Filtering configuration for measurements

Abstract

According to an example aspect of the present disclosure, there is provided a method for an apparatus, the method comprising receiving, from a wireless network node, a filtering configuration configuring the apparatus to predict, for skipped measurement windows, lower layer outputs for upper layer filtering and predicting, based on the filtering configuration, a lower layer output for a skipped measurement window, wherein the lower layer output is based on at least two measurement windows before the skipped measurement window and performing, based at least on the lower layer output, said upper layer filtering for the skipped measurement window.

Description

FILTERING CONFIGURATION FOR MEASUREMENTSFIELD

[0001] Various example embodiments relate to a filtering configuration for measurements in cellular communication network.BACKGROUND

[0002] Measurements by a User Equipment, UE, may be beneficial, or even mandatory, for various reasons in cellular communication networks. For example, measurements of a UE may be exploited to ensure proper resource management. Measurements are thus very important in cellular communication networks, such as in networks operating according to Long Term Evolution, LTE, and / or 5G radio access technology. 5G radio access technology may also be referred to as New Radio, NR, access technology. Since its inception, LTE has been widely deployed and 3rd Generation Partnership Project, 3GPP, still maintains LTE. Similarly, 3GPP also develops standards for 5G / NR. At least one of the topics in the 3GPP discussions is related to measurements and according to the discussions there is a need to provide improved methods, apparatuses and computer programs for measurements.SUMMARY

[0003] According to some aspects, there is provided the subject-matter of the independent claims. Some example embodiments are defined in the dependent claims.

[0004] The scope of protection sought for various example embodiments of the disclosure is set out by the independent claims. The example embodiments and features, if any, described in this specification that do not fall under the scope of the independent claims are to be interpreted as examples useful for understanding various example embodiments of the disclosure.

[0005] According to a first aspect of the present disclosure, there is provided an apparatus comprising at least one processing core, at least one processor; and at least one memory storing instructions that, when executed by the at least one processor, cause the apparatus at least to receive, from a wireless network node, a filtering configuration configuring the apparatus to predict, for skipped measurement windows, lower layer outputsfor upper layer filtering, predict, based on the filtering configuration, a lower layer output for a skipped measurement window, wherein the lower layer output is based on at least two measurement windows before the skipped measurement window and perform, based at least on the lower layer output, said upper layer filtering for the skipped measurement window.

[0006] According to a second aspect of the present disclosure, there is provided an apparatus comprising at least one processing core, at least one processor; and at least one memory storing instructions that, when executed by the at least one processor, cause the apparatus at least to determine a filtering configuration for configuring a user equipment to predict, for skipped measurement windows, lower layer outputs for upper layer filtering and transmit the filtering configuration to the user equipment.

[0007] According to a third aspect of the present disclosure, there is provided a first method, comprising receiving, from a wireless network node, a filtering configuration configuring an apparatus to predict, for skipped measurement windows, lower layer outputs for upper layer filtering, predicting, based on the filtering configuration, a lower layer output for a skipped measurement window, wherein the lower layer output is based on at least two measurement windows before the skipped measurement window and performing, based at least on the lower layer output, said upper layer filtering for the skipped measurement window.

[0008] According to a fourth aspect of the present disclosure, there is provided a second method, comprising determining a filtering configuration for configuring a user equipment to predict, for skipped measurement windows, lower layer outputs for upper layer filtering and transmitting the filtering configuration to the user equipment.

[0009] According to a fifth aspect of the present disclosure, there is provided an apparatus, comprising means for receiving, from a wireless network node, a filtering configuration configuring the apparatus to predict, for skipped measurement windows, lower layer outputs for upper layer filtering, means for predicting, based on the filtering configuration, a lower layer output for a skipped measurement window, wherein the lower layer output is based on at least two measurement windows before the skipped measurement window and means for performing, based at least on the lower layer output, said upper layer filtering for the skipped measurement window.

[0010] According to a sixth aspect of the present disclosure, there is provided an apparatus, comprising means for determining a filtering configuration for configuring a user equipment to predict, for skipped measurement windows, lower layer outputs for upper layer filtering and means for transmitting the filtering configuration to the user equipment.

[0011] According to a seventh aspect of the present disclosure, there is provided a non-transitory computer readable medium having stored thereon a set of computer readable instructions that, when executed by at least one processor, cause an apparatus to at least perform receiving, from a wireless network node, a filtering configuration configuring an apparatus to predict, for skipped measurement windows, lower layer outputs for upper layer filtering, predicting, based on the filtering configuration, a lower layer output for a skipped measurement window, wherein the lower layer output is based on at least two measurement windows before the skipped measurement window and performing, based at least on the lower layer output, said upper layer filtering for the skipped measurement window.

[0012] According to an eighth aspect of the present disclosure, there is provided a non-transitory computer readable medium having stored thereon a set of computer readable instructions that, when executed by at least one processor, cause an apparatus to at least perform determining a filtering configuration for configuring a user equipment to predict, for skipped measurement windows, lower layer outputs for upper layer filtering and transmitting the filtering configuration to the user equipment.

[0013] According to a ninth aspect of the present disclosure, there is provided a computer program comprising instructions which, when the program is executed by an apparatus, cause the apparatus to carry out receiving, from a wireless network node, a filtering configuration configuring an apparatus to predict, for skipped measurement windows, lower layer outputs for upper layer filtering, predicting, based on the filtering configuration, a lower layer output for a skipped measurement window, wherein the lower layer output is based on at least two measurement windows before the skipped measurement window and performing, based at least on the lower layer output, said upper layer filtering for the skipped measurement window.

