WO2025022184A1

WO2025022184A1 - Performance monitoring of channel classification

Info

Publication number: WO2025022184A1
Application number: PCT/IB2024/054448
Authority: WO
Inventors: Taylan SAHIN; Anil KIRMAZ; Muhammad Ikram ASHRAF; Dick CARRILLO MELGAREJO
Original assignee: Nokia Technologies Oy
Current assignee: Nokia Technologies Oy
Priority date: 2023-07-21
Filing date: 2024-05-07
Publication date: 2025-01-30
Anticipated expiration: 2026-01-21

Abstract

Embodiments of the present disclosure relate to apparatuses, methods, and computer readable storage media for monitoring performance of channel classification. A first apparatus obtains, from a second apparatus, first information of one or more parameters used for a channel classification at least associated with at least one feature for classifying a channel and the number of classes. The first apparatus monitors at least the one or more parameters with respect to a performance of the channel classification, and transmits, based on the monitoring and to the second apparatus, second information for improving the performance of the channel classification.

Description

PERFORMANCE MONITORING OF CHANNEL CLASSIFICATION CROSS-REFERENCE TO RELATED APPLICATION [0001] The present application claims priority to, and the benefit of, US Provisional Application No. 63/514955, filed July 21, 2023, the contents of which are hereby incorporated by reference in their entirety. FIELDS [0002] Various example embodiments of the present disclosure generally relate to the field of telecommunication and in particular, to methods, devices, apparatuses and computer readable storage medium for monitoring performance of channel classification. BACKGROUND [0003] Location-awareness enables various location-based services in different applications and thus is a fundamental aspect of wireless communication networks. The integration and utilization of location information in day-to-day applications are growing significantly as the technology's accuracy evolves. [0004] Many positioning technologies that depend on techniques such time of arrival (TOA), time difference of arrival (TDOA) and angle of arrival (AOA) require light-of- sight (LOS) propagation between a reference point (such as a network device) and a mobile device to be positioned. However, as for non-line-of-sight (NLOS) propagation cases in indoor/outdoor environments, positioning accuracy deteriorates remarkably due to incapability in identifying reflected multipath radio frequency (RF) propagations from diverse arriving angles with diverse delay spreads. Artificial intelligence (AI) algorithms, on the other hand, is intrinsically superior in terms of accuracy and efficiency for fingerprint styled positioning inference regardless of LOS or NLOS. Therefore, channel classification is important at least due to its impact on positioning accuracy. SUMMARY [0005] In a first aspect of the present disclosure, there is provided a first apparatus. The first apparatus comprises at least one processor; and at least one memory storing instructions that, when executed by the at least one processor, cause the first apparatus at least to: obtain, from a second apparatus, first information of one or more parameters used for a channel classification at least associated with at least one feature for classifying a channel and the number of classes; monitor at least the one or more parameters with respect to a performance of the channel classification; and transmit, based on the monitoring and to the second apparatus, second information for improving the performance of the channel classification. [0006] In a second aspect of the present disclosure, there is provided a second apparatus. The second apparatus comprises at least one processor; and at least one memory storing instructions that, when executed by the at least one processor, cause the second apparatus at least to: transmit, to a first apparatus, first information of one or more parameters used for a channel classification at least associated with at least one feature for classifying a channel and the number of classes; and receive, from the first apparatus, second information for improving a performance of the channel classification associated with at least the one or more parameters. [0007] In a third aspect of the present disclosure, there is provided a method. The method comprises: obtaining, from a second apparatus, first information of one or more parameters used for a channel classification at least associated with at least one feature for classifying a channel and the number of classes; monitoring at least the one or more parameters with respect to a performance of the channel classification; and transmitting, based on the monitoring and to the second apparatus, second information for improving the performance of the channel classification. [0008] In a fourth aspect of the present disclosure, there is provided a method. The method comprises: transmitting, to a first apparatus, first information of one or more parameters used for a channel classification at least associated with at least one feature for classifying a channel and the number of classes; and receiving, from the first apparatus, second information for improving a performance of the channel classification associated with at least the one or more parameters. [0009] In a fifth aspect of the present disclosure, there is provided a first apparatus. The first apparatus comprises means for obtaining, from a second apparatus, first information of one or more parameters used for a channel classification at least associated with at least one feature for classifying a channel and the number of classes; means for monitoring at least the one or more parameters with respect to a performance of the channel classification; and means for transmitting, based on the monitoring and to the second apparatus, second information for improving the performance of the channel classification. [0010] In a sixth aspect of the present disclosure, there is provided a second apparatus. The second apparatus comprises means for transmitting, to a first apparatus, first information of one or more parameters used for a channel classification at least associated with at least one feature for classifying a channel and the number of classes; and means for receiving, from the first apparatus, second information for improving a performance of the channel classification associated with at least the one or more parameters. [0011] In a seventh aspect of the present disclosure, there is provided a computer readable medium. The computer readable medium comprises instructions stored thereon for causing an apparatus to perform at least the method according to the third aspect. [0012] In an eighth aspect of the present disclosure, there is provided a computer readable medium. The computer readable medium comprises instructions stored thereon for causing an apparatus to perform at least the method according to the fourth aspect. [0013] It is to be understood that the Summary section is not intended to identify key or essential features of 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 easily comprehensible through the following description. BRIEF DESCRIPTION OF THE DRAWINGS [0014] Some example embodiments will now be described with reference to the accompanying drawings, where: [0015] FIG. 1 illustrates an example communication environment in which example embodiments of the present disclosure can be implemented; [0016] FIG. 2 illustrates an example clustering of channel measurements; [0017] FIG. 3 illustrates a signaling chart for performance monitoring according to some example embodiments of the present disclosure; [0018] FIG.4 illustrates a further signaling chart for performance monitoring according to some example embodiments of the present disclosure; [0019] FIG. 5 illustrates a flowchart of a method implemented at a first apparatus according to some example embodiments of the present disclosure; [0020] FIG. 6 illustrates a flowchart of a method implemented at a second apparatus according to some example embodiments of the present disclosure; [0021] FIG. 7 illustrates a simplified block diagram of a device that is suitable for implementing example embodiments of the present disclosure; and [0022] FIG. 8 illustrates a block diagram of an example computer readable medium in accordance with some example embodiments of the present disclosure. [0023] Throughout the drawings, the same or similar reference numerals represent the same or similar element. DETAILED DESCRIPTION [0024] Principle of the present disclosure will now be described with reference to some example embodiments. It is to be understood that these embodiments are described only for the purpose of illustration and help those skilled in the art to understand and implement the present disclosure, without suggesting any limitation as to the scope of the disclosure. Embodiments described herein can be implemented in various manners other than the ones described below. [0025] 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 skills in the art to which this disclosure belongs. [0026] References in the present disclosure to “one embodiment,” “an embodiment,” “an example embodiment,” and the like indicate that the embodiment described may include a particular feature, structure, or characteristic, but it is not necessary