 , the first device 110 determines an uncertainty level based on the degree of difference.

[0099] In the case of model voting for binary classification, if the classification model is more confident about its estimate, the number of model votes for one channel class can be larger, and correspondingly, the number of model votes for another channel class can be smaller. Therefore, if the difference between the first number and the second number is high, it means that the classification model is confident about its classification result, and therefore the uncertainty level of the classification result may be low.

[0100] In some example embodiments, the difference between the first number N1 and the second number N2 may be measured based on the larger value between N1 / N2 or N2 / N1, which may be represented as max(N1 / N2, N2 / N1). In some example embodiments, the sum of N1 and N2 is determined to be K, and the difference between the first number N1 and the second number N2 may be measured based on the larger value between the ratio of N1 to K and the ratio of N2 to K, which may be represented as max(N1, N2) / K. In these cases, if max(N1 / N2, N2 / N1) or max(N1, N2) / K is determined to have a higher value, the uncertainty level (represented as "γ") of the classification result may be determined to be a higher level. In some examples, the uncertainty level γ may be determined as follows: γ=max(N1 / N2, N2 / N1) or max(N1, N2) / K. In other examples, the uncertainty level γ may be determined in other ways based on the degree of difference between N1 and N2.

[0101] In some example embodiments, using the determined uncertainty level of the classification result, the first device 110 may generate importance assessment information to include at least the determined uncertainty level.

[0102] In some example embodiments, the first device 110 may send importance assessment information if a relatively high level of uncertainty is found. Figure 4 In the example, at block 440, the first device 110 may determine whether the uncertainty level γ exceeds an uncertainty threshold denoted as γ0. In the case where the uncertainty level γ exceeds the uncertainty threshold γ0, at block 450, the first device 110 determines to send importance assessment information including the uncertainty level to the second device 120. In the case where the uncertainty level γ does not exceed the uncertainty threshold γ0, at block 460, the first device 110 determines that the importance assessment information does not need to be sent.

[0103] As described above, a high uncertainty level may correspond to a high importance level of the channel measurement information when updating the classification model. By reporting uncertainty levels exceeding the uncertainty threshold to the second device 120, it is possible to further reduce the transmission overhead between the first device 110 and the second device 120.

[0104] In some example embodiments, if applicable, first device 110 may determine an importance level for the channel measurement information based on the uncertainty level and possibly some other factors to generate importance assessment information. First device 110 may determine whether the importance level exceeds an importance threshold and decide to send the importance assessment information if the importance level exceeds the corresponding importance threshold. The importance threshold may be an importance threshold applied at second device 120.

[0105] In some example embodiments, the uncertainty threshold or importance threshold may be configured by the second device 120 to control how strictly the channel measurement information is evaluated as "important" or how "uncertain" the classification model is about its classification results. In some example embodiments, the uncertainty threshold or importance threshold may be determined based on the accuracy level of the classification model.

[0106] For example, if the classification model has a low accuracy level, e.g., 55%, in the initial stage, it generally means that the model may have difficulty distinguishing channel categories. The uncertainty threshold or importance threshold may be set to a relatively low value so that more channel measurement information may be evaluated as important to allow the third device 130 to collect more training data for model updating.

[0107] In some example embodiments, the uncertainty threshold γ0 may be updated based on updates to the classification model. As the classification model is updated and becomes more mature, its accuracy level may increase, and the uncertainty threshold or importance threshold may also be set to a larger value. For example, if the accuracy level of the classification model has climbed to 80%, the classification model may be more confident in its classification results, and the uncertainty threshold or importance threshold may also be increased.

[0108] It should be understood that in some other example embodiments, the first device 110 may instead generate importance assessment information including channel measurement information and send it to the second device 120 to request the second device 120 to perform a calculation instead of calculating the uncertainty level of the first type of classification model. In those embodiments, the operations at blocks 410, 420, and 430 in process 400 may be implemented by the second device 120. Type II classification model and uncertainty level calculation

[0109] In some example embodiments, the second type of classification model is a type of model that determines the level of uncertainty of the classification result by reconstructing the classification model. An example of such a classification model is a deep neural network (DNN) model, which typically provides a hard output (e.g., 0 or 1) to indicate whether the communication channel is classified into the first channel category or the second channel category.

[0110] Figure 5 An example of a second type of classification model 510 and a reference classification model generated therefrom according to some example embodiments of the present disclosure is shown. The classification model 510 may be in the form of a DNN model.