[0014] According to a tenth aspect of the present disclosure, there is provided a computer program comprising instructions which, when the program is executed by an apparatus, cause the apparatus to carry out determining a filtering configuration for configuring a user equipment to predict, for skipped measurement windows, lower layeroutputs for upper layer filtering and transmitting the filtering configuration to the user equipment.BRIEF DESCRIPTION OF THE DRAWINGS

[0015] FIG. 1 illustrates an example of a network scenario in accordance with at least some example embodiments;

[0016] FIG. 2 illustrates a flowchart in accordance with at least some example embodiments;

[0017] FIG. 3 illustrates a first example of operation in accordance with at least some example embodiments;

[0018] FIG. 4 illustrates a second example of operation in accordance with at least some example embodiments;

[0019] FIG. 5 illustrates a third example of operation in accordance with at least some example embodiments;

[0020] FIG. 6 illustrates a fourth example of operation in accordance with at least some example embodiments;

[0021] FIG. 7 illustrates a signalling graph in accordance with at least some example embodiments;

[0022] FIG. 8 illustrates an example apparatus capable of supporting at least some example embodiments;

[0023] FIG. 9 illustrates a flow graph of a first method in accordance with at least some example embodiments;

[0024] FIG. 10 illustrates a flow graph of a second method in accordance with at least some example embodiments.EXAMPLE EMBODIMENTS

[0025] Embodiments of the present disclosure provide enhancements for filtering of measurements, in particular for situations wherein a measurement window is skipped. Ameasurement window may be, e.g., a measurement occasion, a measurement opportunity, a measurement gap or a Synchronization Signal Block, SSB, -based Radio Resource Management, RRM, Measurement Timing Configuration, SMTC, window. Skipping of the measurement window may comprise, e.g., postponing, ignoring, bypassing, cancelling, deprioritizing, relaxing, loosening or anticipating measurements, or moving measurements in time. For example, skipping of a measurement window may mean that some measurements are performed during the measurement window but not all, i.e., measurements may be relaxed. On the other hand, in some cases, skipping a measurement window may mean that all measurements are skipped during the measurement window. Thus, a User Equipment, UE, may be configured to skip all or at least some measurements during a measurement window that is skipped or will be skipped.

[0026] In case of skipping at least one measurement window, a UE may predict lower layer, such as Layer- 1, outputs for upper layer, such as Layer-3, filtering. The prediction may be based on a filtering configuration, configured by a wireless network node. Even though Layer-1 filter is used as an example of a lower layer filter and Layer-3 filter is used as an example of an upper layer filter in various embodiments, embodiments of the present disclosure may be exploited for any other similar filter structure. Layer-1 and Layer-3 may refer to layers of a cellular communication network. In some example embodiments, Layer- 1 may refer to a physical layer and Layer-3 may refer to a network layer of the Open Systems Interconnection, OSI, model. In some other embodiments, Layer-1 may refer to a physical layer and Layer-3 may refer to a control layer such as the Radio Resource Control, RRC, of the 3rd Generation Partnership Project, 3GPP, architecture.

[0027] FIG. 1 illustrates an example of a network scenario in accordance with at least some example embodiments. According to the example scenario of FIG. 1, there may be a communication system, which comprises UE 110, wireless network node 120 and core network element 130. UE 110 may be connected to wireless network node 120 via air interface 115. For example, UE 110 may be connected wirelessly to wireless network node 120 using multiple beams, either simultaneously or one at a time. That is, air interface 115 may be a beam-based air interface.

[0028] UE 110 may comprise, for example, a smartphone, a cellular phone, a Machine-to-Machine, M2M, node, Machine-Type Communications, MTC, node, an Internet of Things, loT, node (e g., loT device), a RedCap node, a car telemetry unit, a laptopcomputer, a tablet computer or, indeed, any kind of suitable wireless terminal. Wireless network node 120 may be considered as a serving node for UE 110, and one cell of wireless network node 120 may be a serving cell for UE 110.

[0029] Air interface between UE 110 and wireless network node 120 may be configured in accordance with a Radio Access Technology, RAT, which both UE 110 and wireless network node 120 are configured to support. Examples of cellular RATs include Long Term Evolution, LTE, New Radio, NR, which may also be known as fifth generation, 5G, radio access technology and MulteFire. Also, a cellular RAT may be referred to as 6G in the future.

[0030] For example, in the context of LTE wireless network node 120 may be referred to as eNB while wireless network node 120 may be referred to as gNB in the context of NR. In some example embodiments, wireless network node 120 may be referred to as a Transmission and Reception Point, TRP, or control multiple TRPs that may be co-located or non-co-located. In any case, example embodiments of the present disclosure are not restricted to any particular wireless technology. Instead, example embodiments may be exploited in any wireless communication system, wherein it would be beneficial to enhance filtering of measurements, such as RRM measurements.

[0031] Example embodiments of the present disclosure may be related to various applications, such as extended Reality, XR. At least related to XR, there is a need to enable transmission and reception even in case of gaps and / or restrictions that are caused by RRM measurements, such as inter-frequency RRM measurement gaps or intra-frequency measurement gaps or other scheduling restrictions. The scheduling gaps and restrictions from RRM measurements may be harmful, e.g., for the XR capacity, given the bounded latency constraints for such services. It is noted that XR applications are merely one example and example embodiments of the present disclosure are not restricted to any particular wireless technology. Instead, example embodiments may be exploited for various applications, for which there is a need to provide enhancements related to RRM measurements and related UE behaviours, such as scheduling restrictions.

[0032] Using 3GPP standard specifications as an example, SSB-based Reference Signal Received Power, RSRP, measurements may be used for Layer-3 Mobility, while also Channel State Information, CSI, Reference Signal, RS, -based RSRP measurements may be applicable for beam management and Lower-layer Triggered Mobility, LTM, purposes.Scheduling restrictions due to intra-frequency RRM measurements of UE 110 may appear though. Moreover, if UE 110 is performing gap-assisted RRM measurements, UE 110 would not be schedulable during the measurement gaps. Certain scheduling restrictions may also appear when UE 110 is performing RLM related measurements. Scheduling availability of UE 110 during beam failure detection may also need to be considered.

[0033] More specifically, at least one challenge is that skipping of some RRM measurement occasions that appears in a measurement window would influence the filtered RRM measurements and hence, there is a need to manage such a situation. The measurement window may be a SMTC window or a measurement gap. In any case, it is important to have known behaviour for UE 110, for performing RRM measurements, as the network needs to properly set handover triggers and conduct proper mobility and beam management actions.