that every embodiment includes the particular feature, structure, or characteristic. Moreover, such phrases are not necessarily referring to the same embodiment. Further, when a particular feature, structure, or characteristic is described in connection with an embodiment, it is submitted that it is within the knowledge of one skilled in the art to affect such feature, structure, or characteristic in connection with other embodiments whether or not explicitly described. [0027] It shall be understood that although the terms “first,” “second,”…, etc. in front of noun(s) and the like may be used herein to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another and they do not limit the order of the noun(s). For example, a first element could be termed a second element, and similarly, a second element could be termed a first element, without departing from the scope of example embodiments. As used herein, the term “and/or” includes any and all combinations of one or more of the listed terms. [0028] As used herein, “at least one of the following: <a list of two or more elements>” and “at least one of <a list of two or more elements>” and similar wording, where the list of two or more elements are joined by “and” or “or”, mean at least any one of the elements, or at least any two or more of the elements, or at least all the elements. [0029] As used herein, unless stated explicitly, performing a step “in response to A” does not indicate that the step is performed immediately after “A” occurs and one or more intervening steps may be included. [0030] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments. As used herein, the singular forms “a”, “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprises”, “comprising”, “has”, “having”, “includes” and/or “including”, when used herein, specify the presence of stated features, elements, and/or components etc., but do not preclude the presence or addition of one or more other features, elements, components and/ or combinations thereof. [0031] As used in this application, the term “circuitry” may refer to one or more or all of the following: (a) hardware-only circuit implementations (such as implementations in only analog and/or digital circuitry) and (b) combinations of hardware circuits and software, such as (as applicable): (i) a combination of analog and/or digital hardware circuit(s) with software/firmware and (ii) any portions of hardware processor(s) with software (including digital signal processor(s)), software, and memory(ies) that work together to cause an apparatus, such as a mobile phone or server, to perform various functions) and (c) hardware circuit(s) and or processor(s), such as a microprocessor(s) or a portion of a microprocessor(s), that requires software (e.g., firmware) for operation, but the software may not be present when it is not needed for operation. [0032] This definition of circuitry applies to all uses of this term in this application, including in any claims. As a further example, as used in this application, the term circuitry also covers an implementation of merely a hardware circuit or processor (or multiple processors) or portion of a hardware circuit or processor and its (or their) accompanying software and/or firmware. The term circuitry also covers, for example and if applicable to the particular claim element, a baseband integrated circuit or processor integrated circuit for a mobile device or a similar integrated circuit in server, a cellular network device, or other computing or network device. [0033] As used herein, the term “communication network” refers to a network following any suitable communication standards, such as New Radio (NR), Long Term Evolution (LTE), LTE-Advanced (LTE-A), Wideband Code Division Multiple Access (WCDMA), High-Speed Packet Access (HSPA), Narrow Band Internet of Things (NB-IoT) and so on. Furthermore, the communications between a terminal device and a network device in the communication network may be performed according to any suitable generation communication protocols, including, but not limited to, the first generation (1G), the second generation (2G), 2.5G, 2.75G, the third generation (3G), the fourth generation (4G), 4.5G, the fifth generation (5G), the sixth generation (6G) communication protocols, and/or any other protocols either currently known or to be developed in the future. Embodiments of the present disclosure may be applied in various communication systems. Given the rapid development in communications, there will of course also be future type communication technologies and systems with which the present disclosure may be embodied. It should not be seen as limiting the scope of the present disclosure to only the aforementioned system. [0034] 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 therefrom. The network device may refer to a base station (BS) or an access point (AP), for example, a node B (NodeB or NB), an evolved NodeB (eNodeB or eNB), an NR NB (also referred to as a gNB), a Remote Radio Unit (RRU), a radio header (RH), a remote radio head (RRH), a relay, an Integrated Access and Backhaul (IAB) node, a low power node such as a femto, a pico, a non-terrestrial network (NTN) or non-ground network device such as a satellite network device, a low earth orbit (LEO) satellite and a geosynchronous earth orbit (GEO) satellite, an aircraft network device, and so forth, depending on the applied terminology and technology. In some example embodiments, radio access network (RAN) split architecture comprises a Centralized Unit (CU) and a Distributed Unit (DU) at an IAB donor node. An IAB node comprises a Mobile Terminal (IAB-MT) part that behaves like a UE toward the parent node, and a DU part of an IAB node behaves like a base station toward the next-hop IAB node. [0035] The term “terminal device” refers to any end device that may be capable of wireless communication. By way of example rather than limitation, a terminal device may also be referred to as a communication device, user equipment (UE), a Subscriber Station (SS), a Portable Subscriber Station, a Mobile Station (MS), or an Access Terminal (AT). The terminal device may include, but not limited to, a mobile phone, a cellular phone, a smart phone, voice over IP (VoIP) phones, wireless local loop phones, a tablet, a wearable terminal device, a personal digital assistant (PDA), portable computers, desktop computer, image capture terminal devices such as digital cameras, gaming terminal devices, music storage and playback appliances, vehicle-mounted wireless terminal devices, wireless endpoints, mobile stations, laptop-embedded equipment (LEE), laptop-mounted equipment (LME), USB dongles, smart devices, wireless customer-premises equipment (CPE), an Internet of Things (IoT) device, a watch or other wearable, a head-mounted display (HMD), a vehicle, a drone, a medical device and applications (e.g., remote surgery), an industrial device and applications (e.g., a robot and/or other wireless devices operating in an industrial and/or an automated processing chain contexts), a consumer electronics device, a device operating on commercial and/or industrial wireless networks, and the like. The terminal device may also correspond to a Mobile Termination (MT) part of an IAB node (e.g., a relay node). In the following description, the terms “terminal device”, “communication device”, “terminal”, “user equipment” and “UE” may be used interchangeably. [0036] As used herein, the term “resource,” “transmission resource,” “resource block,” “physical resource block” (PRB), “uplink resource,” or “downlink resource” may refer to any resource for performing a communication, for example, a communication between a terminal device and a network device, such as a resource in time domain, a resource in frequency domain, a resource in space domain, a resource in code domain, or any other combination of the time, frequency, space and/or code domain resource enabling a communication, and the like. In the following, unless explicitly stated, a resource in both frequency domain and time domain will be used as an example of a transmission resource for describing some example embodiments of the present disclosure. It is noted that example embodiments of the present disclosure are equally applicable to other resources in other domains. [0037] FIG.1 illustrates an example communication environment 100 in which example embodiments of the present disclosure can be implemented. In the communication environment 100, there are a plurality of communication devices, for example, a first apparatus 110 and a second apparatus 120, and a third apparatus 130. Both the second apparatus 120 and the third apparatus 130 can communicate with the first apparatus 110. [0038] In some scenarios, the first apparatus 110 may be a server or a node or a network device that provides positioning related services. For example, the first apparatus 110 may be a Location Management Function (LMF) node. The second apparatus 120 may be a terminal device, for example, a UE, or a network device, for example, a gNB that serves a terminal device. In some embodiments, the first apparatus 110 may be a core network device, and the second apparatus 120 may be a terminal device or a radio network device. [0039] The third apparatus 130 may be a Positioning Reference Unit (PRU), for example, a UE with known location and supporting