[0111] like Figure 5 As shown, the classification model 510 is configured as an input layer 502, one or more intermediate layers 504 and an output layer 506, each layer including a plurality of operation units (sometimes referred to as neurons). The operation units in one layer are connected to the operation units in the subsequent layer. In some example embodiments, the operation units in one layer may be connected to one or more other operation units in the same layer. Channel measurement information is input to the input layer 502 for processing, and according to the connection of the layer, the information is propagated through the inside of the (multiple) intermediate layers 504. The classification result for the channel measurement information is output from the output layer 506. Examples of the layers included in the model may include convolutional layers, batch normalization, activation functions, pooling layers, fully connected layers, LSTM (long short-term memory) layers, and other types of layers.

[0112] Note that the number of operating units and the number of layers in the classification model 510 are independent of the exemplary embodiments of the present disclosure, and these numbers are given values. The structure of the model is also non-limiting, and the connections between the operating units may have recursive or bidirectional properties. Any model suitable for channel classification may be used.

[0113] For DNN type models and similar models, there is no general solution to directly measure the uncertainty level of the prediction results based on model output or immediate information. It is worth noting that in the classification model, even if the model outputs a probability vector, the model output probability vector cannot be directly used to indicate the uncertainty level of the classification result. That is, even with a high output probability, the classification model may be uncertain in its prediction. In some example embodiments, a reconstruction of the classification model is proposed by slightly changing the model to generate multiple reference classification models, and the uncertainty level is determined based on the multiple reference classification models.

[0114] Figure 6 A flowchart of a process 600 for determining importance assessment information according to some further example embodiments of the present disclosure is shown. The process 600 may be implemented, for example, by the second device 120. In those embodiments, the second device 120 receives importance assessment information including channel measurement information from the first device 110. In order to measure the importance of the channel measurement information, the second device 120 may determine an uncertainty level of a classification result output by the classification model for the channel measurement information.

[0115] At box 610, the second device 120 generates multiple reference classification models by reconstructing the classification model. In some example embodiments, the second device 120 may slightly change the classification model by applying random neural connection discarding on the classification model. Specifically, the second device 120 may randomly discard some neural connections between the operating units in the classification model to obtain a reference classification model. In some example embodiments, the second device 120 may apply a Gaussian process to determine which neural connections are discarded from the classification model. The second device 120 may generate multiple different reference classification models by discarding means. The second device 120 may apply other discarding means to generate a reference classification model.

[0116] exist Figure 5 In the example of , the second device 120 may generate P reference classification models 512-1, 512-2, ..., 512-P (collectively or individually referred to as reference classification models 512), where P is an integer greater than 1. The reference classification model 512 may be represented as f AI-p (.).

[0117] At block 620, the second device 120 determines a plurality of reference classification results using a plurality of reference classification models based on the channel measurement information. The second device 120 may apply the channel measurement information provided by the first device 110 as a corresponding reference classification model f with p=1, 2, ..., P AI-p The second device 120 may obtain a reference classification result output by the reference classification model, where the reference classification result is represented as a matrix with p=1, 2, ..., P.

[0118] At block 630, the second device 120 determines the uncertainty level of the classification result based on the variance of the multiple reference classification results. If the variance of the multiple reference classification results is relatively high, it means that the reference classification model is inconsistent in classifying the channel measurement information. In this case, the uncertainty level of the classification result can be determined to be a relatively high level, and the channel measurement information can be determined to be informative and important when updating the original classification model.

[0119] In this way, even if the model structure is slightly changed (e.g., by dropping some connections), the classification model can be updated to be stable and have higher confidence in classifying similar communication channels.

[0120] In some example embodiments, the uncertainty threshold or importance threshold applied to the classification model for the second type may be set in a manner similar to that applied to the classification model of the first type described above. In some example embodiments, the uncertainty threshold or importance threshold applied to the classification models of the first type and the second type may be configured as the same or different thresholds.

[0121] It should be understood that in some other example embodiments, the first device 110 may alternatively determine the uncertainty level locally by performing similar operations in process 600, and then generate and send importance assessment information including the uncertainty level to the second device 120, rather than requesting the second device 120 to calculate the uncertainty level of the second type of classification model. Comparison of performance gains

[0122] Fig. 7A and Figure 7B The model performance gain of some example embodiments of the present disclosure relative to the traditional model training method is shown. According to the traditional model training method, the third device can be randomly requested by the second device to perform field measurement and classification tagging without auxiliary information. According to the exemplary embodiments of the present disclosure, by coordinating with the first device, the second device can trigger the classification tagging if important and informative channel measurement information is found.

