Method for controlling transmission characteristics in a radio network

Abstract

The present disclosure relates to a method (400), the method comprising: obtaining (410) spatial properties of a user equipment, UE (231), the UE (231) being located within a geographical area (240); predicting (420) radio channel characteristics of one or more radio channels between the UE (231) and one or more radio access nodes (211-213) being located within the geographical area (240) using a digital twin, the digital twin being configured to model one or more physical objects located within the geographical area (240) and to emulate radio propagation of the one or more radio channels influenced by the modelled one or more physical objects to predict the radio channel characteristics; controlling (430) transmission characteristics between the UE (231) and the one or more radio access nodes (211-213) using the predicted radio channel characteristics.

Description

[0001] METHOD FOR CONTROLLING TRANSMISSION CHARACTERISTICS IN A RADIO NETWORK

[0002] TECHNICAL FIELD

[0003] The present invention relates to a method for controlling transmission characteristics in a radio network.

[0004] BACKGROUND

[0005] In a radio network, transmission characteristics are controlled to address challenges in a dynamic radio environment. The control of transmission characteristics is generally based on historical knowledge of a radio channel between a radio access node and a User Equipment, UE.

[0006] Knowledge of a radio channel may be referred to as channel state information (CSI). The CSI may include anything from a general statistical model of the set of channel properties that is consistent with a given set of background information and measurements, to a specific estimate of the channel frequency response along with information on its accuracy. As an example, in the 3rdGeneration Partnership Project, 3GPP, 5G standard, CSI is often used to describe the combination of CQI (channel quality index), Rl (rank index) and PMI (precoding matrix index) that with high probability can be successfully received by the UE. The two first provide information about the signal strength at the receiver and the latter information about how the channels between different antenna pairs relate to each other.

[0007] Conventional solutions typically rely on reported or estimated CSI, that frequently is old or outdated. This has the drawback of reduced network performance.

[0008] Thus, there is a need for an improved method for controlling transmission characteristics / features.

[0009] SUMMARY OF THE INVENTION

[0010] The above-described drawbacks are overcome by the subject matter described herein. Further advantageous implementation forms of the invention are described herein.

[0011] According to a first aspect of the invention the object of the invention is achieved by a method, the method comprising: obtaining spatial properties of a user equipment, UE, the UE being located within a geographical area; predicting radio channel characteristics of one or more radio channels between the UE and one or more radio access nodes being located within the geographical area using a digital twin, the digital twin being configured to model one or more physical objects located within the geographical area and to emulate radio propagation of the one or more radio channels influenced by the modelled one or more physical objects to predict the radio channel characteristics; controlling transmission characteristics between the UE and the one or more radio access nodes using the predicted radio channel characteristics.

[0012] In one embodiment, the radio channel characteristics comprises at least radio channel coherence bandwidth and / or radio channel coherence time, wherein controlling transmission characteristics comprises increasing reference signal density based on the radio channel coherence bandwidth and / or radio channel coherence time.

[0013] In one embodiment, the radio channel characteristics comprises at least radio channel coherence bandwidth and / or radio channel coherence time, wherein controlling transmission characteristics comprises decreasing reference signal density based on the radio channel coherence bandwidth and / or radio channel coherence time.

[0014] In one embodiment, the radio channel characteristics comprises at least radio channel coherence bandwidth and / or radio channel coherence time, wherein controlling transmission characteristics comprises controlling transmission power and / or beamforming for an upcoming transmission based on the radio channel coherence bandwidth and / or radio channel coherence time.

[0015] In one embodiment, the radio channel characteristics comprises at least radio channel coherence time, wherein controlling transmission characteristics comprises controlling reference symbol measurement behavior of the UE based on the radio channel coherence time.

[0016] In one embodiment, the radio channel characteristics comprises at least radio channel coherence bandwidth and / or radio channel coherence time, wherein controlling transmission characteristics comprises controlling scheduling behavior of the UE based on the radio channel coherence bandwidth and / or radio channel coherence time.

[0017] In one embodiment, the radio channel characteristics comprises at least radio channel coherence bandwidth and / or radio channel coherence time, wherein controlling transmission characteristics comprises identifying suitable Multi-user multiple-input multiple-output technology, MU-MIMO, pairs based on the radio channel coherence bandwidth and / or radio channel coherence time. In one embodiment, the radio channel characteristics comprises at least radio channel coherence time, wherein controlling transmission characteristics comprises controlling UE reporting behavior based on the radio channel coherence time.

[0018] In one embodiment, the radio channel characteristics comprises at least radio channel coherence bandwidth and / or radio channel coherence time, wherein controlling transmission characteristics comprises controlling beamforming behavior based on the radio channel coherence bandwidth and / or radio channel coherence time.

[0019] In one embodiment, predicting radio channel characteristics further comprises: sending a request to network node comprising a digital twin, the request comprising the spatial properties of a user equipment, UE, receiving a response, the response comprising radio channel characteristics of the one or more radio channels.