[0034] At least in the context of 3GPP, one challenge thus is how to define an input to a Layer-3 RRM filter if certain, originally configured, Layer- 1 RRM measurement occasions are skipped, subject to making sure that behaviour of UE 110 is well defined for such cases. At the same time, it should be ensured that the output of the Layer-3 filtered RRM measurements is still useful for, e.g., handover triggering decisions.

[0035] Embodiments of the present disclosure therefore enable prediction of Layer-1 outputs for Layer-3 filtering In some embodiments, UE 110 may receive, from wireless network node 120, a filtering configuration configuring UE 110 to predict, for skipped measurement windows, Layer- 1 outputs for Layer-3 filtering. The filtering configuration may indicate a number of how many previous measurements are to be used in the prediction. Alternatively, or in addition, the filtering configuration may comprise a lower layer filter for the skipped measurement window.

[0036] In some example embodiments, the lower layer filter may be a neural network. For example, the neural network may take as an input a sequence / set of RRM measurements performed in previous measurement windows and generate as an output the RRM measurement sample of the measurement window that has been skipped. The inputs may be processed in a sequence of stages, wherein each stage may be composed of a number of functions that take as an input the outputs of the previous stage and produce at least one output. The final stage of the neural network may generate at least the RRM measurement sample that has been skipped.

[0037] In some example embodiments, if one measurement window, such as a RRM measurement occurrence, is skipped, the input to the Layer-3 filter may be a linear prediction of the RRM measurement from the past A Layer- 1 filtered RRM measurement samples. The value of N may be fixed, or signalled to UE 110 by wireless network node 120 as a configuration parameter, for example via RRC. UE 110 may thus receive, from wireless network node 120, a filtering configuration configuring UE 110 to predict, for skipped measurement windows, lower layer outputs for upper layer filtering. The filtering configuration may indicate a number TV of how many previous measurements are to be used in the prediction. The filtering configuration may be received via RRC signalling.

[0038] In some example embodiments, UE 110 may be configured to do nothing, i.e., not produce any new input samples to the Layer-3 filter, if at maximum L consecutive measurement windows are skipped. Hence, only if L or more consecutive measurement windows are skipped, UE 110 may start to perform linear interpolation of the past N Lay er- 1 filtered RRM samples as input to the Layer-3 filter. The value of L may be configured via RRC signalling.

[0039] In some example embodiments, UE 110 may be configured by wireless network node 120 to use a certain filter, such as a Machine Learning, ML, -based predictor to estimate RRM measurement values of skipped measurement windows and feed such values as input to the Layer-3 filter. The ML-based predictor may be hardcoded in UE 110, or configured by the network to UE 110, e.g., in the form of a small neural network that has been trained offline and signalled to UE 110.

[0040] In some example embodiments, the Layer-3 filter may be reset when at least M consecutive measurement windows are skipped. The reset of the Layer-3 filter causes a delay in the generation of the next output. In particular, a new output generated by the Layer- 3 filter after its reset will be available only after M inputs are collected, i.e., after N new measurement windows. Parameter M may indicate to UE 110 that UE 110 is to stop using Layer- 1 filter's output as Layer-3 input after M consecutive skipped measurements. Parameter Nl, which may or may not be used along with M, may indicate after how many measurement windows the Layer-3 output may be considered stable to be used for other operations (e.g., mobility handover), since the reset. In other words, if there are M consecutive skipped measurements followed by Nl measurements, after the M consecutive skipped measurements, the Layer-3 filter may be reset. After M+Nl windows since the firstskipped window (i.e, after Nl windows after the reset) the Layer-3 filter may start to produce the output again. Between M+ 1 and M+Nl-1 since the first skipped window, the Layer-3 filter might not produce anything.

[0041] In some example embodiments, a delay in the generation of the output when the Layer-3 filter is reset may be addressed by considering an adaptive buffer size for the Layer-3 filter. Specifically, a parameter Nl, wherein Nl < N, may be used to control when the Layer-3 filter starts generating an output after its reset. The parameter Nl may be used as follows: when M consecutive measurement windows are skipped, the Layer-3 filter may wait X=N1 inputs to produce the output. After the output is generated, X may be incremented by a unit every time a new Layer-1 sample becomes available until A becomes equal to A, i.e., every measurement window where UE 110 performs the RRM measurement. When X becomes equal to N, the Layer-3 filter may use the latest N samples at every new RRM measurement. That is, the window of X=N samples may slide.

[0042] FIG. 2 illustrates a flowchart in accordance with at least some example embodiments. According to the flowchart of FIG. 2, RRM measurement samples for a Layer- 1 filter may be measured or computed by previous measurements depending on whether the measurement window has been skipped or not. For replacing missing samples when the RRM measurement operations are skipped in a measurement window with the last measured value, the skipped samples may be predicted. The prediction may be triggered every time the RRM measurement operations are skipped, e g., based on an implicit or explicit indication transmitted by wireless network node 120.

[0043] At step 210, UE 110 may determine whether an RRM measurement in a measurement window is skipped. If the RRM measurement is not skipped, UE 110 may, at step 220, determine to use the RRM measurement from the measurement window as an input. Consequently, UE 110 may, at step 230, perform the RRM measurement during the measurement window. Alternatively, if the RRM measurement is skipped, UE 110 may, at step 225, determine to use the RRM measurement from previous measurement windows as an input. Consequently, UE 110 may, at step 235, predict the RRM measurement for the measurement window. At step 240, UE 110 may perform Layer- 1 filtering by using the measured or the predicted RRM measurement as an input, to generate a Layer- 1 output. At step 250, UE 110 may perform Layer-3 filtering by using the Layer-1 output as an input, to generate a Layer-3 output. The Layer-3 output may comprise RRM measurement results andUE 110 may transmit the Layer-3 output to wireless network node 120. Wireless network node 120 may, based on the Layer-3 output, for example manage radio resources accordingly, possibly by taking into account application requirements of UE 110.