uplink (UL)/ downlink (DL)positioning measurements. Hence, the third apparatus 130 may provide correction data for positioning calculations. [0040] In some other scenarios, the first apparatus 110 may be a network device, for example, a gNB serving a terminal device and the second apparatus 120 may be a terminal device, for example, a UE. [0041] Furthermore, it is also possible that the first apparatus 110 may be a terminal device, such as a UE and the second apparatus 120 may also be a terminal device, such as another UE or a PRU. In this situation, the first apparatus 110 and the second apparatus 120 may communicate with each other via sidelink (SL). [0042] In the following, for the purpose of illustration, some example embodiments are described with the first apparatus 110 operating as a LMF node, the second apparatus 120 operating as terminal device or a network device. In some embodiments, the third apparatus 130 is also involved and operates as the PRU. However, it is to be understood that the above examples are just discussed for purpose of illustrations rather than limitations. [0043] Communications in the communication environment 100 may be implemented according to any proper communication protocol(s), comprising, but not limited to, cellular communication protocols of the first generation (1G), the second generation (2G), the third generation (3G), the fourth generation (4G), the fifth generation (5G), the sixth generation (6G), and the like, wireless local network communication protocols such as Institute for Electrical and Electronics Engineers (IEEE) 802.11 and the like, and/or any other protocols currently known or to be developed in the future. Moreover, the communication may utilize any proper wireless communication technology, comprising 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 (OFDM), Discrete Fourier Transform spread OFDM (DFT-s-OFDM) and/or any other technologies currently known or to be developed in the future. [0044] As discussed above, AI/ML(Machine Learning) are using for positioning in the following aspects. The first aspect is direct AI/ML positioning and the output of AI/ML model inference is UE location. For example, fingerprinting based on channel observation may be used as the input of an AI/ML model. The second aspect is AI/ML assisted positioning, in which the output of AI/ML model inference is new measurement and/or enhancement of existing measurement, for example, LOS/NLOS identification, timing and/or angle of measurement, likelihood of measurement, and so on. [0045] For the second aspect, i.e., AI/ML-assisted positioning, one application is to classify channel measurements since the accuracy of positioning depends highly on whether the target UE has a line-of-sight (LOS) or non-line-of-sight (NLOS) to the positioning anchors. With clean LOS links, positioning accuracy is expected to be high, i.e., would yield lower error. Whereas in the case of severe NLOS channels, higher positioning accuracy degradation would be expected. Therefore, classifying the channel measurement for positioning beforehand, and then eliminating or prioritizing it accordingly when calculating the position estimate substantially contributes to improved positioning accuracy. Similarly, a channel classification outcome would serve as a “confidence” indicator associated with a given measurement (e.g., LOS-classified Time of Arrival (ToA) measurement is more trustable than a NLOS-classified ToA measurement), which can be utilized to determine whether/how to use the associated positioning-related measurement collected from the channel, to do the positioning estimate. [0046] Classification of the channels may be based on various features (referred to as “F” for purpose of discussion) of the channel measurements such as energy, maximum amplitude, and RMS delay spread of the channel impulse response (CIR). Given a collected set of channel measurements, these can be clustered, such as via unsupervised ML techniques, which results in channel clusters centered at certain feature values, referred to as centroids (referred to as “C” for purpose of discussion). Then, a new measurement may be classified as belonging to one of the formed clusters, e.g., based on how much its extracted features are close to the centroid value of that cluster. FIG. 2 illustrates a set of channel measurements clustered into two classes. These two classes are representative of LOS and NLOS channel conditions, via unsupervised learning. As shown in FIG. 2, a new measurement may be classified into one of the two classes, for example, depending on relative difference to the cluster centroids. [0047] In addition to binary (LOS/NLOS) classification, it is also possible to extract more granular information about the channel by considering multiple classes (i.e., K classes). Such classification would reflect complex radio conditions in a given environment, which depends on the penetration, reflection, diffraction and scattering of radio signals from objects and blockers of various sizes and materials. The optimal number of classes K would depend on several factors such as the set of features F used for the classification, as well as the radio propagation environment. To illustrate, different LOS obstacles in a setting would yield different penetration delays, which in turn cause varying ranging errors. [0048] Various use cases have been introduced for AI/ML-based positioning in the 3GPP study item, for example: [0049] Case 1: UE-based positioning with UE-side model, direct AI/ML or AI/ML- assisted positioning; [0050] Case 2a: UE-assisted/LMF-based positioning with UE-side model, AI/ML- assisted positioning; [0051] Case 2b: UE-assisted/LMF-based positioning with LMF-side model, direct AI/ML positioning; [0052] Case 3a: NG-RAN node assisted positioning with gNB-side model, AI/ML- assisted positioning; and [0053] Case 3b: NG-RAN node assisted positioning with LMF-side model, direct AI/ML positioning. [0054] If the entity performing the positioning calculations (e.g., target UE) does not have access to optimized parameters for channel classification, the positioning performance may easily degrade. This might happen, e.g., when UEs come across a new environment that they have not experienced so far. In general, not all UEs might have access to the best collected data, algorithms, and processing/measurement capabilities, at all times for all environments. [0055] In the case of UE-side model (Case 1 and Case 2a as per above Rel-18 RAN1 agreement), the classification model would reside at the UE side, and in the case of gNB- side model (Case 3a), the classification model would reside at the gNB. As discussed above, while the channel classification is important factor for enhanced positioning, the performance at the UE (or gNB) needs to be monitored since the utilized model parameters or the utilized training data may not yield to a desired performance in an arbitrarily different environment. [0056] Two options have been introduced for model monitoring which involve monitoring based on the ground truth and without ground truth information. However, so far, no specific mechanisms have been defined for monitoring, especially in the absence of ground truth, including aspects such as the monitoring metric, type of measurement(s), and signaling between the positioning entities such as LMF and UE (or gNB). [0057] According to some example embodiments of the present disclosure, there is provided a solution for the network to monitor positioning-related ML performance of UE (or gNB), on the basis of the models used for channel classification, which can be used to indicate LOS/NLOS channel type, or one of multiple channel types/classes/conditions, as well as to indicate a confidence level (e.g., “inference accuracy”) associated with a positioning-related measurement/estimation (e.g., higher confidence of a ToA estimation if it is a LOS-like channel). This in turn provide necessary assistance to improve its positioning-related ML performance, when necessary. [0058] Example embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings. [0059] FIG. 3 illustrates a signaling chart 300 for performance monitoring according to some example embodiments of the present disclosure. For the purposes of discussion, the signaling chart 300 will be discussed with reference to FIG. 1, for example, by using the first apparatus 110 and the second apparatus 120. [0060] In embodiments shown with respect to FIG. 3, the first apparatus 110 may be a core network device, and the second apparatus 120 may be a terminal device or a radio network device. [0061] As shown, the second apparatus 120 transmits (325) the first information of one or more parameters used for a channel classification to the first apparatus 110. Thus, the first apparatus 110 obtains (330) the first information from the second apparatus 120. The first information is at least associated with at least one feature for classifying a channel and the number of classes. [0062] There may be various features associated with the first information, for example, a feature for classifying a channel, F, the number of classes, K, a function associated with the F and K such as the centroids, C, and/or the like. [0063] In some example embodiments, F is a feature set and may comprise at least one or more of the following channel features in Table 1.