[0020] According to a second aspect of the invention the object of the invention is achieved by a radio access node, the radio access node comprising: a processor, and a memory, said memory containing instructions executable by said processor, whereby said channel emulator is operative to: obtain spatial properties of a user equipment, UE, the UE being located within a geographical area; predict radio channel characteristics of one or more radio channels between the UE and one or more radio access nodes being located within the geographical area using a digital twin, the digital twin being configured to model one or more physical objects located within the geographical area and to emulate radio propagation of the one or more radio channels influenced by the modelled one or more physical objects to predict the radio channel characteristics; control transmission characteristics between the UE and the one or more radio access nodes using the predicted radio channel characteristics.

[0021] In one embodiment, the radio channel characteristics comprises at least radio channel coherence bandwidth and / or radio channel coherence time, wherein control transmission characteristics comprises to increase reference signal density based on the radio channel coherence bandwidth and / or radio channel coherence time.

[0022] In one embodiment, the radio channel characteristics comprises at least radio channel coherence bandwidth and / or radio channel coherence time, wherein control transmission characteristics comprise decrease reference signal density based on the radio channel coherence bandwidth and / or radio channel coherence time.

[0023] In one embodiment, the radio channel characteristics comprises at least radio channel coherence bandwidth and / or radio channel coherence time, wherein control transmission characteristics comprises control transmission power and / or beamforming for an upcoming transmission based on the radio channel coherence bandwidth and / or radio channel coherence time.

[0024] In one embodiment, the radio channel characteristics comprises at least radio channel coherence time, wherein control transmission characteristics comprises to control reference symbol measurement behavior of the UE based on the radio channel coherence time.

[0025] In one embodiment, the radio channel characteristics comprises at least radio channel coherence bandwidth and / or radio channel coherence time, wherein control transmission characteristics comprises to control scheduling behavior of the UE based on the radio channel coherence bandwidth and / or radio channel coherence time.

[0026] In one embodiment, the radio channel characteristics comprises at least radio channel coherence bandwidth and / or radio channel coherence time, wherein control transmission characteristics comprises to identify suitable Multi-user multiple-input multiple-output technology, MU-MIMO, pairs based on the radio channel coherence bandwidth and / or radio channel coherence time.

[0027] In one embodiment, the radio channel characteristics comprises at least radio channel coherence time, wherein control transmission characteristics comprises to control UE reporting behavior based on the radio channel coherence time.

[0028] In one embodiment, the radio channel characteristics comprises at least radio channel coherence bandwidth and / or radio channel coherence time, wherein control transmission characteristics comprises to control beamforming behavior based on the radio channel coherence bandwidth and / or radio channel coherence time.

[0029] In one embodiment, the node is configured to: send a request to network node comprising a digital twin, the request comprising the spatial properties of a user equipment, UE, receiving a response, the response comprising radio channel characteristics of the one or more radio channels.

[0030] According to a third aspect of the invention the object of the invention is achieved by a system, the system comprising: a network node configured to generate and provide a digital twin for a geographical area, a radio access node, the radio access node being configured to: obtaining spatial properties of a user equipment, UE, the UE and the radio access node being located within the geographical area; obtain predicted radio channel characteristics of one or more radio channels between the UE and the radio access node based on the obtained spatial properties using the digital twin, the UE and the radio access node being located within the geographical area, the digital twin configured to model one or more physical objects located within the geographical area and emulate radio propagation of the one or more radio channels influenced by the modelled one or more physical objects to predict the radio channel characteristics; control transmission characteristics transmission characteristics between the UE and the one or more radio assess nodes using the predicted radio channel characteristics.

[0031] In one embodiment, wherein the spatial properties of the user equipment are received in a first message from the UE and / or is estimated using received reference signals.

[0032] In one embodiment, the predicted radio channel characteristics is obtained by the radio access node by receiving a second message from the network node.

[0033] In one embodiment, the transmission characteristics is controlled by sending a third message from the radio access node to the network node.

[0034] The scope of the invention is defined by the claims, which are incorporated into this section by reference. Reference will be made to the appended sheets of drawings that will first be described briefly.

[0035] BRIEF DESCRIPTION OF THE DRAWINGS

[0036] It should be appreciated that like reference numerals are used to identify like elements illustrated in one or more of the figures.

[0037] Fig. 1 shows an example of a radio network. The radio network comprises a plurality of nodes configured to transmit radio signals in the radio network.

[0038] Fig. 2 illustrates prediction of radio channel characteristics of one or more radio channels between a UE and one or more radio access nodes being located within a geographical area using a digital twin hosted by a network node according to one or more embodiments of the present disclosure.

[0039] Fig. 3 illustrates a geographical area simulated by a digital twin according to one or more embodiments of the present disclosure.

[0040] Fig. 4 shows a flowchart of a method according to one or more embodiments of the present disclosure.