[0044] Radio signals may be thus measured during a measurement window and the measured samples passed to Layer-1 filtering, at step 240, e.g., suppress noise. The output of Layer-1 filtering may be further passed to Layer-3 filtering. A Layer-3 filter may be an Infinite Impulse Response, IIR, filter that takes as an input the output of a Layer- 1 filter together with a previously generated Layer-3 output to compute a new Layer-3 output at every measurement window.

[0045] FIG. 3 illustrates a first example of operation in accordance with at least some example embodiments. More specifically, FIG. 3 illustrates operation with skipping and RRM prediction. Skipped measurement window is denoted by 313. As shown in FIG. 3, one or more measurement windows may be skipped. Due to the skipping of the measurement window, UE 110 would not be able to perform measurements. In such a case, the input of Layer-1 filtering 333 may be generated by predictions based on previous RRM measurements every time the RRM measurement in a measurement window is skipped. As illustrated in FIG. 3, the input of Layer- 1 filtering 333 may comprise performed RRM measurements 321 and 322. The output of Layer-1 filtering 333 may be used as an input of the Layer-3 filter. UE 110 may perform, based at least on the output of Layer-1 filtering 333, Layer-3 filtering 343 for the skipped measurement window.

[0046] Thus, the prediction may be performed by UE 110 before Layer-1 filtering 333. In this case, the input of Layer-1 filtering may be predicted according to the samples of the measured signal collected in previous measurement windows, i.e., before skipped measurement window 313. UE 110 may predict the output of Layer-1 filtering 333 by predicting RRM measurements 323 of skipped measurement window 313, based on performed RRM measurements 321 and 322. After that, UE 110 may perform, based on the predicted radio resource management measurements, Layer-1 filtering 333 to generate the output of Layer- 1 filtering 333.

[0047] FIG. 4 illustrates a second example of operation in accordance with at least some example embodiments. More specifically, FIG. 4 illustrates operation with skipping and Layer-1 prediction. The input of Layer-1 filtering 433 may be generated by predictions based on previous Layer- 1 filtering outputs every time the RRM measurement in ameasurement window is skipped. As illustrated in FIG. 4, the input of Layer-1 filtering 433 may comprise outputs of Layer-1 filtering 431 and 432. The output of Layer-1 filtering 433 may be used as an input of the Layer-3 filter. UE 110 may perform, based at least on the output of Layer-1 filtering 433, Layer-3 filtering for the skipped measurement window. Then, UE 110 may perform, based at least on the output of Layer-1 filtering 433, upper layer filtering for the skipped measurement window.

[0048] Thus, the output of Layer- 1 filtering 433 may be generated by predictions based on previous Layer-1 filtering outputs when a measurement window is skipped. The output of the Layer-1 filter may be used as an input of the Layer-3 filter.

[0049] FIG. 5 illustrates a third example of operation in accordance with at least some example embodiments. More specifically, FIG. 5 illustrates operation with skipping and reset. The output of the Layer-1 filtering 534 may be generated by predictions based on outputs of previous Layer-1 filtering 531 and 532 when a measurement window is skipped. The output of the LI filter may be used as input of the L3 filter. However, after M=2 consecutive skipped measurement windows the Layer-3 filter may be reset. That is, UE 110 may reset a filter for Layer-3 filtering 545 and a filter for Layer-1 filtering 535 after at least two skipped measurement windows, and after Layer- 1 filtering 534 and Layer-3 filtering associated with Layer-1 filtering 534.

[0050] The network, such as wireless network node 120, may configure a number AT of consecutive measurement windows, to define how many RRM measurement operations need to be skipped to cause the reset of the L3 filter. Resetting the L3 filter may mean that the output generated by the L3 filter of previous measurement windows is ignored to prevent the propagation of old values in future RRM measurement operations. Stopping the propagation of old values that may not be anymore representative of the current status of the channel measured by UE 110. For example, in mobile scenarios, a fast-moving UE may have been instructed to skip several consecutive measurements. When normal RRM measurements are restored, old values may correspond to a UE position that was far away from the cell even if UE 110 would have moved towards the center of the cell. In case of FIG. 5, at the measurement window k+1, the Layer-3 filter ignores the output of generated in the measurement window k since real measurements in both measurement windows k and k-1 have been skipped.

[0051] FIG. 6 illustrates a fourth example of operation in accordance with at least some example embodiments. More specifically, FIG. 6 illustrates Layer-3 filtering with multiple input values 641, 642, 643 and 644 from the Layer- 1 filtering operations. The 4 input values may correspond to Layer-1 filtering operations performed in the current and the 3 previous measurement windows. That is, Layer-3 input buffers are denoted by 641, 642, 643 and 644 in FIG. 6. The output of Layer-3 filtering is denoted by 645.

[0052] If the Layer-3 filter takes W multiple values as inputs from the LI filtering operations executed in the current and previous measurement windows (W=4 in FIG. 6), then when the Layer-3 filter is reset, its buffer becomes empty and a new output is generated by Layer-3 filter only after at least W measurement windows. As illustrated in FIG. 6, the Layer-3 filter begins to generate a new output only from measurement window k+W=k+4 after it has been reset in the (k+ / )-th measurement window.

[0053] In some embodiments, such a delay may be reduced by starting to generate the output from the filter after only NJ measurement windows from its reset considering only min(A7, W) input values. For example, if NJ=J and the Layer-3 filter is reset after measurement window k, the Layer-3 filter may generate an output also in measurement window (k+J) using the single input generated by the Layer- 1 filter. In measurement window (k+2) the Layer-3 filter may generate an output using the input from the Layer-1 filter executed in measurement windows k+2 and k+1, and so on until the Layer-3 input buffer becomes full again. In addition to the ffl values generated in the current and previous measurement windows by the Layer- 1 filter, the Layer-3 filter may use also the output it has generated in the previous measurement window or in previous measurement windows.

[0054] FIG. 7 illustrates a signalling graph in accordance with at least some example embodiments. On the vertical axes are disposed, from the left to the right, wireless network node 120 and UE 110. Time advances from the top towards the bottom.

