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WO2025183598A1 - Noeud de réseau radio, équipement utilisateur et procédés mis en oeuvre dans celui-ci - Google Patents

Noeud de réseau radio, équipement utilisateur et procédés mis en oeuvre dans celui-ci

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Publication number
WO2025183598A1
WO2025183598A1 PCT/SE2024/050179 SE2024050179W WO2025183598A1 WO 2025183598 A1 WO2025183598 A1 WO 2025183598A1 SE 2024050179 W SE2024050179 W SE 2024050179W WO 2025183598 A1 WO2025183598 A1 WO 2025183598A1
Authority
WO
WIPO (PCT)
Prior art keywords
network node
radio network
channel estimation
indication
computational model
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
PCT/SE2024/050179
Other languages
English (en)
Inventor
Adrian GARCIA RODRIGUEZ
Mårten SUNDBERG
David Sandberg
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Telefonaktiebolaget LM Ericsson AB
Original Assignee
Telefonaktiebolaget LM Ericsson AB
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Telefonaktiebolaget LM Ericsson AB filed Critical Telefonaktiebolaget LM Ericsson AB
Priority to PCT/SE2024/050179 priority Critical patent/WO2025183598A1/fr
Publication of WO2025183598A1 publication Critical patent/WO2025183598A1/fr
Pending legal-status Critical Current
Anticipated expiration legal-status Critical

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Classifications

    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L25/00—Baseband systems
    • H04L25/02—Details ; arrangements for supplying electrical power along data transmission lines
    • H04L25/0202—Channel estimation
    • H04L25/024—Channel estimation channel estimation algorithms
    • H04L25/0254—Channel estimation channel estimation algorithms using neural network algorithms
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L25/00—Baseband systems
    • H04L25/02—Details ; arrangements for supplying electrical power along data transmission lines
    • H04L25/0202—Channel estimation
    • H04L25/0224—Channel estimation using sounding signals

Definitions

  • Embodiments herein relate to a radio network node, a user equipment (UE), and methods performed therein for communication. Furthermore, a computer program and a computer readable storage medium are also provided herein. In particular, embodiments herein relate to usage of computational models such as an artificial intelligence (Al) module in a wireless communications network.
  • Al artificial intelligence
  • UEs also known as wireless communication devices, mobile stations, stations (STA) and/or wireless devices, communicate via for example a Radio Access Network (RAN) with one or more core networks (CN).
  • the RAN covers a geographical area which is divided into service areas or cell areas, with each service area or cell area being served by radio network node such as an access node e.g. a Wi-Fi access point or a radio base station (RBS), which in some networks may also be called, for example, a NodeB, a gNodeB, or an eNodeB.
  • the service area or cell area is a geographical area where radio coverage is provided by the radio network node.
  • the radio network node operates on radio frequencies to communicate over an air interface with the UEs within range of the radio network node.
  • the radio network node communicates over a downlink (DL) to the UE and the UE communicates over an uplink (UL) to the radio network node.
  • DL downlink
  • UL uplink
  • a Universal Mobile Telecommunications System is a third generation telecommunications network, which evolved from the second generation (2G) Global System for Mobile Communications (GSM).
  • the UMTS terrestrial radio access network (UTRAN) is essentially a RAN using wideband code division multiple access (WCDMA) and/or High-Speed Packet Access (HSPA) for communication with user equipment.
  • WCDMA wideband code division multiple access
  • HSPA High-Speed Packet Access
  • 3GPP Third Generation Partnership Project
  • telecommunications suppliers propose and agree upon standards for present and future generation networks and UTRAN specifically, and investigate enhanced data rate and radio capacity.
  • 3GPP Third Generation Partnership Project
  • radio network nodes may be connected, e.g., by landlines or microwave, to a controller node, such as a radio network controller (RNC) or a base station controller (BSC), which supervises and coordinates various activities of the plural radio network nodes connected thereto.
  • RNC radio network controller
  • BSC base station controller
  • the RNCs are typically connected to one or more core networks.
  • the Evolved Packet System comprises the Evolved Universal Terrestrial Radio Access Network (E-UTRAN), also known as the Long-Term Evolution (LTE) radio access network, and the Evolved Packet Core (EPC), also known as System Architecture Evolution (SAE) core network.
  • E-UTRAN also known as the Long-Term Evolution (LTE) radio access network
  • EPC also known as System Architecture Evolution (SAE) core network.
  • E-UTRAN/LTE is a 3GPP radio access technology wherein the radio network nodes are directly connected to the EPC core network.
  • the Radio Access Network (RAN) of an EPS has an architecture comprising radio network nodes connected directly to one or more core networks.
  • Transmit-side beamforming means that the transmitter can amplify the transmitted signals in a selected direction or directions, while suppressing the transmitted signals in other directions.
  • a receiver can amplify signals from a selected direction or directions, while suppressing unwanted signals from other directions.
  • ICT Information and communication technology
  • Example use cases include using autoencoders for channel state information (CSI) compression to reduce the feedback overhead and improve channel prediction accuracy; using deep neural networks for classifying line-of-sight (LOS) and non-line-of-sight (NLOS) conditions to enhance the positioning accuracy; and using reinforcement learning for beam selection at the network side and/or the UE side to reduce the signaling overhead and beam alignment latency; using deep reinforcement learning to learn an optimal precoding policy for complex multiple-input multiple-output (MIMO) precoding problems.
  • CSI channel state information
  • LOS line-of-sight
  • NLOS non-line-of-sight
  • a proprietary ML model operating with the existing standard air interface is applied at one end of the communication chain, e.g., at the UE side, and the model life cycle management (LCM), e.g., model selection/training, model monitoring, model retraining, model update, is done at this node without inter-node assistance, e.g., no assistance information provided by the network node.
  • LCM model life cycle management
  • a ML model is operating at one end of the communication chain, e.g., at the UE side, but this node gets assistance from the node(s) at the other end of the communication chain, e.g., a gNB, for its Al model LCM, e.g., for training/retraining the Al model, model update.
  • the Al model may be split into one part located at the network side and another part located at the UE side.
  • the Al model requires joint training between the network and UE, and the Al model life cycle management involves both ends of a communication chain.
  • the Al model LCM typically comprises
  • a training (re-training) pipeline top pipeline of Fig.1
  • data ingestion which refers to gathering raw training data from a data storage. After data ingestion, there may also be a step that controls the validity of the gathered data.
  • data pre-processing which refers to some feature engineering applied to the gathered data. For example, this may include data normalization and possibly a data transformation required for the input data to the Al model.
  • model evaluation which refers to benchmarking the performance to some baseline. The iterative steps of model training and model evaluation continues until an acceptable level of performance is achieved.
  • model registration which refers to registering the Al model, including any corresponding Al-meta data that provides information on how the Al model was developed, and possibly Al model evaluations performance outcomes.
  • An inference pipeline (lower pipeline of Fig. 1), o With data ingestion, which refers to gathering raw inference data from a data storage. o With data pre-processing stage, which may be identical to the corresponding processing that occurs in the training pipeline. o With model operational, which refers to using the trained and deployed model in an operational mode. o With data & model monitoring, which refers to validating that the inference data are from a distribution that aligns well with the training data, as well as monitoring model outputs for detecting any performance, or operational, drifts.
  • Fig. 1 thus, illustrates training and inference pipelines, and their interactions within a model LCM.
  • the AI/ML-based CSI feedback compression work within 3GPP Release 18 aims enhancing the CSI quality available at the gNB to improve spatial precoding/detection capabilities, and • reducing the excessive feedback overhead.
  • the gNB transmits downlink CSI-RSs to enable the estimation of the downlink channel at the UE,
  • the gNB schedules uplink time/frequency resources for the transmission of the CSI feedback, (not shown in Fig. 2),
  • the UE estimates the channel and compresses the channel estimates into a latent space with the help of an Al-designed encoder
  • the UE quantizes each of the latent space coefficients and conveys them in the uplink time/frequency resources scheduled by the gNB in Step 2),
  • the gNB dequantizes the information received by the UE and feeds the latent space coefficients to an Al-designed decoder to obtain the CSI estimates.
  • Fig. 3 shows a conceptual illustration of the AI/ML-based downlink CSI estimation based on uplink CSI in Frequency Division Duplex (FDD) systems.
  • FDD Frequency Division Duplex
  • Fig. 3 extracted from M. Arnold, S. Ddrner, S. Cammerer, S. Yan, J. Hoydis and S. ten Brink, "Enabling FDD Massive MIMO through Deep Learning-based Channel Prediction," available as arXiv: 1901.03664, an alternative procedure to estimate downlink CSI in FDD systems consists in utilizing AI/ML models to perform the extrapolation through the frequency gap by relying on uplink measurements, see, e.g., M. Alrabeiah and A.
  • a straightforward solution to determine whether to use the AI/ML-based uplink-to- downlink CSI mapping may be the following:
  • Network configures and UE measures on downlink CSI-RSs.
  • UE reports the measurements e.g., CSI Type II and/or the AI/ML-based compressed CSI feedback.
  • Network compares i) the output of the AI/ML uplink-to-downlink CSI mapping model with ii) the UE-reported measurements to determine whether the AI/ML uplink-to-downlink CSI mapping is performing adequately.
  • An object herein is to handle channel estimations or data related to channel estimations in an efficient manner.
  • the object is achieved by providing a method performed by a UE for handling communication in a wireless communications network.
  • the UE obtains a first indication locally or from a radio network node, wherein the first indication indicates a computational model.
  • the UE transmits a result indication of a preferred process out of a first and second process for a downlink channel estimation at the radio network node, to the radio network node, wherein the result indication is obtained from the indicated computational model.
  • the object is achieved by providing a method performed by a radio network node for handling communication in a wireless communications network.
  • the radio network node trains a computational model to indicate whether to use a first process or a second process for obtaining data related to a downlink channel estimation.
  • the radio network node transfers a first indication of the trained computational model to a UE, and receives a result indication from the UE, wherein the result indication indicates a preferred process out of the first and second processes for obtaining the data related to the downlink channel estimation at the radio network node.
  • the radio network node executes the computational model to obtain a network result indication of a network preferred process out of the first and second processes for obtaining the data related to the downlink channel estimation at the radio network node.
  • a computer program comprising instructions, which, when executed on at least one processor, cause the at least one processor to carry out any of the methods above, as performed by the radio network node and the UE, respectively.
  • a computer-readable storage medium having stored thereon a computer program comprising instructions which, when executed on at least one processor, cause the at least one processor to carry out the method according to any of the methods above, as performed by the radio network node and the UE, respectively.
  • a radio network node and a UE are herein provided to be configured to perform the methods herein, respectively.
  • a UE for handling communication in a wireless communications network.
  • the UE is configured to obtain a first indication locally or from a radio network node, wherein the first indication indicates a computational model.
  • the UE is configured to transmit a result indication of a preferred process out of a first and second process for a downlink channel estimation at the radio network node, to the radio network node, wherein the result indication is obtained from the indicated computational model.
  • a radio network node for handling communication in a wireless communications network.
  • the radio network node is configured to train a computational model to indicate whether to use a first process or a second process for obtaining data related to a downlink channel estimation.
  • the radio network node is configured to transfer a first indication of the trained computational model to a UE, and to receive a result indication from the UE, wherein the result indication indicates a preferred process out of the first and second processes for obtaining the data related to the downlink channel estimation at the radio network node.
  • the radio network node is configured to execute the computational model to obtain a network result indication of a network preferred process out of the first and second processes for obtaining the data related to the downlink channel estimation at the radio network node.
  • the first process such as an uplink-to-downlink CSI mapping, for example, having an Al model that predicts DL CSI from UL measurements; or,
  • the second process such as a conventional DL CSI acquisition method, such as existing CSI reporting framework from the UE.
  • the embodiments herein may apply to, e.g., FDD systems or multi-carrier TDD configurations in where UL measurements in one carrier could perform a mapping to DL CSI for another carrier.
  • the embodiments herein may be applied to, e.g., typical cellular systems with where the network communicates with UEs or fixed wireless access (FWA) deployments where two network nodes communicate with each other.
  • FWA fixed wireless access
  • Embodiments herein may achieve a minimal allocation of RS resources for CSI acquisition, in both UL and DL, while ensuring a certain level of targeted network Key Performance Indicator (KPI), such as DL precoding quality achieving, e.g., a targeted DL throughput.