[0041] Fig. 5A-B shows an examples of controlling reference symbol density according to one or more embodiments of the present disclosure.

[0042] Fig. 6 illustrates controlling transmission for an upcoming transmission according to one or more embodiments of the present disclosure.

[0043] Fig. 7 illustrates controlling filtering of reference symbols according to one or more embodiments of the present disclosure. Fig. 8A-B shows an examples of controlling scheduling behavior according to one or more embodiments of the present disclosure.

[0044] Fig. 9 illustrates an example of selecting MU -Ml MO pairs according to one or more embodiments of the present disclosure.

[0045] Fig. 10A-B shows an examples of controlling UE reporting behavior according to one or more embodiments of the present disclosure.

[0046] Fig. 11 shows details of a network node 250 according to one or more embodiments of the present disclosure.

[0047] A more complete understanding of embodiments of the invention will be afforded to those skilled in the art, as well as a realization of additional advantages thereof, by a consideration of the following detailed description of one or more embodiments. It should be appreciated that like reference numerals are used to identify like elements illustrated in one or more of the figures.

[0048] DETAILED DESCRIPTION

[0049] When a radio signal is transmitted in a radio network, the energy of the signal will spread from the transmitting antenna / s. To put it a bit simpler, this can be described as the information carried by a sinusoidal radio signal splits up in multiple rays that each propagates into different directions and interacts with the environment through reflection, refraction and by losing energy to the medium that it travels through. Typically, only a very small fraction of the transmitted radio frequency power will reach the intended destination.

[0050] These multiple rays will add up either constructively or destructively at the receiver / mobile user depending on the relative phase shifts of the sinusoidal signals. These phase shifts depend on a distance that of the paths each component has travelled. The received power will differ for different locations in space, thereby forming a standing wave pattern. The standing wave pattern that appears is sometimes referred to as a small-scale fading pattern and the weak signal, which occurs at approximately half of the carrier wavelength in distance and are called fading dips.

[0051] For a wideband signal, a similar behavior can be observed in frequency domain. Then, the signal may be strong for some subcarriers and weak for others. This is called frequency selectivity. The bandwidth over which the channel strength is approximately equal (flat) is called coherence bandwidth and may vary from case to case. Analogously, the channel coherence time is the time during which the channel is approximately constant for a moving user. Typically, a non-line-of-sight channel is very frequency selective, i.e., has a small coherence bandwidth, while a line-of-sight-channel has more flat characteristics, i.e., has a large coherence bandwidth.

[0052] The standing wave pattern in time and frequency may be defined by its statistics, represented e.g., by second-order statistics such as spatial and frequency autocorrelation functions / matrices. The spatial autocorrelation function may also have information of the correlation of radio channels between different antenna and / or beam combinations.

[0053] Knowledge of a radio channel may be referred to as channel state information (CSI). The CSI may include anything from a general statistical model of the set of channel properties that is consistent with a given set of background information and measurements, to a specific estimate of the channel frequency response along with information on its accuracy. As an example, in the 3rd Generation Partnership Project, 3GPP, 5G standard, CSI is often used to describe the combination of CQI (channel quality index), Rl (rank index) and PMI (precoding matrix index) that with high probability can be successfully received by the UE. The two first provide information about the signal strength at the receiver and the latter information about how the channels between different antenna pairs relate to each other.

[0054] The quality of the CSI itself depends on the reference Signal to Noise ratio, SNR, but also on the delay to retrieve the CSI, e.g., due to reference signal processing and CSI handling. During the delay, the actual channel might change resulting in a mismatch between the CSI and the actual current channel.

[0055] Accurate CSI is important for multiple reasons. For example, if a base station and a user attempt to communicate while the user is in a fading dip using high MCS and Rl (i.e., with the incorrect assumption that the user has a good signal quality), then there is a large risk that the data will be lost. However, if the radio access node has knowledge of or can predict when these dips occur, it can schedule all communication between that user equipment and the base station to when the channel gain is high.

[0056] This is even more important if multiple users are co-scheduled within the same time-frequency resources and served using multi-user multiple input multiple output transmission. Then accurate phase information is of high importance to ensure that inter-user interference can be mitigated.

[0057] To help with acquiring good CSI, reference signals are transmitted regularly. A reference signal is a signal whose characteristics are known at both the receiver and the transmitter and can therefore be used to estimate the channel at any given time. How dense the reference signals need to be in time and frequency depends on the fading, the frequency selectivity of the channel and the required reference signal SNR. If the channel is flat assuming good enough reference signal SNR, then the reference signals can be sparsely spaced in frequency while a frequency selective channel needs densely spaced reference signals in frequency. Similarly, if the channel is fading slowly (due to a user moving slowly), then the reference signals can be sparsely spaced in time while densely spaced reference signals in time are required for fast fading channels (which occurs if a user is moving fast). It is of interest not to have more reference signals than necessary as they use up radio resources that could otherwise have been used to transmit data.