[0123] Fig. 7AAn accuracy trend curve 710 for a conventional model training method and an accuracy trend curve 720 for a method proposed according to some exemplary embodiments of the present disclosure are shown. The two trend curves show the relationship between the accuracy climb and the number of labeled training data for training a first type of classification model. In order to achieve a satisfactory accuracy level close to 0.75, the number of labeled training data required for the classification model using the proposed method is reduced by approximately 60% compared to the conventional method.

[0124] Figure 7B The accuracy trend curve 712 of the traditional model training method and the accuracy trend curve 722 of the proposed method according to some example embodiments of the present disclosure are shown. The two trend curves show the relationship between the accuracy climb and the amount of labeled training data for training the first type of classification model. As shown in the figure, in order to achieve similar performance in terms of classification accuracy, the number of labeled training data required for the classification model can be reduced by approximately 50% using the proposed method according to some example embodiments of the present disclosure compared to the traditional method. Example Method

[0125] Figure 8 800 is a flowchart showing an example method 800 implemented at a first device according to some example embodiments of the present invention. For discussion purposes, Figure 1 The method 800 is described from the perspective of the first device 110.

[0126] At block 810 , the first device 110 determines a classification result of a communication channel using a classification model based at least in part on channel measurement information related to the communication channel.

[0127] At block 820 , the first device 110 determines, based at least in part on the type of the classification model, importance assessment information indicating an importance level of the channel measurement information in updating the classification model.

[0128] In block 830 , the first device 110 sends importance evaluation information to the second device 120 .

[0129] In some example embodiments, determining importance assessment information includes: determining whether the type of the classification model is the first type or the second type; based on determining that the type of the classification model is the first type, determining an uncertainty level of the classification result, and generating importance assessment information to include at least the uncertainty level of the classification result; and based on determining that the type of the classification model is the second type, generating importance assessment information to include at least channel measurement information.

[0130] In some example embodiments, the first type of classification model is a model type that has an uncertainty level of the classification result determined without reconstructing the classification model. In some example embodiments, the second type of classification model is a model type that has an uncertainty level of the classification result determined by reconstructing the classification model.

[0131] In some example embodiments, the classification result indicates whether the communication channel is classified as a first channel category or a second channel category, and the classification result determined using the first type of classification model is based on a ratio of a first number of model votes for the first channel category to a second number of model votes for the second channel category. In some example embodiments, determining the level of uncertainty includes: determining a degree of difference between the first number and the second number; and determining the level of uncertainty based on the degree of difference.

[0132] In some example embodiments, sending the importance assessment information to the second device includes sending the importance assessment information to the second device based on determining that the determined uncertainty level exceeds an uncertainty threshold.

[0133] In some example embodiments, the method 800 further comprises receiving an uncertainty threshold from the second device.

[0134] In some exemplary embodiments, the classification result is determined based on a predicted probability provided by a second type of classification model to indicate whether the communication channel is classified into the first channel category or the second channel category.

[0135] In some example embodiments, the method 800 further comprises receiving an update to at least the classification model from the second device.

[0136] In some example embodiments, the classification result indicates whether the communication channel is classified as a line-of-sight channel or a non-line-of-sight channel.

[0137] In some exemplary embodiments, the first device comprises a terminal device and the second device comprises a location management function. In some exemplary embodiments, the communication channel comprises a channel between the terminal device and the network device.

[0138] Fig. 9 A flowchart of an example method 900 implemented at a second device according to some example embodiments of the present invention is shown. For discussion purposes, Figure 1 Method 900 is described from the perspective of the second device 120.

[0139] In block 910 , the second device 120 receives importance evaluation information from the first device, the importance evaluation information indicating an importance level of the channel measurement information in updating a classification model used to determine a classification result of the communication channel based on the channel measurement information.

[0140] At block 920 , the second device 120 determines whether the importance level of the channel measurement information exceeds an importance threshold.

[0141] If the importance level of the channel measurement information exceeds the importance threshold, then at block 930 , the second device 120 causes the third device to perform classification marking for at least the communication channel at a location associated with the first device.