Table 1 [0064] The first information may comprise a variety of values or parameters, for example, but not limited to, one or more features used for a channel classification, the number of classes, a centroid value with respect to the one or more features and the number of classes; or a function associated with at least one of the one or more features, the number of classes, or the centroid value. [0065] In some embodiments, there may be several cases in which the second apparatus 120 is triggered to transmit the first information to the first apparatus 110. In one example, the second apparatus 120 may transmit (305) a request of monitoring the performance of the channel classification to the first apparatus 110. Upon receiving (310) the request, the first apparatus 110 may transmit (315), to the second apparatus 120, a request of monitoring the performance of the channel classification, which indicates that the one or more parameters are to be monitored for the performance. [0066] Alternatively, or in addition, the second apparatus 120 may transmit (305) the one or more parameters to be monitored for the performance to the first apparatus 110. Similarly, upon receiving (310) the one or more parameters, the first apparatus 110 may transmit (315), to the second apparatus 120, a request of monitoring the performance of the channel classification indicating that the one or more parameters are to be monitored for the performance. [0067] As a further alternative, or in addition, the second apparatus 120 may transmit (305) an indication of an unavailability of ground truth to the first apparatus 110. Upon receiving (310) such an indication, the first apparatus 110 may transmit (315), to the second apparatus 120, a request of monitoring the performance of the channel classification indicating that the one or more parameters are to be monitored for the performance. [0068] Upon receiving (320) the request of monitoring the performance from the first apparatus 110, the second apparatus 120 transmits (325) the first information to the first apparatus 110. It is to be understood that the above examples of triggering the transmission (325) of the first information are just described for purpose of illustration. Other situations regarding other trigger manner(s)/condition(s) are also applicable. In some embodiments, the second apparatus 120 may transmit the first information unsolicitedly or periodically, without triggering or request. [0069] With the first information, the first apparatus 110 monitors (335) at least the one or more parameters with respect to a performance of the channel classification. In some embodiments, during the monitoring process, the first apparatus 110 may test the performance of the channel classification associated with the one or more parameters by using assistance information. The assistance information may include, for example, but not limited to, at least one channel measurement data with or without a ground truth, at least one channel measurement data with a position label, and/or the like. Based on the testing, the first apparatus 110 may determine an optimality of the one or more parameters based on the testing. In this way, the first apparatus 110 may have the knowledge that whether a parameter in the one or more parameters is optimal. [0070] Alternatively, in some embodiments, the first apparatus 110 may obtain a reference value corresponding to the one or more parameters. Then, the first apparatus 110 may compare an actual value of the one or more parameters with the reference value. Thus, the first apparatus 110 may determine the optimality based on a difference between the reference value and the actual value. [0071] The reference value may be any one of the values of F, K, or C. In some embodiments, one or more reference values may be obtained from the third apparatus 130. Alternatively, one or more reference values may be determined by the first apparatus 110. [0072] Based on the monitoring (335) as discussed above, the first apparatus 110 may obtain various information or data related to the performance of the channel classification. For example, the first apparatus 110 may determine one or more reference values corresponding to the one or more parameters. The first apparatus 110 may also determine difference(s) between a reference value and a corresponding actual value of one of the one or more parameters. Furthermore, the first apparatus 110 may determine data associated with at least one further feature related to the channel classification, and/or determine data available for finetuning or retraining a model, e.g., a classification model, associated with the channel classification at the second apparatus 120. [0073] Then, the first apparatus 110 transmits (350) second information for improving the performance of the channel classification to the second apparatus 120. Upon receiving (355) the second information, the second apparatus 120 may optimize the channel classification at least based on the second information. [0074] The second information may include various information or data related to the performance of the channel classification. In some embodiments, the second information may comprise at least one reference value corresponding to the one or more parameters, at least one difference between a reference value and a corresponding actual value of the one or more parameters, data associated with at least one further feature associated with the channel classification, and/or data available for finetuning or retraining a model associated with the channel classification at the second apparatus. Furthermore, the second information may include timestamp of data to allow the second apparatus 120 to determine up-to-date/outdatedness of the parameters. It is also possible that the second apparatus 120 may provide a validity or applicable condition for the data to the first apparatus 110, such as time frame, area/zone, etc, for the first apparatus 110 determine whether the above-mentioned data to be applied to optimize the channel classification. [0075] Optionally, the first apparatus 110 may not unsolicitedly provide the second information, but passively transmit it. For example, in some embodiments, the second apparatus 120 may transmit (345) a request of the second information to first apparatus receive. As a response, the second apparatus 120 transmits (350) the second information so as to improve the performance of the channel classification. [0076] In view of the above, it is possible for the network to monitor positioning-related ML performance of UE (or gNB), on the basis of models used for channel classification. As such, necessary assistance can be provided to improve positioning-related AI/ML performance. [0077] More details of embodiments of the present disclosure will be discussed with respect to FIG. 4, which illustrates a further signaling chart 400 for performance monitoring according to some example embodiments of the present disclosure. For the purposes of discussion, the signaling chart 400 will be discussed with reference to FIG. 1, for example, by using the first apparatus 110, the second apparatus 120 and the third apparatus 130. [0078] In embodiments shown with respect to FIG. 4, the first apparatus 110 may be a core network device (e.g., a LMF node), the second apparatus 120 may be a terminal device (e.g., a UE) or a radio network device (e.g., a gNB), and the third apparatus 130 may be a PRU (e.g., a UE). [0079] In example embodiments discussed with respect to FIG. 4, it is assumed that the first apparatus 110, e.g., the LMF node has access to more useful data pertaining to a certain positioning environment (e.g., ground truths of UE positions, positioning-related measurements such as LOS/NLOS, ToA, channel classes, etc.), and possesses a channel classification model with optimally determined set of features F used for channel classification, number of channel classes K, and cluster centroid values C(F, K). [0080] Whereas, the second apparatus 120 served by the first apparatus 110 possesses a different (e.g., suboptimal) set of features F’, number of classes K’, and cluster centroids C’(F’, K’), e.g., that were determined for a different target environment. [0081] In example embodiments of the present disclosure, there may be various examples of the model for the channel classification, which is also referred to as the channel classification model. An example of a channel classification model, e.g., at the second apparatus 120 will be described below. [0082] Given a new DL channel measurement (e.g., CIR, Power Delay Profile (PDP), etc.) [0083] 1. Calculating a set of pre-determined features F (e.g., maximum amplitude, mean excess delay, kurtosis, etc.) from the measurement [0084] 2. Finding difference of the calculated values to the centroids C of pre- determined K channel classes in terms of the calculated feature values [0085] 3. Using the differences to the class centroids, classify the measurement as one of K classes (hard classification): e.g., “this is a LOS channel”, or as a function of difference/proximity/likeness/weight with respect to (one or more of) K classes (soft classification), e.g., “this is 0.8-likely a LOS channel”. [0086] The classification outcome could be then used to do any of the following: [0087] • indicate the confidence of an associated positioning-related measurement (e.g., ToA, TDoA, etc.), e.g., based on soft classification – especially to be used for AI/ML-assisted positioning, [0088] • eliminate and/or weight the measurement, e.g., based on hard or soft classification – especially to be used for direct AI/ML positioning. [0089] It is to be understood that how to determine F and K (e.g., optimally for a given environment), how to utilize the classification outcome and the clustering outcome, i.e., centroids C (F, K) which can be determined via unsupervised learning, are known and thus will not be detailed here. [0090] In some example embodiments, the second apparatus 120 may first transmit (405) a request of monitoring the performance of the channel classification to the first apparatus 110. Upon receiving (410) the request, the first apparatus 110 may transmit (415), to the second apparatus 120, a request of monitoring the performance of the channel classification, which indicates that the one or more parameters are to be monitored for the performance. [0091] Alternatively, or in addition, the second apparatus 120 may transmit (405) the one or more parameters to be monitored for the performance to the first apparatus 110. Similarly, upon receiving (310) the one or more parameters, the first apparatus 110 may transmit (415), to the second apparatus 120, a request of monitoring the performance of the channel classification indicating that the one