[0058] The problem for mobile users is that, as a user moves through the standing wave pattern the CSI becomes outdated. There are then typically two options, either increase the reference signal density in time and frequency or to predict the radio channels of upcoming transmissions.

[0059] To predict a radio channel, the present, and possibly also older, channel estimates are combined with a model of the channel in order to estimate what the channel will look like a time T into the future, where T is called the prediction horizon.

[0060] The possibility to predict the channel depends on the environment, e.g., an environment with a strong line-of-sight (LOS) channel has a longer prediction horizon compared to a more non- line-of-sight (NLOS) environment with many rays with similar strength arriving at different delays and angles. The radio environment changes over a cell e.g., some parts are more LOS and could requires less reference signals and other parts of the cell are more NLOS and requires more dense reference signals.

[0061] Autoregressive models and neural networks are examples of ways to model the radio channel. All models have in common that to design them, training data (past channel measurements) and / or higher-order statistics of the channel, which is also derived from training data, is needed.

[0062] Similar trade-offs as for the reference signals apply to UE reports. For downlink transmissions the base station may rely on CSI reported from the UE. If the report period is longer than the channel coherence time, the CSI reported may be outdated and the downlink transmission sub-optimal. On the other hand, the shorter the UE report period, the higher amount of radio resources are needed.

[0063] Another aspect of transmission is the beamforming scheme; its performance depends on the sensitivity to channel aging. For example, reciprocity-based beamforming tends to be better than codebook-based beamforming for long channel coherence time relative to the CSI update period but worse otherwise. Conventional solutions for predicting channels to control transmission characteristics / features, commonly use second-order statistics of the channel to create a model that can be used to extrapolate current channel measurements into future channel measurements. The second- order statistics can also be used to control transmission characteristics / features such as selecting appropriate UE report periods and selecting the best beamforming scheme for a UE.

[0064] These second-order statistics can be based on previous channel measurements, so called training data.

[0065] The accuracy of the prediction of conventional solutions depends, among other factors, on the accuracy of the second-order statistics which in turns depends on the available amount of training data. Therefore, when a good prediction accuracy is required for a prediction horizon of for example 0.1 wavelengths (corresponding to e.g., 10 ms at a user speed of 36 km / h and a carrier frequency of 3GHz), then the training data may need to consist of several seconds of measurements.

[0066] This has the drawback that high quality predictions for this distance cannot be available immediately when a new user enters a system. It also means that when there are abrupt changes in the second-order statistics of the channel, for example if a user turns a corner, there will be a warm-up process before long range predictions are of high quality again.

[0067] A further drawback is that storing large amounts of training data, either at the UE or at the base station requires large memory allocation, and therefore a more complex design.

[0068] Conventionally transmission characteristics of a User Equipment, UE, or a network node are based on channels that are modelled statically and statistically, e.g., modelled as an increased level of noise.

[0069] The present disclosure exploits the increasing computational power available today to mathematically and dynamically to model a plurality of nodes transmitting simultaneously in the radio network, e.g., as a digital twin model.

[0070] In one example application of the disclosure channel statistics are generated off-line, i.e., not necessarily in the mobile network itself. The statistics is generated for different potential user positions within a network, using a digital twin model.

[0071] One alternative is to have a database of statistics generated off-line that is tied to a user’s location but based on measurements from previous users in the same location. Such a method may work especially well along highly trafficked roads, where the UE / user positions are almost always the same over time, and where this data can be collected and frequently updated but will work less well when that is not the case. The training data needed to be stored for this disclosure could be large, and there could be a need to store it for a long time to get enough statistics from different UEs in different locations and scenarios.

[0072] The proposed solution assumes that a geographical area within a wireless network system is divided into regions, such that in each region all positions have similar channel characteristics / statistics. This may be indicated by statistics such as coherence time and bandwidth of a channel.

[0073] For each such region, a digital twin, placed for example in a central node, is used to generate statistics of the channel in space and frequency. A representation of this statistics is stored, for example in the base station or somewhere more central in the radio network, such as in a cloud.

[0074] Before controlling transmission characteristics, the radio access node acquires spatial information / spatial properties, e.g., about one or more of the UE's position, speed and direction of motion, and uses this to extract the relevant synthetically generated channel statistics. The radio access node needs at least information about each UE's position but may benefit from information about the user speed and moving direction as well as mobile orientation.

[0075] The synthetically generated channel statistic is used to aid the channel prediction and / or configuration of channel time-coherence or frequency-coherence dependent features, e.g., one or several of the following: a. Using the channel statistics to adopt the reference signal density in time and / or frequency. This is typically performed by the radio access node / base station. b. Using the synthetically generated channel statistics to predict a future channel to the user / UE based on measurements of a current reference signal. c. Using the synthetically generated second-order channel statistics to adopt the filtering of the reference signal to improve the reference signal SNR and improve channel estimates. d. Using the first moment, for example, synthetically generated average channel power to predict the SNR at the receiver, which could improve the UE scheduling by using more optimal initial values. e. Spatial statistics on e.g., channel angular power profiles could support MU -Ml MO in terms of UE scheduling, i.e. , telling which combination of UEs have a low spatial correlation and therefore are suitable MU-MI MO pairs. f. First and second order statistics could support an efficient usage of time-frequency reference signals to reduce the air interface load and cell interference. g. Using second order statistics to configure the period and content of UE reports, including for example, CSI reports and mobility related reports such as received signal strength indicator (RSSI) and measurement timing configuration (RMTC). h. Using statistics when selecting beamforming mode, for example whether to use codebook beams or reciprocity-based beamforming.

