Ai / ML predictions for deactivated scells or deactivated scg

Abstract

Methods, apparatuses, and systems for performing artificial intelligence / machine learning-based measurements in wireless networks In an example method, carried out by a wireless device in such networks, the wireless device predicts (210) at least one measurement value for a signal measurement for a cell that is configured for the wireless device as a serving cell but that is deactivated. The wireless device then reports (220) the predicted measurement value or measurement values to the wireless network.

Description

[0001] AI / ML PREDICTIONS FOR DEACTIVATED SCELLS OR DEACTIVATED SCG

[0002] TECHNICAL FIELD

[0003] The present disclosure is generally related to wireless communications networks and is more particularly related to improved techniques for the use of artificial intelligence / machine-leaming models in such networks.

[0004] BACKGROUND

[0005] Carrier Aggregation

[0006] In fourth-generation (4G) and fifth-generation (5G) wireless networks specified by the 3rd-Generation Partnership Project (3GPP), a UE can be configured with carrier aggregation (CA) where two or more component carriers are aggregated, so that the UE can transmit and / or receive on more than one carrier at the same time. This increases the data rates for the UE. When carrier aggregation is configured, one cell is configured as a Primary cell (PCell). The PCell handles the Radio Resource Control (RRC) connection and provides the security information at handover and re-establishment. In addition, secondary cells (SCells) can be configured for the additional carriers and together with the PCell they form a set of serving cells.

[0007] Deactivated SCells

[0008] A feature has been introduced for user equipment (UE) power savings in which a Secondary Cell (S Cell) is deactivated, so that a UE is not required to monitor the physical downlink control channel (PDCCH). Even if an SCell is deactivated, the UE is still required to perform layer 3 (L3) measurements, e.g., Reference Signal Received Power (RSRP) for the SCell which is deactivated.

[0009] In NR (3GPP terminology for the 5G radio access technology), for that purpose, an activation / deactivation mechanism of cells is supported. When an SCell is deactivated, the UE does not need to receive the corresponding PDCCH or physical downlink shared channel (PDSCH), cannot transmit in the corresponding uplink, nor is it required to perform channel quality indicator (CQI) measurements. Conversely, when an SCell is active, the UE shall receive PDSCH and PDCCH (if the UE is configured to monitor PDCCH from this SCell) and is expected to be able to perform CQI measurements. The next-generation radio access network (NG-RAN) ensures that while a physical uplink control channel (PUCCH) SCell (a Secondary Cell configured with PUCCH) is deactivated, SCells of a secondary PUCCH group (a group of SCells whose PUCCH signalling is associated with the PUCCH on the PUCCH SCell) should not be activated. NG-RAN ensures that SCells mapped to PUCCH SCell are deactivated before the PUCCH SCell is changed or removed.

[0010] When reconfiguring the set of serving cells:

[0011] - SCells added to the set are initially activated or deactivated;- SCells which remain in the set (either unchanged or reconfigured) do not change their activation status (activated or deactivated).

[0012] At handover or connection resume from RRC INACTIVE:

[0013] 1) SCells are activated or deactivated.

[0014] To enable reasonable UE battery consumption when CA is configured, only one uplink bandwidth part (UL BWP) for each uplink carrier and one downlink bandwidth part (DL BWP) or only one DL / UL BWP pair can be active at a time in an active serving cell, all other BWPs that the UE is configured with being deactivated. On deactivated BWPs, the UE does not monitor the PDCCH, does not transmit on PUCCH, the physical random access channel (PRACH), and the uplink shared channel (UL-SCH).

[0015] To enable fast SCell activation when CA is configured, one dormant BWP can be configured for an SCell. If the active BWP of the activated SCell is a dormant BWP, the UE stops monitoring PDCCH and transmitting SRS / PUSCH / PUCCH on the SCell but continues performing channel state information (CSI) measurements, AGC and beam management, if configured. A DCI is used to control entering / leaving the dormant BWP for one or more SCell(s) or one or more SCell group(s).

[0016] The dormant BWP is one of the UE's dedicated BWPs configured by network via dedicated RRC signalling. The SpCell and PUCCH SCell cannot be configured with a dormant BWP.

[0017] Dual Connectivity

[0018] In 3GPP Rel-12, the LTE feature Dual Connectivity (DC) was introduced, to enable the UE to be connected in two cell groups, each controlled by an LTE access node, eNBs, labelled as the Master eNB, MeNB and the Secondary eNB, SeNB. The UE still only has one RRC connection with the network. In 3GPP, the Dual Connectivity (DC) solution has since then been evolved and is now also specified for NR as well as between LTE and NR. Multi-connectivity (MC) is the case when there are more than 2 nodes involved. With introduction of 5G, the term MR-DC (Multi-Radio Dual Connectivity, see also 3GPP TS 37.340) was defined as a generic term for all dual connectivity options which includes at least one NR access node. Using the MR-DC generalized terminology, the UE is connected in a Master Cell Group (MCG), controlled by the Master Node (MN), and in a Secondary Cell Group (SCG) controlled by a Secondary Node (SN).

[0019] Further, in MR-DC, when dual connectivity is configured for the UE, within each of the two cell groups, MCG and SCG, carrier aggregation may be used as well. In this case, within the Master Cell Group, MCG, controlled by the master node (MN), the UE may use one PCell and one or more SCell(s). And within the Secondary Cell Group, SCG, controlled by the secondary node (SN), the UE may use one Primary SCell (PSCell, also known as the primary SCG cell in NR) and one ormore SCell(s). This combined case is illustrated in Figure 1. In NR, the primary cell of a master or secondary cell group is sometimes also referred to as the Special Cell (SpCell). Hence, the SpCell in the MCG is the PCell and the SpCell in the SCG is the PSCell.

[0020] There are different ways to deploy 5G network with or without interworking with LTE (also referred to as E-UTRA) and evolved packet core (EPC). In principle, NR and LTE can be deployed without any interworking, denoted by NR stand-alone (SA) operation, that is gNB in NR can be connected to 5G core network (5GC) and eNB in LTE can be connected to EPC with no interconnection between the two.

[0021] Deactivated Secondary Cell Groups (SCGs)

[0022] A feature has been later introduced for UE power savings, in Rel-16, in which a whole Secondary Cell Group (SCG), for a UE configured with Multi-Radio Dual connectivity (MR-DC) is deactivated. In that case, the UE is not required to monitor PDCCH for the PSCell and the SCells of the SCG, but must perform

[0023] Even if the whole SCG is deactivated, the UE is still required to perform L3 measurements, e.g., RSRP for the PScell and the SCells, to support network controlled mobility. However, since there is a goal of UE power savings, measurement cycles may be relaxed.

[0024] AI / ML modeling and associated principles

[0025] Artificial intelligence (Al) or machine learning (ML) techniques utilize one or more algorithms to model a certain system and make inferences, or predictions of certain system parameters or outputs, based on measurements from that system. These algorithms use a set of data as input for training one or more AI / ML models. The output of the AI / ML model may be used by a device in a wireless communications system, such as a user equipment (UE), base station (BS) or another node, for performing certain operations or taking certain decisions, such as handover decisions, whether fully or partially based on the model’s predictions, which in turn depend on the trained model. The AI / ML model can be trained in the device online (or on-the-fly while processing the data) or offline in the background. More specifically:

[0026] • Online training is an AI / ML training process where the model being used for inference is (typically continuously) trained in (near) real-time with the arrival of new training samples or data.

[0027] • Offline training is an AI / ML training process where the model is trained based on collected samples or data, and where the trained model is later used or delivered for inference.

[0028] AI / ML model inference refers to a process of using a trained AI / ML model to produce a set of outputs based on a set of inputs.As noted above, the AL / ML models can be trained in a device, which can be a UE, a network node, or another node. In this respect the AI / ML modes can be broadly classified as:

[0029] • Case I: UE-side (AI / ML) model, i.e., an AI / ML model whose inference is performed entirely at the UE.

[0030] • Case II: Network-side (AI / ML) model, i.e., an AI / ML model whose inference is performed entirely at the network.

[0031] • Case III: One-sided (AI / ML) model, i.e., a UE-side (AI / ML) model or a Network-side (AI / ML) model.

[0032] • Case IV: Two-sided (AI / ML) model, a paired AI / ML model(s) over which joint inference is performed, where joint inference comprises AI / ML inference performed jointly across the UE and the network, i.e., the first part of inference is performed by UE and then the remaining part is performed by gNB, or vice versa.

[0033] An AI / ML model can be transferred or delivered over the air interface either in terms of one or more parameters of a model structure known at the receiving end or a new model with parameters. The model delivery may contain a full model or a partial model.

[0034] The term lifecycle management (LCM) of an AI / ML model refers to the process of developing, deploying and maintaining the AI / ML model. An AI / ML model training pipeline includes several processing stages, such as gathering unprocessed input data from data repositories (data ingestion), finding high-quality input features (data pre-processing), finding the optimal mapping of the model input features to a desired model output target in a sense determined by a loss function (model training), and evaluating model performance on unseen data from a functional level as well as from a system level when relevant (model evaluation). The training pipeline typically ends with a model registration stage, which may comprise of operations to make the ML model executable via compilation to a specific hardware and of steps like versioning and packaging of the model so that it can be executed.

[0035] According to a technical report issued by 3GPP, “Technical Specification Group Radio Access Network; Study on Artificial Intelligence (AI)ZMachine Learning (ML) for NR air interface (Release 18),” 3GPP TR 38.843, Dec. 2023, AI / ML LCM covers the following components:

[0036] ■ Data collection

[0037] ■ Model training

[0038] ■ Lunctionality / model identification

[0039] ■ Model inference

[0040] ■ Lunctionality / model selection, activation, deactivation, switching, and fallback operation ■ Functionality / model monitoring

[0041] ■ Model updateUE capability.

[0042] In functionality-based LCM, the network indicates activation / deactivation / fallback / switching of AI / ML functionality via 3GPP signaling, e.g., using Radio Resource Control (RRC) signaling, Medium Access Control Control Elements (MAC-CEs), or Downlink Control Information (DCI), as specified by 3GPP standards. Models might not be specifically identified at the network, in some cases, and a UE may perform model-level LCM. A UE may have one AI / ML model per functionality or multiple AI / ML models per functionality. Functionality refers to an AI / ML-enabled feature / feature group enabled by configuration(s), where configuration(s) is(are) supported based on conditions indicated by UE capability. In addition to functionality identification, the UE can also report updates on applicable functionality(ies) among functionality (ies).

[0043] In model-based LCM, models are identified at the network and the network and / or UE may activate / deactivate / select / switch individual AI / ML models using a model ID. A model may be associated with specific configurations / conditions and additional conditions (e.g., scenarios, sites, datasets) may be determined / identified between a UE and the network.

[0044] AI / ML model validation is a subprocess of training, to evaluate the quality of an AI / ML model using a dataset different from the one used for model training, that helps selecting model parameters that generalize beyond the dataset used for model training.