[0142] In some example embodiments, method 900 further includes: receiving at least one pair of sample channel measurement information related to the communication channel and a true classification result marked for the sample channel measurement information from a third device; and updating at least the classification model based on the at least one pair of sample channel measurement information and the true classification result.

[0143] In some example embodiments, the method 900 further comprises sending an update to at least the classification model to the first device.

[0144] In some example embodiments, receiving importance assessment information includes: based on determining that the classification model is of the first type, receiving importance assessment information including at least an uncertainty level of the classification result; and based on determining that the classification model is of the second type, receiving importance assessment information including at least channel measurement information.

[0145] In some example embodiments, the first type of classification model is a model type that has an uncertainty level of the classification result determined without reconstructing the classification model. In some example embodiments, the second type of classification model is a model type that has an uncertainty level of the classification result determined by reconstructing the classification model.

[0146] In some example embodiments, method 900 further includes: determining an uncertainty level of the classification result based on the channel measurement information in response to determining that the importance assessment information includes at least channel measurement information.

[0147] In some example embodiments, the classification model is of the second type. In some example embodiments, determining the uncertainty level of the classification result includes: generating multiple reference classification models by reconstructing the classification model; determining multiple reference classification results using the multiple reference classification models based on channel measurement information; and determining the uncertainty level of the classification result based on the variance of the multiple reference classification results.

[0148] In some example embodiments, a plurality of reference classification models are generated by applying random neural connection dropout to the classification models.

[0149] In some example embodiments, an uncertainty level for a classification result exceeding an uncertainty threshold is received from the first device.

[0150] In some example embodiments, the method 900 further comprises: sending the uncertainty threshold to the first device.

[0151] In some example embodiments, the uncertainty threshold is determined based on a level of accuracy of the classification model. In some example embodiments, the uncertainty threshold is updated based on an update to the classification model.

[0152] In some example embodiments, the classification result indicates whether the communication channel is classified as a line-of-sight channel or a non-line-of-sight channel.

[0153] In some exemplary embodiments, the first device comprises a terminal device, the second device comprises a location management function, and the third device comprises a positioning reference unit. In some exemplary embodiments, the communication channel comprises a channel between the terminal device and a network device. Example devices, equipment, and media

[0154] In some example embodiments, any of the methods 800 can be performed (e.g., Figure 1 The first device 110 in the first apparatus 110 may include a component for performing the corresponding operation of method 800. The component may be implemented in any suitable form. For example, the component may be implemented in a circuit system or a software module. The first apparatus may be implemented as or included in Figure 1 In the first device 110.

[0155] In some example embodiments, a first device includes: a component for determining a classification result of a communication channel using a classification model based at least in part on channel measurement information related to the communication channel; a component for determining importance assessment information based at least in part on a type of the classification model, the importance assessment information indicating an importance level of the channel measurement information in updating the classification model; and a component for sending the importance assessment information to a second device.

[0156] In some example embodiments, a unit for determining importance assessment information includes: a component for determining whether the type of a classification model is a first type or a second type; a component for determining an uncertainty level of a classification result based on determining that the type of the classification model is the first type, and a component for generating importance assessment information to at least include the uncertainty level of the classification result; and a component for generating importance assessment information to at least include channel measurement information based on determining that the type of the classification model is the second type.

[0157] In some example embodiments, the first type of classification model is a model type that has an uncertainty level of the classification result determined without reconstructing the classification model. In some example embodiments, the second type of classification model is a model type that has an uncertainty level of the classification result determined by reconstructing the classification model.

[0158] In some example embodiments, the classification result indicates whether the communication channel is classified into a first channel category or a second channel category, and the classification result determined using the first type of classification model is based on a ratio of a first number of model votes for the first channel category to a second number of model votes for the second channel category. In some example embodiments, the means for determining the level of uncertainty includes: means for determining a degree of difference between the first number and the second number; and means for determining the level of uncertainty based on the degree of difference.

[0159] In some example embodiments, means for sending the importance assessment information to the second device includes means for sending the importance assessment information to the second device based on determining that the determined uncertainty level exceeds an uncertainty threshold.

[0160] In some example embodiments, the first apparatus further comprises means for receiving the uncertainty threshold from the second apparatus.

[0161] In some exemplary embodiments, the classification result is determined based on a predicted probability provided by a second type of classification model to indicate whether the communication channel is classified into the first channel category or the second channel category.