or more parameters are to be monitored for the performance. [0092] As a further alternative, or in addition, the second apparatus 120 may transmit (405) an indication of an unavailability of ground truth to the first apparatus 110. Upon receiving (410) such an indication, the first apparatus 110 may transmit (415), to the second apparatus 120, a request of monitoring the performance of the channel classification indicating that the one or more parameters are to be monitored for the performance. [0093] To monitor the performance of the second apparatus 120, the first apparatus 110 first gathers (upon a request from the second apparatus 120 or unsolicited) the parameters F’, K’, and/or C’(F’, K’) from the second apparatus 120. [0094] In some embodiments, first information including, but not limited to, the parameters F’, K’, and/or C’(F’, K’) are transmitted (425) from the second apparatus 120 based on the available channel measurements and statistics, to indicate the first apparatus 110 about the required performance monitoring metric, unavailability of ground truth, and/or the like. Such transmission (425) may be unsolicitedly or may be as a response to the receipt (420) of the request, indication or parameters from the second apparatus 120. For example, the monitoring metric could be (or a function of) C'(F’, K’) (and C(F,K)). Upon receipt (430) of the first information, for example, an indication related to presence of ground truth, the first apparatus (e.g., LMF) 110 may use it to determine and provide the necessary assistance data for the second apparatus 120 with the indicated monitoring metric. [0095] Then, the first apparatus 110 may monitor (455) the performance of the channel classification. In some embodiments, the first apparatus 110 evaluates the optimality of any of reference values {F’,K’,C’}, such as by testing the classification performance using a testing dataset (e.g., channel measurements with ground truth channel class and/or position labels), or by comparing any of reference values {F’, K’, C’} reported by the second apparatus 120 with respect to reference values {F, K, C} determined by the network, for example, the first apparatus 110. [0096] Alternatively, or in addition, the first apparatus 110 may obtain any of parameters {F’’, K’’, C’’} from the third apparatus 130, e.g., a PRU (which is a UE with known location, and supporting UL/DL positioning measurements, hence can provide correction data for positioning calculations), or any other devices (e.g., another UE) in general, so as to utilize these parameters to make a performance comparison. In some embodiments, the first apparatus 110 may transmit (435) to the third apparatus 130 a request for reference values {F’’, K’’, C’’}. The third apparatus 130, upon receiving (440) this request, may transmit (445) the reference values {F’’, K’’, C’’} to the first apparatus 110. Thus, the first apparatus 110 may use these reference values {F’’, K’’, C’’} to make a performance comparison with the reference values {F’, K’, C’} received from the second apparatus 120. [0097] Based on the evaluation, the first apparatus 110 may then transmit (460) some information (also referred to as “second information” for purpose of discussion), to assist the second apparatus 120. [0098] In some example embodiments, the first apparatus 110 may provide any of {F, K, C(F, K)} or {F’’, K’’, C’’} to be utilized by UE (which are more optimal, i.e., yielding better accuracy than {F’,K’,C’}). [0099] In another example embodiment, the first apparatus 110 may share proactively (e.g., periodically) any of {F, K, C(F, K)}, optionally associated with performance indicators (e.g., time stamp, positioning accuracy) and configurations (e.g., bandwidth, number of TRPs) with the second apparatus 120. [0100] Alternatively or additionally, in some example embodiments, the first apparatus 110 may indicate, to the second apparatus 120, the evaluated monitoring metric, e.g., as (a function of) {F’,K’,C’}, including difference/dissimilarity between any of {F’,K’,C’} and {F,K,C} or between {F’, K’, C’} and {F’’, K’’, C’'}. [0101] As a further alternative or in addition, the first apparatus 110 may provide new data to the second apparatus 120 for improving its performance of ML model(s), e.g., to retrain/finetune them. The ML model may be the same one used for the channel classification but may also be any other model residing at the UE that is used for other tasks, e.g., direct AI/ML positioning using fingerprinting. The new data may contain further channel measurements, e.g., CIRs collected from the target environment, which might be accompanied with positioning-related measurements (e.g., TOA, Angle of Arrival (AoA), etc.) and/or positioning accuracy indications (e.g., in terms of absolute/relative horizontal and/or vertical positioning error). [0102] In some embodiments, the second information may trigger/request collection of new data including any new configuration (e.g., of different bandwidth) to collect data by the second apparatus 120. As shown in FIG. 4, upon receiving (465) the second information from the first apparatus 110, the second apparatus 120 transmits (470) to the third apparatus 130 a request for reference values {F’’, K’’, C’’}. After receiving (475) the request, the third apparatus 130 provides (480) the requested reference values {F’’, K’’, C’’} to the second apparatus 120, as a response. [0103] Based on the received assistance, the UE optimizes its classification model so as to perform as desired, e.g., to meet a certain positioning accuracy target. For example, The second apparatus 120, upon receiving (485) {F’’, K’’, C’’}, may optimize (490) the classification model based on these reference values. [0104] As such, the first apparatus 110, the second apparatus 120 and the third apparatus 130 may perform channel classification/positioning with improved performance. [0105] Additionally, in some example embodiments, the second apparatus 120 may be a UE or a gNB. For example, if the second apparatus 120 is a gNB, the first apparatus 110 checks the performance of a model at the gNB. In this case, gNB utilizes the UL (and/or DL) channel measurements. [0106] In an example embodiment, the first apparatus 110, e.g., NW, requests a subset of F’, K’, C’ from the second apparatus 120, e.g., UE. The UE reports a subset of F’, K’, C’; and NW provides a subset of F, K, C. [0107] Alternatively, or in addition, in an example embodiment, the second apparatus 120, e.g., a UE requests/provides F’, K’, C’ (and/or the third apparatus 130, e.g., a UE, requests/provides F’’, K’’,C’’) information from/to other UEs, e.g., PRUs over sidelink (SL). [0108] Alternatively, or in addition, in an example embodiment, the second apparatus 120, e.g., a UE requests/provides F’, K’, C’ information with a timestamp (i.e., time at which the parameters were obtained from NW) from/to other UEs over SL to allow UEs assess the up-to-date/outdatedness of the parameters. That is, the NW may provide F,K,C to a first UE at time T, in a case where the first UE also acquires F,K,C from a second UE, which the second UE has acquired from NW at time T+k, the first UE may discard the old one and use the new data. [0109] Alternatively, or in addition, in an example embodiment, the first apparatus 110, e.g., NW, configures the UE to perform performance monitoring based on the area the UE resides. The reason being that the network may have the set of – valid - {F, K, C} parameters for a given area with successful channel classification but has not sufficient training for other areas. [0110] In such case, the first apparatus 110, e.g., network device, may configure the second apparatus 120, e.g., a UE, request for performance monitoring if it finds itself outside of a specified geofenced area. [0111] The network may respond to such request by providing the valid set of {F,K,C} parameters, or respond with an “unknown” message if such parameters are not known to the network for the requested area. [0112] Alternatively, or in addition, in an example embodiment, the first apparatus 110, e.g., NW, may configure the second apparatus 120, e.g., UE, to periodically request classification performance monitoring, with configured periodicity. [0113] Alternatively, or in addition, in an example embodiment, the first apparatus 110, e.g., NW, configures the UE to request classification performance monitoring if some event is identified. For example, this triggering event may be defined as: “the rate at which the measurement from one TRP changes over time is larger than the threshold defined as, e.g., X (dBm RSRP) / msec”. [0114] Alternatively, or in addition, in an example embodiment, the communication between LMF and UE may take place using LPP protocol or via gNBs using NR positioning protocol A (NRPPa)+RRC protocols; whereas the communication between LMF and gNB may take place using NRPPa protocol. [0115] Alternatively, or in addition, in an example embodiment, the information provided by LMF could be broadcast/groupcast i.e., not intending a single target UE, but any (potential) target UE. [0116] FIG. 5 shows a flowchart of an example method 500 implemented at a first apparatus in accordance with some example embodiments of the present disclosure. For the purpose of discussion, the method 500 will be described from the perspective of the first apparatus 110 in FIG. 1. [0117] At block 510, the first apparatus 110 obtains, from a second apparatus 120, first information of one or more parameters used for a channel classification at least associated with at least one feature for classifying a channel and the number of classes. [0118] At block 520, the first apparatus 110 monitors at least the one or more parameters with respect to a performance of the channel classification. [0119] At block 530, the first apparatus 110 transmits, based on the monitoring and to the second apparatus 120, second information for improving the performance of the channel classification. [0120] In some example embodiments, the method 500 further comprises: receiving, from the second apparatus 120, at least one of the following: a request of monitoring the performance of the channel classification, the one or more parameters to be monitored for the performance, or an indication of an unavailability of ground truth. [0121] In some example embodiments, the method 500 further comprises: transmitting, to the second apparatus 120, a request of monitoring the performance of the channel classification indicating that the one or more parameters are to be monitored for the performance. [0122] In some example embodiments, the method 500 further comprises: testing the performance of the channel classification associated with the one or more parameters by using assistance information