  • KPI Key Performance Indicator
  • embodiments herein handle channel estimations or data related to channel estimations in an efficient manner.
  • Fig. 1 illustrates training and inference pipelines according to prior art
  • Fig. 2 shows an illustration of the AI/ML-based CSI feedback framework according to prior art
  • Fig. 3 shows a conceptual illustration of mapping UL to DL
  • Fig. 4 is a schematic overview depicting a wireless communications network according to embodiments herein;
  • Fig. 5a shows a combined flowchart and signalling scheme according to some embodiments herein;
  • Fig. 5b shows a combined flowchart and signalling scheme according to some embodiments herein;
  • Fig. 6 is a schematic flowchart depicting a method performed by a UE according to embodiments herein;
  • Fig. 7 is a schematic flowchart depicting a method performed by a radio network node according to embodiments herein;
  • FIG. 8a are schematic flowcharts depicting methods according to some embodiments herein;
  • FIG. 8b are schematic flowcharts depicting methods according to some embodiments herein
  • Fig. 8c is a schematic flowchart depicting a method according to some embodiments herein
  • Fig. 8d is a schematic flowchart depicting methods according to some embodiments herein
  • Fig. 9 is a schematic flowchart depicting methods according to some embodiments herein;
  • Fig. 10 is a schematic flowchart depicting a method according to some embodiments herein;
  • Fig. 11 is a schematic flowchart depicting a method according to some embodiments herein;
  • Fig. 12 is a schematic flowchart depicting a method according to some embodiments herein;
  • Fig. 13 is a block diagram depicting a UE according to embodiments herein;
  • Fig. 14 is a block diagram depicting a radio network node according to embodiments herein;
  • Fig. 15 shows an example of a communication system QQ100 in accordance with some embodiments
  • Fig. 16 shows a UE QQ200 in accordance with some embodiments
  • Fig. 17 shows a network node QQ300 in accordance with some embodiments
  • Fig. 18 is a block diagram of a host QQ400, which may be an embodiment of the host QQ1 16 of Fig. 15, in accordance with various aspects described herein;
  • Fig. 19 is a block diagram illustrating a virtualization environment QQ500 in which functions implemented by some embodiments may be virtualized.
  • Fig. 20 shows a communication diagram of a host QQ602 communicating via a network node QQ604 with a UE QQ606 over a partially wireless connection in accordance with some embodiments.
  • Fig. 4 is a schematic overview depicting a wireless communications network 1.
  • the wireless communications network 1 comprises one or more RANs and one or more CNs.
  • the wireless communications network 1 may use a number of different technologies, such as Wi-Fi, Long Term Evolution (LTE), LTE-Advanced, NR, Wideband Code Division Multiple Access (WCDMA), Global System for Mobile communications/Enhanced Data rate for GSM Evolution (GSM/EDGE), Worldwide Interoperability for Microwave Access (WiMax), or Ultra Mobile Broadband (UMB), just to mention a few possible implementations.
  • WCDMA Wideband Code Division Multiple Access
  • GSM/EDGE Global System for Mobile communications/Enhanced Data rate for GSM Evolution
  • WiMax Worldwide Interoperability for Microwave Access
  • UMB Ultra Mobile Broadband
  • wireless devices e.g.
  • a user equipment (UE) 10 such as a mobile station, a non-access point (non-AP) STA, a STA, a wireless device and/or a wireless terminal, communicate via one or more Access Networks (AN), e.g. a RAN, to one or more core networks (CN).
  • AN e.g. a RAN
  • CN core networks
  • UE is a non-limiting term which means any terminal, wireless communication terminal, internet of things (loT) capable device, Machine Type Communication (MTC) device, Device to Device (D2D) terminal, or node e.g. smart phone, laptop, mobile phone, sensor, relay, mobile tablets or even a base station communicating within a cell.
  • MTC Machine Type Communication
  • D2D Device to Device
  • the wireless communications network 1 comprises a radio network node 12 providing radio coverage over a geographical area, e.g. a first service area, of a first radio access technology (RAT), such as NR, LTE, UMTS, Wi-Fi or similar.
  • the radio network node 12 may be a radio access network node such as radio network controller or an access point such as a wireless local area network (WLAN) access point or an Access Point Station (AP STA), an access controller, a base station, e.g.
  • a radio base station such as a NodeB, an evolved Node B (eNB, eNodeB), a base transceiver station, Access Point Base Station, base station router, a transmission arrangement of a radio base station, a stand-alone access point or any other network unit capable of serving a UE within the service area served by the radio network node 12 depending e.g. on the first radio access technology and terminology used.
  • a radio base station such as a NodeB, an evolved Node B (eNB, eNodeB), a base transceiver station, Access Point Base Station, base station router, a transmission arrangement of a radio base station, a stand-alone access point or any other network unit capable of serving a UE within the service area served by the radio network node 12 depending e.g. on the first radio access technology and terminology used.
  • the radio network node 12 and/or the UE 10 may handle one or more computational models such as Al models or ML models.
  • the training of a computational model may be performed in a centralized, decentralized, and hierarchical manner at the radio network node 12.
  • the radio network node 12 may be a standalone server, a cloud-implemented server, a distributed server or processing resources in a server farm or same node.
  • Embodiments herein may be implemented as physical bare metal, virtual or cloud native such as Kubernetes environment in, e.g., hyper-cloud networks.
  • the UE 10 obtains a first indication, locally or from the radio network node 12.
  • the UE may receive the computational model of an index value indicating the computational model, or may retrieve the computational model of an index value indicating the computational model from a memory of the UE 10.
  • the first indication indicates a computational model, which computational model is used to indicate whether to use a first process or a second process for obtaining data related to a downlink channel estimation at the radio network node 12.
  • the UE 10 further executes the indicated computational model to obtain a result indication of a preferred process out of the first and second processes for the downlink channel estimation at the radio network node 12.
  • the UE 10 transmits the result indication to the radio network node 12.
  • the radio network node 12 trains the computational model and executes the computational model to obtain a network result indication of a network preferred process out of the first and second processes for obtaining the data related to the downlink channel estimations at the radio network node 12. It should be understood that the radio network node 12 may take the received result indication into account when determining preferred process such as the first process.
  • the first process relates to an uplink- to-downlink CSI mapping predictor with the following main features:
  • Model inputs At least
  • such downlink CSI may refer to, e.g., o Type I CSI feedback as per the existing specifications, o Type II CSI feedback as per the existing specifications, o Type II CSI feedback with a reduced quantization resolution w.r.t. the existing specifications (not high-fidelity measurements), o Type II CSI feedback with a reduced number of quantized phase and amplitude coefficients w.r.t. the existing specifications.
  • Model output I ground truth High-fidelity complete version of the downlink CSI from a third frequency band.
  • the third and the second frequency bands are the same.
  • Loss function Metric that measures the quality of the resultant downlink CSI. Examples of loss functions include mean squared error, normalized mean squared error, cosine similarity, beamforming gain, etc.
  • the concept “network” may refer to one of a generic network node, a gNB (or the corresponding node in a 6G network), a base station, a unit within the base station to handle at least some ML operation, a relay node, a core network node, a core network node that handle at least some ML operations, or a device supporting D2D communication.
  • a computational model may refer to an ML-based model, a configuration of an ML-based model, a non-ML-based functionality, or a configuration of a non- ML-based functionality.
  • An AI/ML model can be defined as a functionality or be part of a functionality that is deployed/implemented in a first node. This first node can receive a message from a second node indicating that the functionality is not performing correctly. Further, an AI/ML model can be defined as a feature or part of a feature that is implemented/supported in a first node. This first node can indicate the feature version to a second node. If the ML-model is updated, the feature version maybe changed by the first node.
  • Fig. 5a is a combined flowchart and signalling scheme according to some embodiments herein. The order of the actions may be performed in any suitable manner.
  • the radio network node 12 trains the computational model.
  • the radio network node 12 transmit the first indication indicating the trained computational model.
  • the computational model is used to indicate whether to use the first process or the second process for obtaining data related to a downlink channel estimation at the radio network node 12.
  • Data herein meaning DL precoder to use, precoder data to use, and/or channel estimates.
  • the UE 10 may execute the computational model to indicate process to obtain the data.
  • the UE 10 may use the obtained computational model to indicate use or not of the first process.
  • the UE 10 then transmits the result indication to the radio network node 12.
  • the result indication may be a flag, a value, an out-of-distribution (OoD) distribution or indication, such as a OoD value, or similar
  • the radio network node 12 determines the preferred process to use. This may be based on the result indication or the radio network node 12 may take the result indication into account.
  • the radio network node 12 may also configure resources to perform the first process such as activate or deactivate reference signals and/or allocate resources for uplink signals. Action 506. The radio network node 12 then uses the preferred process such as the first process to obtain the data related to the downlink channel estimation, such as CSI data, at the radio network node 12.
  • Fig. 5b is a combined flowchart and signalling scheme according to some alternative embodiments herein. The order of the actions may be performed in any suitable manner.
  • the radio network node 12 trains the computational model.
  • the radio network node 12 may execute the computational model to indicate process to obtain the data. Thus, the radio network node 12 may use the obtained computational model to indicate use or not of the first process.
  • a network result indication may be a flag, a value, an OoD distribution or indication, such as a OoD value, or similar. As a result, the radio network node 12 determines the preferred process to use.
  • the radio network node 12 may also configure resources to perform the first process such as activate or deactivate reference signals and/or allocate resources for uplink signals.
  • the radio network node 12 uses the preferred process such as the first process to obtain the data related to the downlink channel estimation, such as CSI data, at the radio network node 12.
  • Example embodiments of the method performed by the UE 10 for handling communication in the wireless communications network 1 will now be described with reference to a flowchart depicted in Fig. 6.
  • the actions do not have to be taken in the order stated below, but may be taken in any suitable order.
  • Optional actions are marked with dashed boxes.
  • the UE 10 obtains the first indication locally or from the radio network node 12, wherein the first indication indicates a computational model.
  • the computational model may be used to indicate whether to use the first process or the second process for obtaining data related to the downlink channel estimation at the radio network node 12.
  • the data may comprise one or more values of the channel estimation, and/or one or more downlink precoders or parameters related to a downlink precoder.
  • the one or more downlink precoders or parameters may be derived from the downlink channel estimation.
  • the first process for obtaining the data related to the downlink channel estimation may be associated with a mapping of a measured uplink channel estimation at a first frequency band to the downlink channel estimation at a second frequency band
  • the second process for obtaining the data related to the downlink channel estimation may be associated with performed one or more measurements for the downlink channel estimation.
  • the first and second bands may be different frequency bands or same frequency bands using different carriers. It should be noted that the mapping may be performed by using an Al model, ML model or an algorithm.
  • the UE may obtain the first indication by receiving it from the radio network node 12, or by retrieving the first indication from a data storage.
  • the UE 10 may further obtain a purpose of the computational model and/or a required pre-processing to generate inputs to the computational model. This may be obtained by receiving a further indication from the radio network node 12.
  • the UE 10 may execute the indicated computational model to obtain the result indication of the preferred process out of the first and second processes for the downlink channel estimation at the radio network node 12. For example, an output of the computational model may indicate that the first process is accurate within a set margin, and thus the result indication indicates the first process. Otherwise, when the output of the computational model is above the set margin, the result indication indicates the second process.
  • the UE 10 transmits the result indication of the preferred process out of the first and second process for the downlink channel estimation at the radio network node 12, to the radio network node 12, wherein the result indication is obtained from the indicated computational model.
  • the result indication may be a flag, a value, an OoD distribution or indication, such as a OoD value, or similar.
  • the UE 10 may furthermore obtain a resource indication indicating one or more resources related to CSI feedback and/or UL SRS to achieve a set key performance indicator, such as an indicator of an accuracy of the first process.
  • the resource indication may be obtained from a resource computational model and/or from the radio network node 12.
  • the UE 10 determines whether to use, or not to use, the first process such as an estimation computational model to determine data of a DL channel estimation.
  • the UE 10 further transmits to the radio network node 12 the result indication indicating the determination.
  • the UE 10 may determine to use a channel estimation type out of at least two channel estimation types based on an output from the obtained computational model.
  • the UE 10 may then indicate back to the radio network node 12 the determined channel estimation type to use.
  • Example embodiments of a method performed by the radio network node 12 for handling communication in the wireless communications network 1 will now be described with reference to a flowchart depicted in Fig. 7. The actions do not have to be taken in the order stated below, but may be taken in any suitable order. Optional actions are marked with dashed boxes.