[0055] At step 702, wireless network node 120 and UE 110 may agree on UE behaviour and configuration, to perform RRM measurement filtering and prediction when measurement window(s) are skipped. For example, wireless network node 120 and UE 110 may agree on M consecutive skipped windows, prediction parameters, etc. In some example embodiments, wireless network node 120 may transmit the filtering configuration to UE

[0056] Wireless network node 120 may configure UE 110 with parameters for the RRM measurement operation in the presence of skipping and activating the behaviour. Thus, UE 110 and wireless network node 120 may agree on the behaviour and configuration used during RRM measurement operation in measurement windows, e.g., by extending the content exchanged during RRC connection set and reconfiguration procedure.

[0057] At step 704, UE 110 may perform RRM measurements and filtering during measurement window k-N+J. At step 706, UE 110 may perform RRM measurements and filtering during measurement window k-N+2.

[0058] At step 708, wireless network node 120 may transmit a command to UE 110 before measurement window k-1, to command UE 110 to skip measurements. According to the command, UE 110 skips the RRM measurement operations at steps 710 and 712, but performs predictions in accordance with embodiments of the present disclosure.

[0059] At step 710, UE 110 may skip performing RRM measurements and filtering during measurement window k-1. At step 712, UE 110 may perform RRM measurement prediction during measurement window k-1.

[0060] Similarly, at step 714, wireless network node 120 may transmit a command to UE 110 before measurement window k, to command UE 110 to skip measurements. According to the command, UE 110 skips the RRM measurement operations at steps 716 and 718, but performs predictions in accordance with embodiments of the present disclosure.

[0061] At step 716, UE 110 may skip performing RRM measurements and filtering during measurement window k. At step 718, UE 110 may perform RRM measurement prediction during measurement window k. If UE 110 would be configured to reset the Layer- 3 filter after M=2 consecutive skipped measurement windows, UE 110 would need to reset the Layer-3 filter after the k-th window.

[0062] Embodiments of the present disclosure therefore define a deterministic way to replace samples in the Layer-3 filter of RRM measurement operations when measurements in a certain measurement window are skipped. In addition, a configurable way to replace the input of the Layer-3 filtering operation is provided, to avoid the propagation of old values in future measurements that may affect intra- and inter-cell mobility. Also, acceleration of the generation of the output, when the Layer-3 filter is reset, is enabled.

[0063] UE 110 may thus perform Layer-3 filtering for the skipped measurement window, to generate Layer-3 output comprising RRM measurement results and transmit the Layer-3 filtering output to wireless network node 120. Wireless network node 120 may further take the Layer-3 filtering output into account in mobility environment, e.g., to schedule resources accordingly. For example, wireless network node 120 may take the Layer-3 filtering output into account for beam management and LTM purposes, possibly in particular to ensure sufficient resources for at least one XR application used by UE 110.

[0064] FIG. 8 illustrates an example apparatus capable of supporting at least some example embodiments. Illustrated is device 800, which may comprise, for example, UE 110 or wireless network node 120, or a control device configured to control the functioning thereof, possibly when installed therein. Comprised in device 800 is processor 810, which may comprise, for example, a single- or multi-core processor wherein a single-core processor comprises one processing core and a multi-core processor comprises more than one processing core. Processor 810 may comprise, in general, a control device. Processor 810 may comprise more than one processor. Processor 810 may be a control device. A processing core may comprise, for example, a Cortex-A8 processing core manufactured by ARM Holdings or a Steamroller processing core produced by Advanced Micro Devices Corporation. Processor 810 may comprise at least one Qualcomm Snapdragon and / or Intel Atom processor. Processor 810 may comprise at least one application-specific integrated circuit, ASIC. Processor 810 may comprise at least one field-programmable gate array, FPGA. Processor 810 may be means for performing method steps in device 800. Processor 810 may be configured, at least in part by computer instructions, to perform actions.

[0065] A processor may comprise circuitry, or be constituted as circuitry or circuitries, the circuitry or circuitries being configured to perform phases of methods in accordance with example embodiments described herein. As used in this application, the term “circuitry” may refer to one or more or all of the following: (a) hardware-only circuit implementations, such as implementations in only analog and / or digital circuitry, and (b) combinations of hardware circuits and software, such as, as applicable: (i) a combination of analog and / or digital hardware circuit(s) with software / firmware and (ii) any portions of hardware processor(s) with software (including digital signal processor(s)), software, and memory(ies) that work together to cause an apparatus, such as a mobile phone or server, to perform various functions) and (c) hardware circuit(s) and or processor(s), such as a microprocessor(s) or aportion of a microprocessor(s), that requires software (e g., firmware) for operation, but the software may not be present when it is not needed for operation.

[0066] This definition of circuitry applies to all uses of this term in this application, including in any claims. As a further example, as used in this application, the term circuitry also covers an implementation of merely a hardware circuit or processor (or multiple processors) or portion of a hardware circuit or processor and its (or their) accompanying software and / or firmware. The term circuitry also covers, for example and if applicable to the particular claim element, a baseband integrated circuit or processor integrated circuit for a mobile device or a similar integrated circuit in server, a cellular network device, or other computing or network device.

[0067] Device 800 may comprise memory 820. Memory 820 may comprise randomaccess memory and / or permanent memory. Memory 820 may comprise at least one RAM chip. Memory 820 may comprise solid-state, magnetic, optical and / or holographic memory, for example. Memory 820 may be at least in part accessible to processor 810. Memory 820 may be at least in part comprised in processor 810. Memory 820 may be means for storing information. Memory 820 may comprise computer instructions that processor 810 is configured to execute. When computer instructions configured to cause processor 810 to perform certain actions are stored in memory 820, and device 800 overall is configured to run under the direction of processor 810 using computer instructions from memory 820, processor 810 and / or its at least one processing core may be considered to be configured to perform said certain actions. Memory 820 may be at least in part comprised in processor 810. Memory 820 may be at least in part external to device 800 but accessible to device 800.