[0076] In other words, an advantage of the present disclosure is that radio channels are more accurately emulated by providing a digital representation / digital twin of the real world. More precisely, a digital representation / digital twin of radio channels for nodes in the radio network.

[0077] A further advantage of the proposed solution is that it provides a flexible way to generate new channel statistics, e.g., in advance of a user entering a new position such. Thereby the channel of a new user can be predicted as soon as a user enters the system.

[0078] A further advantage of the proposed solution is that the channel of a user that has a sudden change in the radio channel, for example when turning a corner can be instantly predicted as soon as the change has occurred.

[0079] A further advantage of the proposed solution is that there is no need to have large memory for training data or use lots of computational power to compute the higher-order statistics in the base station or at the user since calculation in the digital twin can be done off-line.

[0080] A further advantage of the proposed solution is that the channel statistics can easily be updated when relevant changes to the radio environment occurs, such as seasonal change, new buildings, or temporary changes, e.g., a car becoming a reflector.

[0081] A further advantage of the proposed solution is that the reference signal density (time and / or frequency) can be adjusted to balance the prediction performance with the reference signal overhead.

[0082] In the present disclosure, the term “digital twin” denotes a digital representation of reality that can be used for simulation purposes amongst other. In particular, digital twins of radio environments often have representations of important objects in the environment, such as buildings and trees, and utilize ray-tracing techniques to recreate the radio channel at different geographical areas in the network. In other words, the digital twin is configured to model one or more physical objects located within the geographical area and to emulate radio propagation of the one or more radio channels influenced by the modelled one or more physical objects to predict the radio channel characteristics. This enables real-time radio channel characteristics to be considered when controlling transmission characteristics. In one example, the digital applies mathematical models representing the physical radio signal transmission medium. In the present disclosure, the term “spatial properties” denotes properties of the UE related to location, orientation and movement of the UE. this may e.g., include information about UE geographical position, UE moving speed, UE moving direction as well as UE orientation, e.g., in relation to gravity and / or orientation of transmitting antennas of the radio access nodes.

[0083] In the present disclosure, the term “channels in a radio network” denotes channels over which a physical radio signal is transmitted.

[0084] In the present disclosure, the term “transmission characteristics” denotes characteristics influencing transmission of the physical radio signal. Examples of transmission characteristics includes, spectrum usage, number of bits, transmission power, antenna characteristics, modulation, encoding, timing, beamforming etc.

[0085] In the present disclosure, the expression “trained model” or “training of a model” signifies the step of producing a model from a training set of data by using AI / ML. In one example this involves obtaining a training data set, having predetermined input values and / or desired output values. The model may then be trained using AI / ML, e.g., unsupervised, or supervised learning, by repeatedly providing the input values to candidate models and registering model output values and calculating performance measure for each candidate model based on the training output values and model output values. The candidate model producing the highest performance measure is then selected as the trained model. The model may in one example be trained by selecting from any number of candidate models forming a hypothesis space, based on a performance measure. The performance measure may indicate how well the model output corresponds to the desired output in the training set for the model. The candidate model producing the highest performance measure may then be selected as the trained model. This may also be seen as tuning parameters of the model. Examples of AI / ML training methods / techniques are supervised classification methods such as neural networks, Support Vector Machines, Naive Bayes, and k-Nearest Neighbor.

[0086] In the present disclosure the term “User Equipment / UE” denotes a user node or user device typically carried by or associated to a user. Examples of a UE is a mobile phone, a tablet computer, or a laptop.

[0087] Fig. 1 shows an example of a radio network 100. The radio network comprises a plurality of nodes N1-N21 configured to transmit radio signals in the radio network 100. Each node comprises one or more antennas and circuitry capable of performing radio transmission. In one non-limiting example the nodes are radio access nodes and / or user operated nodes such as UEs. A radio signal transmission between nodes and / or UEs is then performed using certain transmission characteristics.

[0088] Fig. 2 illustrates prediction of radio channel characteristics of one or more radio channels between a UE 231 and one or more radio access nodes 211-213 being located within a geographical area 240 using a digital twin, e.g., hosted by a network node 250, according to one or more embodiments of the present disclosure.

[0089] In Fig. 2, an example of a system 200 that includes three radio access nodes 211-213 and a UE 231. The UE 231 moves along a path 210 within cells C1-C3 of the radio access nodes 211-213.