  • the radio network node 12 trains the computational model to indicate whether to use the first process or the second process for obtaining data related to a downlink channel estimation.
  • the data may comprise one or more values of the channel estimation, and/or one or more downlink precoders or parameters related to a downlink precoder.
  • the one or more downlink precoders or parameters may be derived from the downlink channel estimation.
  • the first process for obtaining the data related to the downlink channel estimation may be associated with the mapping of a measured uplink channel estimation at the first frequency band to the downlink channel estimation at the second frequency band, and the second process for obtaining the data related to the downlink channel estimation may be associated with performed one or more measurements for the downlink channel estimation.
  • the first and second bands may be different frequency bands or same frequency bands using different carriers. It should be noted that the mapping may be performed by using an Al model, ML model or an algorithm.
  • the radio network node 12 transfers the first indication of the trained computational model to the UE 10.
  • the first indication may comprise the model as such or an index indicating the computational model.
  • the radio network node 12 may in addition transfer to the UE 10 the purpose of the trained computational model and/or the required pre-processing to generate inputs to the trained computational model.
  • the radio network node 12 may indicate the purpose of the model — identify out-of-distribution channel samples for the uplink-to-downlink CSI mapping predictor at the network — to the UE 10 prior or simultaneously to the transfer of the computational model.
  • the radio network node 12 may explicitly indicate the required pre-processing to generate the model inputs, e.g., selection of a specific pre-processing among the options defined in the specification. In other embodiments, the UE 10 will implicitly determine the pre-processing required to generate the model inputs as per the specification text.
  • the radio network node 12 then receives the result indication from the UE 10.
  • the result indication indicates a preferred process out of the first and second processes for obtaining the data related to the downlink channel estimations at the radio network node 12.
  • the result indication may be a flag, a value, an OoD distribution or indication, such as a OoD value, or similar.
  • the radio network node 12 executes the computational model to obtain a network result indication of a network preferred process out of the first and second processes for obtaining the data related to the downlink channel estimation at the radio network node 12.
  • the network result indication may be a flag, a value, an OoD distribution or indication, such as a OoD value, or similar. Thus, this may be an alternative to the transmission of the first indication or in addition also taking the result indication from the UE 10 into account.
  • the radio network node 12 may, based on received result indication and/or the obtained network result indication, determine one or more of the following: i) to execute the preferred process or the network preferred process at the radio network node, ii) activation and/or deactivation of one or more reference signals, and/or iii) a requested amount of resources related to channel state information, CSI, feedback and/or uplink sounding reference signals.
  • the requested amount of resources may be determined to reach a set key performance indicator when executing the preferred process or the network preferred process at the radio network node 12.
  • the requested amount of resources may be indicated by a resource indication obtained from a resource computational model.
  • the radio network node 12 may then transmit the resource indication to the UE 10.
  • the radio network node 12 may configure resources based on the requested amount of resources.
  • the radio network node 12 may in some embodiments herein, generate and/or train a computational model indicating whether to use, or not use, the first process such as an estimation computational model to determine data related to a DL channel estimation.
  • the radio network node 12 may transfer the computational model to the UE 10, and receive from the UE 10 the result indication indicating a result of the computational model.
  • the radio network node 12 further uses or not uses the first process to determine the data related to the DL channel estimation taking the result indication into consideration.
  • the radio network node 12 may execute the trained computational model to determine whether to use the first process or not.
  • Embodiments herein introduce efficient and dynamic methods to determine at the radio network node 12 when the data such as downlink CSI should be acquired via:
  • an uplink-to-downlink CSI mapping (for example having an Al model that predicts DL CSI from UL measurements), referred to as the first process, or,
  • the embodiments herein may apply to, e.g., FDD systems or multi-carrier TDD configurations in where UL measurements in one carrier could perform a mapping to DL CSI for another carrier.
  • one or more computational models defined i.e., trained in the case of AI/ML models
  • these one or more computational models are transferred to and executed at the UE 10.
  • Fig. 8a shows a sequence of network-based (left) and UE-based (right) OoD detection.
  • Network trains AI/ML model to identify out-of-distribution channel samples for the uplink-to-downlink CSI mapping predictor at the network based on UL measurements (SRS and/or UL DM RS) and optionally CSI feedback provided by the UE
  • the model is executed at the network to predict for a given sample if it is (or the probability that it is) OoD.
  • Action 803. Network trains AI/ML model to identify out-of-distribution channel samples for the uplink-to-downlink CSI mapping predictor at the network based on DL measurements (CSI-RS).
  • Action 804. Network transfers trained AI/ML models to UE/s, indicating the purpose of the transferred AI/ML model/s and required pre-processing to generate the model inputs.
  • the model is executed at the UEto predict for a given sample if it is (or the probability that it is) OoD.
  • Action 806. UE/s report the prediction back to the network.
  • the decision if the first process such as an uplink-to-downlink CSI mapping may be effectively carried out is not black-and-white and the associated performance of the downstream task making use of the mapping, for example calculating precoding weights for beamforming purposes, may gracefully degrade I improve with different experienced channels.
  • the input data to the computational model is assumed to be OoD.
  • the second computational model may be trained to predict the amount and frequency required in time/frequency/spatial domain resources of SRS and/or UL DM RS and/or CSI-RS and/or CSI feedback.
  • This functionality is exemplified and illustrated in Fig. 8b and referred to as network-based (left) and UE-based KPI prediction (right), respectively.
  • Network trains AI/ML model to determine the amount of RS resources and [optionally] CSI-feedback required to reach a given KPI when executing the uplink-to- downlink CSI mapping predictor at the network.
  • the model is executed at the network to predict for a given sample the configuration of resources required to achieve the targeted KPI.
  • Action 813 The network takes action to configure the predicted required resources.
  • Action 814 Network trains AI/ML model to determine the amount of RS resources [optionally] CSI-feedback required to reach a given KPI when executing the uplink-to- downlink CSI mapping predictor at the UE.
  • Action 815 Network transfers trained AI/ML models to UE/s, indicating the purpose of the transferred AI/ML model/s and required pre-processing to generate the model inputs.
  • Action 816 The model is executed at the UE to predict for a given sample the configuration of resources required to achieve the targeted KPI.
  • Action 817 UE/s report the predicted resource requirement to the network and/or adjusts the amount of CSI feedback.
  • Action 818 The network takes action to configure the predicted required resources.
  • Fig. 8c shows a method according to some embodiments herein.
  • Network trains AI/ML model/s to i) identify out-of-distribution channel samples for the uplink-to-downlink CSI mapping predictor at the network, and/or ii) determine the amount of CSI feedback required to reach a given KPI when executing the uplink-to-downlink CSI mapping predictor at the network.
  • Action 822 Network transfers trained AI/ML models to UE/s, indicating the purpose of the transferred AI/ML model/s and, in some embodiments, the required preprocessing to generate the model inputs.
  • Action 823 UE/s execute the trained AI/ML models and report the output back to the network.
  • Network determines and indicates to the UE i) whether the uplink-to-downlink CSI mapping predictor at the network will be executed, ii) the activation/deactivation of reference signals (i.e. , SRSs or CSI-RSs), iii) the amount of resources allocated for CSI feedback purposes.
  • reference signals i.e. , SRSs or CSI-RSs
  • Fig. 8d shows a method according to some embodiments herein.
  • Action 831 Network trains AI/ML model/s to identify out-of-distribution channel samples for the uplink-to-downlink CSI mapping predictor at the network
  • Network trains a model to determine the amount of CSI feedback required to reach a given KPI when executing the uplink-to-downlink CSI mapping predictor at the network.
  • Action 832 Network transfers trained AI/ML models to UE/s, indicating the purpose of the transferred AI/ML model/s and, in some embodiments, the required preprocessing to generate the model inputs ⁇
  • Action 833 UE/s execute the trained AI/ML models and report the output back to the network.
  • Action 834 Based on the outputs from the AI/ML model/s executed at the network and/or the UE/s, network determines and indicates to the UE i) whether the uplink-to-downlink CSI mapping predictor at the network will be executed, ii) the activation/deactivation of reference signals (i.e. , SRSs or CSI-RSs), iii) the amount of resources allocated for CSI feedback purposes.
  • reference signals i.e. , SRSs or CSI-RSs
  • Fig. 9 shows a determination of whether the first process at the radio network node 12 such as a uplink-to-downlink CSI mapping predictor or model, produces accurate outputs, using a network-based OoD detection.
  • the radio network node 12 trains the computational model such as a first model to determine the type of downlink CSI acquisition, i.e., the first process or the second process that is preferred.
  • the radio network node 12 may train the first model to identify out-of-distribution channel samples for the uplink-to-downlink CSI mapping predictor at the radio network node 12 with the following main features: o Model inputs: At least one of the following: a) (Pre-processed) SRSs from a first uplink frequency band, e.g., uplink channel estimates derived from SRSs, and/or b) (Pre-processed) uplink DMRSs from a first uplink frequency band, and/or c) Downlink CSI from a second downlink frequency band.
  • such downlink CSI may refer to, e.g., o Type I CSI feedback as per the existing specification, o Type II CSI feedback as per the existing specification, o Type II CSI feedback with a reduced quantization resolution w.r.t. the existing specifications (i.e., not high-fidelity measurements), o Type II CSI feedback with a reduced number of quantized phase and amplitude coefficients w.r.t. the existing specification, o High-resolution Type II CSI feedback with a larger number of phase and amplitude coefficients quantized with a larger number of bits than in the existing specification.
  • o Model output / around truth a) Boolean indicating whether the loss function of the uplink-to-downlink CSI mapping predictor is below a predetermined value, and/or b) floating point number indicating, e.g., the probability that the loss function of the uplink-to-downlink CSI mapping predictor is below a predetermined value.
  • Action 902. The radio network node 12 executes the first model, and may then use the first process based on the output of the first model.
  • the radio network node 12 may indicate to the UE 10 that the first process such as the uplink-to-downlink CSI mapping predictor is being executed. In other embodiments, the UE 10 will implicitly understand that the radio network node 12 is using or executing the uplink-to-downlink CSI mapping predictor based on the presence or absence of reference signals. As an example, the UE 10 may infer that the uplink-to- downlink CSI mapping predictor is executed at the radio network node 12 if SRSs are scheduled, or that the uplink-to-downlink CSI mapping predictor is not executed if no SRSs are scheduled and Type II CSI feedback as per the existing specification is requested by the radio network node 12.
  • the UE 10 finds or determines that the radio network node 12 executes the first process it can for example enable detailed performance monitoring to discover potential problems related to downlink CSI acquisition in the radio network node 12 or to enable efficient switching between SRS transmissions and CSI-RS reception/transmission together with CSI feedback.
  • the radio network node 12 Based on the outputs of the first model, i.e. , indicating the first process or the second process, the radio network node 12 activates/deactivate reference signals utilized for CSI acquisition and/or activates/deactivate the execution of the first process.
  • the radio network node 12 may activate the execution of the uplink-to-downlink CSI mapping predictor and a. activate the transmission of SRSs from a first frequency band, and/or b. deactivate CSI-RSs enabling downlink CSI acquisition at the UEs, and/or c. do not dedicate resources for Uplink Control Information (UCI) and request UEs to transmit CSI feedback in such resources.
  • the network may deactivate the execution of the first process and a.
  • Fig. 10 shows a determination of whether the first process at the radio network node 12 such as an uplink-to-downlink CSI mapping predictor or model, produces accurate outputs, using a UE-based OoD detection.
  • the radio network node 12 may train a computational model such as a second model — with a same target of the first model, i.e. , identifying out-of-distribution (OoD) channel samples for the first process at the network.
  • a computational model such as a second model — with a same target of the first model, i.e. , identifying out-of-distribution (OoD) channel samples for the first process at the network.
  • OoD out-of-distribution
  • the radio network node 12 uses (pre-processed) CSI-RS measurements from a UE.
  • the subsequent intention is to transfer the model to a relevant UE such as the UE 10 (1002) for its subsequent execution (1003) and outcome reporting (1004).
  • the execution of the Network-based OoD detection requires the presence of active SRSs for uplink CSI acquisition purposes — therefore introducing additional overhead when not used for other purposes — and/or uplink DMRSs — which may not be present if the UE 10 does not generate uplink data traffic.
  • first model and the second model may be identical or differ.