including at least one of the following: at least one channel measurement data with or without a ground truth, the ground truth comprises at least one of: a position label or respective channel class associated with at least one channel measurement; and determining an optimality of the one or more parameters based on the testing. [0123] In some example embodiments, the method 500 further comprises: obtaining a reference value corresponding to the at least one of the one or more parameters; comparing an actual value of at least one of the one or more parameters with the reference value; and determining an optimality based on a difference between the reference value and the actual value. [0124] In some example embodiments, the reference value is obtained from a third apparatus or determined by the first apparatus. [0125] In some example embodiments, the first information comprises at least one of: one or more features used for a channel classification, the number of classes, a centroid value with respect to the one or more features and the number of classes; or a function associated with at least one of the one or more features, the number of classes, or the centroid value. [0126] In some example embodiments, the second information comprises at least one of the following: at least one reference value corresponding to the at least one of the one or more parameters, at least one difference between a reference value and a corresponding actual value of the at least one of the one or more parameters, data associated with at least one further feature associated with the channel classification, or data available for finetuning or retraining a model associated with the channel classification at the second apparatus, or a timestamp indicating a validity of the data; or a validity or applicable condition for the data. [0127] In some example embodiments, the method 500 further comprises: receiving, from the second apparatus 120, a request of the second information. [0128] In some example embodiments, the first apparatus 110 comprises a location management function, a radio network device, or a terminal device, and the second apparatus 120 comprises a terminal device or a radio network device. [0129] FIG. 6 shows a flowchart of an example method 600 implemented at a second apparatus in accordance with some example embodiments of the present disclosure. For the purpose of discussion, the method 600 will be described from the perspective of the second apparatus 120 in FIG. 1. [0130] At block 610, the second apparatus 120 transmits, to a first apparatus 110, first information of one or more parameters used for a channel classification at least associated with at least one feature for classifying a channel and the number of classes. [0131] At block 620, the second apparatus 120 receives, from the first apparatus, second information for improving a performance of the channel classification associated with at least the one or more parameters. [0132] In some example embodiments, the method 600 further comprises: transmitting, to the first apparatus 110, at least one of the following: a request of monitoring the performance of the channel classification, the one or more parameters to be monitored for the performance, or an indication of an unavailability of ground truth. [0133] In some example embodiments, the method 600 further comprises: receiving, from the first apparatus 110, a request of monitoring the performance of the channel classification indicating that the one or more parameters are to be monitored for the performance. [0134] In some example embodiments, the second information comprises at least one of the following: at least one reference value corresponding to at least one of the one or more parameters, at least one difference between a reference value and a corresponding actual value of the at least one of the one or more parameters, data associated with at least one further feature associated with the channel classification, data available for finetuning or retraining a model associated with the channel classification at the second apparatus or a timestamp indicating a validity of the data; or a validity or applicable condition for the data. [0135] In some example embodiments, the method 600 further comprises: transmitting, to the first apparatus, a request of the second information. [0136] In some example embodiments, the method 600 further comprises: transmitting, to a third apparatus 130, a request of at least one reference value corresponding to at least one of the one or more parameters and receiving the at least one of the one or more parameters from the third apparatus 130. [0137] In some example embodiments, the method 600 further comprises: improving the performance of the channel classification at the second apparatus based at least on the second information. [0138] In some example embodiments, the first information comprises at least one of: one or more features used for a channel classification, the number of classes, a centroid value with respect to the one or more features and the number of classes; or a function associated with at least one of the one or more features, the number of classes, or the centroid value. [0139] In some example embodiments, the first apparatus 110 comprises a location management function, a radio network device, or a terminal device, and the second apparatus 120 comprises a terminal device or a radio network device. [0140] In some example embodiments, a first apparatus capable of performing any of the method 500 (for example, the first apparatus 110 in FIG. 1) may comprise means for performing the respective operations of the method 500. The means may be implemented in any suitable form. For example, the means may be implemented in a circuitry or software module. The first apparatus may be implemented as or included in the first apparatus 110 in FIG. 1. [0141] In some example embodiments, the first apparatus comprises means for obtaining, from a second apparatus, first information of one or more parameters used for a channel classification at least associated with at least one feature for classifying a channel and the number of classes; means for monitoring at least the one or more parameters with respect to a performance of the channel classification; and means for transmitting, based on the monitoring and to the second apparatus, second information for improving the performance of the channel classification. [0142] In some example embodiments, the first apparatus further comprises: means for receiving, from the second apparatus, at least one of the following: a request of monitoring the performance of the channel classification, the one or more parameters to be monitored for the performance, or an indication of an unavailability of ground truth. [0143] In some example embodiments, the first apparatus further comprises: means for transmitting, to the second apparatus, a request of monitoring the performance of the channel classification indicating that the one or more parameters are to be monitored for the performance. [0144] In some example embodiments, the first apparatus further comprises: testing the performance of the channel classification associated with the one or more parameters by using assistance information including at least one of the following: at least one channel measurement data with or without a ground truth, wherein the ground truth at least comprises at least one of: a position label or respective channel class associated with at least one channel measurement, means for determining an optimality of the one or more parameters based on the testing. [0145] In some example embodiments, the first apparatus further comprises: means for obtaining a reference value corresponding to at least one of the one or more parameters; means for comparing an actual value of the at least one of the one or more parameters with the reference value; and means for determining an optimality based on a difference between the reference value and the actual value. [0146] In some example embodiments, the reference value is obtained from a third apparatus or determined by the first apparatus. [0147] In some example embodiments, the first information comprises at least one of: means for one or more features used for a channel classification, the number of classes, a centroid value with respect to the one or more features and the number of classes; or a function associated with at least one of the one or more features, the number of classes, or the centroid value. [0148] In some example embodiments, the second information comprises at least one of the following: at least one reference value corresponding to at least one of the one or more parameters, at least one difference between a reference value and a corresponding actual value of the at least one of the one or more parameters, data associated with at least one further feature associated with the channel classification, or data available for finetuning or retraining a model associated with the channel classification at the second apparatus or a timestamp indicating a validity of the data; or a validity or applicable condition for the data. [0149] In some example embodiments, the first apparatus further comprises: means for receiving, from the second apparatus, a request of the second information. [0150] In some example embodiments, the first apparatus comprises a location management function, a radio network device, or a terminal device, and the second apparatus comprises a terminal device or a radio network device. [0151] In some example embodiments, the first apparatus further comprises means for performing other operations in some example embodiments of the method 500 or the first apparatus 110. In some example embodiments, the means comprises at least one processor; and at least one memory storing instructions that, when executed by the at least one processor, cause the performance of the first apparatus. [0152] In some example embodiments, a second apparatus capable of performing any of the method 600 (for example, the second apparatus 120 in FIG.1) may comprise means for performing the respective operations of the method 600. The means may be implemented in any suitable form. For example, the means may be implemented in a circuitry or software module. The second apparatus may be implemented as or included in the second apparatus 120 in FIG. 1. [0153] In some example embodiments, the second apparatus comprises means for transmitting, to a first apparatus, first information of one or more parameters used for a channel classification at least associated with at least one feature for classifying a channel and the number of classes; and means for receiving, from the first apparatus, second information for improving a performance of the channel classification