[0090] In one example, a first radio access node 212, obtains spatial properties of the user equipment UE 231. The system 200 further optionally comprises a network node 250. As can be seen in Fig. 2, the UE 231 is located within the geographical area 240.

[0091] The spatial properties of the user equipment UE 231 may e.g., comprise user position based on Global Positioning System, GPS, information from the UE.

[0092] Alternatively, the spatial properties of the user equipment UE 231 could be estimated based on reference signals. Such reference signals could be signals from different base station received at the UE 231 or it could be reference signals transmitted by the UE 231 , received at the serving and neighboring radio access nodes 211-213.

[0093] If the user position is estimated at the user, this must be signaled to the radio access node / s 211-213.

[0094] Spatial properties of the user equipment UE 231 , such as the UE's speed, may for example be estimated based on an estimate of the Doppler frequency of the UE, which in turn may be based on reference signals.

[0095] Alternatively, spatial properties of the user equipment UE 231 in the form of user speed may be estimated from two or more position estimates of the UE either in the UE or in the radio access node / base station.

[0096] Spatial properties of the user equipment UE 231 , such as the direction of motion of the UE, may also be estimated based on two or more position estimates of the UE either in the UE or in the radio access node / base station.

[0097] Alternatively, Spatial properties of the user equipment UE 231 , such as the direction of motion of the UE, may be based on the Doppler-delay profile and compared with a Doppler-delay profile of the digital twin given the UE’s position. Additionally, or alternatively, the radio access node / base station will estimate the UE’s upcoming position, based on for example current position, speed and direction of motion and feed this information to the digital twin, which can then update the higher-order statistics in this position, taking into account changes in the environment, such as cars that may reflect radio signal before the UE reaches said position.

[0098] The first radio access node 212 then predicts radio channel characteristics of one or more radio channels between the UE 231 and the first radio access nodes 212 using a digital twin hosted in the network node 250. This may e.g., be performed by sending the obtained spatial properties of the user equipment UE 231 to the network node 250 and receive pre-processed radio channel characteristics, e.g., using the position of the UE 231.

[0099] The digital twin is configured to model one or more physical objects located within the geographical area 240 and to emulate radio propagation of the one or more radio channels influenced by the modelled one or more physical objects to predict the radio channel characteristics. The digital twin typically has representations of important objects, such as buildings and trees and utilize ray-tracing techniques to recreate the radio channel at different positions in a network.

[0100] Transmission characteristics is then controlled using the predicted using radio channel characteristics. Transmission characteristics between the UE 231 and the one or more radio access nodes 211-213 may be controlled, either directly or by sending a control message / signal. In one non-limiting example, the transmission characteristics include radio channel coherence bandwidth and / or radio channel coherence time.

[0101] Fig. 3 illustrates a geographical area 240 simulated by a digital twin according to one or more embodiments of the present disclosure.

[0102] Based on a two- or three-dimensional map, the digital twin, using e.g., raytracing, simulates the standing wave pattern of the channel in different regions of the map, i.e. , models the radio propagation channel.

[0103] To retrieve the radio propagation channel based on a digital twin, a huge computational effort is required which is performed off-line on some computer cluster or similar 250, hence it is typically not executed in the base station or in the communication network 100 itself.

[0104] Based on the simulated standing wave pattern, the channel statistics for the position-specific channel is generated.

[0105] These statistics may for example include the autocorrelation function of the channel in space, the frequency correlation of the channel over the different subcarriers and the cross correlation between different channel components. This includes channels between different transmit / receiver antenna pairs or the channels to a user through different beams.

[0106] The statistics may be represented by channel models that can be used for channel prediction. Examples of such models are autocorrelation models, Doppler-delay domain taps or machine learning models that are trained on the standing wave pattern of the channel.

[0107] Different regions might refer to different positions in a two-dimensional or three-dimensional grid. As an example, such a grid may have a resolution of 0.1-.1 meters, 1-10 meters or 1-100 meters or any other suitable resolution.

[0108] The grid could be regular or irregular, that is, the resolution may be finer in some areas than others.

[0109] Different regions may refer to different streets and / or crossings in a city and / or different floors or rooms in a building.

[0110] A look-up table may be used to associate each position of the UE with the representation the channels higher-order statistics.

[0111] The statistics may be associated with a direction of motion as well as a positions. For example, if the statistics is represented by a model, this model may be associated with both a direction of motion and a position.

[0112] Fig. 4 shows a flowchart of a method according to one or more embodiments of the present disclosure. The method comprises:

[0113] Step 410: obtaining spatial properties of a user equipment, LIE 231 , the LIE 231 being located within a geographical area 240.

[0114] As described previously, the term “spatial properties” denotes properties of the UE related to location, orientation, and movement of the UE. this may e.g., include information about UE geographical position, UE moving speed, UE moving direction as well as UE orientation in relation to gravity and / or orientation of transmitting antennas of the radio access nodes.