[0045] Al for Mobility

[0046] The AI / ML for PHY work in Rel-18 has been limited to lower layer features, such as Beam Management, which is sometimes referred as intra-cell mobility. Other features, such as L3 handovers, RRC measurements configuration and reporting of predictions have not been part of the Rel-18.

[0047] Hence, a Rel-19 Study Item to study the usage of Al / ML for L3 Mobility and / or RRM measurements is considered. The study will focus on mobility enhancement in RRC CONNECTED mode over air interface by following existing mobility framework, i.e., handover decision is always made in the network side. Mobility use cases focus on standalone NR PCell change. UE-side and network-side AI / ML model can both be considered, respectively. The objectives of the study item include:

[0048] • Study and evaluate potential benefits and gains of AI / ML aided mobility for network triggered L3 -based handover, considering the following aspects:

[0049] o AI / ML based RRM measurement and event prediction.

[0050] o Cell-level measurement prediction including intra and inter-frequency (UE sided and NW sided model).o Inter-cell Beam-level measurement prediction for L3 Mobility (UE sided and NW sided model).

[0051] o HO failure / RLF prediction (UE sided model).

[0052] o Measurement events prediction (UE sided model).

[0053] • Study the need / benefits of any other UE assistance information for the network side model.

[0054] • The evaluation of the AI / ML aided mobility benefits should consider handover (HO) performance key performance indicators (KPIs) (e.g., Ping-pong HO, HOF / RLF, Time of stay, Handover interruption, prediction accuracy, and measurement reduction) etc.) and complexity tradeoffs.

[0055] • Potential Al mobility specific enhancement should be based on the Rell9 AI / ML-air interface WID general framework (e.g. LCM, performance monitoring etc.).

[0056] • Potential specification impacts of AI / ML aided mobility.

[0057] • Evaluate testability, interoperability, and impacts on RRM requirements and performance.

[0058] The rel-18 study item has focused on performing simulations and showing the gain of AI / ML in the context of mobility. The results shown are promising with a prediction error of less than 1 dB for both temporal and frequency domain predictions.

[0059] In rel-20 the work on AI / ML mobility will continue in a work item and the scope was discussed at the RAN Plenary meeting RAN#107 in March 2025. It is still not discussed what uses cases measurement predictions can be used for, which means that more cases than L3 handover may be included in the work item.

[0060] Problems with the existing technology

[0061] A problem in existing technology is that measurements are very costly for the UE to perform in terms of capacity and UE energy consumption. In particular, measurements on multiple frequencies (e.g., serving and / or non-serving) are extra costly as the UE is normally unable to transmit / receive data on the serving frequency when measuring on another frequency, which means that there is a service degradation during the time when measurements on a different frequency than the serving frequency is performed. This means that the network may not receive as many measurement results as it actually needs to be able to maintain the network in the best way.

[0062] SUMMARY

[0063] Embodiments of the methods, apparatuses, and systems described herein address these problems. According to some embodiments, a UE that is configured with at least one serving cell that is deactivated (or that is associated with a cell group which is deactivated) reports to a network node at least one frequency-domain (FD) measurement prediction on the at least one serving cell, e.g., predictions of RSRP value(s) of the at least serving cell. The at least one serving cell may be asecondary cell (SCell), and may be associated with, i.e., part of, a deactivated secondary cell group (SCG).

[0064] According to the techniques described herein, a UE configured with carrier aggregation or dual connectivity may provide frequency domain measurement predictions to the network for one or more deactivated SCells, in place of actual measurements performed on those SCells. In various embodiments or implementations, the UE may be permitted to do this on its own, or it may be instructed to do so, e.g., with respect to certain or all deactivated SCells.

[0065] Embodiments include methods carried out by a wireless device, or user equipment (UE) as wireless devices are referred to by 3GPP, operating in a wireless network. An example method comprises predicting at least one measurement value for a signal measurement for a cell that is configured for the UE as a serving cell but that is deactivated, and reporting the predicted measurement value or measurement values to the wireless network. The signal measurement for which the measurement value is predicted may be a frequency-domain measurement, for example. The cell that is deactivated may be an SCell and / or associated with a deactivated SCG, in some instances or embodiments.

[0066] Other embodiments include methods carried out by a node in a radio access network or core network of a wireless network, such as a gNB. An example method comprises receiving, from a user equipment (UE), a report including a predicted measurement value for a signal measurement for a cell that is configured for the UE but that is deactivated for the UE or was deactivated at the time the predicted measurement value was predicted. The node carrying out the method may perform at least one Radio Resource Configuration (RRC) operation based on the predicted measurement value, in some instances or embodiments. Again, the cell that is deactivated may be an SCell and / or associated with a deactivated SCG, in some instances or embodiments.

[0067] Numerous variations of these methods, including variations of signaling techniques for configuring the UE and reporting the predicted measurement values are described herein. Corresponding apparatuses and systems are also described herein. These include an example user equipment (UE), which comprises communication interface circuitry configured to communicate with a wireless network, and processing circuitry operatively coupled to the communication interface circuitry. The processing circuitry is configured to predict at least one measurement value for a signal measurement for a cell that is configured for the UE as a serving cell but that is deactivated, and report the predicted measurement value or measurement values to the wireless network.

[0068] Another example is a network node comprising communication interface circuitry configured to communicate with one or more other nodes in a wireless network and processing circuitry operatively coupled to the communication interface circuitry and configured to receive, via the communication interface circuitry, from a user equipment (UE), a report including a predicted measurement value fora signal measurement for a cell that is configured for the UE but that is deactivated for the UE or was deactivated at the time the predicted measurement value was predicted.

[0069] An advantage of various embodiments of the solutions described herein is that the UE can save capacity and UE battery when the amount of frequency domain measurements are reduced. The tradeoff for this may be minimal, as the network may not require the most accurate measurements, e.g., when a serving cell is deactivated and / or when a cell group is deactivated, since the UE is not being scheduled for data transmission / reception while these are deactivated. The network can use the predicted measurements to trigger activation of SCell or PSCell or to select SCell or PSCell when the cell needs to be activated.

[0070] BRIEF DESCRIPTION OF THE FIGURES

[0071] Figure 1 illustrates dual connectivity combined with carrier aggregation in MR-DC.

[0072] Figure 2 is a process flow diagram illustrating an example method carried out by a UE, according to various embodiments.

[0073] Figure 3 is a process flow diagram illustrating an example method carried out by a network node, according to various embodiments.

[0074] Figure 4 shows a communication system according to various embodiments of the present disclosure.

[0075] Figure 5 shows a UE according to various embodiments of the present disclosure.

[0076] Figure 6 shows a network node according to various embodiments of the present disclosure.

[0077] Figure 7 shows a host computing system according to various embodiments of the present disclosure.

[0078] Figure 8 is a block diagram of a virtualization environment in which functions implemented by some embodiments of the present disclosure may be virtualized.

[0079] Figure 9 illustrates communication between a host computing system, a network node, and a UE via multiple connections, at least one of which is wireless, according to various embodiments of the present disclosure.

[0080] DETAILED DESCRIPTION

[0081] In this document, the term “predictions” refer to predictions based on the use of any Artificial intelligence (AI)ZMachine Learning (ML) model, namely the results of the inference of the AI / ML engine, that is performed based on measurements and / or some additional information or any non-AL / ML based predictions. Per various embodiments, these predictions may be reported to the network by a UE.Overview of Predictions

[0082] In the context of this document, the predictions may be time-domain predictions: thus, the input of the ML-model may comprise at least one or more measurements at (or starting at) a time instance tO (and / or a time interval such as T1 or tO+Tl, which may comprise one or more samples or measurement time occasions, from 1 to K time occasions) of at least one cell, and the output of the ML-model may comprise one or more predicted measurements at (or starting at) a future time instance e.g. tO + T, possibly comprising future time instances within a time window of duration T2 and having F predictions.

[0083] In spatial domain predictions, the UE may use cell level or beam level measurements as input and produce cell level or beam level predictions in different cells or beams (at the same time instance). In frequency domain predictions, the UE may use cell level or beam level measurements on one or more frequencies as input and produce cell level or beam level predictions in a different frequency (at the same time instance).

[0084] An AI / ML model can be designed to produce the beam-level measurement prediction in frequency domain or spatial domain. Utilizing the predicted beam-level measurement quality(es) and beam IDs generated as output of the AI / ML model inference, a predicted cell-level measurement quality for a cell X can be derived using the approaches described above. An AI / ML model can also be designed to directly predict the cell-level measurement by taking LI and or L3 measurements of a set of beams and or cells as model input. Besides predicted beam-level or / and cell-level measurement quantities and beam / cell IDs, the model may also provide additional information like confidence level of the model output, the validation time of the predicted measurements, etc.

[0085] The designed AI / ML model can be deployed at the UE or at the network side and associated with a beam / cell prediction feature or a Radio Resource Management (RRM) prediction feature. When connecting to a network node, a UE can report its support of the AI / ML model for spatial and / or interfrequency beam and or cell prediction feature or RRM prediction feature to the network node, via UE capability reporting. In addition, UE can report applicability of its AI / ML model for spatial and / or inter-frequency beam and or cell prediction feature or RRM prediction feature to the network node. The applicability indication can be seen as a dynamic UE capability on conducting predictions under certain network configuration and conditions. Based on the received UE capability and applicability indications, together with other conditions, the network node can make decisions on whether to configure / active the AI / ML model at the UE or not.

[0086] Below we give different examples on how to design an AI / ML model to achieve the beam / cell-level measurement quality prediction in spatial or / and frequency domain, i.e., inter-frequency prediction.For the AI / ML model used for cell prediction, in an example, a neural network-based model is composed of multiple connected neurons. Optionally it contains one or a few of input layer, one or a few of hidden layer, and one output layer. For the input layer, it takes UE measurements results as the model input, where the beam or cell level measurement results are obtained based on measuring some reference signals, e.g., SSBs and / or CSI-RSs. Optionally, the measurement results would be normalized before input to the hidden layers. The normalization can change the value of the numeric variable in the dataset to a typical scale which improve model training. For the hidden layer(s), it is located between the input and output, in which the function applies weights to the inputs and directs them through an activation function to the output layer. Optionally, activation function can be one of Softmax function, Sigmoid function, ReLU function, Leaky ReLU, tanh function and Maxout. For the output layer, the output can be predicted RSRP values for each beam or cell in a different friquency. Optionally, the output can be the probability values where each value means the probability of the beam / cell in a target frequency to be the best beam / cell.

[0087] In an example for the AI / ML model, the AI / ML model used for spatial and inter-frequency cell level measurement prediction is based on convolutional neural networks, optionally it contains one or a few of input layers, one of a few of convolution layer, one or a few of pooling layer and output layer. For the input layer, it takes UE cell / beam level measurements results as the model input, where the beam level measurement results are obtained based on measuring some reference signals, e.g., SSBs and / or CSI-RSs and cell level measurement results are derived from the beam level measurements using cell quality derivation procedure. Optionally, the beam / cell level measurement results would be normalized before input to the convention layers. The normalization can change the value of the numeric variable in the dataset to a typical scale which improve model training. For the convention layer(s), it is used to extract the feature from the input. It applies a set of learnable fdters (known as the kernels) to the input with smaller size than the whole input. These fdters and kernels slide over the input data and computes the dot product between kernel weight and the corresponding input. The output of convention layer is referred as feature maps coming from the input measured RSRP values. For pooling layers, it involves sliding a two-dimensional fdter over each channel of feature map and summarizing the features lying within the region covered by the fdter. Before the output layer, there can be a fully connected layer to interpret / summarize the features obtained and directs them through activation function to the output layer. For the output layer, the output can be predicted RSRP values for each beam or each cell operating in a target frequency. Optionally, the output can be the probability values where each value means the probability of the beam to be the best beam or best cell in a target frequency.