associated with at least the one or more parameters. [0154] In some example embodiments, the second apparatus further comprises: means for transmitting, to the first apparatus, at least one of the following: a request of monitoring the performance of the channel classification, the one or more parameters to be monitored for the performance, or an indication of an unavailability of ground truth. [0155] In some example embodiments, the second apparatus further comprises: means for receiving, from the first apparatus, a request of monitoring the performance of the channel classification indicating that the one or more parameters are to be monitored for the performance. [0156] In some example embodiments, the second information comprises at least one of the following: at least one reference value corresponding to at least one of the one or more parameters, at least one difference between a reference value and a corresponding actual value of the at least one of the one or more parameters, data associated with at least one further feature associated with the channel classification, or data available for finetuning or retraining a model associated with the channel classification at the second apparatus or a timestamp indicating a validity of the data; or a validity or applicable condition for the data. [0157] In some example embodiments, the second apparatus further comprises: means for transmitting, to the first apparatus, a request of the second information. [0158] In some example embodiments, the second apparatus further comprises: means for transmitting, to a third apparatus, a request of at least one reference value corresponding to at least one of the one or more parameters and means for receiving the at least one of the one or more parameters from the third apparatus. [0159] In some example embodiments, the second apparatus further comprises: means for improving the performance of the channel classification at the second apparatus based at least on the second information. [0160] In some example embodiments, the first information comprises at least one of: means for one or more features used for a channel classification, the number of classes, a centroid value with respect to the one or more features and the number of classes; or a function associated with at least one of the one or more features, the number of classes, or the centroid value. [0161] In some example embodiments, the first apparatus comprises a location management function, a radio network device, or a terminal device, and the second apparatus comprises a terminal device or a radio network device. [0162] In some example embodiments, the second apparatus further comprises means for performing other operations in some example embodiments of the method 600 or the second apparatus 120. In some example embodiments, the means comprises at least one processor; and at least one memory storing instructions that, when executed by the at least one processor, cause the performance of the second apparatus. [0163] FIG. 7 is a simplified block diagram of a device 700 that is suitable for implementing example embodiments of the present disclosure. The device 700 may be provided to implement a communication device, for example, the first apparatus 110 or the second apparatus 120 as shown in FIG. 1. As shown, the device 700 includes one or more processors 710, one or more memories 720 coupled to the processor 710, and one or more communication modules 740 coupled to the processor 710. [0164] The communication module 740 is for bidirectional communications. The communication module 740 has one or more communication interfaces to facilitate communication with one or more other modules or devices. The communication interfaces may represent any interface that is necessary for communication with other network elements. In some example embodiments, the communication module 740 may include at least one antenna. [0165] The processor 710 may be of any type suitable to the local technical network and may include one or more of the following: general purpose computers, special purpose computers, microprocessors, digital signal processors (DSPs) and processors based on multicore processor architecture, as non-limiting examples. The device 700 may have multiple processors, such as an application specific integrated circuit chip that is slaved in time to a clock which synchronizes the main processor. [0166] The memory 720 may include one or more non-volatile memories and one or more volatile memories. Examples of the non-volatile memories include, but are not limited to, a Read Only Memory (ROM) 724, an electrically programmable read only memory (EPROM), a flash memory, a hard disk, a compact disc (CD), a digital video disk (DVD), an optical disk, a laser disk, and other magnetic storage and/or optical storage. Examples of the volatile memories include, but are not limited to, a random access memory (RAM) 722 and other volatile memories that will not last in the power-down duration. [0167] A computer program 730 includes computer executable instructions that are executed by the associated processor 710. The instructions of the program 730 may include instructions for performing operations/acts of some example embodiments of the present disclosure. The program 730 may be stored in the memory, e.g., the ROM 724. The processor 710 may perform any suitable actions and processing by loading the program 730 into the RAM 722. [0168] The example embodiments of the present disclosure may be implemented by means of the program 730 so that the device 700 may perform any process of the disclosure as discussed with reference to FIG. 3 to FIG. 6. The example embodiments of the present disclosure may also be implemented by hardware or by a combination of software and hardware. [0169] In some example embodiments, the program 730 may be tangibly contained in a computer readable medium which may be included in the device 700 (such as in the memory 720) or other storage devices that are accessible by the device 700. The device 700 may load the program 730 from the computer readable medium to the RAM 722 for execution. In some example embodiments, the computer readable medium may include any types of non-transitory storage medium, such as ROM, EPROM, a flash memory, a hard disk, CD, DVD, and the like. The term “non-transitory,” as used herein, is a limitation of the medium itself (i.e., tangible, not a signal) as opposed to a limitation on data storage persistency (e.g., RAM vs. ROM). [0170] FIG. 8 shows an example of the computer readable medium 800 which may be in form of CD, DVD or other optical storage disk. The computer readable medium 800 has the program 730 stored thereon. [0171] Generally, various embodiments of the present disclosure may be implemented in hardware or special purpose 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 embodiments of the present disclosure are illustrated and described as block diagrams, flowcharts, or using some other pictorial representations, it is to be understood that the block, apparatus, system, technique or method described herein may be implemented in, as non-limiting examples, hardware, software, firmware, special purpose circuits or logic, general purpose hardware or controller or other computing devices, or some combination thereof. [0172] Some example embodiments of the present disclosure also provide at least one computer program product tangibly stored on a computer readable medium, such as a non- transitory computer readable medium. The computer program product includes computer- executable instructions, such as those included in program modules, being executed in a device on a target physical or virtual processor, to carry out any of the methods as described above. Generally, program modules include routines, programs, libraries, objects, classes, components, data structures, or the like that perform particular tasks or implement particular abstract data types. The functionality of the program modules may be combined or split between program modules as desired in various embodiments. Machine-executable instructions for program modules may be executed within a local or distributed device. In a distributed device, program modules may be located in both local and remote storage media. [0173] Program code for carrying out methods of the present disclosure may be written in any combination of one or more programming languages. The program code may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the program code, when executed by the processor or controller, cause the functions/operations specified in the flowcharts and/or block diagrams to be implemented. The program code may execute entirely on a machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server. [0174] In the context of the present disclosure, the computer program code or related data may be carried by any suitable carrier to enable the device, apparatus or processor to perform various processes and operations as described above. Examples of the carrier include a signal, computer readable medium, and the like. [0175] The computer readable medium may be a computer readable signal medium or a computer readable storage medium. A computer readable medium may include but not limited to an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of the computer readable storage medium would include an electrical connection having one or more wires, a portable computer diskette, 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 disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. [0176] Further, although operations are depicted in a particular order, this should not be understood as requiring that such operations be performed in the particular order shown or in sequential order, or that all illustrated operations be performed, to achieve desirable results. In certain circumstances, multitasking and parallel processing may be advantageous. Likewise, although several specific implementation details are contained in the above discussions, these should not be construed as limitations on the scope of the present disclosure, but rather as descriptions of features that may be specific to particular embodiments. Unless explicitly stated, certain features that are described in the context of separate embodiments may also be implemented in combination in a single embodiment. Conversely, unless explicitly stated, various features that are described in the context of a single embodiment may also be implemented in a plurality of embodiments separately or in any suitable sub-combination. [0177] Although the present disclosure has been described in languages specific to structural features and/or methodological acts, it is to be understood that the present 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