[0068] Device 800 may comprise a transmitter 830. Device 800 may comprise a receiver 840. Transmitter 830 and receiver 840 may be configured to transmit and receive, respectively, information in accordance with at least one cellular or non-cellular standard. Transmitter 830 may comprise more than one transmitter. Receiver 840 may comprise more than one receiver. Transmitter 830 and / or receiver 840 may be configured to operate in accordance with Global System for Mobile communication, GSM, Wideband Code Division Multiple Access, WCDMA, Long Term Evolution, LTE, and / or 5G / NR standards, for example.

[0069] Device 800 may comprise a Near-Field Communication, NFC, transceiver 850. NFC transceiver 850 may support at least one NFC technology, such as Bluetooth, Wibree or similar technologies.

[0070] Device 800 may comprise User Interface, UI, 860. UI 860 may comprise at least one of a display, a keyboard, a touchscreen, a vibrator arranged to signal to a user by causing device 800 to vibrate, a speaker and a microphone. A user may be able to operate device 800 via UI 860, for example to accept incoming telephone calls, to originate telephone calls or video calls, to browse the Internet, to manage digital files stored in memory 820 or on a cloud accessible via transmitter 830 and receiver 840, or via NFC transceiver 850, and / or to play games.

[0071] Device 800 may comprise or be arranged to accept a user identity module 870. User identity module 870 may comprise, for example, a Subscriber Identity Module, SIM, card installable in device 800. A user identity module 870 may comprise information identifying a subscription of a user of device 800. A user identity module 870 may comprise cryptographic information usable to verify the identity of a user of device 800 and / or to facilitate encryption of communicated information and billing of the user of device 800 for communication effected via device 800.

[0072] Processor 810 may be furnished with a transmitter arranged to output information from processor 810, via electrical leads internal to device 800, to other devices comprised in device 800. Such a transmitter may comprise a serial bus transmitter arranged to, for example, output information via at least one electrical lead to memory 820 for storage therein. Alternatively to a serial bus, the transmitter may comprise a parallel bus transmitter. Likewise processor 810 may comprise a receiver arranged to receive information in processor 810, via electrical leads internal to device 800, from other devices comprised in device 800. Such a receiver may comprise a serial bus receiver arranged to, for example, receive information via at least one electrical lead from receiver 840 for processing in processor 810. Alternatively to a serial bus, the receiver may comprise a parallel bus receiver.

[0073] Device 800 may comprise further devices not illustrated in FIG. 8. For example, where device 800 comprises a smartphone, it may comprise at least one digital camera. Some devices 800 may comprise a back-facing camera and a front-facing camera, wherein the back-facing camera may be intended for digital photography and the front-facing camera for video telephony. Device 800 may comprise a fingerprint sensor arranged to authenticate, at least in part, a user of device 800. In some example embodiments, device 800 lacks at least one device described above. For example, some devices 800 may lack a NFC transceiver 850 and / or user identity module 870.

[0074] Processor 810, memory 820, transmitter 830, receiver 840, NFC transceiver 850, UI 860 and / or user identity module 870 may be interconnected by electrical leads internal to device 800 in a multitude of different ways. For example, each of the aforementioned devices may be separately connected to a master bus internal to device 800, to allow for the devices to exchange information. However, as the skilled person will appreciate, this is only one example and depending on the example embodiment, various ways of interconnecting at least two of the aforementioned devices may be selected without departing from the scope of the example embodiments.

[0075] FIG. 9 is a flow graph of a first method in accordance with at least some example embodiments. The first method may be performed by an apparatus, such as UE 110.

[0076] The first method may comprise, at step 910, receiving, from a wireless network node, a filtering configuration configuring the apparatus to predict, for skipped measurement windows, lower layer outputs for upper layer filtering. The first method may also comprise, at step 920, predicting, based on the filtering configuration, a lower layer output for a skipped measurement window, wherein the lower layer output is based on at least two measurement windows before the skipped measurement window. Further, the first method may comprise, at step 930, performing, based at least on the lower layer output, said upper layer filtering for the skipped measurement window. In an embodiment, the apparatus may determine whether or not a measurement window is skipped. Based on determining that the measurement window is skipped, the apparatus may be configured to perform prediction e.g. as discussed in step 920.

[0077] FIG. 10 is a flow graph of a second method in accordance with at least some example embodiments. The second method may be performed by an apparatus, such as wireless network node 120.

[0078] The second method may comprise, at step 1010, determining a filtering configuration for configuring a user equipment to predict, for skipped measurementwindows, lower layer outputs for upper layer filtering. The second method may also comprise, at step 1020, transmit the filtering configuration to the user equipment.

[0079] It is to be understood that the example embodiments disclosed are not limited to the particular structures, process steps, or materials disclosed herein, but are extended to equivalents thereof as would be recognized by those ordinarily skilled in the relevant arts. It should also be understood that terminology employed herein is used for the purpose of describing particular example embodiments only and is not intended to be limiting.

[0080] Reference throughout this specification to one example embodiment or an example embodiment means that a particular feature, structure, or characteristic described in connection with the example embodiment is included in at least one example embodiment. Thus, appearances of the phrases “in one example embodiment” or “in an example embodiment” in various places throughout this specification are not necessarily all referring to the same example embodiment. Where reference is made to a numerical value using a term such as, for example, about or substantially, the exact numerical value is also disclosed.

[0081] As used herein, a plurality of items, structural elements, compositional elements, and / or materials may be presented in a common list for convenience. However, these lists should be construed as though each member of the list is individually identified as a separate and unique member. Thus, no individual member of such list should be construed as a de facto equivalent of any other member of the same list solely based on their presentation in a common group without indications to the contrary. In addition, various example embodiments and examples may be referred to herein along with alternatives for the various components thereof. It is understood that such example embodiments, examples, and alternatives are not to be construed as de facto equivalents of one another, but are to be considered as separate and autonomous representations.

[0082] In an example embodiment, an apparatus, such as, for example, UE 110 or wireless network node 120, may comprise means for carrying out the example embodiments described above and any combination thereof.