[0115] Various examples of obtaining spatial properties are described in relation to Fig. 2.

[0116] Step 420: predicting radio channel characteristics of one or more radio channels between the UE 231 and one or more radio access nodes 211-213 being located within the geographical area 240 using a digital twin. The digital twin is configured to model one or more physical objects located within the geographical area 240 and to emulate radio propagation of the one or more radio channels influenced by the modelled one or more physical objects to predict the radio channel characteristics. In other words, the digital twin dynamically predicts the radio channel characteristics in the geographical area 240. In one example, the digital twin models objects and associated surfaces of those objects to perform ray tracing, which traces multiple rays that each propagates into different directions and interacts with the environment through reflection, refraction and by losing energy to the medium that it travels through.

[0117] Step 430: controlling transmission characteristics. The transmission characteristics may be controlled for the UE 231 and / or for the one or more radio access nodes 211-213 using the predicted radio channel characteristics.

[0118] In one embodiment, transmission characteristics in the form of the density of reference symbols is controlled.

[0119] In this embodiment, the radio channel characteristics comprises at least radio channel coherence bandwidth and / or radio channel coherence time, wherein controlling transmission characteristics comprises controlling reference signal density based on the radio channel coherence bandwidth and / or radio channel coherence time.

[0120] In a first embodiment, reference signal density is decreased based on the radio channel coherence bandwidth and / or radio channel coherence time.

[0121] Fig. 5A shows an example of controlling reference symbol density according to one or more embodiments of the present disclosure. Fig. 5A shows radio resources arranged in a time / frequency grid 510. Radio resources allocated for reference symbols are marked with the symbol x.

[0122] In this example, the digital twin generates predicted radio channel characteristics indicating a relatively long coherence time and / or relatively large coherence bandwidth. The transmission characteristics comprising density in time / frequency of reference symbols may be set to a relatively low level, e.g., 4 reference symbols as indicated in Fig. 5A.

[0123] In a second embodiment, reference signal density is increased based on the radio channel coherence bandwidth and / or radio channel coherence time.

[0124] Fig. 5B shows a further example of controlling reference symbol density according to one or more embodiments of the present disclosure. Fig. 5B shows radio resources arranged in a time / frequency grid 520. Radio resources allocated for reference symbols are marked with the symbol x.

[0125] In this example, the digital twin generates predicted radio channel characteristics indicating a relatively short coherence time and / or relatively large coherence bandwidth. The transmission characteristics comprising density in time / frequency of reference symbols may be set to a relatively high level, e.g., 9 reference symbols as indicated in Fig. 5B. Fig. 6 illustrates controlling transmission for an upcoming transmission according to one or more embodiments of the present disclosure. Fig. 6 shows a radio access node 211 serving a cell C1. A UE 231 is moving along a path 210 and is located in cell C1 at a first position Pi at a first point in time TN. It is estimated that the UE 231 will continue to follow its path 210, e.g., a highway, and will be located at a second position P2 at a second point in time TN+I .

[0126] T ransmission characteristics such as transmission power and / or beamforming for an upcoming transmission at the second point in time TN+I can then be optimized for the second position P2 which reduces interference and noise in the radio network 100. In other words, as the digital twin can predict radio channel characteristics at the second position P2, a beamforming vector with the best fit can be selected and / or the least amount of power can be used to transmit from the serving radio access node 211 to the UE 231 , or vice versa.

[0127] In this embodiment, the radio channel characteristics comprises at least radio channel coherence bandwidth and / or radio channel coherence time, wherein controlling transmission characteristics comprises controlling transmission power and / or beamforming for an upcoming transmission TN+1 based on the radio channel coherence bandwidth and / or radio channel coherence time.

[0128] Fig. 7 illustrates controlling filtering of reference symbols according to one or more embodiments of the present disclosure. Fig. 7 illustrates a signal S comprising a reference symbol.

[0129] Using the predicted radio channel characteristics from the digital twin, the signal S may be filtered by selecting a filter F1-FN based on the predicted radio channel characteristics from the digital twin. The signal S may either be pre-filtered before transmission or after reception by the receiver.

[0130] Examples of filters are: low-pass filters implemented as finite impulse response filters or infinite impulse response filters where the cutoff frequency is low for long radio channel coherence time and high for short radio channel coherence time. Similarly low-pass filters can be implemented to perform smoothing over subcarriers, where the cutoff is low for large radio channel coherence bandwidth and high for short radio channel coherence bandwidth.

[0131] The effect of this is that reference signal SNR can be improved and / or quality of radio channel estimates can be improved.

[0132] In one embodiment, the radio channel characteristics comprises at least radio channel coherence time. Controlling transmission characteristics comprises controlling reference symbol measurement behavior of the UE 231 based on the radio channel coherence time. The network can configure a measurement filter for Layer 3 filtering of e.g., cell measurement quantities and beam measurement quantities such that the cutoff frequency is low for long radio channel coherence time and high for short radio channel coherence time.