  • the first and second models may have a different architecture, e.g., the first model may be a fully connected neural network vs. a convolutional neural network or vice versa.
  • the second model may be a low-complexity version of the first model (reduced number of layers/neurons, less bits used for weight quantization, ).
  • the radio network node 12 transfers the trained computational model, such as the second model, to the UE 10.
  • the UE 10 may indicate its capability to execute the second model to the radio network node 12.
  • the UE 10 may communicate to the radio network node such capability indication, e.g., when responding to a network-sent UECapabilityEnquiry message with a UECapabilitylnformation message.
  • the transfer of the second model is performed over-the-air.
  • This may be suitable, e.g., when different radio network nodes utilize different cell-specific models, when an updated version of the second model is available, e.g., when the second model is trained with updated data, if the transfer of the second model may be performed without affecting the network performance, and/or when there is no inter-vendor agreement for performing the model transfer during the UE manufacturing stage.
  • the second model may be made available by the network manufacturer to the UE manufacturer/s during the UE manufacturing stage.
  • Action 1003. The UE 10 executes the second model. The execution of the second model could be conditioned on an intended communication to the radio network node 12 of the output of the second model. See next action.
  • the UE 10 communicates the output of the second model to the radio network node 12 via signaling, e.g., within UCI/Radio Resource Control (RRC).
  • RRC Radio Resource Control
  • the radio network node 12 may indicate to the UE 10 to only convey the output of the second model in circumstances where the execution status of the first process is recommended to be modified. This may be based on an instantaneous model output or after a number of consistent model outputs with a similar outcome. Such number may be explicitly indicated by the radio network node 12, e.g., via Downlink Control Information (DCI)/RRC, and/or specification text. This number may be used, for instance, o If the first process is inactive and the output of the second model suggests that the first process will be accurate. o If the first process is active and the output of the second model suggests that the first process will be inaccurate.
  • DCI Downlink Control Information
  • the radio network node 12 may configure uplink resources in a periodic, aperiodic, or a semi-persistent manner so that the UE 10 can communicate the result indication of action 1003.
  • Such configurations may be based on RRC signaling in case of periodic or semi-persistent scheduling and activated and/or deactivated through semi-static signaling such as RRC or Medium Access Control (MAC) Control Element (CE) or through dynamic signaling in DCI.
  • Aperiodic triggering of resources is typically performed dynamically through signaling in DCI.
  • the uplink signaling of the result indication may be event-based and included in an UL transmission by the UE 10, e.g., when transmitting other uplink control information (UCI) or based on requesting UL resources for the purpose of communicating the model output.
  • UCI uplink control information
  • the radio network node 12 Based on the result indication from the UE 10, i.e., the output of the second model, i.e., indicating the first process or the second process, the radio network node 12 activates/deactivate reference signals utilized for CSI acquisition and/or activates/deactivate the execution of the first process.
  • Figs. 11-12 show determinations of the amount of RS resources and CSI feedback that may be used for the first process at the radio network node 12 to produce accurate outputs.
  • the purpose of the resource computational model is to quantify the level of assistance information, e.g., used as input to a model of the first process such as an uplink-to-downlink CSI mapping model, required to make the first process accurate enough (fulfilling a targeted KPI).
  • a model of the first process such as an uplink-to-downlink CSI mapping model
  • the resource computational model may be an example of the computational model mentioned in Action 601.
  • the models of Fig. 9 and Fig. 10 may be based on models of Fig. 11 and 12, i.e., the outputs of at least one of a third model and a fourth model may be used to determine whether the first process at the radio network node 12 produces accurate outputs. o For instance, if the output of the third and/or the fourth models indicates that the maximum ora very large number of downlink channel estimates should be used at the input of the first process to achieve the target KPI, the radio network node 12 may infer that the first process — together the relevant signals used to produce its input — should be deactivated.
  • the radio network node 12 may infer that the first process — together the relevant signals used to produce its input — should be activated.
  • the output of the resource computational model may be used to optimize the downlink CSI acquisition process.
  • Fig. 11 shows a determination of the amount of RS resources and CSI feedback for the first process at the radio network node 12 to produce accurate outputs, also referred to as Network-based KPI prediction.
  • the radio network node 12 trains a third model to determine the amount of RS resources and CSI feedback required to reach a targeted KPI by the uplink- to-downlink CSI mapping predictor at the network.
  • o Model inputs See action 901 of the Network-based OoD detection model.
  • o Model outputs The reference signal and/or CSI feedback configuration required to reach the targeted KPI. This includes: o At least the targeted SRSs configuration and/or modifications to the current configuration used, and/or o The UL DM RS configuration to be used o [optionally] CSI-RSs configuration (in the case of the traditional CSI feedback approaches), and/or the CSI feedback configuration (size, procedure to encode the CSI, etc.).
  • Action 1102. The radio network node 12 executes the third model.
  • the radio network node 12 Based on the outputs of the third model, the radio network node 12 either does not take action (no configuration changes needed to reach the target KPI), or reconfigures resources indicated by the third model output to the predicted configuration and [optionally] activates/deactivates the uplink-to-downlink CSI mapping predictor.
  • Fig. 12 shows a determination of the amount of RS resources and CSI feedback for the first process at the radio network node 12 to produce accurate outputs, also referred to as UE-based KPI prediction.
  • the radio network node 12 trains a fourth model for determining amount of resources. In UE-based OoD detection with the model output, instead of identifying out-of-distribution samples, the radio network node 12 determines the amount of RS resources and CSI feedback required to reach a target KPI.
  • the radio network node 12 transfers the trained computational model, such as the fourth model, to the UE 10.
  • the UE 10 executes the fourth model indicating resources needed or required.
  • the UE 10 instead of identifying out-of-distribution samples, may with the output of the fourth model determine the amount of RS resources and CSI feedback required to reach a target KPI.
  • the signaling from the UE 10 could include: o An indication of an increase/decrease in SRS allocation and/or UL DM RS allocation and/or CSI-RS allocation and/or resolution/size of the CSI feedback. o As a more detailed targeted configuration also whether the increase should be in time domain and/or frequency domain and/or spatial domain. o As the most detailed configuration, the UE could send recommended settings based on the RRC configurations available in the specification text, essentially recommending to the network which configurations the UE foresees to be needed to reach the targeted KPI.
  • Action 1205. Based on the result indication from the UE 10, i.e., the output of the fourth model, may be used by the radio network node 12 to decide the reference signals to schedule (i.e., at least SRSs in the case of uplink-to-downlink CSI mapping and at least CSI-RSs in the case of the traditional CSI feedback approaches), and/or to configure the CSI feedback properties (size, procedure to encode the CSI, etc.) to reach the targeted KPI.
  • the UE 10 may also utilize the output of such models to determine the amount of CSI feedback that should be transmitted to the radio network node 12.
  • Embodiments herein comprises one or more of the following below and Table 1 summarizes one or more features of the computational models that may be considered.
  • the network will indicate
  • the purpose of the model identify out-of-distribution channel samples for the uplink-to-downlink CSI mapping predictor at the network
  • the required pre-processing to generate the model inputs may be provided by the specification.
  • the transfer of the second model is performed through RRC signaling, or a separate dedicated bearer for model transfer.
  • o UE may communicate the output of such model to the network via new signaling, e.g., within UCI I RRC I MAC CE transmitted on Physical Uplink Control Channel (PUCCH) or Physical uplink Shared Channel (PUSCH).
  • PUCCH Physical Uplink Control Channel
  • PUSCH Physical uplink Shared Channel
  • such output will consist of a binary value indicating whether the uplink-to-downlink CSI mapping may be recommended or not.
  • the network will indicate
  • the purpose of the model determine the amount of CSI feedback and/or the amount of UL sounding reference signals for a given KPI when executing the uplink-to-downlink CSI mapping predictor at the network
  • the required pre-processing to generate the model inputs may be provided by the specification.
  • the transfer of the model is performed through RRC signaling, or a separate dedicated bearer for model transfer.
  • o UE may communicate the output of the model to the network via new signaling (e.g., within UCI I RRC I MAC CE transmitted on PUCCH or PUSCH)
  • such output will consist of code point selected from a set of code points provided in the specification (for example four bits, with different information associated to each of the 16 states).
  • Such output may indicate the resources recommended to be subsequently reserved by the network for CSI feedback purposes (e.g., number of feedback bits or resource elements) and/or changes in the RS configurations.
  • the functional input/output may be implemented as a single model, for example a single trained model that with the prediction target to both decide the type of downlink CSI acquisition to use (model 1 and 2) and the amount of CSI feedback (if any) and/or amount of reference signals required (model 3 and 4).
  • the models 1 and 2 may be a single model and the models 3 and 4 may be a single model.
  • Fig. 13 shows a block diagram depicting the UE 10 for communication in a wireless in the wireless communications network.
  • the UE 10 may comprise processing circuitry 1301 , e.g. one or more processors, configured to perform the methods herein.
  • processing circuitry 1301 e.g. one or more processors, configured to perform the methods herein.
  • the UE 10 and/or the processing circuitry 1301 is configured to obtain the first indication locally or from the radio network node 12, wherein the first indication indicates the computational model.
  • the computational model may be used to indicate whether to use the first process or the second process for obtaining data related to a downlink channel estimation at the radio network node 12.
  • the UE 10 and/or the processing circuitry 1301 may be configured to execute the indicated computational model to obtain the result indication of the preferred process out of the first and second processes for the downlink channel estimation at the radio network node 12.
  • the UE 10 and/or the processing circuitry 1301 is configured to transmit the result indication of the preferred process out of the first and second process for the downlink channel estimation at the radio network node 12, to the radio network node 12, wherein the result indication is obtained from the indicated computational model.
  • the first process for obtaining the data related to the downlink channel estimation may be associated with the mapping of the measured uplink channel estimation at the first frequency band to the downlink channel estimation at the second frequency band.
  • the second process for obtaining the data related to the downlink channel estimation may be associated with performed one or more measurements for the downlink channel estimation.
  • the UE 10 and/or the processing circuitry 1301 may be configured to obtain the purpose of the computational model and/or the required pre-processing to generate inputs to the computational model. This may be received from the radio network node 12.
  • the UE 10 and/or the processing circuitry 1301 may be configured to obtain the resource indication indicating one or more resources related to CSI feedback and/or uplink sounding reference signals to achieve the set key performance indicator.
  • the resource indication may be obtained from the second computational model and/or from the radio network node 12.
  • the UE 10 further comprises a memory 1305.
  • the memory comprises one or more units to be used to store data on, such as indications, computational model, indications, reconfiguration, applications to perform the methods disclosed herein when being executed, and similar.
  • the UE 10 comprises a communication interface 1306 comprising transmitter, receiver, transceiver and/or one or more antennas.
  • the UE 10 for handling communication in a wireless communications network, wherein the UE 10 comprises processing circuitry and a memory, said memory comprising instructions executable by said processing circuitry whereby said UE 10 is operative to perform any of the methods herein.
  • the methods according to the embodiments described herein for the UE 10 are respectively implemented by means of e g. a computer program product 1307 or a computer program product, comprising instructions, i.e. , software code portions, which, when executed on at least one processor, cause the at least one processor to carry out the actions described herein, as performed by the UE 10.
  • the computer program product 1307 may be stored on a computer-readable storage medium 1308, e g. a universal serial bus (USB) stick, a disc or similar.
  • the computer-readable storage medium 1308, having stored thereon the computer program product may comprise the instructions which, when executed on at least one processor, cause the at least one processor to carry out the actions described herein, as performed by the UE 10.
  • the computer-readable storage medium may be a non-transitory or transitory computer- readable storage medium.
  • Fig. 14 shows a block diagram depicting the radio network node 12 for handling communication in the wireless communications network.
  • the radio network node 12 may comprise processing circuitry 1401 , e.g. one or more processors, configured to perform the methods herein.
  • processing circuitry 1401 e.g. one or more processors, configured to perform the methods herein.
  • the radio network node 12 and/or the processing circuitry 1401 is configured to train the computational model to indicate whether to use the first process or the second process for obtaining data related to the downlink channel estimation.
  • the radio network node 12 and/or the processing circuitry 1401 is configured to transfer the first indication of the trained computational model to the UE 10, and to receive the result indication from the UE 10.
  • the result indication indicates the preferred process out of the first and second processes for obtaining the data related to the downlink channel estimation at the radio network node 12.