[0162] In some example embodiments, the first apparatus further comprises means for receiving an update to at least the classification model from the second apparatus.

[0163] In some example embodiments, the classification result indicates whether the communication channel is classified as a line-of-sight channel or a non-line-of-sight channel.

[0164] In some exemplary embodiments, the first device comprises a terminal device and the second device comprises a location management function. In some exemplary embodiments, the communication channel comprises a channel between the terminal device and the network device.

[0165] In some example embodiments, the first apparatus further comprises means for performing other operations in some example embodiments of the method 800 or the first device 110. In some example embodiments, the apparatus comprises at least one processor; and at least one memory stores instructions that, when executed by the at least one processor, cause the first apparatus to execute the instructions.

[0166] In some example embodiments, any of the methods 900 can be performed (e.g., Figure 1 The second device (the second device 120 in the method 900) may include a device for performing the corresponding operation of the method 900. The device may be implemented in any suitable form. For example, the device may be implemented in a circuit system or a software module. The second device may be implemented as or included in Figure 1 In the second device 120 in.

[0167] In some example embodiments, the second device includes: a component for receiving importance assessment information from the first device, the importance assessment information indicating an importance level of channel measurement information when updating a classification model, the classification model being used to determine a classification result of a communication channel based on the channel measurement information; a component for determining whether the importance level of the channel measurement information exceeds an importance threshold; and a component for causing a third device to perform at least classification marking for the communication channel at a location associated with the first device based on determining that the importance level of the channel measurement information exceeds the importance threshold.

[0168] In some example embodiments, the second device also includes: a component for receiving at least one pair of sample channel measurement information related to the communication channel and a true classification result marked for the sample channel measurement information from a third device; and a component for at least updating the classification model based on the at least one pair of sample channel measurement information and the true classification result.

[0169] In some example embodiments, the second apparatus further comprises means for sending an update to at least the classification model to the first apparatus.

[0170] In some example embodiments, an apparatus for receiving importance assessment information includes: a component for receiving importance assessment information including at least an uncertainty level of a classification result based on determining that a classification model is of a first type; and a component for receiving importance assessment information including at least channel measurement information based on determining that the classification model is of a second type.

[0171] In some example embodiments, the first type of classification model is a model type that has an uncertainty level of the classification result determined without reconstructing the classification model. In some example embodiments, the second type of classification model is a model type that has an uncertainty level of the classification result determined by reconstructing the classification model.

[0172] In some example embodiments, the second apparatus further comprises: a component for determining an uncertainty level of the classification result based on the channel measurement information in response to determining that the importance assessment information includes at least channel measurement information.

[0173] In some example embodiments, the classification model is of the second type. In some example embodiments, the means for determining the level of uncertainty of the classification result includes: means for generating a plurality of reference classification models by reconstructing the classification model; means for determining a plurality of reference classification results using the plurality of reference classification models based on the channel measurement information; and means for determining the level of uncertainty of the classification result based on the variance of the plurality of reference classification results.

[0174] In some example embodiments, a plurality of reference classification models are generated by applying random neural connection dropout to the classification models.

[0175] In some example embodiments, an uncertainty level for a classification result exceeding an uncertainty threshold is received from the first device.

[0176] In some example embodiments, the second apparatus further comprises means for sending the uncertainty threshold to the first apparatus.

[0177] In some example embodiments, the uncertainty threshold is determined based on a level of accuracy of the classification model. In some example embodiments, the uncertainty threshold is updated based on updates to the classification model.

[0178] In some example embodiments, the classification result indicates whether the communication channel is classified as a line-of-sight channel or a non-line-of-sight channel.

[0179] In some example embodiments, the first device comprises a terminal device, the second device comprises a location management function, and the third device comprises a positioning reference unit. In some example embodiments, the communication channel comprises a channel between the terminal device and a network device.

[0180] In some example embodiments, the second apparatus further comprises means for performing other operations in some example embodiments of the method 900 or the second device 120. In some example embodiments, the means comprises at least one processor; and at least one memory stores instructions that, when executed by the at least one processor, cause the second apparatus to execute the instructions.