WHAT IS CLAIMED IS: 1. A first apparatus comprising: at least one processor; and at least one memory storing instructions that, when executed by the at least one processor, cause the first apparatus at least to: obtain, from a second apparatus, first information of one or more parameters used for a channel classification at least associated with at least one feature for classifying a channel and the number of classes; monitor at least the one or more parameters with respect to a performance of the channel classification; and transmit, based on the monitoring and to the second apparatus, second information for improving the performance of the channel classification.

2. The first apparatus of claim 1, wherein the first apparatus is caused to: receive, from the second apparatus, at least one of the following: a request of monitoring the performance of the channel classification, the one or more parameters to be monitored for the performance, or an indication of an unavailability of ground truth.

3. The first apparatus of claim 1 or 2, wherein the first apparatus is caused to: transmit, to the second apparatus, a request of monitoring the performance of the channel classification indicating that the one or more parameters are to be monitored for the performance.

4. The first apparatus of any of claims 1-3, wherein the first apparatus is caused to: testing the performance of the channel classification associated with the one or more parameters by using assistance information including at least one channel measurement data with or without a ground truth, wherein the ground truth at least comprises at least one of: a position label; or respective channel class associated with at least one channel measurement; and determine an optimality of the one or more parameters based on the testing.

5. The first apparatus of any of claims 1-3, wherein the first apparatus is caused to: obtain a reference value corresponding to at least one of the one or more parameters; compare an actual value of the at least one of the one or more parameters with the reference value; and determine an optimality based on a difference between the reference value and the actual value.

6. The first apparatus of claim 1, wherein the reference value is obtained from a third apparatus or determined by the first apparatus.

7. The first apparatus of any of claims 1-6, wherein the first information comprises at least one of: one or more features used for a channel classification, the number of classes, a centroid value with respect to the one or more features and the number of classes; or a function associated with at least one of the one or more features, the number of classes, or the centroid value.

8. The first apparatus of any of claims 1-7, wherein the second information comprises at least one of the following: at least one reference value corresponding to at least one of the one or more parameters, at least one difference between a reference value and a corresponding actual value of the at least one of the one or more parameters, data associated with at least one further feature associated with the channel classification, or data available for finetuning or retraining a model associated with the channel classification at the second apparatus, or a timestamp indicating a validity of the data; or a validity or applicable condition for the data..

9. The first apparatus of any of claims 1-8, wherein the first apparatus is caused to: receive, from the second apparatus, a request of the second information.

10. The first apparatus of any of claims 1-9, wherein the first apparatus comprises a location management function, a radio network device, or a terminal device, and the second apparatus comprises a terminal device or a radio network device.

11. A second apparatus comprising: at least one processor; and at least one memory storing instructions that, when executed by the at least one processor, cause the second apparatus at least to: transmit, to a first apparatus, first information of one or more parameters used for a channel classification at least associated with at least one feature for classifying a channel and the number of classes; and receive, from the first apparatus, second information for improving a performance of the channel classification associated with at least the one or more parameters.

12. The second apparatus of claim 11, wherein the second apparatus is caused to: transmit, to the first apparatus, at least one of the following: a request of monitoring the performance of the channel classification, the one or more parameters to be monitored for the performance, or an indication of an unavailability of ground truth.

13. The second apparatus of claim 11, wherein the second apparatus is caused to: receive, from the first apparatus, a request of monitoring the performance of the channel classification indicating that the one or more parameters are to be monitored for the performance.

14. The second apparatus of any of claims 11-13, wherein the second information comprises at least one of the following: at least one reference value corresponding to at least one of the one or more parameters, at least one difference between a reference value and a corresponding actual value of the at least one of the one or more parameters, data associated with the at least one further feature associated with the channel classification, or data available for finetuning or retraining a model associated with the channel classification at the second apparatus, a timestamp indicating a validity of the data; or a validity or applicable condition for the data.

15. The second apparatus of any of claims 11-14, wherein the second apparatus is caused to: transmit, to the first apparatus, a request of the second information.

16. The second apparatus of any of claims 11-13, wherein the second apparatus is caused to: transmit, to a third apparatus, a request of at least one reference value corresponding to at least one of the one or more parameters; and receive the at least one reference value corresponding to the at least one of the one or more parameters from the third apparatus.

17. The second apparatus of any of claims 11-16, wherein the second apparatus is caused to: improve the performance of the channel classification at the second apparatus based at least on the second information.

18. The second apparatus of any of claims 11-17, wherein the first information comprises at least one of: one or more features used for a channel classification, the number of classes, a centroid value with respect to the one or more features and the number of classes; or a function associated with at least one of the one or more features, the number of classes, or the centroid value.

19. The second apparatus of any of claims 11-18, wherein the first apparatus comprises a location management function, a radio network device, or a terminal device, and the second apparatus comprises a terminal device or a radio network device.

20. A method comprising: obtaining, from a second apparatus, first information of one or more parameters used for a channel classification at least associated with at least one feature for classifying a channel and the number of classes; monitoring at least the one or more parameters with respect to a performance of the channel classification; and transmitting, based on the monitoring and to the second apparatus, second information for improving the performance of the channel classification.

21. A method comprising: transmitting, to a first apparatus, first information of one or more parameters used for a channel classification at least associated with at least one feature for classifying a channel and the number of classes; and receiving, from the first apparatus, second information for improving a performance of the channel classification associated with at least the one or more parameters.

22. A first apparatus comprising: means for obtaining, from a second apparatus, first information of one or more parameters used for a channel classification at least associated with at least one feature for classifying a channel and the number of classes; means for monitoring at least the one or more parameters with respect to a performance of the channel classification; and means for transmitting, based on the monitoring and to the second apparatus, second information for improving the performance of the channel classification.

23. A second apparatus comprising: means for transmitting, to a first apparatus, first information of one or more parameters used for a channel classification at least associated with at least one feature for classifying a channel and the number of classes; and means for receiving, from the first apparatus, second information for improving a performance of the channel classification associated with at least the one or more parameters.

24. A computer readable medium comprising instructions stored thereon for causing an apparatus at least to perform the method of claim 20 or the method of claim 21.