[0088] In another set of examples, an AI / ML model is designed to predict the beam measurements of one or more beams in the spatial or frequency domain. A predicted cell-level measurement quality for a cell X in spatial or frequency domain (i.e., a cell operating in a frequency different from the cells / beamsused as input to the model) can be derived based on the predicted beam-level measurement quality(es) or / and beam IDs generated from the AI / ML model output. In another example an AI / ML model is designed to directly predict the cell level measurements of one or more cells in spatial or frequency domain.

[0089] The present document discloses methods at a User Equipment (UE) in which a UE is configured with at least one serving cell that is deactivated (or is associated with a cell group which is deactivated) and that reports to a network node at least one frequency-domain (FD) measurement prediction on the at least one serving cell e.g. predictions of RSRP value(s) of the at least serving cell.

[0090] In one main option, the at least one serving cell corresponds to a Secondary Cell (SCell) or any other network entity the UE is configured with for operating in Carrier Aggregation (CA)

[0091] In one sub-option, the SCell is associated with a Master cell Group (MCG);

[0092] In one sub-option, the SCell is associated with a Secondary Cell Group (SCG);

[0093] In one main option, the at least one serving cell is associated with a deactivated cell group

[0094] In one sub-option, an SCG is deactivated and the UE performs FD measurement prediction(s) on at least one serving cell of the deactivated SCG e.g. one FD measurement prediction on the PSCell, or an SCell of the deactivated SCG.

[0095] In one sub-option, an MCG is deactivated and the UE performs FD measurement prediction(s) on at least one serving cell of the deactivated MCG e.g. one FD measurement prediction on the PCell, or an SCell of the deactivated MCG.

[0096] In some embodiments or instances, the UE configured with a serving cell determines to perform frequency-domain (FD) measurement prediction on the serving cell (e.g. predictions of RSRP value(s)) when it is indicated that the serving cell is deactivated.

[0097] A key idea underlying the disclosed methods is for a UE, configured with carrier aggregation or dual connectivity, to provide frequency domain measurement predictions to the network according to one or more of the following:

[0098] 1) Replacing measurements of deactivated SCells with frequency domain measurement predictions and reporting actual measurements of some SCells.

[0099] i. Replacing in this context means that the UE performs FD measurement prediction(s) on an Scell instead of performing measurements on that SCell and / or replacing in this context means that the UE reports FD measurement prediction(s) on an Scell instead of reporting measurements on that SCell.2) Providing frequency domain measurement prediction(s) of an SCell when the SCell is deactivated.

[0100] ii. Providing in this context means performing and / or reporting.

[0101] 3) Providing frequency domain measurement predictions of a PSCell when the SCG is deactivated.

[0102] iii. Providing in this context means performing and / or reporting.

[0103] 4) Replacing some or all of the measurements of the PSCell with frequency domain measurement predictions when the SCG is deactivated.

[0104] iv. Replacing in this context means that the UE performs FD measurement prediction(s) on an Scell instead of performing measurements on that SCell and / or replacing in this context means that the UE reports FD measurement prediction(s) on an Scell instead of reporting measurements on that SCell.

[0105] According to variations of these methods, the UE is allowed to replace the measurements with predictions, or, alternatively, the UE is instructed to perform frequency domain predictions.

[0106] Further details of these and related methods are provided below.

[0107] AI / ML predictions for UEs configured with CA or DC

[0108] Described herein are solutions for a UE that is configured with CA (Carrier Aggregation) and / or DC (Dual Connectivity) and where AI / ML measurement predictions (in this context, frequency domain measurement prediction(s)) are performed instead of or in addition to actual measurements in cases related to the configuration of CA or DC. More specifically, in the mentioned scenario the UE can be configured with a master cell group (including a PCell and one or more SCells). In another scenario the UE can be configured with a master cell group (including a PCell and one or more SCells) and a secondary cell group (including a PSCell and one or more SCells).

[0109] Embodiments include methods at a User Equipment (UE) where the UE is configured with at least one serving cell that is deactivated (or that is associated with a cell group that is deactivated) and reports to a network node at least one frequency-domain (FD) measurement prediction on the at least one serving cell, e.g., predictions of RSRP value(s) of the at least serving cell.

[0110] • In one main option, the at least one serving cell corresponds to a Secondary Cell (SCell) or any other network entity the UE is configured with for operating in Carrier Aggregation (CA)

[0111] v. In one sub-option, the SCell is associated with a Master cell Group (MCG); vi. In one sub-option, the SCell is associated with a Secondary Cell Group

[0112] (SCG);• In one main option, the at least one serving cell is associated with a deactivated cell group

[0113] i. In one sub-option, an SCG is deactivated and the UE performs FD measurement prediction(s) on at least one serving cell of the deactivated SCG e.g. one FD measurement prediction on the PSCell, or an SCell of the deactivated SCG.

[0114] ii. In one sub-option, an MCG is deactivated and the UE performs FD measurement prediction(s) on at least one serving cell of the deactivated MCG e.g. one FD measurement prediction on the PCell, or an SCell of the deactivated MCG.

[0115] In some embodiments or instances, the UE configured with a serving cell may determine to perform frequency-domain (FD) measurement prediction on the serving cell (e.g., predictions of RSRP value(s)) when it is indicated that the serving cell is deactivated.

[0116] • In one option, the UE in Connected state is initially performing one or more measurements on the serving cell (e.g., actual RSRP measurements on the serving cell) and, upon reception of a command from the network indicating that the serving cell is to be deactivated (e.g. ‘SCell Activation / Deactivation MAC CE’, or an Information element or parameter in an RRC Reconfiguration message), the UE stops performing the measurements in the serving cell and starts performing frequency domain measurement prediction(s), so that when the UE needs to transmit a report to the network (e.g., if a measurement report is triggered in which the UE is required to report on that deactivated serving cell) the UE includes the FD measurement prediction(s) on that deactivated serving cell.

[0117] i. In one sub-option, this does not necessarily prevent the UE from performing measurements on other configured serving cells that may be activated (e.g., in case these measurements are to be used as input for the UE to perform the FD prediction on the serving cell being deactivated), e.g., other SCells, or the PCell.

[0118] ii. In one sub-option, the command from the network indicating that the serving cell is to be deactivated also indicates whether the UE is allowed to perform FD measurement prediction(s) for the serving cell to be deactivated. Then, the UE determines whether it has spare capacity and / or UE energy to perform measurements or FD measurement prediction(s) for the serving cell to be deactivated.iii. In one sub-option, the command corresponds to a lower layer signaling, below the RRC layer in the UE’s protocol stack.

[0119] 1. In one alternative of that sub-option, the lower layer signaling is a MAC Control element (MAC CE), such as a ‘SCell Activation / Deactivation MAC CE’;

[0120] 2. In one alternative of that sub-option, the lower layer signaling is a PDCCH order;

[0121] 3. In one alternative of that sub-option, the lower layer signaling is Downlink Control Indication (DCI);

[0122] 4. In one alternative of that sub-option, the lower layer signaling is a MAC Control element (MAC CE), which is also a mobility command (e.g. for L1 / L2 - triggered Mobility). In such command the UE is indicated to chance cell and to deactivate an SCell associated with the target cell indicated in the command.

[0123] • In one option, the UE in Connected state is performing frequency domain measurement prediction(s) on a serving cell that is deactivated so that when the UE needs to transmit a report to the network (e.g., if a measurement report is triggered in which the UE is required to report on that deactivated serving cell) the UE includes the FD measurement prediction(s) on that deactivated serving cell; then, upon reception of a command from the network indicating that the serving cell is to be activated (e.g. ‘SCell Activation / Deactivation MAC CE’), the stops performing the FD measurement prediction(s) in the serving cell and starts performing real measurement, so that when the UE needs to transmit a report to the network (e.g. if a measurement report is triggered in which the UE is required to report on that activated serving cell) the UE includes the measurements e.g. RSRP, RSRQ.

[0124] • In one sub-option, this does not necessarily prevent the UE from performing measurements on other configured serving cells which may be activated (e.g., in case these measurements are to be used as input for the UE to perform the FD prediction on the serving cell being deactivated), e.g., other SCells, or the PCell.

[0125] • In one sub-option, the command corresponds to a lower layer signaling, below the RRC layer in the UE’s protocol stack.

[0126] • In one alternative of that sub-option, the lower layer signaling is a MAC Control element (MAC CE), such as a ‘SCell Activation / Deactivation MAC CE’;• In one alternative of that sub-option, the lower layer signaling is a PDCCH order;

[0127] • In one alternative of that sub-option, the lower layer signaling is Downlink Control Indication (DCI);

[0128] • In one alternative of that sub-option, the lower layer signaling is a MAC Control element (MAC CE), which is also a mobility command (e.g. for L1 / L2 - triggered Mobility). In such command the UE is indicated to chance cell and to deactivate an SCell associated with the target cell indicated in the command.

[0129] • In one sub-option, the command corresponds to an RRC message e.g. RRC reconfiguration, including an information element or parameter which indicates to the UE to activate that SCell.

[0130] • In one alternative of that sub-option, the RRC message is also a mobility command (e.g. for a handover), e.g., indicating a reconfiguration with sync. In such command the UE is indicated to chance cell and to activate an SCell associated with the target cell indicated in the command.

[0131] • (RRC) In one option, the UE in Connected state receives in the serving cell configuration (e.g., Scell configuration) an indication that the serving cell is to be deactivated and based on that the UE performs frequency domain measurement prediction(s), so that when the UE needs to transmit a report to the network (e.g. if a measurement report is triggered in which the UE is required to report on that deactivated serving cell) the UE includes the FD measurement prediction(s) on that deactivated serving cell.

[0132] • In one option, this does not necessarily prevents the UE to perform measurements on other configured serving cells which may be activated (e.g. in case these measurements are to be used as input for the UE to perform the FD prediction on the serving cell being deactivated), e.g., other SCells, or the PCell.

[0133] • (RRC) In one option, the UE in Connected state receives in the serving cell configuration (e.g. Scell configuration) an indication that the serving cell which is deactivated is to be activated and based on that the UE stops performs frequency domain measurement prediction(s) and starts performing measurements.

[0134] • In one option, this does not necessarily prevents the UE to perform measurements on other configured serving cells which may be activated (e.g. in case these measurements are to be used as input for the UE to perform theFD prediction on the serving cell being deactivated), e.g., other SCells, or the PCell.