  • the radio network node 12 and/or the processing circuitry 1401 is configured to execute the computational model to obtain the network result indication of the network preferred process out of the first and second processes for obtaining the data related to the downlink channel estimation at the radio network node 12.
  • the first process for obtaining the data related to the downlink channel estimation may be associated with the mapping of a measured uplink channel estimation at the first frequency band to the downlink channel estimation at the second frequency band, and the second process for obtaining the data related to the downlink channel estimation may be associated with the performed one or more measurements for the downlink channel estimation.
  • the radio network node 12 and/or the processing circuitry 1401 may be configured to, based on the received result indication and/or the obtained network result indication, determine one or more of the following: i) to execute the preferred process or the network preferred process at the radio network node, ii) activation and/or deactivation of one or more reference signals, and/or iii) a requested amount of resources related to channel state information, CSI, feedback and/or uplink sounding reference signals.
  • the requested amount of resources may be determined to reach the set key performance indicator when executing the preferred process or the network preferred process at the radio network node 12.
  • the requested amount of resources may be indicated by the resource indication obtained from the resource computational model.
  • the radio network node 12 and/or the processing circuitry 1401 may be configured to transmit the resource indication to the UE 10.
  • the radio network node 12 and/or the processing circuitry 1401 may be configured to configure resources based on the requested amount of resources.
  • the radio network node 12 and/or the processing circuitry 1401 may be configured to transfer to the UE 10 the purpose of the trained computational model and/or the required pre-processing to generate inputs to the trained computational model.
  • the radio network node 12 further comprises a memory 1405.
  • the memory comprises one or more units to be used to store data on, such as indications, computational model, indications, reconfiguration, applications to perform the methods disclosed herein when being executed, and similar.
  • the radio network node 12 comprises a communication interface 1406 comprising transmitter, receiver, transceiver and/or one or more antennas.
  • the radio network node 12 for handling communication in a wireless communications network, wherein the radio network node 12 comprises processing circuitry and a memory, said memory comprising instructions executable by said processing circuitry whereby said radio network node 12 is operative to perform any of the methods herein.
  • the methods according to the embodiments described herein for the radio network node 12 are respectively implemented by means of e.g. a computer program product 1407 or a computer program product, comprising instructions, i.e. , software code portions, which, when executed on at least one processor, cause the at least one processor to carry out the actions described herein, as performed by the radio network node 12.
  • the computer program product 1407 may be stored on a computer-readable storage medium 1408, e.g. a universal serial bus (USB) stick, a disc or similar.
  • the computer- readable storage medium 1408, having stored thereon the computer program product may comprise the instructions which, when executed on at least one processor, cause the at least one processor to carry out the actions described herein, as performed by the radio network node 12.
  • the computer-readable storage medium may be a non-transitory or transitory computer-readable storage medium.
  • functions means or modules may be implemented using digital logic and/or one or more microcontrollers, microprocessors, or other digital hardware. In some embodiments, several or all of the various functions may be implemented together, such as in a single application-specific integrated circuit (ASIC), or in two or more separate devices with appropriate hardware and/or software interfaces between them. Several of the functions may be implemented on a processor shared with other functional components of a radio network node, for example. Alternatively, several of the functional elements of the processing means discussed may be provided through the use of dedicated hardware, while others are provided with hardware for executing software, in association with the appropriate software or firmware.
  • ASIC application-specific integrated circuit
  • processor or “controller” as used herein does not exclusively refer to hardware capable of executing software and may implicitly include, without limitation, digital signal processor (DSP) hardware, read-only memory (ROM) for storing software, random-access memory for storing software and/or program or application data, and non-volatile memory.
  • DSP digital signal processor
  • ROM read-only memory
  • RAM random-access memory
  • non-volatile memory non-volatile memory
  • Other hardware conventional and/or custom, may also be included.
  • Designers of communications receivers will appreciate the cost, performance, and maintenance trade-offs inherent in these design choices.
  • Fig. 15 shows an example of a communication system QQ100 in accordance with some embodiments.
  • the communication system QQ100 includes a telecommunication network QQ102 that includes an access network QQ104, such as a radio access network (RAN), and a core network QQ106, which includes one or more core network nodes QQ108.
  • the access network QQ104 includes one or more access network nodes, such as network nodes QQ110a and QQ110b (one or more of which may be generally referred to as network nodes QQ110) being examples of the first radio network node 12 and second radio network node 13, or any other similar 3 rd Generation Partnership Project (3GPP) access nodes or non-3GPP access points.
  • 3GPP 3 rd Generation Partnership Project
  • a network node being examples of the entities herein, is not necessarily limited to an implementation in which a radio portion and a baseband portion are supplied and integrated by a single vendor.
  • network nodes include disaggregated implementations or portions thereof.
  • the telecommunication network QQ102 includes one or more Open-RAN (ORAN) network nodes.
  • ORAN Open-RAN
  • An ORAN network node is a node in the telecommunication network QQ102 that supports an ORAN specification (e.g., a specification published by the O-RAN Alliance, or any similar organization) and may operate alone or together with other nodes to implement one or more functionalities of any node in the telecommunication network QQ102, including one or more network nodes QQ110 and/or core network nodes QQ108.
  • ORAN specification e.g., a specification published by the O-RAN Alliance, or any similar organization
  • Examples of an ORAN network node include an open radio unit (0-Rll), an open distributed unit (0-Dll), an open central unit (O-CU), including an O-CU control plane (O-CU-CP) or an O-CU user plane (O-CU-UP), a RAN intelligent controller (near- real time or non-real time) hosting software or software plug-ins, such as a near-real time control application (e.g., xApp) or a non-real time control application (e.g., rApp), or any combination thereof (the adjective “open” designating support of an ORAN specification).
  • a near-real time control application e.g., xApp
  • rApp non-real time control application
  • the network node may support a specification by, for example, supporting an interface defined by the ORAN specification, such as an A1 , F1 , W1, E1, E2, X2, Xn interface, an open fronthaul user plane interface, or an open fronthaul management plane interface.
  • an ORAN access node may be a logical node in a physical node.
  • an ORAN network node may be implemented in a virtualization environment (described further below) in which one or more network functions are virtualized.
  • the virtualization environment may include an O-Cloud computing platform orchestrated by a Service Management and Orchestration Framework via an 0-2 interface defined by the O-RAN Alliance or comparable technologies.
  • the network nodes QQ110 facilitate direct or indirect connection of the user equipment (UE) 10, such as by connecting UEs QQ112a, QQ112b, QQ112c, and QQ112d (one or more of which may be generally referred to as UEs QQ112) to the core network QQ106 over one or more wireless connections.
  • UE user equipment
  • Example wireless communications over a wireless connection include transmitting and/or receiving wireless signals using electromagnetic waves, radio waves, infrared waves, and/or other types of signals suitable for conveying information without the use of wires, cables, or other material conductors.
  • the communication system QQ100 may include any number of wired or wireless networks, network nodes, UEs, and/or any other components or systems that may facilitate or participate in the communication of data and/or signals whether via wired or wireless connections.
  • the communication system QQ100 may include and/or interface with any type of communication, telecommunication, data, cellular, radio network, and/or other similar type of system.
  • the UEs QQ112 may be any of a wide variety of communication devices, including wireless devices arranged, configured, and/or operable to communicate wirelessly with the network nodes QQ110 and other communication devices.
  • the network nodes QQ110 are arranged, capable, configured, and/or operable to communicate directly or indirectly with the UEs QQ112 and/or with other network nodes or equipment in the telecommunication network QQ102 to enable and/or provide network access, such as wireless network access, and/or to perform other functions, such as administration in the telecommunication network QQ102.
  • the core network QQ106 connects the network nodes QQ110 to one or more hosts, such as host QQ116. These connections may be direct or indirect via one or more intermediary networks or devices. In other examples, network nodes may be directly coupled to hosts.
  • the core network QQ106 includes one more core network nodes (e.g., core network node QQ108) such as network node 15 that are structured with hardware and software components. Features of these components may be substantially similar to those described with respect to the UEs, network nodes, and/or hosts, such that the descriptions thereof are generally applicable to the corresponding components of the core network node QQ108.
  • Example core network nodes include functions of one or more of a Mobile Switching Center (MSC), Mobility Management Entity (MME), Home Subscriber Server (HSS), Access and Mobility Management Function (AMF), Session Management Function (SMF), Authentication Server Function (ALISF), Subscription Identifier De-concealing function (SIDF), Unified Data Management (UDM), Security Edge Protection Proxy (SEPP), Network Exposure Function (NEF), and/or a User Plane Function (UPF).
  • MSC Mobile Switching Center
  • MME Mobility Management Entity
  • HSS Home Subscriber Server
  • AMF Access and Mobility Management Function
  • SMF Session Management Function
  • ALISF Authentication Server Function
  • SIDF Subscription Identifier De-concealing function
  • UDM Unified Data Management
  • SEPP Security Edge Protection Proxy
  • NEF Network Exposure Function
  • UPF User Plane Function
  • the host QQ116 may be under the ownership or control of a service provider other than an operator or provider of the access network QQ104 and/or the telecommunication network QQ102, and may be operated by the service provider or on behalf of the service provider.
  • the host QQ116 may host a variety of applications to provide one or more service. Examples of such applications include live and prerecorded audio/video content, data collection services such as retrieving and compiling data on various ambient conditions detected by a plurality of UEs, analytics functionality, social media, functions for controlling or otherwise interacting with remote devices, functions for an alarm and surveillance center, or any other such function performed by a server.
  • the communication system QQ100 of Fig. 15 enables connectivity between the UEs, network nodes, and hosts.
  • the communication system may be configured to operate according to predefined rules or procedures, such as specific standards that include, but are not limited to: Global System for Mobile Communications (GSM); Universal Mobile Telecommunications System (UMTS); Long Term Evolution (LTE), and/or other suitable 2G, 3G, 4G, 5G standards, or any applicable future generation standard (e.g., 6G); wireless local area network (WLAN) standards, such as the Institute of Electrical and Electronics Engineers (IEEE) 802.11 standards (WiFi); and/or any other appropriate wireless communication standard, such as the Worldwide Interoperability for Microwave Access (WiMax), Bluetooth, Z-Wave, Near Field Communication (NFC) ZigBee, LiFi, and/or any low-power wide-area network (LPWAN) standards such as LoRa and Sigfox.
  • GSM Global System for Mobile Communications
  • UMTS Universal Mobile Telecommunications System
  • LTE Long
  • the telecommunication network QQ102 is a cellular network that implements 3GPP standardized features. Accordingly, the telecommunications network QQ102 may support network slicing to provide different logical networks to different devices that are connected to the telecommunication network QQ102. For example, the telecommunications network QQ102 may provide Ultra Reliable Low Latency Communication (URLLC) services to some UEs, while providing Enhanced Mobile Broadband (eMBB) services to other UEs, and/or Massive Machine Type Communication (mMTC)/Massive loT services to yet further UEs.
  • URLLC Ultra Reliable Low Latency Communication
  • eMBB Enhanced Mobile Broadband
  • mMTC Massive Machine Type Communication
  • the UEs QQ112 are configured to transmit and/or receive information without direct human interaction.
  • a UE may be designed to transmit information to the access network QQ104 on a predetermined schedule, when triggered by an internal or external event, or in response to requests from the access network QQ104.
  • a UE may be configured for operating in single- or multi- RAT or multi-standard mode.
  • a UE may operate with any one or combination of Wi-Fi, NR (New Radio) and LTE, i.e. being configured for multi-radio dual connectivity (MR-DC), such as E-UTRAN (Evolved-UMTS Terrestrial Radio Access Network) New Radio - Dual Connectivity (EN-DC).
  • MR-DC multi-radio dual connectivity
  • the hub QQ114 communicates with the access network QQ104 to facilitate indirect communication between one or more UEs (e.g., UE QQ112c and/or QQ112d) and network nodes (e.g., network node QQ110b).
  • the hub QQ114 may be a controller, router, content source and analytics, or any of the other communication devices described herein regarding UEs.
  • the hub QQ114 may be a broadband router enabling access to the core network QQ106 for the UEs.
  • the hub QQ114 may be a controller that sends commands or instructions to one or more actuators in the UEs.
  • the hub QQ114 may be a data collector that acts as temporary storage for UE data and, in some embodiments, may perform analysis or other processing of the data.