[0181] Fig.10 1 is a simplified block diagram of a device 1000 suitable for implementing an example embodiment of the present disclosure. The device 1000 is provided to implement a communication device, such as Figure 1 A first device 110 or a second device 120 is shown. As shown, the device 1000 includes one or more processors 1010, one or more memories 1020 coupled to the processor 1010, and one or more communication modules 1040 coupled to the processor 1010.

[0182] The communication module 1040 is used for two-way communication. The communication module 1040 has one or more communication interfaces to facilitate communication with one or more other modules or devices. The communication interface may represent any interface required to communicate with other network elements. In some example embodiments, the communication module 1040 may include at least one antenna.

[0183] As non-limiting examples, processor 1010 may be of any type suitable for a local technology network, and may include one or more of the following: a general purpose computer, a special purpose computer, a microprocessor, a digital signal processor (DSP), and a processor based on a multi-core processor architecture. Device 1000 may have multiple processors, such as application specific integrated circuit chips that are time slaved to a clock that synchronizes a main processor.

[0184] The memory 1020 may include one or more non-volatile memories and one or more volatile memories. Examples of non-volatile memories include, but are not limited to, read-only memory (ROM) 1024, electrically programmable read-only memory (EPROM), flash memory, hard disk, compact disk (CD), digital video disk (DVD), optical disk, laser disk, and other magnetic storage and / or optical storage. Examples of volatile memories include, but are not limited to, random access memory (RAM) 1022 and other volatile memories that do not persist during the duration of a power outage.

[0185] Computer program 1030 includes computer executable instructions executed by associated processor 1010. Program 1030 may be stored in a memory, such as ROM 1024. Processor 1010 may perform any suitable actions and processes by loading program 1030 into RAM 1022.

[0186] The exemplary embodiments of the present disclosure may be implemented by the program 1030 so that the device 1000 may execute the procedures described in reference to FIG. 3 to FIG. Figure 5 Any process of the present disclosure discussed. The exemplary embodiments of the present disclosure may also be implemented by hardware or a combination of software and hardware.

[0187] In some example embodiments, the program 1030 may be tangibly embodied in a computer-readable medium that may be included in the device 1000 (such as in the memory 1020) or other storage device accessible by the device 1000. The device 1000 may load the program 1030 from the computer-readable medium to the RAM 1022 for execution. The computer-readable medium may include any type of tangible non-volatile storage device, such as a ROM, EPROM, flash memory, hard disk, CD, DVD, etc. Fig.11An example of a computer readable medium 1100 is shown which may be in the form of a CD, DVD, or other optical storage disk. The computer readable medium has a program 1030 stored thereon.

[0188] Generally, various embodiments of the present disclosure may be implemented in hardware or dedicated circuits, software, logic, or any combination thereof. Some aspects may be implemented in hardware, and other aspects may be implemented in firmware or software, which may be executed by a controller, microprocessor, or other computing device. Although various aspects of the embodiments of the present disclosure are shown and described as block diagrams, flow charts, or using some other graphical representations, it should be understood that, as non-limiting examples, the blocks, devices, systems, techniques, or methods described herein may be implemented in hardware, software, firmware, dedicated circuits or logic, general hardware or controllers or other computing devices, or some combination thereof.

[0189] The present disclosure also provides at least one computer program product tangibly stored on a non-transitory computer-readable storage medium. As used herein, the term "non-transitory" is a limitation of the medium itself (i.e., tangible rather than a signal), not a limitation on data storage persistence (e.g., RAM versus ROM). The computer program product includes computer executable instructions (such as instructions included in a program module) executed in a device on a target physical or virtual processor to perform the above-referenced Figures 2 to 6 Any method described. Typically, program modules include routines, programs, libraries, objects, classes, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The functions of program modules can be combined or split between program modules as needed in various embodiments. Machine executable instructions for program modules can be executed in local or distributed devices. In distributed devices, program modules can be located in local and remote storage media.

[0190] The program code for executing the method of the present disclosure can be written in any combination of one or more programming languages. The program code can be provided to a processor or controller of a general-purpose computer, a special-purpose computer or other programmable data processing device so that the program code enables the functions / operations specified in the flow chart and / or block diagram to be implemented when executed by the processor or controller. The program code can be executed entirely on the machine, partially on the machine, as an independent software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0191] In the context of the present disclosure, computer program codes or related data may be carried by any suitable carrier to enable a device, apparatus or processor to perform various processes and operations as described above. Examples of carriers include signals, computer readable media, etc.