[0135] • (RRC) In one option, the UE in Connected state receives in the serving cell configuration (e.g. See 11 configuration) an indication indicating whether that the serving cell is to be deactivated or activated (e.g. presence of a parameter indicates the SCell to be ‘activated’, and absence indication ‘deactivated’), and based on that indication the UE determines to either perform measurements or FD measurement prediction(s) on that serving cell.

[0136] • When the indication indicates that the serving cell is to be deactivated, the UE stops performing the measurements in the serving cell (if it was performing any, e.g., when that is received while the serving cell was activated) and starts performing frequency domain measurement prediction(s), so that when the UE needs to transmit a report to the network (e.g. if a measurement report is triggered in which the UE is required to report on that deactivated serving cell) the UE includes the FD measurement prediction(s) on that deactivated serving cell.

[0137] • In one option, this does not necessarily prevents the UE to perform measurements on other configured serving cells which may be activated (e.g. in case these measurements are to be used as input for the UE to perform the FD prediction on the serving cell being deactivated), e.g., other SCells, or the PCell.

[0138] • When the indication indicates that the serving cell is to be activated, the performs measurements in the serving cell, so that when the UE needs to transmit a report to the network (e.g. if a measurement report is triggered in which the UE is required to report on that deactivated serving cell) the UE includes the performed measuremens on that deactivated serving cell.

[0139] • Command indicating allowance ....MAC CE ... RRC ... part of the Scell configuration.

[0140] A ‘serving cell’ that is deactivated may correspond more generically to a portion of a bandwidth (BW), or network entity the UE is configured with to operate while in Connected state (e.g. RRC CONNECTED), like in carrier aggregation (CA). In 6G, such a configured BW might not necessarily be called a serving cell (e.g. a Pcell, SCell. SpCell), but as long as such network entity is considered ‘deactivated’, meaning that the UE is supposed to operate in a UE power saving mode (e.g., without monitoring a Downlink Control Channel, such as PDCCH), and is required to perform measurements (e.g. RSRP), the UE performs FD predictions according to the method, and the method is applicable.Signaling related to the use of predicted measurements

[0141] Initially, according to various embodiments of the presently disclosed invention, the UE may transmit information to the network related to UE capabilities and / or UE preferences related to AI / ML based frequency domain measurement predictions, e.g., in the context of CA or DC. This information may be transmitted prior to the configuration of CA / DC or prior to the configuration of frequency domain measurement predictions, e.g., in a procedure for UE Attach, UE RRC connection establishment, RRC connection setup, IDLE to CONNECTED transition, UE Assistance Information.

[0142] According to some methods, then, the UE, which is capable of performing AI / ML predictions in frequency domain, is configured with a CA or DC configuration (or DC with CA configuration for each cell group). The UE performs one or more of the following steps:

[0143] receiving from a serving network node a message, e.g., an RRC Reconfiguration, comprising one or more of the following:

[0144] o A configuration of measurements for SCell(s) when the UE is configured with CA or DC. The measurements are configured in an RRC MeasConfig and have an associated measId,ReportConfig and a measObject.

[0145] o A configuration of measurements for a PSCell when the UE is configured with DC.

[0146] The measurements are configured in an RRC MeasConfig and have an associated measld and ReportConfig and a measObject.

[0147] o In one option, the UE is instructed to report measurement predictions of SCell(s) when the SCell(s) are deactivated. Receiving measurement predictions of SCell(s) is of great benefit for the network to determine which SCell(s) to activate when the amount of traffic increases.

[0148] o In one option, the UE is allowed to replace actual RRM measurements of a deactivated SCG with predicted measurements. In legacy solutions, when an SCG is deactivated, the UE may perform relaxed measurements, i.e., less frequent measurements than when the SCG is activated. In this method, the UE is allowed to replace the relaxed measurements with predicted measurements, i.e., the measurement effort in the UE is even further reduced. The UE may be allowed to replace some or all of the relaxed measurements with predicted measurements. It may be further indicated how large amount of the actual measurements that can be replaced by predicted measurements. In one option, the UE sends measurement predictions in addition to the relaxed measurements, i.e. the network receives more (predicted) measurement results but without any additional measurements being performed in the UE. In an option for the time occasions at which the UE is not needed to perform the measurements (according to the measurement relaxation), theUE performs predictions wherein the predictions can be one of the temporal or spatial or frequency domain predictions.

[0149] o In one option, the UE receives a separate configuration for the measurement predictions. The predictions may be configured as a measurement configuration or as a prediction configuration, e.g. in a prediction object associated with an identity and a report configuration.

[0150] o In one option, the UE is instructed to report predictions instead of measurements when the SCG is deactivated, i.e., it is not the UEs choice whether to report measurements or predictions, it is determined in the configuration from the network. o In one option, the frequency domain measurement predictions are combined with spatial domain measurement predictions, so that the UE performs frequency domain predictions of some cells in a different frequency and then performs spatial domain predictions in that same frequency based on the cells which were predicted in the frequency domain.

[0151] ■ The configuration of the spatial prediction may comprise further information, e g- • set of serving / neighbor cells to predict

[0152] • number of cells to predict using spatial domain predictions

[0153] • usage of clustering in input / output for the prediction • In one option, the UE perform FD measurement prediction(s) on a serving cell which is deactivated, but also for one or more cells in the same serving frequency as that serving cell.

[0154] performing the measurement predictions according to the received configuration.

[0155] o In one option, the measurement predictions are performed only if the AIML functionality is determined by the UE to be applicable. If the model becomes inapplicable the UE indicates for which SCells or PSCells the prediction model(s)functionality(es) is / are inapplicable. The network uses per SCell or PSCell applicability information to reconfigure the UE with the measurements.

[0156] transmitting to a network node, a message (e.g., RRC MeasurementReport) including the FD measurement predictions. The transmitted measurement predictions may comprise:

[0157] o Transmitting frequency predictions instead of measurements when the SCell is activated. The frequency predictions may replace some or all of the actual measurements, depending on the configuration received.■ In one option, the UE includes a serving cell identifier (e.g. serving cell identifier, or a serving frequency identifier, servCellld, physical cell identifier, global cell identifier) associated to the frequency predictions. ■ In another option, the UE includes a beam identifier for a serving cell (e.g.

[0158] SSB index, CSI-RS resource identifier, beam identifier) associated to the frequency prediction.

[0159] o Transmitting frequency predictions of SCell(s) when the SCell is deactivated.

[0160] o Transmitting frequency predictions of the PSCell when the SCG is deactivated. The frequency predictions may replace some or all instances of the relaxed measurements. Alternatively, the UE may transmit predicted measurements in addition to the relaxed measurements increasing the knowledge in the network.

[0161] In one option, when providing frequency domain measurement predictions for one or more deactivated SCells or a deactivated SCG (including PSCell), the UE includes a confidence metric, quality indicator, or error bound associated with each prediction in the measurement report. This confidence indication allows the network to assess the reliability of the prediction and e.g. decide whether to accept the prediction in place of real-time measurement, or, e.g., trigger activation / reactivation of the corresponding cell to obtain updated actual measurements.

[0162] Additionally, the confidence metric could be checked by UE and compared to a previously configured threshold by the network, then reported to the network only if the threshold is met.

[0163] Embodiments of the techniques described herein include methods performed at / by a network node, a Master Node (MN) if configured with dual connectivity, the method comprising one or more of the following:

[0164] transmitting to a UE a message, e.g., an RRC Reconfiguration, comprising one or more of the following:

[0165] o A configuration of measurements for SCell(s) when the UE is configured with CA or DC. The measurements are configured in an RRC MeasConfig and have an associated measId,ReportConfig and a measObject.

[0166] o A configuration of measurements for a PSCell when the UE is configured with DC.

[0167] The measurements are configured in an RRC MeasConfig and have an associated measld and ReportConfig and a measObject.

[0168] o In one option, the UE is instructed to report measurement predictions of SCell(s) when the SCell(s) are deactivated. Receiving measurement predictions of SCell(s) is of great benefit for the network to determine which SCell(s) to activate when the amount of traffic increases.In one option, the UE is allowed to replace actual RRM measurements of a deactivated SCG with predicted measurements. In legacy solutions, when an SCG is deactivated, the UE may perform relaxed measurements, i.e. less frequent measurements than when the SCG is activated. In this method, the UE is allowed to replace the relaxed measurements with predicted measurements, i.e. the measurement effort in the UE is even further reduced. The UE may be allowed to replace some or all of the relaxed measurements with predicted measurements. It may be further indicated how many or what proportion of the actual measurements can be replaced by predicted measurements. In one option, the UE sends measurement predictions in addition to the relaxed measurements, i.e. the network receives more (predicted) measurement results but without any additional measurements being performed in the UE. In an option for the time occasions at which the UE is not needed to perform the measurements (according to the measurement relaxation), the UE performs predictions wherein the predictions can be one of the temporal or spatial or frequency domain predictions.

[0169] In one option, the UE receives a separate configuration for the measurement predictions. The predictions may be configured as a measurement configuration or as a prediction configuration, e.g. in a prediction object associated with an identity and a report configuration.

[0170] In one option, the UE is instructed to report predictions instead of measurements when the SCG is deactivated, i.e. it is not the UEs choice whether to report measurements or predictions, it is determined in the configuration from the network. In one option, the frequency domain measurement predictions are combined with spatial domain measurement predictions, so that the UE performs frequency domain predictions of some cells in a different frequency and then performs spatial domain predictions in that same frequency based on the cells which were predicted in the frequency domain.

[0171] ■ The configuration of the spatial prediction may comprise further information, e g- • set of serving / neighbor cells to predict

[0172] • number of cells to predict using spatial domain predictions • usage of clustering in input / output for the prediction

[0173] • In one option, the UE perform FD measurement prediction(s) on a serving cell which is deactivated, but also for one or more cells in the same serving frequency as that serving cell.receiving from a UE, a message (e.g. RRC MeasurementReport) including the FD measurement predictions. The transmitted measurement predictions may comprise:

[0174] o Frequency predictions instead of measurements when the SCell is activated. The frequency predictions may replace some or all of the actual measurements, depending on the configuration received.

[0175] ■ In one option, the UE includes a serving cell identifier (e.g. serving cell identifier, or a serving frequency identifier, servCellld, physical cell identifier, global cell identifier) associated to the frequency predictions. ■ In another option, the UE includes a beam identifier for a serving cell (e.g.

[0176] SSB index, CSI-RS resource identifier, beam identifier) associated to the frequency prediction.

[0177] o Frequency predictions of SCell(s) when the SCell is deactivated.

[0178] o Frequency predictions of the PSCell when the SCG is deactivated. The frequency predictions may replace some or all instances of the relaxed measurements.

[0179] Alternatively, the UE may transmit predicted measurements in addition to the relaxed measurements increasing the knowledge in the network.

[0180] In view of the various examples and details provided above, it will be appreciated that Figure 2 illustrates an example method for predicting measurements and reporting predicted measurements to the wireless network, as performed by a UE operating in a wireless network. The illustrated method, as described below, is intended to be a generalization of the various UE-based techniques described above. Thus, where the terminology differs slightly from that used above, the terminology used to describe Figure 2 should be understood to be synonymous with or, more generally, to at least encompass similar terminology used above. Furthermore, while several variations are described below, these are not the only variants - many additional details and variants were described above.