  • the hub QQ114 may be a content source. For example, for a UE that is a VR headset, display, loudspeaker or other media delivery device, the hub QQ114 may retrieve VR assets, video, audio, or other media or data related to sensory information via a network node, which the hub QQ114 then provides to the UE either directly, after performing local processing, and/or after adding additional local content.
  • the hub QQ114 acts as a proxy server or orchestrator for the UEs, in particular if one or more of the UEs are low energy loT devices.
  • the hub QQ114 may have a constant/persistent or intermittent connection to the network node QQ110b.
  • the hub QQ114 may also allow for a different communication scheme and/or schedule between the hub QQ114 and UEs (e.g., UE QQ112c and/or QQ112d), and between the hub QQ114 and the core network QQ106.
  • the hub QQ114 is connected to the core network QQ106 and/or one or more UEs via a wired connection.
  • the hub QQ114 may be configured to connect to an M2M service provider over the access network QQ104 and/or to another UE over a direct connection.
  • UEs may establish a wireless connection with the network nodes QQ110 while still connected via the hub QQ114 via a wired or wireless connection.
  • the hub QQ114 may be a dedicated hub - that is, a hub whose primary function is to route communications to/from the UEs from/to the network node QQ110b.
  • the hub QQ114 may be a non-dedicated hub - that is, a device which is capable of operating to route communications between the UEs and network node QQ110b, but which is additionally capable of operating as a communication start and/or end point for certain data channels.
  • a UE refers to a device capable, configured, arranged and/or operable to communicate wirelessly with network nodes and/or other UEs.
  • a UE include, but are not limited to, a smart phone, mobile phone, cell phone, voice over IP (VoIP) phone, wireless local loop phone, desktop computer, personal digital assistant (PDA), wireless cameras, gaming console or device, music storage device, playback appliance, wearable terminal device, wireless endpoint, mobile station, tablet, laptop, laptop-embedded equipment (LEE), laptop-mounted equipment (LME), smart device, wireless customer-premise equipment (CPE), vehicle, vehicle-mounted or vehicle embedded/integrated wireless device, etc.
  • Other examples include any UE identified by the 3rd Generation Partnership Project (3GPP), including narrow band internet of things (NB-loT) UE, a machine type communication (MTC) UE, and/or an enhanced MTC (eMTC) UE.
  • 3GPP 3rd Generation Partnership Project
  • NB-loT narrow band internet of things
  • a UE may support device-to-device (D2D) communication, for example by implementing a 3GPP standard for sidelink communication, Dedicated Short-Range Communication (DSRC), vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I), or vehicle-to-everything (V2X).
  • D2D device-to-device
  • DSRC Dedicated Short-Range Communication
  • V2V vehicle-to-vehicle
  • V2I vehicle-to-infrastructure
  • V2X vehicle-to-everything
  • a UE may not necessarily have a user in the sense of a human user who owns and/or operates the relevant device.
  • a UE may represent a device that is intended for sale to, or operation by, a human user but which may not, or which may not initially, be associated with a specific human user (e.g., a smart sprinkler controller).
  • a UE may represent a device that is not intended for sale
  • the UE QQ200 includes processing circuitry QQ202 that is operatively coupled via a bus QQ204 to an input/output interface QQ206, a power source QQ208, a memory QQ210, a communication interface QQ212, and/or any other component, or any combination thereof.
  • Certain UEs may utilize all or a subset of the components shown in Figure 16. The level of integration between the components may vary from one UE to another UE. Further, certain UEs may contain multiple instances of a component, such as multiple processors, memories, transceivers, transmitters, receivers, etc.
  • the processing circuitry QQ202 is configured to process instructions and data and may be configured to implement any sequential state machine operative to execute instructions stored as machine-readable computer programs in the memory QQ210.
  • the processing circuitry QQ202 may be implemented as one or more hardware-implemented state machines (e.g., in discrete logic, field-programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), etc.); programmable logic together with appropriate firmware; one or more stored computer programs, general-purpose processors, such as a microprocessor or digital signal processor (DSP), together with appropriate software; or any combination of the above.
  • the processing circuitry QQ202 may include multiple central processing units (CPUs).
  • the input/output interface QQ206 may be configured to provide an interface or interfaces to an input device, output device, or one or more input and/or output devices.
  • Examples of an output device include a speaker, a sound card, a video card, a display, a monitor, a printer, an actuator, an emitter, a smartcard, another output device, or any combination thereof.
  • An input device may allow a user to capture information into the UE QQ200.
  • Examples of an input device include a touch-sensitive or presence-sensitive display, a camera (e.g., a digital camera, a digital video camera, a web camera, etc.), a microphone, a sensor, a mouse, a trackball, a directional pad, a trackpad, a scroll wheel, a smartcard, and the like.
  • the presence-sensitive display may include a capacitive or resistive touch sensor to sense input from a user.
  • a sensor may be, for instance, an accelerometer, a gyroscope, a tilt sensor, a force sensor, a magnetometer, an optical sensor, a proximity sensor, a biometric sensor, etc., or any combination thereof.
  • An output device may use the same type of interface port as an input device. For example, a Universal Serial Bus (USB) port may be used to provide an input device and an output device.
  • USB Universal Serial Bus
  • the power source QQ208 is structured as a battery or battery pack. Other types of power sources, such as an external power source (e.g., an electricity outlet), photovoltaic device, or power cell, may be used.
  • the power source QQ208 may further include power circuitry for delivering power from the power source QQ208 itself, and/or an external power source, to the various parts of the UE QQ200 via input circuitry or an interface such as an electrical power cable. Delivering power may be, for example, for charging of the power source QQ208.
  • Power circuitry may perform any formatting, converting, or other modification to the power from the power source QQ208 to make the power suitable for the respective components of the UE QQ200 to which power is supplied.
  • the memory QQ210 may be or be configured to include memory such as random access memory (RAM), read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), magnetic disks, optical disks, hard disks, removable cartridges, flash drives, and so forth.
  • the memory QQ210 includes one or more application programs QQ214, such as an operating system, web browser application, a widget, gadget engine, or other application, and corresponding data QQ216.
  • the memory QQ210 may store, for use by the UE QQ200, any of a variety of various operating systems or combinations of operating systems.
  • the memory QQ210 may be configured to include a number of physical drive units, such as redundant array of independent disks (RAID), flash memory, USB flash drive, external hard disk drive, thumb drive, pen drive, key drive, high-density digital versatile disc (HD-DVD) optical disc drive, internal hard disk drive, Blu-Ray optical disc drive, holographic digital data storage (HDDS) optical disc drive, external mini-dual inline memory module (DIMM), synchronous dynamic random access memory (SDRAM), external micro-DIMM SDRAM, smartcard memory such as tamper resistant module in the form of a universal integrated circuit card (UICC) including one or more subscriber identity modules (SIMs), such as a USIM and/or ISIM, other memory, or any combination thereof.
  • RAID redundant array of independent disks
  • HD-DVD high-density digital versatile disc
  • HDDS holographic digital data storage
  • DIMM external mini-dual inline memory module
  • SDRAM synchronous dynamic random access memory
  • SDRAM synchronous dynamic random access memory
  • the UICC may for example be an embedded UICC (eUlCC), integrated IIICC (illlCC) or a removable IIICC commonly known as ‘SIM card.’
  • the memory QQ210 may allow the UE QQ200 to access instructions, application programs and the like, stored on transitory or non-transitory memory media, to off-load data, or to upload data.
  • An article of manufacture, such as one utilizing a communication system may be tangibly embodied as or in the memory QQ210, which may be or comprise a device-readable storage medium.
  • the processing circuitry QQ202 may be configured to communicate with an access network or other network using the communication interface QQ212.
  • the communication interface QQ212 may comprise one or more communication subsystems and may include or be communicatively coupled to an antenna QQ222.
  • the communication interface QQ212 may include one or more transceivers used to communicate, such as by communicating with one or more remote transceivers of another device capable of wireless communication (e.g., another UE or a network node in an access network).
  • Each transceiver may include a transmitter QQ218 and/or a receiver QQ220 appropriate to provide network communications (e.g., optical, electrical, frequency allocations, and so forth).
  • the transmitter QQ218 and receiver QQ220 may be coupled to one or more antennas (e.g., antenna QQ222) and may share circuit components, software or firmware, or alternatively be implemented separately.
  • communication functions of the communication interface QQ212 may include cellular communication, Wi-Fi communication, LPWAN communication, data communication, voice communication, multimedia communication, short-range communications such as Bluetooth, near-field communication, location-based communication such as the use of the global positioning system (GPS) to determine a location, another like communication function, or any combination thereof.
  • GPS global positioning system
  • Communications may be implemented in according to one or more communication protocols and/or standards, such as IEEE 802.11, Code Division Multiplexing Access (CDMA), Wideband Code Division Multiple Access (WCDMA), GSM, LTE, New Radio (NR), UMTS, WiMax, Ethernet, transmission control protocol/internet protocol (TCP/IP), synchronous optical networking (SONET), Asynchronous Transfer Mode (ATM), QUIC, Hypertext Transfer Protocol (HTTP), and so forth.
  • CDMA Code Division Multiplexing Access
  • WCDMA Wideband Code Division Multiple Access
  • WCDMA Wideband Code Division Multiple Access
  • GSM Global System for Mobile communications
  • LTE Long Term Evolution
  • NR New Radio
  • UMTS Worldwide Interoperability for Microwave Access
  • WiMax Ethernet
  • TCP/IP transmission control protocol/internet protocol
  • SONET synchronous optical networking
  • ATM Asynchronous Transfer Mode
  • QUIC Hypertext Transfer Protocol
  • HTTP Hypertext Transfer Protocol
  • a UE may provide an output of data captured by its sensors, through its communication interface QQ212, via a wireless connection to a network node.
  • Data captured by sensors of a UE can be communicated through a wireless connection to a network node via another UE.
  • the output may be periodic (e.g., once every 15 minutes if it reports the sensed temperature), random (e.g., to even out the load from reporting from several sensors), in response to a triggering event (e.g., when moisture is detected an alert is sent), in response to a request (e.g., a user initiated request), or a continuous stream (e.g., a live video feed of a patient).
  • a UE comprises an actuator, a motor, or a switch, related to a communication interface configured to receive wireless input from a network node via a wireless connection.
  • the states of the actuator, the motor, or the switch may change.
  • the UE may comprise a motor that adjusts the control surfaces or rotors of a drone in flight according to the received input or to a robotic arm performing a medical procedure according to the received input.
  • a UE when in the form of an Internet of Things (loT) device, may be a device for use in one or more application domains, these domains comprising, but not limited to, city wearable technology, extended industrial application and healthcare.
  • loT device are a device which is or which is embedded in: a connected refrigerator or freezer, a TV, a connected lighting device, an electricity meter, a robot vacuum cleaner, a voice controlled smart speaker, a home security camera, a motion detector, a thermostat, a smoke detector, a door/window sensor, a flood/moisture sensor, an electrical door lock, a connected doorbell, an air conditioning system like a heat pump, an autonomous vehicle, a surveillance system, a weather monitoring device, a vehicle parking monitoring device, an electric vehicle charging station, a smart watch, a fitness tracker, a head-mounted display for Augmented Reality (AR) or Virtual Reality (VR), a wearable for tactile augmentation or sensory enhancement, a water sprinkler, an animal- or item-tracking
  • AR Aug
  • a UE may represent a machine or other device that performs monitoring and/or measurements, and transmits the results of such monitoring and/or measurements to another UE and/or a network node.
  • the UE may in this case be an M2M device, which may in a 3GPP context be referred to as an MTC device.
  • the UE may implement the 3GPP NB-loT standard.
  • a UE may represent a vehicle, such as a car, a bus, a truck, a ship and an airplane, or other equipment that is capable of monitoring and/or reporting on its operational status or other functions associated with its operation.
  • any number of UEs may be used together with respect to a single use case.
  • a first UE might be or be integrated in a drone and provide the drone’s speed information (obtained through a speed sensor) to a second UE that is a remote controller operating the drone.
  • the first UE may adjust the throttle on the drone (e.g. by controlling an actuator) to increase or decrease the drone’s speed.
  • the first and/or the second UE can also include more than one of the functionalities described above.
  • a UE might comprise the sensor and the actuator, and handle communication of data for both the speed sensor and the actuators.
  • FIG 17 shows a network node QQ300 in accordance with some embodiments.
  • network node refers to equipment capable, configured, arranged and/or operable to communicate directly or indirectly with a UE and/or with other network nodes or equipment, in a telecommunication network.