[0083] In an example embodiment, a computer program may be configured to cause a method in accordance with the example embodiments described above and any combination thereof. In an example embodiment, a computer program product, embodied on a non-transitoiy computer readable medium, may be configured to control a processor toperform a process comprising the example embodiments described above and any combination thereof.

[0084] In an example embodiment, an apparatus, such as, for example, UE 110 or wireless network node 120, may comprise at least one processor, and at least one memory including computer program code, wherein the at least one memory and the computer program code are configured to, with the at least one processor, cause the apparatus at least to perform the example embodiments described above and any combination thereof.

[0085] Furthermore, the described features, structures, or characteristics may be combined in any suitable manner in one or more example embodiments. In the preceding description, numerous specific details are provided, such as examples of lengths, widths, shapes, etc., to provide a thorough understanding of example embodiments of the disclosure. One skilled in the relevant art will recognize, however, that the disclosure can be practiced without one or more of the specific details, or with other methods, components, materials, etc. In other instances, well-known structures, materials, or operations are not shown or described in detail to avoid obscuring aspects of the disclosure.

[0086] While the forgoing examples are illustrative of the principles of the example embodiments in one or more particular applications, it will be apparent to those of ordinary skill in the art that numerous modifications in form, usage and details of implementation can be made without the exercise of inventive faculty, and without departing from the principles and concepts of the disclosure. Accordingly, it is not intended that the disclosure be limited, except as by the claims set forth below.

[0087] The verbs “to comprise” and “to include” are used in this document as open limitations that neither exclude nor require the existence of also un-recited features. The features recited in depending claims are mutually freely combinable unless otherwise explicitly stated. Furthermore, it is to be understood that the use of "a" or "an", that is, a singular form, throughout this document does not exclude a plurality.

[0088] Examples:Example 1. An apparatus, comprising: at least one processor; and at least one memory storing instructions that, when executed by the at least one processor, cause the apparatus at least to:- receive, from a wireless network node, a filtering configuration configuring the apparatus to predict, for skipped measurement windows, lower layer outputs for upper layer filtering;- predict, based on the filtering configuration, a lower layer output for a skipped measurement window, wherein the lower layer output is based on at least two measurement windows before the skipped measurement window; and- perform, based at least on the lower layer output, said upper layer filtering for the skipped measurement window.Example 2. The apparatus according to example 1, wherein the measurement window is a synchronization signal block -based radio resource management measurement timing configuration window or a measurement gap.Example 3. The apparatus according to example 1 or example 2, wherein the lower layer output is an output from Layer-1 filtering and said upper layer filtering is Layer-3 filtering.Example 4. The apparatus according to any of the preceding examples, wherein the filtering configuration is received via radio resource control signalling.Example 5. The apparatus according to any of the preceding examples, wherein the stored instructions further cause, when executed by the at least one processor, the apparatus at least to:- perform said upper layer filtering using an infinite impulse response filter, wherein an input of the infinite impulse response filter comprises at least the lower layer output and an upper layer output of a measurement window before the skipped measurement window.Example 6. The apparatus according to any of the preceding examples, wherein the lower layer output is based on radio resource management measurements and lower layer filtering.Example 7. The apparatus according to any of the preceding examples, wherein the stored instructions further cause, when executed by the at least one processor, the apparatus at least to:- predict the lower layer output by predicting radio resource management measurements of the skipped measurement window and by performing, based on the predicted radio resource management measurements, lower layer filtering to generate the lower layer output.Example 8. The apparatus according to any of examples 1 to 6, wherein the stored instructions further cause, when executed by the at least one processor, the apparatus at least to:- predict the lower layer output based on lower layer outputs of at least two measurement windows before the skipped measurement window.Example 9. The apparatus according to any of the preceding examples, wherein the stored instructions further cause, when executed by the at least one processor, the apparatus at least to:- reset a filter for said upper layer filtering after at least two skipped measurement windows.Example 10. The apparatus according to example 9, wherein the stored instructions further cause, when executed by the at least one processor, the apparatus at least to:- receive, from the wireless network node, a reset configuration configuring a number of the at least two skipped measurement windows.Example 11. The apparatus according to any of the preceding examples, wherein the filtering configuration comprises a lower layer filter for the skipped measurement window.Example 12. The apparatus according to example 11, wherein the lower layer filter is a neural network.Example 13. The apparatus according to any of the preceding examples, wherein the filtering configuration indicates a number of how many previous measurements are to be used in the prediction.Example 14. The apparatus according to any of the preceding examples, wherein the stored instructions further cause, when executed by the at least one processor, the apparatus at least to:- perform said upper layer filtering for the skipped measurement window, to generate an upper layer filtering output comprising radio resource management measurement results; and- transmit the upper layer filtering output to the wireless network nodeExample 15. An apparatus comprising at least one processing core and at least one memory storing instructions that, when executed by the at least one processing core, cause the apparatus at least to:- determine a filtering configuration for configuring a user equipment to predict, for skipped measurement windows, lower layer outputs for upper layer filtering; and- transmit the filtering configuration to the user equipment.INDUSTRIAL APPLICABILITY

[0089] At least some example embodiments find industrial application in cellular communication networks, for example in 3 GPP networks.ACRONYMS LIST3 GPP 3rd Generation Partnership ProjectBS Base StationCSI Channel State InformationGSM Global System for Mobile communicationHR Infinite Impulse Response loT Internet of ThingsLTE Long-Term EvolutionLTM Lower-layer Triggered MobilityM2M Machine-to-MachineMIMO Multiple-Input Multiple-OutputML Machine LearningNFC Near-Field CommunicationNR New RadioOSI Open Systems InterconnectionRAT Radio Access TechnologyRRC Radio Resource ControlRRM Radio Resource ManagementRS Reference SignalRSRP Reference Signal Received PowerSMTC S SB-based RRM Measurement Timing ConfigurationSSB Synchronization Signal BlockTRP Transmission and Reception PointUE User EquipmentUI User InterfaceWCDMA Wideband Code Division Multiple AccessWiMAX Worldwide Interoperability for Microwave AccessWLAN Wireless Local Area NetworkXR extended RealityREFERENCE SIGNS LIST

Claims

CLAIMS:

1. An apparatus, comprising: at least one processor; and at least one memory storing instructions that, when executed by the at least one processor, cause the apparatus at least to:- receive, from a wireless network node, a filtering configuration configuring the apparatus to predict, for skipped measurement windows, lower layer outputs for upper layer filtering;- predict, based on the filtering configuration, a lower layer output for a skipped measurement window, wherein the lower layer output is based on at least two measurement windows before the skipped measurement window; and- perform, based at least on the lower layer output, said upper layer filtering for the skipped measurement window.