[0181] The example method shown in Figure 2 includes the steps of predicting at least one measurement value for a signal measurement for a cell that is configured for the UE as a serving cell but that is deactivated, and reporting the predicted measurement value or measurement values to the wireless network. These steps are shown at blocks 210 and 220 in Figure 2. Note that the term “measurement value” is used here, for better precision. The term “measurement” might be construed as referring to the measurement process, so the term “measurement value” is used here to refer to the result of a measurement process. That said, the description above uses the term “measurement” more broadly, to refer to either the measurement process or the result of a measurement process. Accordingly, the term “measurement” as used herein should be interpreted according to its context, to refer to either a process or instance of measuring, or the result of such a process or instance of measuring. Further, the term “signal measurement” is used here to more clearly indicate that the measurements at issue areperformed on received radio signals or signals derived from radio signals, such as an RSRP measurement. While the term “signal measurement” is used here for improved clarity, the numerous examples and details provided above should also be understood as referring to signal measurements, even when the word “signal” is omitted from the discussion.

[0182] Referring again to Figure 2, in some embodiments or instances of the illustrated method, the signal measurement for which the measurement value is predicted is a frequency-domain measurement as described herein.

[0183] In some embodiments or instances, the method comprises, prior to the predicting step shown at block 210, performing an actual signal measurement for the cell. This is shown at block 205. The predicting at step 210 may then be based on the actual signal measurement.

[0184] In some embodiments, performing the actual signal measurement for the cell is in response to receiving an indication that the cell is to be deactivated. The receiving of such an indication is shown at block 203. In some of these embodiments or instances, the method further comprises ceasing actual signal measurements for the cell upon deactivation of the cell, as shown at block 208.

[0185] In some embodiments or instances, the method further comprises ceasing predicting of measurement values for the signal measurement for the cell in response to receiving an indication that the cell is to be activated. This is shown at blocks 225 and 227.

[0186] In some embodiments or instances, the predicting of the at least one measurement value for the signal measurement for the cell is performed in response to receiving an instruction that the UE is to perform the predicting for the cell. In some other embodiments or instances, predicting of the at least one measurement value for the signal measurement for the cell is performed in response to receiving an indication that the UE is allowed to perform the predicting for the cell.

[0187] As was discussed in detail above, the cell at issue may be a secondary cell (SCell) that is deactivated at the time of the predicting. In some embodiments or instances, the cell is associated with a secondary cell group (SCG) that is deactivated at the time of the predicting.

[0188] Figure 3 illustrates an example method for facilitating and using predicted measurement values in a node operating in a wireless communications network, such as radio access network (RAN) node (e.g., a gNB), or a node in a core network. Again, the illustrated method, as described below, is intended to be a generalization of the various techniques described above. Thus, where the terminology differs slightly from that used above, the terminology used to describe Figure 3 should be understood to be synonymous with or, more generally, to at least encompass similar terminology used above. Furthermore, while several variations are described below, these are not the only variants -many additional details and variants were described above. The same caveats regarding the terms “measurement value” and “signal measurement” provided above apply to the following discussion as well.

[0189] The example method shown in Figure 3 includes the step of receiving, from a user equipment (UE), a report including a predicted measurement value for a signal measurement for a cell that is configured for the UE but that is deactivated for the UE or was deactivated at the time the predicted measurement value was predicted. This is shown at block 310. In some embodiments or instances, the method may further comprise performing at least one Radio Resource Configuration (RRC) operation based on the predicted measurement value, as shown at block 320.

[0190] In some embodiments or instances, the signal measurement for which the measurement value was predicted is a frequency-domain measurement.

[0191] In some embodiments or instances, the method further comprises, prior to receiving the report, configuring the UE to report and to perform prediction of the measurement value for the cell. This is shown at block 305. In some embodiments or instances, configuring the UE comprises sending the UE an indication that the cell is to be deactivated. In some embodiments or instances, configuring the UE comprises sending the UE an indication that the UE must perform prediction and reporting of the measurement value for the cell in response to the cell being deactivated. In other embodiments or instances, configuring the UE may comprise sending the UE an indication that the UE is allowed to perform prediction and reporting of the measurement value for the cell in response to the cell being deactivated.

[0192] Note that the term “configuring” as used herein may involve the use of one message or several; if several messages are used, they may be sent at different times. One or more of these messages may be RRC messages, but the use of other message types is also possible.

[0193] In some embodiments or instances, the cell at issue is a secondary cell (SCell) that is deactivated at the time of the predicting. In some embodiments or instances, the cell is associated with a secondary cell group (SCG) that is deactivated at the time of the predicting.

[0194] Embodiments of the presently disclosed techniques include apparatuses, such as UE apparatuses and gNB apparatuses, configured to carry out any of the methods describe above. Below is a detailed description of an example wireless system and several of its nodes - it should be understood that the method described above may be implemented in one or more of these nodes, in various embodiments and instances.Figure 4 shows an example of a communication system 400 in accordance with some embodiments. This provides a context for the techniques described herein, which can be implemented by devices operating in such a communication system 400. In this example, communication system 400 includes telecommunication network 402 that includes access network 404 (e.g., RAN) and a core network 406, which includes one or more core network nodes 408. Access network 404 includes one or more access network nodes, such as network nodes 410a-b (one or more of which may be generally referred to as network nodes 410), or any other similar 3GPP access node or non-3GPP access point. Network nodes 410 facilitate direct or indirect connection of UEs, such as by connecting UEs 412a-d (one or more of which may be generally referred to as UEs 412) to core network 406 over one or more wireless connections.

[0195] 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. Moreover, in different embodiments, communication system 400 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. Communication system 400 may include and / or interface with any type of communication, telecommunication, data, cellular, radio network, and / or other similar type of system.

[0196] UEs 412 may be any of a wide variety of communication devices, including wireless devices arranged, configured, and / or operable to communicate wirelessly with network nodes 410 and other communication devices. Similarly, network nodes 410 are arranged, capable, configured, and / or operable to communicate directly or indirectly with UEs 412 and / or with other network nodes or equipment in telecommunication network 402 to enable and / or provide network access, such as wireless network access, and / or to perform other functions, such as administration in telecommunication network 402.

[0197] In the depicted example, core network 406 connects network nodes 410 to one or more hosts, such as host 416. 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. Core network 406 includes one or more core network nodes (e.g., 408) 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 core network node 408. 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 (AUSF), 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).

[0198] Host 416 may be under the ownership or control of a service provider other than an operator or provider of access network 404 and / or telecommunication network 402, and may be operated by the service provider or on behalf of the service provider. Host 416 may host a variety of applications to provide one or more service. Examples of such applications include live and pre-recorded 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.

[0199] As a whole, communication system 400 of Figure 4 enables connectivity between the UEs, network nodes, and hosts. In that sense, 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.

[0200] In some examples, telecommunication network 402 is a cellular network that implements 3GPP standardized features. Accordingly, telecommunication network 402 may support network slicing to provide different logical networks to different devices that are connected to telecommunication network 402. For example, telecommunication network 402 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)ZMassive loT services to yet further UEs.

[0201] In some examples, UEs 412 are configured to transmit and / or receive information without direct human interaction. For instance, a UE may be designed to transmit information to access network 404 on a predetermined schedule, when triggered by an internal or external event, or in response to requests from access network 404. Additionally, a UE may be configured for operating in single- or multi -RAT or multi-standard mode. For example, 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).

[0202] In the example, hub 414 communicates with access network 404 to facilitate indirect communication between one or more UEs (e.g., UE 412c and / or 412d) and network nodes (e.g., network node 410b). In some examples, hub 414 may be a controller, router, content source and analytics, or any of the other communication devices described herein regarding UEs. For example, hub 414 may be a broadband router enabling access to core network 406 for the UEs. As another example, hub 414 may be a controller that sends commands or instructions to one or more actuators in the UEs. Commands or instructions may be received from the UEs, network nodes 410, or by executable code, script, process, or other instructions in hub 414. As another example, hub 414 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. As another example, hub 414 may be a content source. For example, for a UE that is a VR headset, display, loudspeaker or other media delivery device, hub 414 may retrieve VR assets, video, audio, or other media or data related to sensory information via a network node, which hub 414 then provides to the UE either directly, after performing local processing, and / or after adding additional local content. In still another example, hub 414 acts as a proxy server or orchestrator for the UEs, in particular in if one or more of the UEs are low energy loT devices.

[0203] Hub 414 may have a constant / persistent or intermittent connection to network node 410b. Hub 414 may also allow for a different communication scheme and / or schedule between hub 414 and UEs (e.g., UE 412c and / or 412d), and between hub 414 and core network 406. In other examples, hub 414 is connected to core network 406 and / or one or more UEs via a wired connection. Moreover, hub 414 may be configured to connect to an M2M service provider over access network 404 and / or to another UE over a direct connection. In some scenarios, UEs may establish a wireless connection with network nodes 410 while still connected via hub 414 via a wired or wireless connection. In some embodiments, hub 414 may be a dedicated hub - that is, a hub whose primary function is to route communications to / from the UEs from / to network node 410b. In other embodiments, hub 414 may be a non-dedicated hub - that is, a device which is capable of operating to route communications between the UEs and network node 410b, but which is additionally capable of operating as a communication start and / or end point for certain data channels.

[0204] Figure 5 shows a UE 500 in accordance with some embodiments. Examples of 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-mounted or vehicleembedded / integrated wireless device, etc. Other examples include any UE identified by 3GPP, including a narrow band internet of things (NB-IoT) UE, a machine type communication (MTC) UE, and / or an enhanced MTC (eMTC) UE.

[0205] 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). In other examples, a UE may not necessarily have a user in the sense of a human user who owns and / or operates the relevant device. Instead, 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). Alternatively, a UE may represent a device that is not intended for sale to, or operation by, an end user but which may be associated with or operated for the benefit of a user (e.g., a smart power meter).

[0206] UE 500 includes processing circuitry 502 that is operatively coupled via bus 504 to input / output interface 506, power source 508, memory 510, communication interface 512, and possibly other components not explicitly shown. Certain UEs may utilize all or a subset of the components shown in Figure 5. 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.

[0207] Processing circuitry 502 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 memory 510. Processing circuitry 502 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. For example, processing circuitry 502 may include multiple central processing units (CPUs).

[0208] In the example, input / output interface 506 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 UE 500. 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, asmartcard, 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.

[0209] In some embodiments, power source 508 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. Power source 508 may further include power circuitry for delivering power from power source 508 itself, and / or an external power source, to the various parts of UE 500 via input circuitry or an interface such as an electrical power cable. Delivering power may be, for example, for charging power source 508. Power circuitry may perform any formatting, converting, or other modification to the power from power source 508 to make the power suitable for the respective components of UE 500 to which power is supplied.