  • network nodes include, but are not limited to, access points (APs) (e.g., radio access points), base stations (BSs) (e.g., radio base stations, Node Bs, evolved Node Bs (eNBs) and NR NodeBs (gNBs)), O-RAN nodes or components of an O-RAN node (e.g., O-RU, O-DU, O-CU).
  • APs access points
  • BSs base stations
  • eNBs evolved Node Bs
  • gNBs NR NodeBs
  • O-RAN nodes or components of an O-RAN node e.g., O-RU, O-DU, O-CU.
  • Base stations may be categorized based on the amount of coverage they provide (or, stated differently, their transmit power level) and so, depending on the provided amount of coverage, may be referred to as femto base stations, pico base stations, micro base stations, or macro base stations.
  • a base station may be a relay node or a relay donor node controlling a relay.
  • a network node may also include one or more (or all) parts of a distributed radio base station such as centralized digital units, distributed units (e.g., in an O-RAN access node) and/or remote radio units (RRUs), sometimes referred to as Remote Radio Heads (RRHs). Such remote radio units may or may not be integrated with an antenna as an antenna integrated radio.
  • Parts of a distributed radio base station may also be referred to as nodes in a distributed antenna system (DAS).
  • DAS distributed antenna system
  • network nodes include multiple transmission point (multi- TRP) 5G access nodes, multi-standard radio (MSR) equipment such as MSR BSs, network controllers such as radio network controllers (RNCs) or base station controllers (BSCs), base transceiver stations (BTSs), transmission points, transmission nodes, multi-cell/multicast coordination entities (MCEs), Operation and Maintenance (O&M) nodes, Operations Support System (OSS) nodes, Self-Organizing Network (SON) nodes, positioning nodes (e.g., Evolved Serving Mobile Location Centers (E-SMLCs)), and/or Minimization of Drive Tests (MDTs).
  • MSR multi-standard radio
  • RNCs radio network controllers
  • BSCs base station controllers
  • BTSs base transceiver stations
  • OFDM Operation and Maintenance
  • OSS Operations Support System
  • SON Self-Organizing Network
  • positioning nodes e.g., Evolved Serving Mobile Location Centers (E-SMLCs)
  • the network node QQ300 includes a processing circuitry QQ302, a memory QQ304, a communication interface QQ306, and a power source QQ308.
  • the network node QQ300 may be composed of multiple physically separate components (e.g., a NodeB component and a RNC component, or a BTS component and a BSC component, etc.), which may each have their own respective components.
  • the network node QQ300 comprises multiple separate components (e.g., BTS and BSC components)
  • one or more of the separate components may be shared among several network nodes.
  • a single RNC may control multiple NodeBs.
  • each unique NodeB and RNC pair may in some instances be considered a single separate network node.
  • the network node QQ300 may be configured to support multiple radio access technologies (RATs).
  • RATs radio access technologies
  • some components may be duplicated (e.g., separate memory QQ304 for different RATs) and some components may be reused (e.g., a same antenna QQ310 may be shared by different RATs).
  • the network node QQ300 may also include multiple sets of the various illustrated components for different wireless technologies integrated into network node QQ300, for example GSM, WCDMA, LTE, NR, WiFi, Zigbee, Z-wave, LoRaWAN, Radio Frequency Identification (RFID) or Bluetooth wireless technologies. These wireless technologies may be integrated into the same or different chip or set of chips and other components within network node QQ300.
  • RFID Radio Frequency Identification
  • the processing circuitry QQ302 may comprise a combination of one or more of a microprocessor, controller, microcontroller, central processing unit, digital signal processor, application-specific integrated circuit, field programmable gate array, or any other suitable computing device, resource, or combination of hardware, software and/or encoded logic operable to provide, either alone or in conjunction with other network node QQ300 components, such as the memory QQ304, to provide network node QQ300 functionality.
  • the processing circuitry QQ302 includes a system on a chip (SOC). In some embodiments, the processing circuitry QQ302 includes one or more of radio frequency (RF) transceiver circuitry QQ312 and baseband processing circuitry QQ314. In some embodiments, the radio frequency (RF) transceiver circuitry QQ312 and the baseband processing circuitry QQ314 may be on separate chips (or sets of chips), boards, or units, such as radio units and digital units. In alternative embodiments, part or all of RF transceiver circuitry QQ312 and baseband processing circuitry QQ314 may be on the same chip or set of chips, boards, or units.
  • SOC system on a chip
  • the processing circuitry QQ302 includes one or more of radio frequency (RF) transceiver circuitry QQ312 and baseband processing circuitry QQ314.
  • the radio frequency (RF) transceiver circuitry QQ312 and the baseband processing circuitry QQ314 may be on separate chips (or sets of chips
  • the memory QQ304 may comprise any form of volatile or non-volatile computer-readable memory including, without limitation, persistent storage, solid-state memory, remotely mounted memory, magnetic media, optical media, random access memory (RAM), read-only memory (ROM), mass storage media (for example, a hard disk), removable storage media (for example, a flash drive, a Compact Disk (CD) or a Digital Video Disk (DVD)), and/or any other volatile or non-volatile, non-transitory device-readable and/or computer-executable memory devices that store information, data, and/or instructions that may be used by the processing circuitry QQ302.
  • volatile or non-volatile computer-readable memory including, without limitation, persistent storage, solid-state memory, remotely mounted memory, magnetic media, optical media, random access memory (RAM), read-only memory (ROM), mass storage media (for example, a hard disk), removable storage media (for example, a flash drive, a Compact Disk (CD) or a Digital Video Disk (DVD)), and/or any other volatile
  • the memory QQ304 may store any suitable instructions, data, or information, including a computer program, software, an application including one or more of logic, rules, code, tables, and/or other instructions capable of being executed by the processing circuitry QQ302 and utilized by the network node QQ300.
  • the memory QQ304 may be used to store any calculations made by the processing circuitry QQ302 and/or any data received via the communication interface QQ306.
  • the processing circuitry QQ302 and memory QQ304 is integrated.
  • the communication interface QQ306 is used in wired or wireless communication of signalling and/or data between a network node, access network, and/or UE. As illustrated, the communication interface QQ306 comprises port(s)/terminal(s) QQ316 to send and receive data, for example to and from a network over a wired connection.
  • the communication interface QQ306 also includes radio frontend circuitry QQ318 that may be coupled to, or in certain embodiments a part of, the antenna QQ310. Radio front-end circuitry QQ318 comprises filters QQ320 and amplifiers QQ322. The radio front-end circuitry QQ318 may be connected to an antenna QQ310 and processing circuitry QQ302.
  • the radio front-end circuitry may be configured to condition signals communicated between antenna QQ310 and processing circuitry QQ302.
  • the radio front-end circuitry QQ318 may receive digital data that is to be sent out to other network nodes or UEs via a wireless connection.
  • the radio front-end circuitry QQ318 may convert the digital data into a radio signal having the appropriate channel and bandwidth parameters using a combination of filters QQ320 and/or amplifiers QQ322.
  • the radio signal may then be transmitted via the antenna QQ310.
  • the antenna QQ310 may collect radio signals which are then converted into digital data by the radio front-end circuitry QQ318.
  • the digital data may be passed to the processing circuitry QQ302.
  • the communication interface may comprise different components and/or different combinations of components.
  • the network node QQ300 does not include separate radio front-end circuitry QQ318, instead, the processing circuitry QQ302 includes radio front-end circuitry and is connected to the antenna QQ310. Similarly, in some embodiments, all or some of the RF transceiver circuitry QQ312 is part of the communication interface QQ306. In still other embodiments, the communication interface QQ306 includes one or more ports or terminals QQ316, the radio front-end circuitry QQ318, and the RF transceiver circuitry QQ312, as part of a radio unit (not shown), and the communication interface QQ306 communicates with the baseband processing circuitry QQ314, which is part of a digital unit (not shown).
  • the antenna QQ310 may include one or more antennas, or antenna arrays, configured to send and/or receive wireless signals.
  • the antenna QQ310 may be coupled to the radio front-end circuitry QQ318 and may be any type of antenna capable of transmitting and receiving data and/or signals wirelessly.
  • the antenna QQ310 is separate from the network node QQ300 and connectable to the network node QQ300 through an interface or port.
  • the antenna QQ310, communication interface QQ306, and/or the processing circuitry QQ302 may be configured to perform any receiving operations and/or certain obtaining operations described herein as being performed by the network node. Any information, data and/or signals may be received from a UE, another network node and/or any other network equipment. Similarly, the antenna QQ310, the communication interface QQ306, and/or the processing circuitry QQ302 may be configured to perform any transmitting operations described herein as being performed by the network node. Any information, data and/or signals may be transmitted to a UE, another network node and/or any other network equipment.
  • the power source QQ308 provides power to the various components of network node QQ300 in a form suitable for the respective components (e.g., at a voltage and current level needed for each respective component).
  • the power source QQ308 may further comprise, or be coupled to, power management circuitry to supply the components of the network node QQ300 with power for performing the functionality described herein.
  • the network node QQ300 may be connectable to an external power source (e.g., the power grid, an electricity outlet) via an input circuitry or interface such as an electrical cable, whereby the external power source supplies power to power circuitry of the power source QQ308.
  • the power source QQ308 may comprise a source of power in the form of a battery or battery pack which is connected to, or integrated in, power circuitry. The battery may provide backup power should the external power source fail.
  • Embodiments of the network node QQ300 may include additional components beyond those shown in Figure 17 for providing certain aspects of the network node’s functionality, including any of the functionality described herein and/or any functionality necessary to support the subject matter described herein.
  • the network node QQ300 may include user interface equipment to allow input of information into the network node QQ300 and to allow output of information from the network node QQ300. This may allow a user to perform diagnostic, maintenance, repair, and other administrative functions for the network node QQ300.
  • FIG 18 is a block diagram of a host QQ400, which may be an embodiment of the host QQ116 of Figure 15, in accordance with various aspects described herein.
  • the host QQ400 may be or comprise various combinations hardware and/or software, including a standalone server, a blade server, a cloud-implemented server, a distributed server, a virtual machine, container, or processing resources in a server farm.
  • the host QQ400 may provide one or more services to one or more UEs.
  • the host QQ400 includes processing circuitry QQ402 that is operatively coupled via a bus QQ404 to an input/output interface QQ406, a network interface QQ408, a power source QQ410, and a memory QQ412.
  • processing circuitry QQ402 that is operatively coupled via a bus QQ404 to an input/output interface QQ406, a network interface QQ408, a power source QQ410, and a memory QQ412.
  • Other components may be included in other embodiments. Features of these components may be substantially similar to those described with respect to the devices of previous figures, such as Figures 16 and 17, such that the descriptions thereof are generally applicable to the corresponding components of host QQ400.
  • the memory QQ412 may include one or more computer programs including one or more host application programs QQ414 and data QQ416, which may include user data, e.g., data generated by a UE for the host QQ400 or data generated by the host QQ400 for a UE.
  • Embodiments of the host QQ400 may utilize only a subset or all of the components shown.
  • the host application programs QQ414 may be implemented in a container-based architecture and may provide support for video codecs (e.g., Versatile Video Coding (WC), High Efficiency Video Coding (HEVC), Advanced Video Coding (AVC), MPEG, VP9) and audio codecs (e.g., FLAG, Advanced Audio Coding (AAC), MPEG, G.711), including transcoding for multiple different classes, types, or implementations of UEs (e.g., handsets, desktop computers, wearable display systems, heads-up display systems).
  • the host application programs QQ414 may also provide for user authentication and licensing checks and may periodically report health, routes, and content availability to a central node, such as a device in or on the edge of a core network.
  • the host QQ400 may select and/or indicate a different host for over-the-top services for a UE.
  • the host application programs QQ414 may support various protocols, such as the HTTP Live Streaming (HLS) protocol, Real-Time Messaging Protocol (RTMP), Real-Time Streaming Protocol (RTSP), Dynamic Adaptive Streaming over HTTP (MPEG-DASH), etc.
  • HLS HTTP Live Streaming
  • RTMP Real-Time Messaging Protocol
  • RTSP Real-Time Streaming Protocol
  • MPEG-DASH Dynamic Adaptive Streaming over HTTP
  • FIG 19 is a block diagram illustrating a virtualization environment QQ500 in which functions implemented by some embodiments may be virtualized.
  • virtualizing means creating virtual versions of apparatuses or devices which may include virtualizing hardware platforms, storage devices and networking resources.