[0192] The computer readable medium may be a computer readable signal medium or a computer readable storage medium. The computer readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared or semiconductor system, device or apparatus, or any suitable combination of the foregoing. More specific examples of computer readable storage media would include an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0193] In addition, although the operations are depicted in a particular order, this should not be understood as requiring such operations to be performed in the particular order shown or in the order of sequence, or performing all the operations shown to achieve the desired result. In some cases, multitasking and parallel processing may be advantageous. Similarly, although several specific implementation details are included in the above discussion, these details should not be interpreted as limitations on the scope of the present disclosure, but should be interpreted as descriptions of features that may be specific to a particular embodiment. Certain features described in the context of a separate embodiment may also be implemented in combination in a single embodiment. On the contrary, the various features described in the context of a single embodiment may also be implemented in multiple embodiments individually or in any suitable sub-combination.

[0194] Although the disclosure has been described in language specific to structural features and / or methodological acts, it should be understood that the disclosure defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are disclosed as example forms of implementing the claims.

Claims

1. A first device, comprising: at least one processor; as well as at least one memory storing instructions, which when executed by the at least one processor cause the first device to at least: Determining a classification result for the communication channel using a classification model based at least in part on channel measurement information associated with the communication channel; determining, based at least in part on the type of the classification model, importance assessment information indicating an importance level of the channel measurement information in updating the classification model; as well as The importance evaluation information is sent to the second device.

2. The first device according to claim 1, wherein the at least one memory stores instructions, which, when executed by the at least one processor, cause the first device to determine the importance assessment information by: determining whether the type of the classification model is the first type or the second type; According to determining that the type of the classification model is the first type, determining a level of uncertainty for the classification result; as well as generating the importance assessment information to include at least the uncertainty level of the classification result; as well as Based on determining that the type of the classification model is the second type, the importance evaluation information is generated to include at least the channel measurement information.

3. The first device according to claim 2, wherein the classification model of the first type is a model type having an uncertainty level of a classification result determined without reconstructing the classification model, and The classification model of the second type is a model type having an uncertainty level of a classification result determined by reconstructing the classification model.

4. The first device according to claim 2 or 3, wherein the classification result indicates whether the communication channel is classified into a first channel category or a second channel category, and the classification result determined using the classification model of the first type is based on a ratio of a first number of model votes for the first channel category to a second number of model votes for the second channel category; and wherein the at least one memory stores instructions that, when executed by the at least one processor, cause the first device to determine the uncertainty level by: determining a degree of difference between the first number and the second number, and The level of uncertainty is determined based on the degree of difference.

5. The first device according to any one of claims 2 to 4, wherein the at least one memory stores instructions, which, when executed by the at least one processor, cause the first device to send the importance assessment information to the second device by: Based on determining that the determined uncertainty level exceeds an uncertainty threshold, the importance assessment information is sent to the second device.

6. The first device of claim 5, wherein the at least one memory stores instructions that, when executed by the at least one processor, further cause the first device to: The uncertainty threshold is received from the second device.

7. The first device according to any one of claims 2 to 6, wherein the classification result is determined based on a predicted probability provided by the classification model of the second type to indicate whether the communication channel is classified into the first channel category or the second channel category.

8. The first device according to any one of claims 2 to 7, wherein the at least one memory stores instructions which, when executed by the at least one processor, further cause the first device to: An update to at least the classification model is received from the second device. 9 . The first device according to claim 1 , wherein the classification result indicates whether the communication channel is classified as a line-of-sight channel or a non-line-of-sight channel.

10. The first device according to any one of claims 1 to 9, wherein the first device comprises a terminal device, and the second device comprises a location management function; and The communication channel includes a channel between the terminal device and the network device.

11. A second device, comprising: at least one processor; as well as At least one memory stores instructions that, when executed by the at least one processor, cause the second device to at least: receiving importance evaluation information from the first device, the importance evaluation information indicating an importance level of the channel measurement information in updating a classification model, the classification model being used to determine a classification result of the communication channel based on the channel measurement information; Determining whether the importance level of the channel measurement information exceeds an importance threshold; as well as Based on determining that the importance level of the channel measurement information exceeds the importance threshold, a third device is caused to perform classification marking for at least the communication channel at a location associated with the first device.