[0210] Memory 510 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 readonly memory (EPROM), electrically erasable programmable read-only memory (EEPROM), magnetic disks, optical disks, hard disks, removable cartridges, flash drives, and so forth. In one example, memory 510 includes one or more application programs 514, such as an operating system, web browser application, a widget, gadget engine, or other application, and corresponding data 516. Memory 510 may store, for use by UE 500, any of a variety of various operating systems or combinations of operating systems.

[0211] Memory 510 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 in-line 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. The UICC may for example be an embedded UICC (eUICC), integrated UICC (iUICC) or a removable UICC commonly known as ‘SIM card.’ Memory 510 may allow UE 500 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 memory 510, which may be or comprise a device-readable storage medium.Processing circuitry 502 may be configured to communicate with an access network or other network using communication interface 512. Communication interface 512 may comprise one or more communication subsystems and may include or be communicatively coupled to antenna 522.

[0212] Communication interface 512 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 transmitter 518 and / or receiver 520 appropriate to provide network communications (e.g., optical, electrical, frequency allocations, and so forth). Moreover, transmitter 518 and receiver 520 may be coupled to one or more antennas (e.g., 522) and may share circuit components, software or firmware, or alternatively be implemented separately.

[0213] In the illustrated embodiment, communication functions of communication interface 512 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. 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 / intemet protocol (TCP / IP), synchronous optical networking (SONET), Asynchronous Transfer Mode (ATM), QUIC, Hypertext Transfer Protocol (HTTP), and so forth.

[0214] Regardless of the type of sensor, a UE may provide an output of data captured by its sensors, through its communication interface 512, 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 19 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., an alert is sent when moisture is detected), in response to a request (e.g., a user initiated request), or a continuous stream (e.g., a live video feed of a patient).

[0215] As another example, 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. In response to the received wireless input the states of the actuator, the motor, or the switch may change. For example, 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. Non-limiting examples of such an 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 device, a sensor for monitoring a plant or animal, an industrial robot, an Unmanned Aerial Vehicle (UAV), and any kind of medical device, like a heart rate monitor or a remote controlled surgical robot. A UE in the form of an loT device comprises circuitry and / or software in dependence of the intended application of the loT device in addition to other components as described in relation to UE 500 shown in Figure 5.

[0216] As yet another specific example, in an loT scenario, 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. As one particular example, the UE may implement the 3GPP NB-IoT standard. In other scenarios, 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.

[0217] In practice, any number of UEs may be used together with respect to a single use case. For example, 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. When the user makes changes from the remote controller, 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. For example, a UE might comprise the sensor and the actuator, and handle communication of data for both the speed sensor and the actuators.

[0218] Figure 6 shows a network node 600 in accordance with some embodiments. Examples of network nodes include, but are not limited to, access points (e.g., radio access points) and base stations (e.g., radio base stations, Node Bs, eNBs, and gNBs).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. Abase 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 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).

[0219] Other examples of 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).

[0220] Network node 600 includes processing circuitry 602, memory 604, communication interface 606, and power source 608. Network node 600 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. In certain scenarios in which network node 600 comprises multiple separate components (e.g., BTS and BSC components), one or more of the separate components may be shared among several network nodes. For example, a single RNC may control multiple NodeBs. In such a scenario, each unique NodeB and RNC pair, may in some instances be considered a single separate network node. In some embodiments, network node 600 may be configured to support multiple radio access technologies (RATs). In such embodiments, some components may be duplicated (e.g., separate memory 604 for different RATs) and some components may be reused (e.g., a same antenna 610 may be shared by different RATs). Network node 600 may also include multiple sets of the various illustrated components for different wireless technologies integrated into network node 600, 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 600.

[0221] Processing circuitry 602 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 inconjunction with other network node 600 components, such as memory 604, to provide network node 600 functionality.

[0222] In some embodiments, processing circuitry 602 includes a system on a chip (SOC). In some embodiments, processing circuitry 602 includes one or more of radio frequency (RF) transceiver circuitry 612 and baseband processing circuitry 614. In some embodiments, RF transceiver circuitry 612 and baseband processing circuitry 614 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 612 and baseband processing circuitry 614 may be on the same chip or set of chips, boards, or units.

[0223] Memory 604 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 processing circuitry 602. Memory 604 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 (collectively denoted computer program product 604a) capable of being executed by processing circuitry 602 and utilized by network node 600. Memory 604 may be used to store any calculations made by processing circuitry 602 and / or any data received via communication interface 606. In some embodiments, processing circuitry 602 and memory 604 is integrated.

[0224] Communication interface 606 is used in wired or wireless communication of signaling and / or data between a network node, access network, and / or UE. As illustrated, communication interface 606 comprises port(s) / terminal(s) 616 to send and receive data, for example to and from a network over a wired connection. Communication interface 606 also includes radio front-end circuitry 618 that may be coupled to, or in certain embodiments a part of, antenna 610. Radio front-end circuitry 618 comprises filters 620 and amplifiers 622. Radio front-end circuitry 618 may be connected to antenna 610 and processing circuitry 602. The radio front-end circuitry may be configured to condition signals communicated between antenna 610 and processing circuitry 602. Radio front-end circuitry 618 may receive digital data that is to be sent out to other network nodes or UEs via a wireless connection. Radio front-end circuitry 618 may convert the digital data into a radio signal having the appropriate channel and bandwidth parameters using a combination of filters 620 and / or amplifiers 622. The radio signal may then be transmitted via antenna 610. Similarly, when receiving data, antenna 610 may collect radio signals which are then converted into digital data by radio front-end circuitry 618. Thedigital data may be passed to processing circuitry 602. In other embodiments, the communication interface may comprise different components and / or different combinations of components.

[0225] In certain alternative embodiments, network node 600 does not include separate radio front-end circuitry 618, instead, processing circuitry 602 includes radio front-end circuitry and is connected to antenna 610. Similarly, in some embodiments, all or some of RF transceiver circuitry 612 is part of communication interface 606. In still other embodiments, communication interface 606 includes one or more ports or terminals 616, radio front-end circuitry 618, and RF transceiver circuitry 612, as part of a radio unit (not shown), and communication interface 606 communicates with the baseband processing circuitry 614, which is part of a digital unit (not shown).

[0226] Antenna 610 may include one or more antennas, or antenna arrays, configured to send and / or receive wireless signals. Antenna 610 may be coupled to radio front-end circuitry 618 and may be any type of antenna capable of transmitting and receiving data and / or signals wirelessly. In certain embodiments, antenna 610 is separate from network node 600 and connectable to network node 600 through an interface or port.

[0227] Antenna 610, communication interface 606, and / or processing circuitry 602 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, antenna 610, communication interface 606, and / or processing circuitry 602 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.

[0228] Power source 608 provides power to the various components of network node 600 in a form suitable for the respective components (e.g., at a voltage and current level needed for each respective component). Power source 608 may further comprise, or be coupled to, power management circuitry to supply the components of network node 600 with power for performing the functionality described herein. For example, network node 600 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 power source 608. As a further example, power source 608 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.

[0229] Embodiments of network node 600 may include additional components beyond those shown in Figure 6 for providing certain aspects of the network node’s functionality, including any of the functionalitydescribed herein and / or any functionality necessary to support the subject matter described herein. For example, network node 600 may include user interface equipment to allow input of information into network node 600 and to allow output of information from network node 600. This may allow a user to perform diagnostic, maintenance, repair, and other administrative functions for network node 600.

[0230] Figure 7 is a block diagram of a host 700, which may be an embodiment of host 416 of Figure 4, in accordance with various aspects described herein. Host 700 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. Host 700 may provide one or more services to one or more UEs.

[0231] Host 700 includes processing circuitry 702 that is operatively coupled via a bus 704 to an input / output interface 706, a network interface 708, a power source 710, and a memory 712. 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 5 and 6, such that the descriptions thereof are generally applicable to the corresponding components of host 700.

[0232] Memory 712 may include one or more computer programs including one or more host application programs 714 and data 716, which may include user data, e.g., data generated by a UE for host 700 or data generated by host 700 for a UE. Embodiments of host 700 may utilize only a subset or all of the components shown. Host application programs 714 may be implemented in a container-based architecture and may provide support for video codecs (e.g., Versatile Video Coding (VVC), High Efficiency Video Coding (HEVC), Advanced Video Coding (AVC), MPEG, VP9) and audio codecs (e.g., FLAC, 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). Host application programs 714 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. Accordingly, host 700 may select and / or indicate a different host for over-the-top services for a UE. Host application programs 714 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.

[0233] Figure 8 is a block diagram illustrating a virtualization environment 800 in which functions implemented by some embodiments may be virtualized. In the present context, virtualizing means creating virtual versions of apparatuses or devices which may include virtualizing hardware platforms, storage devices and networking resources. As used herein, virtualization can be applied to any device described herein, or components thereof, and relates to an implementation in which at least a portionof 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 800 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. Further, in embodiments in which the virtual node does not require radio connectivity (e.g., a core network node or host), then the node may be entirely virtualized.

[0234] Applications 802 (which may alternatively be called software instances, virtual appliances, network functions, virtual nodes, virtual network functions, etc.) are run in the virtualization environment 800 to implement some of the features, functions, and / or benefits of some of the embodiments disclosed herein.

[0235] Hardware 804 includes processing circuitry, memory that stores software and / or instructions (collectively denoted computer program product 804a) 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 806 (also referred to as hypervisors or virtual machine monitors (VMMs)), provide VMs 808a-b (one or more of which may be generally referred to as VMs 808), and / or perform any of the functions, features and / or benefits described in relation with some embodiments described herein. The virtualization layer 806 may present a virtual operating platform that appears like networking hardware to VMs 808.

[0236] VMs 808 comprise virtual processing, virtual memory, virtual networking or interface and virtual storage, and may be run by a corresponding virtualization layer 806. Different embodiments of the instance of a virtual appliance 802 may be implemented on one or more of VMs 808, and the implementations may differ. 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.

[0237] In the context of NFV, each VM 808 may be a software implementation of a physical machine that runs programs as if they were executing on a physical, non-virtualized machine. Each of VMs 808, and that part of hardware 804 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. Still in the context of NFV, a virtual network function is responsible for handling specific network functions that run in one or more VMs 808 on top of hardware 804 and corresponds to application 802.Hardware 804 may be implemented in a standalone network node with generic or specific components. Hardware 804 may implement some functions via virtualization. Alternatively, hardware 804 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 810, which, among others, oversees lifecycle management of applications 802. In some embodiments, hardware 804 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. In some embodiments, some signaling can be provided with the use of control system 812 which may alternatively be used for communication between hardware nodes and radio units.

[0238] Figure 9 shows a communication diagram of a host 902 communicating via a network node 904 with a UE 906 over a partially wireless connection in accordance with some embodiments. Example implementations, in accordance with various embodiments, of the UE (such as a UE 412a of Figure 4 and / or UE 500 of Figure 5), network node (such as network node 410a of Figure 4 and / or network node 600 of Figure 6), and host (such as host 416 of Figure 4 and / or host 700 of Figure 7) discussed in the preceding paragraphs will now be described with reference to Figure 9.