2. The apparatus according to claim 1, wherein the measurement window is a synchronization signal block -based radio resource management measurement timing configuration window or a measurement gap.

3. The apparatus according to claim 1 or claim 2, wherein the lower layer output is an output from Layer-1 filtering and said upper layer filtering is Layer-3 filtering.

4. The apparatus according to any of the preceding claims, wherein the filtering configuration is received via radio resource control signalling.

5. The apparatus according to any of the preceding claims, wherein the stored instructions further cause, when executed by the at least one processor, the apparatus at least to:- perform said upper layer filtering using an infinite impulse response filter, wherein an input of the infinite impulse response filter comprises at least the lower layer output and an upper layer output of a measurement window before the skipped measurement window.

6. The apparatus according to any of the preceding claims, wherein the lower layer output is based on radio resource management measurements and lower layer filtering.

7. The apparatus according to any of the preceding claims, wherein the stored instructions further cause, when executed by the at least one processor, the apparatus at least to:- predict the lower layer output by predicting radio resource management measurements of the skipped measurement window and by performing, based on the predicted radio resource management measurements, lower layer filtering to generate the lower layer output.

8. The apparatus according to any of claims 1 to 6, wherein the stored instructions further cause, when executed by the at least one processor, the apparatus at least to:- predict the lower layer output based on lower layer outputs of at least two measurement windows before the skipped measurement window.

9. The apparatus according to any of the preceding claims, wherein the stored instructions further cause, when executed by the at least one processor, the apparatus at least to:- reset a filter for said upper layer filtering after at least two skipped measurement windows.

10. The apparatus according to claim 9, wherein the stored instructions further cause, when executed by the at least one processor, the apparatus at least to:- receive, from the wireless network node, a reset configuration configuring a number of the at least two skipped measurement windows.

11. The apparatus according to any of the preceding claims, wherein the filtering configuration comprises a lower layer filter for the skipped measurement window.

12. The apparatus according to claim 11, wherein the lower layer filter is a neural network.

13. The apparatus according to any of the preceding claims, wherein the filtering configuration indicates a number of how many previous measurements are to be used in the prediction.

14. The apparatus according to any of the preceding claims, wherein the stored instructions further cause, when executed by the at least one processor, the apparatus at least to:- perform said upper layer filtering for the skipped measurement window, to generate an upper layer filtering output comprising radio resource management measurement results; and- transmit the upper layer filtering output to the wireless network node15. An apparatus comprising at least one processing core and at least one memory storing instructions that, when executed by the at least one processing core, cause the apparatus at least to:- determine a filtering configuration for configuring a user equipment to predict, for skipped measurement windows, lower layer outputs for upper layer filtering; and- transmit the filtering configuration to the user equipment.

16. A method, comprising:- receiving, from a wireless network node, a filtering configuration configuring an apparatus to predict, for skipped measurement windows, lower layer outputs for upper layer filtering,- predicting, based on the filtering configuration, a lower layer output for a skipped measurement window, wherein the lower layer output is based on at least two measurement windows before the skipped measurement window and- performing, based at least on the lower layer output, said upper layer filtering for the skipped measurement window.

17. A method, comprising:- determining a filtering configuration for configuring a user equipment to predict, for skipped measurement windows, lower layer outputs for upper layer filtering and- transmitting the filtering configuration to the user equipment.

18. An apparatus, comprising:- means for receiving, from a wireless network node, a filtering configuration configuring the apparatus to predict, for skipped measurement windows, lower layer outputs for upper layer filtering,- means for predicting, based on the filtering configuration, a lower layer output for a skipped measurement window, wherein the lower layer output is based on at least two measurement windows before the skipped measurement window and- means for performing, based at least on the lower layer output, said upper layer filtering for the skipped measurement window.

19. An apparatus, comprising:- means for determining a filtering configuration for configuring a user equipment to predict, for skipped measurement windows, lower layer outputs for upper layer filtering and- means for transmitting the filtering configuration to the user equipment.

20. A non-transitoiy computer readable medium having stored thereon a set of computer readable instructions that, when executed by at least one processor, cause an apparatus to at least perform:- receiving, from a wireless network node, a filtering configuration configuring an apparatus to predict, for skipped measurement windows, lower layer outputs for upper layer filtering,- predicting, based on the filtering configuration, a lower layer output for a skipped measurement window, wherein the lower layer output is based on at least two measurement windows before the skipped measurement window and- performing, based at least on the lower layer output, said upper layer filtering for the skipped measurement window.

21. A non-transitory computer readable medium having stored thereon a set of computer readable instructions that, when executed by at least one processor, cause an apparatus to at least perform:- determining a filtering configuration for configuring a user equipment to predict, for skipped measurement windows, lower layer outputs for upper layer filtering and- transmitting the filtering configuration to the user equipment.

22. A computer program comprising instructions which, when the program is executed by an apparatus, cause the apparatus to carry out: - receiving, from a wireless network node, a filtering configuration configuring an apparatus to predict, for skipped measurement windows, lower layer outputs for upper layer filtering,- predicting, based on the filtering configuration, a lower layer output for a skipped measurement window, wherein the lower layer output is based on at least two measurement windows before the skipped measurement window and- performing, based at least on the lower layer output, said upper layer filtering for the skipped measurement window.

23. A computer program comprising instructions which, when the program is executed by an apparatus, cause the apparatus to carry out:- determining a filtering configuration for configuring a user equipment to predict, for skipped measurement windows, lower layer outputs for upper layer filtering and- transmitting the filtering configuration to the user equipment.

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