  • virtualization can be applied to any device described herein, or components thereof, and relates to an implementation in which at least a portion of the functionality is implemented as one or more virtual components.
  • Some or all of the functions described herein may be implemented as virtual components executed by one or more virtual machines (VMs) implemented in one or more virtual environments QQ500 hosted by one or more of hardware nodes, such as a hardware computing device that operates as a network node, UE, core network node, or host.
  • VMs virtual machines
  • the virtualization environment QQ500 includes components defined by the O-RAN Alliance, such as an O-Cloud environment orchestrated by a Service Management and Orchestration Framework via an 0-2 interface.
  • Applications QQ502 (which may alternatively be called software instances, virtual appliances, network functions, virtual nodes, virtual network functions, etc.) are run in the virtualization environment Q400 to implement some of the features, functions, and/or benefits of some of the embodiments disclosed herein.
  • Hardware QQ504 includes processing circuitry, memory that stores software and/or instructions executable by hardware processing circuitry, and/or other hardware devices as described herein, such as a network interface, input/output interface, and so forth.
  • Software may be executed by the processing circuitry to instantiate one or more virtualization layers QQ506 (also referred to as hypervisors or virtual machine monitors (VMMs)), provide VMs QQ508a and QQ508b (one or more of which may be generally referred to as VMs QQ508), and/or perform any of the functions, features and/or benefits described in relation with some embodiments described herein.
  • the virtualization layer QQ506 may present a virtual operating platform that appears like networking hardware to the VMs QQ508.
  • the VMs QQ508 comprise virtual processing, virtual memory, virtual networking or interface and virtual storage, and may be run by a corresponding virtualization layer QQ506.
  • Different embodiments of the instance of a virtual appliance QQ502 may be implemented on one or more of VMs QQ508, and the implementations may be made in different ways.
  • Virtualization of the hardware is in some contexts referred to as network function virtualization (NFV). NFV may be used to consolidate many network equipment types onto industry standard high volume server hardware, physical switches, and physical storage, which can be located in data centers, and customer premise equipment.
  • NFV network function virtualization
  • a VM QQ508 may be a software implementation of a physical machine that runs programs as if they were executing on a physical, nonvirtualized machine.
  • Each of the VMs QQ508, and that part of hardware QQ504 that executes that VM be it hardware dedicated to that VM and/or hardware shared by that VM with others of the VMs, forms separate virtual network elements.
  • a virtual network function is responsible for handling specific network functions that run in one or more VMs QQ508 on top of the hardware QQ504 and corresponds to the application QQ502.
  • Hardware QQ504 may be implemented in a standalone network node with generic or specific components. Hardware QQ504 may implement some functions via virtualization. Alternatively, hardware QQ504 may be part of a larger cluster of hardware (e.g. such as in a data center or CPE) where many hardware nodes work together and are managed via management and orchestration QQ510, which, among others, oversees lifecycle management of applications QQ502. In some embodiments, hardware QQ504 is coupled to one or more radio units that each include one or more transmitters and one or more receivers that may be coupled to one or more antennas.
  • Radio units may communicate directly with other hardware nodes via one or more appropriate network interfaces and may be used in combination with the virtual components to provide a virtual node with radio capabilities, such as a radio access node or a base station.
  • some signalling can be provided with the use of a control system QQ512 which may alternatively be used for communication between hardware nodes and radio units.
  • Figure 20 shows a communication diagram of a host QQ602 communicating via a network node QQ604 with a UE QQ606 over a partially wireless connection in accordance with some embodiments.
  • host QQ602 Like host QQ400, embodiments of host QQ602 include hardware, such as a communication interface, processing circuitry, and memory.
  • the host QQ602 also includes software, which is stored in or accessible by the host QQ602 and executable by the processing circuitry.
  • the software includes a host application that may be operable to provide a service to a remote user, such as the UE QQ606 connecting via an over-the-top (OTT) connection QQ650 extending between the UE QQ606 and host QQ602.
  • OTT over-the-top
  • a host application may provide user data which is transmitted using the OTT connection QQ650.
  • the network node QQ604 includes hardware enabling it to communicate with the host QQ602 and UE QQ606.
  • the connection QQ660 may be direct or pass through a core network (like core network QQ106 of Figure 15) and/or one or more other intermediate networks, such as one or more public, private, or hosted networks.
  • an intermediate network may be a backbone network or the Internet.
  • the UE QQ606 includes hardware and software, which is stored in or accessible by UE QQ606 and executable by the UE’s processing circuitry.
  • the software includes a client application, such as a web browser or operator-specific “app” that may be operable to provide a service to a human or non-human user via UE QQ606 with the support of the host QQ602.
  • a client application such as a web browser or operator-specific “app” that may be operable to provide a service to a human or non-human user via UE QQ606 with the support of the host QQ602.
  • an executing host application may communicate with the executing client application via the OTT connection QQ650 terminating at the UE QQ606 and host QQ602.
  • the UE's client application may receive request data from the host's host application and provide user data in response to the request data.
  • the OTT connection QQ650 may transfer both the request data and the user data.
  • the UE's client application may interact with the user to generate the user data that it provides to the host application through the OTT connection QQ650.
  • the OTT connection QQ650 may extend via a connection QQ660 between The host QQ602 and the network node QQ604 and via a wireless connection QQ670 between the network node QQ604 and the UE QQ606 to provide the connection between the host QQ602 and the UE QQ606.
  • connection QQ660 and wireless connection QQ670 over which the OTT connection QQ650 may be provided, have been drawn abstractly to illustrate the communication between the host QQ602 and the UE QQ606 via the network node QQ604, without explicit reference to any intermediary devices and the precise routing of messages via these devices.
  • the host QQ602 provides user data, which may be performed by executing a host application.
  • the user data is associated with a particular human user interacting with the UE QQ606.
  • the user data is associated with a UE QQ606 that shares data with the host QQ602 without explicit human interaction.
  • the host QQ602 initiates a transmission carrying the user data towards the UE QQ606.
  • the host QQ602 may initiate the transmission responsive to a request transmitted by the UE QQ606.
  • the request may be caused by human interaction with the UE QQ606 or by operation of the client application executing on the UE QQ606.
  • the transmission may pass via the network node QQ604, in accordance with the teachings of the embodiments described throughout this disclosure. Accordingly, in step QQ612, the network node QQ604 transmits to the UE QQ606 the user data that was carried in the transmission that the host QQ602 initiated, in accordance with the teachings of the embodiments described throughout this disclosure. In step QQ614, the UE QQ606 receives the user data carried in the transmission, which may be performed by a client application executed on the UE QQ606 associated with the host application executed by the host QQ602.
  • the UE QQ606 executes a client application which provides user data to the host QQ602.
  • the user data may be provided in reaction or response to the data received from the host QQ602.
  • the UE QQ606 may provide user data, which may be performed by executing the client application.
  • the client application may further consider user input received from the user via an input/output interface of the UE QQ606. Regardless of the specific manner in which the user data was provided, the UE QQ606 initiates, in step QQ618, transmission of the user data towards the host QQ602 via the network node QQ604.
  • step QQ620 in accordance with the teachings of the embodiments described throughout this disclosure, the network node QQ604 receives user data from the UE QQ606 and initiates transmission of the received user data towards the host QQ602. In step QQ622, the host QQ602 receives the user data carried in the transmission initiated by the UE QQ606.
  • One or more of the various embodiments improve the performance of OTT services provided to the UE QQ606 using the OTT connection QQ650, in which the wireless connection QQ670 forms the last segment. More precisely, the teachings of these embodiments may improve handling of channel estimations and thereby provide benefits such as reduced user waiting time, better responsiveness, and/or extended battery lifetime.
  • factory status information may be collected and analyzed by the host QQ602.
  • the host QQ602 may process audio and video data which may have been retrieved from a UE for use in creating maps.
  • the host QQ602 may collect and analyze real-time data to assist in controlling vehicle congestion (e.g., controlling traffic lights).
  • the host QQ602 may store surveillance video uploaded by a UE.
  • the host QQ602 may store or control access to media content such as video, audio, VR or AR which it can broadcast, multicast or unicast to UEs.
  • the host QQ602 may be used for energy pricing, remote control of non-time critical electrical load to balance power generation needs, location services, presentation services (such as compiling diagrams etc. from data collected from remote devices), or any other function of collecting, retrieving, storing, analyzing and/or transmitting data.
  • a measurement procedure may be provided for the purpose of monitoring data rate, latency and other factors on which the one or more embodiments improve.
  • the measurement procedure and/or the network functionality for reconfiguring the OTT connection may be implemented in software and hardware of the host QQ602 and/or UE QQ606.
  • sensors (not shown) may be deployed in or in association with other devices through which the OTT connection QQ650 passes; the sensors may participate in the measurement procedure by supplying values of the monitored quantities exemplified above, or supplying values of other physical quantities from which software may compute or estimate the monitored quantities.
  • the reconfiguring of the OTT connection QQ650 may include message format, retransmission settings, preferred routing etc.; the reconfiguring need not directly alter the operation of the network node QQ604. Such procedures and functionalities may be known and practiced in the art.
  • measurements may involve proprietary UE signalling that facilitates measurements of throughput, propagation times, latency and the like, by the host QQ602.
  • the measurements may be implemented in that software causes messages to be transmitted, in particular empty or ‘dummy’ messages, using the OTT connection QQ650 while monitoring propagation times, errors, etc.
  • computing devices described herein may include the illustrated combination of hardware components, other embodiments may comprise computing devices with different combinations of components. It is to be understood that these computing devices may comprise any suitable combination of hardware and/or software needed to perform the tasks, features, functions and methods disclosed herein. Determining, calculating, obtaining or similar operations described herein may be performed by processing circuitry, which may process information by, for example, converting the obtained information into other information, comparing the obtained information or converted information to information stored in the network node, and/or performing one or more operations based on the obtained information or converted information, and as a result of said processing making a determination.
  • processing circuitry may process information by, for example, converting the obtained information into other information, comparing the obtained information or converted information to information stored in the network node, and/or performing one or more operations based on the obtained information or converted information, and as a result of said processing making a determination.
  • computing devices may comprise multiple different physical components that make up a single illustrated component, and functionality may be partitioned between separate components.
  • a communication interface may be configured to include any of the components described herein, and/or the functionality of the components may be partitioned between the processing circuitry and the communication interface.
  • non-computationally intensive functions of any of such components may be implemented in software or firmware and computationally intensive functions may be implemented in hardware.
  • processing circuitry executing instructions stored on in memory, which in certain embodiments may be a computer program product in the form of a non- transitory computer-readable storage medium.
  • some or all of the functionality may be provided by the processing circuitry without executing instructions stored on a separate or discrete device-readable storage medium, such as in a hard-wired manner.
  • the processing circuitry can be configured to perform the described functionality.

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  • Engineering & Computer Science (AREA)
  • Power Engineering (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Artificial Intelligence (AREA)
  • Evolutionary Computation (AREA)
  • Mobile Radio Communication Systems (AREA)

Abstract

Des modes de réalisation de la présente invention concernent, par exemple, un procédé mis en oeuvre par un UE (10) pour gérer une communication dans un réseau de communication sans fil. L'UE (10) obtient une première indication localement ou à partir d'un noeud de réseau radio (12), la première indication indiquant un modèle de calcul. L'UE (10) transmet une indication de résultat d'un processus préféré parmi un premier et un second processus pour une estimation de canal de liaison descendante au niveau du noeud de réseau radio (12), au noeud de réseau radio (12), l'indication de résultat étant obtenue à partir du modèle de calcul indiqué.
PCT/SE2024/050179 2024-02-26 2024-02-26 Noeud de réseau radio, équipement utilisateur et procédés mis en oeuvre dans celui-ci Pending WO2025183598A1 (fr)

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Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2023280143A1 (fr) * 2021-07-05 2023-01-12 中国移动通信有限公司研究院 Procédé de commande de tâche d'intelligence artificielle (ia), terminal, station de base et support de stockage

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2023280143A1 (fr) * 2021-07-05 2023-01-12 中国移动通信有限公司研究院 Procédé de commande de tâche d'intelligence artificielle (ia), terminal, station de base et support de stockage
EP4366362A1 (fr) * 2021-07-05 2024-05-08 China Mobile Communication Co., Ltd. Research Institute Procédé de commande de tâche d'intelligence artificielle (ia), terminal, station de base et support de stockage

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