[0239] Like host 700, embodiments of host 902 include hardware, such as a communication interface, processing circuitry, and memory. Host 902 also includes software, which is stored in or accessible by host 902 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 UE 906 connecting via an over-the-top (OTT) connection 950 extending between UE 906 and host 902. In providing the service to the remote user, a host application may provide user data which is transmitted using OTT connection 950.

[0240] Network node 904 includes hardware enabling it to communicate with host 902 and UE 906.

[0241] Connection 960 may be direct or pass through a core network (like core network 406 of Figure 4) and / or one or more other intermediate networks, such as one or more public, private, or hosted networks. For example, an intermediate network may be a backbone network or the Internet.

[0242] UE 906 includes hardware and software, which is stored in or accessible by UE 906 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 906 with the support of host 902. In host 902, an executing host application may communicate with the executing client application via OTT connection 950 terminating at UE 906 and host 902. In providing the service to the user, the UE's client application may receive request data from the host'shost application and provide user data in response to the request data. OTT connection 950 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 OTT connection 950.

[0243] OTT connection 950 may extend via a connection 960 between host 902 and network node 904 and via wireless connection 970 between network node 904 and UE 906 to provide the connection between host 902 and UE 906. Connection 960 and wireless connection 970, over which OTT connection 950 may be provided, have been drawn abstractly to illustrate the communication between host 902 and UE 906 via network node 904, without explicit reference to any intermediary devices and the precise routing of messages via these devices.

[0244] As an example of transmitting data via OTT connection 950, in step 908, host 902 provides user data, which may be performed by executing a host application. In some embodiments, the user data is associated with a particular human user interacting with UE 906. In other embodiments, the user data is associated with a UE 906 that shares data with host 902 without explicit human interaction. In step 910, host 902 initiates a transmission carrying the user data towards UE 906. Host 902 may initiate the transmission responsive to a request transmitted by UE 906. The request may be caused by human interaction with UE 906 or by operation of the client application executing on UE 906. The transmission may pass via network node 904, in accordance with the teachings of the embodiments described throughout this disclosure. Accordingly, in step 912, network node 904 transmits to UE 906 the user data that was carried in the transmission that host 902 initiated, in accordance with the teachings of the embodiments described throughout this disclosure. In step 914, UE 906 receives the user data carried in the transmission, which may be performed by a client application executed on UE 906 associated with the host application executed by host 902.

[0245] In some examples, UE 906 executes a client application which provides user data to host 902. The user data may be provided in reaction or response to the data received from host 902. Accordingly, in step 916, UE 906 may provide user data, which may be performed by executing the client application. In providing the user data, the client application may further consider user input received from the user via an input / output interface of UE 906. Regardless of the specific manner in which the user data was provided, UE 906 initiates, in step 918, transmission of the user data towards host 902 via network node 904. In step 920, in accordance with the teachings of the embodiments described throughout this disclosure, network node 904 receives user data from UE 906 and initiates transmission of the received user data towards host 902. In step 922, host 902 receives the user data carried in the transmission initiated by UE 906.

[0246] One or more of the various embodiments improve the performance of OTT services provided to UE 906 using OTT connection 950, in which wireless connection 970 forms the last segment. Moreprecisely, embodiments can reduce and / or prevent undesired recovery actions by UEs. For example, due to the conditions for considering an LTM cell switch procedure successful (causing UE to stop a supervision timer), undesired recovery actions due to supervision timer expiration are prevented at the UE. This is especially an issue in the scenarios where LTM cell switch needs to be performed without a RA procedure (i.e., “RACH-less”). Preventing undesired recovery actions makes LTM RACH-less solutions more efficient, which reduces the delay to access an LTM candidate cell. Embodiments can facilitate predictable UE behavior in LTM execution failures and can reduce and / or eliminate ambiguity for UE actions in the event of LTM failures that are concurrent other failures such as radio link failure (RLF). By improving operation of UEs and RANs in this manner, embodiments increase the value of OTT services delivered to / from the UE via the RAN, to both end users and service providers.

[0247] In an example scenario, factory status information may be collected and analyzed by host 902. As another example, host 902 may process audio and video data which may have been retrieved from a UE for use in creating maps. As another example, host 902 may collect and analyze real-time data to assist in controlling vehicle congestion (e.g., controlling traffic lights). As another example, host 902 may store surveillance video uploaded by a UE. As another example, host 902 may store or control access to media content such as video, audio, VR or AR which it can broadcast, multicast or unicast to UEs. As other examples, host 902 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.

[0248] In some examples, 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. There may further be an optional network functionality for reconfiguring OTT connection 950 between host 902 and UE 906, in response to variations in the measurement results. The measurement procedure and / or the network functionality for reconfiguring the OTT connection may be implemented in software and hardware of host 902 and / or UE 906. In some embodiments, sensors (not shown) may be deployed in or in association with other devices through which OTT connection 950 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 OTT connection 950 may include message format, retransmission settings, preferred routing etc.; the reconfiguring need not directly alter the operation of network node 904. Such procedures and functionalities may be known and practiced in the art. In certain embodiments, measurements may involve proprietary UE signaling that facilitates measurements of throughput, propagation times, latency and the like, by host 902. The measurementsmay be implemented in that software causes messages to be transmitted, in particular empty or ‘dummy’ messages, using OTT connection 950 while monitoring propagation times, errors, etc.

[0249] The foregoing merely illustrates the principles of the disclosure. Various modifications and alterations to the described embodiments will be apparent to those skilled in the art in view of the teachings herein. It will thus be appreciated that those skilled in the art will be able to devise numerous systems, arrangements, and procedures that, although not explicitly shown or described herein, embody the principles of the disclosure and can be thus within the spirit and scope of the disclosure. Various embodiments can be used together with one another, as well as interchangeably therewith, as should be understood by those having ordinary skill in the art.

[0250] The term unit, as used herein, can have conventional meaning in the field of electronics, electrical devices and / or electronic devices and can include, for example, electrical and / or electronic circuitry, devices, modules, processors, memories, logic solid state and / or discrete devices, computer programs or instructions for carrying out respective tasks, procedures, computations, outputs, and / or displaying functions, and so on, as such as those that are described herein.

[0251] Any appropriate steps, methods, features, functions, or benefits disclosed herein may be performed through one or more functional units or modules of one or more virtual apparatuses. Each virtual apparatus may comprise a number of these functional units. These functional units may be implemented via processing circuitry, which may include one or more microprocessor or microcontrollers, as well as other digital hardware, which may include Digital Signal Processor (DSPs), special-purpose digital logic, and the like. The processing circuitry may be configured to execute program code stored in memory, which may include one or several types of memory such as Read Only Memory (ROM), Random Access Memory (RAM), cache memory, flash memory devices, optical storage devices, etc. Program code stored in memory includes program instructions for executing one or more telecommunications and / or data communications protocols as well as instructions for carrying out one or more of the techniques described herein. In some implementations, the processing circuitry may be used to cause the respective functional unit to perform corresponding functions according to one or more embodiments of the present disclosure.

[0252] As described herein, device and / or apparatus can be represented by a semiconductor chip, a chipset, or a (hardware) module comprising such chip or chipset; this, however, does not exclude the possibility that a functionality of a device or apparatus, instead of being hardware implemented, be implemented as a software module such as a computer program or a computer program product comprising executable software code portions for execution or being run on a processor. Furthermore, functionality of a device or apparatus can be implemented by any combination of hardware and software. A device or apparatus can also be regarded as an assembly of multiple devices and / orapparatuses, whether functionally in cooperation with or independently of each other. Moreover, devices and apparatuses can be implemented in a distributed fashion throughout a system, so long as the functionality of the device or apparatus is preserved. Such and similar principles are considered as known to a skilled person.

[0253] Furthermore, functions described herein as being performed by a wireless device or a network node may be distributed over a plurality of wireless devices and / or network nodes. In other words, it is contemplated that the functions of the network node and wireless device described herein are not limited to performance by a single physical device and, in fact, can be distributed among several physical devices.

[0254] In addition, certain terms used in the present disclosure, including the specification, drawings and embodiments thereof, can be used synonymously in certain instances, including, but not limited to, e.g., data and information. It should be understood that, while these words and / or other words that can be synonymous to one another, can be used synonymously herein, that there can be instances when such words can be intended to not be used synonymously. Further, to the extent that the prior art knowledge has not been explicitly incorporated by reference herein above, it is explicitly incorporated herein in its entirety. All publications referenced are incorporated herein by reference in their entireties.

[0255] Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs. It will be further understood that terms used herein should be interpreted as having a meaning that is consistent with their meaning in the context of this specification and the relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.

[0256] In addition, certain terms used in the present disclosure, including the specification and drawings, can be used synonymously in certain instances (e.g., “data” and “information”). It should be understood, that although these terms (and / or other terms that can be synonymous to one another) can be used synonymously herein, there can be instances when such words can be intended to not be used synonymously.

[0257] EXAMPLE EMBODIMENTS

[0258] Embodiments of the techniques, apparatuses, and systems described herein include, but are not limited to, the following enumerated examples:

[0259] 1. A method, in a user equipment (UE) operating in a wireless network, comprising:

[0260] predicting at least one measurement value for a signal measurement for a cell that is configured for the UE as a serving cell but that is deactivated; andreporting the predicted measurement value or measurement values to the wireless network.

[0261] 2. The method of example embodiment 1, wherein the signal measurement for which the measurement value is predicted is a frequency-domain measurement.

[0262] 3. The method of example embodiment 1 or 2, wherein the method comprises, prior to said predicting, performing an actual signal measurement for the cell, and wherein the predicting is based on the actual signal measurement.

[0263] 4. The method of example embodiment 3, wherein said performing the actual signal measurement for the cell is in response to receiving an indication that the cell is to be deactivated.

[0264] 5. The method of example embodiment 4, further comprising ceasing actual signal measurements for the cell upon deactivation of the cell.

[0265] 6. The method of any one of example embodiments 1-5, wherein the method further comprises ceasing predicting of measurement values for the signal measurement for the cell in response to receiving an indication that the cell is to be activated.

[0266] 7. The method of any one of example embodiments 1-6, wherein said predicting the at least one measurement value for the signal measurement for the cell is performed in response to receiving an instruction that the UE is to perform the predicting for the cell.

[0267] 8. The method of any one of example embodiments 1-6, wherein said predicting the at least one measurement value for the signal measurement for the cell is performed in response to receiving an indication that the UE is allowed to perform the predicting for the cell.

[0268] 9. The method of any one of example embodiments 1-8, wherein the cell is a secondary cell (SCell) that is deactivated at the time of the predicting.

[0269] 10. The method of any one of example embodiments 1-8, wherein the cell is associated with a secondary cell group (SCG) that is deactivated at the time of the predicting.

[0270] 11. A method, in a node in a radio access network or core network of a wireless network, the method comprising:

[0271] receiving, from a user equipment (UE), a report including a predicted measurement value for a signal measurement for a cell that is configured for the UE but that is deactivatedfor the UE or was deactivated at the time the predicted measurement value was predicted.

[0272] 12. The method of example embodiment 11, further comprising performing at least one Radio Resource Configuration (RRC) operation based on the predicted measurement value.