12. The second device of claim 11, wherein the at least one memory stores instructions that, when executed by the at least one processor, further cause the second device to: receiving from the third device at least one pair of sample channel measurement information related to the communication channel and a ground truth classification result marked for the sample channel measurement information; and At least the classification model is updated based on the at least one pair of sample channel measurement information and the true classification result.

13. The second device of claim 12, wherein the at least one memory stores instructions that, when executed by the at least one processor, further cause the second device to: An update to at least the classification model is sent to the first device.

14. The second device according to any one of claims 11 to 13, wherein the at least one memory stores instructions, which when executed by the at least one processor, cause the second device to receive the importance assessment information by: Based on determining that the classification model is of the first type, receiving the importance assessment information including at least an uncertainty level of the classification result; and Based on determining that the classification model is of the second type, receiving the importance evaluation information including at least the channel measurement information.

15. The second device according to claim 14, wherein the classification model of the first type is a model type having an uncertainty level of a classification result determined without reconstructing the classification model, and The classification model of the second type is a model type having an uncertainty level of a classification result determined by reconstructing the classification model.

16. The second device of claim 14, wherein the at least one memory stores instructions that, when executed by the at least one processor, further cause the second device to: According to determining that the importance evaluation information at least includes the channel measurement information, the uncertainty level of the classification result is determined based on the channel measurement information.

17. The second device of claim 16, wherein the classification model is of a second type, and wherein the at least one memory stores instructions that, when executed by the at least one processor, cause the second device to determine the uncertainty level of the classification result by: Generate multiple reference classification models by reconstructing the classification model; Based on the channel measurement information, determine a plurality of reference classification results using the plurality of reference classification models; and The uncertainty level of the classification result is determined based on the variance of the plurality of reference classification results.

18. The second device of claim 17, wherein the at least one memory stores instructions that, when executed by the at least one processor, cause the second device to: generate the plurality of reference classification models by applying random neural connection dropout to the classification models.

19. The second device of claim 15, wherein the at least one memory stores instructions that, when executed by the at least one processor, cause the second device to: receive from the first device the uncertainty level of the classification result that exceeds an uncertainty threshold.

20. The second device according to any one of claims 11 to 19, wherein the at least one memory stores instructions that, when executed by the at least one processor, further cause the second device to: The uncertainty threshold is sent to the first device.

21. The second device according to any one of claims 11 to 20, wherein the uncertainty threshold is determined based on the accuracy level of the classification model, and The uncertainty threshold is updated based on an update to the classification model.

22. The second device according to any one of claims 11 to 21, wherein the classification result indicates whether the communication channel is classified as a line-of-sight channel or a non-line-of-sight channel.

23. The second device according to any one of claims 11 to 22, wherein the first device comprises a terminal device, the second device comprises a location management function, and the third device comprises a positioning reference unit; and The communication channel includes a channel between the terminal device and the network device.

24. A method comprising: determining, at the first device and using the classification model, a classification result for the communication channel based at least in part on channel measurement information associated with the communication channel; determining, based at least in part on the type of the classification model, importance assessment information indicating an importance level of the channel measurement information in updating the classification model; as well as The importance evaluation information is sent to the second device.

25. A method comprising: receiving, at a second device, from a first device, importance assessment information indicating an importance level of the channel measurement information in updating a classification model used to determine a classification result of a communication channel based on the channel measurement information; Determining whether the importance level of the channel measurement information exceeds an importance threshold; as well as Based on determining that the importance level of the channel measurement information exceeds the importance threshold, a third device is caused to perform classification marking for at least the communication channel at a location associated with the first device.

26. A first device, comprising: means for determining a classification result for a communication channel using a classification model based at least in part on channel measurement information associated with the communication channel; means for determining importance assessment information based at least in part on the type of the classification model, the importance assessment information indicating an importance level of the channel measurement information in updating the classification model; and A means for sending the importance assessment information to a second device.

27. A second device, comprising: means for receiving importance evaluation information from the first device, the importance evaluation information indicating an importance level of the channel measurement information in updating a classification model used to determine a classification result of the communication channel based on the channel measurement information; A component for determining whether the importance level of the channel measurement information exceeds an importance threshold; as well as means for causing a third device to perform classification marking for at least the communication channel at a location associated with the first device based on determining that the importance level of the channel measurement information exceeds the importance threshold.

28. A computer-readable medium comprising instructions stored thereon, the instructions being configured to cause an apparatus to at least perform the method according to claim 24 or the method according to claim 25.

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