[0273] 13. The method of example embodiment 11 or 12, wherein the signal measurement for which the measurement value was predicted is a frequency-domain measurement.

[0274] 14. The method of any one of example embodiments 11-13, wherein the method further comprises, prior to receiving the report, configuring the UE to report and to perform prediction of the measurement value for the cell.

[0275] 15. The method of example embodiment 14, wherein said configuring the UE comprises sending the UE an indication that the cell is to be deactivated.

[0276] 16. The method of example embodiment 14 or 15, wherein said configuring the UE comprises sending the UE an indication that the UE must perform prediction and reporting of the measurement value for the cell in response to the cell being deactivated.

[0277] 17. The method of example embodiment 14 or 15, wherein said configuring the UE comprises sending the UE an indication that the UE is allowed to perform prediction and reporting of the measurement value for the cell in response to the cell being deactivated.

[0278] 18. The method of any one of example embodiments 11-17, wherein the cell is a secondary cell (SCell) that is deactivated at the time of the predicting.

[0279] 19. The method of any one of example embodiments 11-18, wherein the cell is associated with a secondary cell group (SCG) that is deactivated at the time of the predicting.

[0280] 20. A user equipment (UE) adapted to carry out a method according to any one of example embodiments 1-10.

[0281] 21. A user equipment (UE) comprising:

[0282] communication interface circuitry configured to communicate with a wireless network; and processing circuitry operatively coupled to the communication interface circuitry and configured to:predict at least one measurement value for a signal measurement for a cell that is configured for the UE as a serving cell but that is deactivated; and report the predicted measurement value or measurement values to the wireless network.

[0283] 22. The UE of example embodiment 21, wherein the processing circuitry is configured to carry out the method of any one of example embodiments 2-10.

[0284] 23. A network node adapted to carry out a method according to any one of example embodiments 11-19.

[0285] 24. A network node, comprising:

[0286] communication interface circuitry configured to communicate with one or more other nodes in a wireless network; and

[0287] processing circuitry operatively coupled to the communication interface circuitry and configured to:

[0288] receive, via the communication interface circuitry, from a user equipment (UE), a report including a predicted measurement value for a signal measurement for a cell that is configured for the UE but that is deactivated for the UE or was deactivated at the time the predicted measurement value was predicted.

[0289] 25. The network node of example embodiment 24, wherein the processing circuitry is configured to carry out the method of any one of example embodiments 12-19.

[0290] 26. A computer program product comprising program instructions for execution by a processing circuity of a UE operating in a wireless network, the program instructions being configured to cause the UE to carry out a method according to any one of example embodiments 1-10.

[0291] 27. A computer program product comprising program instructions for execution by a processing circuity of a network node operating in a wireless network, the program instructions being configured to cause the network node to carry out a method according to any one of example embodiments 11-19.

[0292] REFERENCES

[0293] Technical Specification Group Radio Access Network; Study on Artificial Intelligence (AI)ZMachine Learning (ML) for NR air interface (Release 18), 3GPPTR 38.843, Dec. 2023.ABBREVIATIONS 3GPP 3rd Generation Partnership Project 5GC / 5GCN 5G Core Network

[0294] 5GS 5G System

[0295] AF Application Function

[0296] AMF Access and Mobility Management Function AN Access Network

[0297] API Application Programming Interface ASN.l Abstract Syntax Notation One

[0298] CA Carrier Aggregation

[0299] CE Control Element

[0300] CGI Cell Global Identity

[0301] CHO Conditional Handover

[0302] CN Core Network

[0303] CP Control Plane

[0304] CPC Conditional PSCell Change

[0305] cu Central Unit

[0306] DAPS Dual Active Protocol Stacks

[0307] DC Dual Connectivity

[0308] DRB Data Radio Bearer

[0309] DU Distributed Unit

[0310] eNB E-UTRAN NodeB

[0311] EN-DC E-UTRA-NR Dual Connectivity

[0312] E-UTRA Evolved UTRA

[0313] E-UTRAN Evolved UTRAN

[0314] gNB Radio base station in NR

[0315] GNSS Global Navigation Satellite System GPS Global Positioning System

[0316] ID Identifier / Identity

[0317] IE Information Element

[0318] LTE Long Term Evolution

[0319] MAC Medium Access Control

[0320] MBS Multicast Broadcast Service

[0321] MCE Measurement Collector Entity

[0322] MCG Master Cell GroupMME Mobility Management Entity

[0323] MN Master Node

[0324] MR-DC Multi-Radio Dual Connectivity

[0325] NE-DC NR-E-UTRA Dual Connectivity

[0326] NEF Network Exposure Function

[0327] NG Next Generation

[0328] NGEN-DC NG-RAN E-UTRA-NR Dual Connectivity NG-RAN NG Radio Access Network

[0329] NR New Radio

[0330] OAM / O&M Operation and Maintenance

[0331] PCell Primary Cell

[0332] PCF Policy Control Function

[0333] PCI Physical Cell Identity

[0334] PSCell Primary Secondary Cell

[0335] PDU Protocol Data Unit

[0336] PLMN Public Land Mobile Network

[0337] PTM Point to Multipoint

[0338] PTP Point to Point

[0339] QCI QoS Class Identifier

[0340] QFI QoS Flow Identifier

[0341] QMC QoE Measurement Collection

[0342] QoE Quality of Experience

[0343] QoS Quality of Service

[0344] RACH Random Access Channel

[0345] RAN Radio Access Network

[0346] RAT Radio Access Technology

[0347] RRC Radio Resource Control

[0348] RSRP Reference Signal Received Power

[0349] RSRQ Reference Signal Received Quality

[0350] RS SI Received Signal Strength Indicator

[0351] RV-QOE RAN Visible QoE

[0352] S 1 The interface between the RAN and the CN in LTE. S 1 AP S 1 Application Protocol

[0353] SCell Secondary Cell

[0354] SCG Secondary Cell Group

[0355] SDT Small Data Transmission

[0356] SINR Signal to Interference and Noise RatioSMF Session Management Function SMO Service Management and Orchestration SN Secondary Node

[0357] SNR Signal to Noise Ratio

[0358] SRB Signaling Radio Bearer

[0359] TA Terminal Adaptor

[0360] TCE Trace Collector Entity

[0361] TE Terminal Equipment

[0362] TS Technical Specification

[0363] UDM User Data Management

[0364] UE User Equipment

[0365] UP User Plane

[0366] URI Uniform Resource Identifier

[0367] URL Uniform Resource Locator

[0368] URLLC Ultra-Reliable Low-Latency Communication VR Virtual Reality

[0369] XML Extensible Markup Language

Claims

CLAIMS1. A method, in a user equipment, UE, operating in a wireless network, comprising:predicting (210) at least one measurement value for a signal measurement for a cell that is configured for the UE as a serving cell but that is deactivated; andreporting (220) the predicted measurement value or measurement values to the wireless network.

2. The method of claim 1, wherein the signal measurement for which the measurement value is predicted is a frequency-domain measurement.

3. The method of claim 1 or 2, wherein the method comprises, prior to said predicting (210), performing (205) an actual signal measurement for the cell, and wherein the predicting is based on the actual signal measurement.

4. The method of claim 3, wherein said performing (205) the actual signal measurement for the cell is in response to receiving (203) an indication that the cell is to be deactivated.

5. The method of claim 4, further comprising ceasing (208) actual signal measurements for the cell upon deactivation of the cell.

6. The method of any one of claims 1-5, wherein the method further comprises ceasing (227) predicting of measurement values for the signal measurement for the cell in response to receiving (225) an indication that the cell is to be activated.

7. The method of any one of claims 1-6, wherein said predicting (210) the at least one measurement value for the signal measurement for the cell is performed in response to receiving an instruction that the UE is to perform the predicting for the cell.

8. The method of any one of claims 1-6, wherein said predicting (210) the at least one measurement value for the signal measurement for the cell is performed in response to receiving an indication that the UE is allowed to perform the predicting for the cell.

9. The method of any one of claims 1-8, wherein the cell is a secondary cell, SCell, that is deactivated at the time of the predicting.

10. The method of any one of claims 1-8, wherein the cell is associated with a secondary cell group, SCG, that is deactivated at the time of the predicting.

11. A method, in a node in a radio access network or core network of a wireless network, the method comprising:receiving (310), from a user equipment, UE, a report including a predicted measurement value for a signal measurement for a cell that is configured for the UE but that is deactivated for the UE or was deactivated at the time the predicted measurement value was predicted.

12. The method of claim 11, further comprising performing (320) at least one Radio Resource Configuration, RRC, operation based on the predicted measurement value.

13. The method of claim 11 or 12, wherein the signal measurement for which the measurement value was predicted is a frequency-domain measurement.

14. The method of any one of claims 11-13, wherein the method further comprises, prior to receiving the report, configuring (305) the UE to report and to perform prediction of the measurement value for the cell.

15. The method of claim 14, wherein said configuring (305) the UE comprises sending the UE an indication that the cell is to be deactivated.

16. The method of claim 14 or 15, wherein said configuring (305) the UE comprises sending the UE an indication that the UE must perform prediction and reporting of the measurement value for the cell in response to the cell being deactivated.

17. The method of claim 14 or 15, wherein said configuring (305) the UE comprises sending the UE an indication that the UE is allowed to perform prediction and reporting of the measurement value for the cell in response to the cell being deactivated.

18. The method of any one of claims 11-17, wherein the cell is a secondary cell, SCell, that is deactivated at the time of the predicting.

19. The method of any one of claims 11-18, wherein the cell is associated with a secondary cell group, SCG, that is deactivated at the time of the predicting.

20. A user equipment, UE, (500) adapted to carry out a method according to any one of claims 1-10.

21. A user equipment, UE, (500) comprising:communication interface circuitry (506) configured to communicate with a wireless network;andprocessing circuitry (502) operatively coupled to the communication interface circuitry (506) and configured to:predict at least one measurement value for a signal measurement for a cell that is configured for the UE as a serving cell but that is deactivated; and report the predicted measurement value or measurement values to the wireless network.

22. The UE of claim 21, wherein the processing circuitry (502) is configured to carry out the method of any one of claims 2-10.

23. A network node (600) adapted to carry out a method according to any one of claims 11-19.

24. A network node (600), comprising:communication interface circuitry (606) configured to communicate with one or more other nodes in a wireless network; andprocessing circuitry (602) operatively coupled to the communication interface circuitry (606) and configured to:receive, via the communication interface circuitry, from a user equipment (UE), a report including a predicted measurement value for a signal measurement for a cell that is configured for the UE but that is deactivated for the UE or was deactivated at the time the predicted measurement value was predicted.

25. The network node of claim 24, wherein the processing circuitry (602) is configured to carry out the method of any one of claims 12-19.

26. A computer program product comprising program instructions for execution by a processing circuity of a UE operating in a wireless network, the program instructions being configured to cause the UE to carry out a method according to any one of claims 1-10.

27. A computer program product comprising program instructions for execution by a processing circuity of a network node operating in a wireless network, the program instructions being configured to cause the network node to carry out a method according to any one of claims 11-19.

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