[0133] In one further embodiment of the method described in relation to Fig. 4, scheduling behavior is controlled. In other words, a relatively large amount of data is scheduled when the digital twin predicts that channel gain is high, and a relatively small amount of data is scheduled when the digital twin predicts that channel gain is low.

[0134] In this embodiment the radio channel characteristics comprises at least radio channel coherence bandwidth and / or radio channel coherence time. Controlling transmission characteristics comprises controlling scheduling behavior based on the radio channel coherence bandwidth and / or radio channel coherence time. The scheduling behavior may be controlled for UE 231 and / or the one or more radio access nodes 211-213.

[0135] Fig. 8A shows an example of controlling scheduling behavior according to one or more embodiments of the present disclosure. Fig. 8A shows radio resources, e.g., resource blocks, arranged in a time / frequency grid 810. Radio resources allocated for scheduled data are marked with the symbol D.

[0136] In this example, the digital twin generates predicted radio channel characteristics indicating a relatively large coherence time and / or coherence bandwidth. The transmission characteristics comprising density in time / frequency of data may be set to a relatively high level, e.g., 9 resource blocks as indicated in Fig. 8A.

[0137] Fig. 8B shows a further example of controlling scheduling behavior according to one or more embodiments of the present disclosure. Fig. 8B shows radio resources, e.g., resource blocks, arranged in a time / frequency grid 820. Radio resources allocated for scheduled data are marked with the symbol D.

[0138] In this example, the digital twin generates predicted radio channel characteristics indicating a relatively small coherence time and / or coherence bandwidth. The transmission characteristics comprising density in time / frequency of data may be set to a relatively low level, e.g., 1 resource block as indicated in Fig. 8B.

[0139] In one further embodiment of the method described in relation to Fig. 4, suitable Multi-user multiple-input multiple-output technology, MU-MIMO, pairs 931 , 932 are controlled. In other words, a pair of UEs 931 , 932 having relatively low spatial correlation is selected. This typically means that the same radio resources can be reused for both UEs in the pair 931 , 932 by the radio serving access node 911 in the cell C91 with little interference experienced by the respective UE of the pair 931 , 932. This has the effect of increased radio resource efficiency and reduced interference in the radio network 100. A further advantage is that inter-user interference can be mitigated.

[0140] In this embodiment, the radio channel characteristics comprises at least radio channel coherence bandwidth and / or radio channel coherence time, wherein controlling transmission characteristics comprises identifying suitable Multi-user multiple-input multiple-output technology, MU-MIMO, pairs 931 , 932 based on the radio channel coherence bandwidth and / or radio channel coherence time.

[0141] Fig. 9 illustrates an example of selecting MU-MIMO pairs according to one or more embodiments of the present disclosure. Fig. 9 shows a serving radio access node 911 and a corresponding serving cell 911. Two UEs 931 , 932 are served by the serving radio access node 911.

[0142] In this example, the digital twin generates predicted radio channel characteristics indicating relatively low spatial correlation between the UEs 931 , 932. This may then be exploited, e.g., by selecting the UEs 931 , 932 as a MU-MIMO pair.

[0143] In one further embodiment of the method described in relation to Fig. 4, UE reporting behavior is controlled. In other words, a relatively large amount of UE reports is scheduled when the digital twin predicts that coherence time relatively small / low. A relatively small amount of UE reports is scheduled when the digital twin predicts that coherence time and / or coherence bandwidth is relatively large / high.

[0144] In this embodiment, the radio channel characteristics comprises at least radio channel coherence time. Controlling transmission characteristics comprises controlling UE reporting behavior based on the radio channel coherence time.

[0145] Fig. 10A shows an example of controlling UE reporting behavior according to one or more embodiments of the present disclosure. Fig. 10A shows radio resources, e.g., resource blocks, arranged in a time / frequency grid 1010. Radio resources allocated for UE reports are marked with the symbol R.

[0146] In this example, the digital twin generates predicted radio channel characteristics indicating a relatively small coherence time. The transmission characteristics comprising report density in time / frequency of reports may be set to a relatively high level, e.g., 5 resource blocks as indicated in Fig. 10A.

[0147] Fig. 10B shows a further example of controlling UE reporting behavior according to one or more embodiments of the present disclosure. Fig. 10B shows radio resources, e.g., resource blocks, arranged in a time / frequency grid 1020. Radio resources allocated for scheduled data are marked with the symbol R. In this example, the digital twin generates predicted radio channel characteristics indicating a relatively large coherence time. The transmission characteristics comprising report density in time / frequency of reports may be set to a relatively low level, e.g., 1 resource block as indicated in Fig. 10B.

[0148] In one further embodiment of the method described in relation to Fig. 4, beamforming behavior is controlled. In other words, codebook-based beamforming is selected when the digital twin predicts that coherence time and / or coherence bandwidth is relatively small / low. A reciprocitybased beamforming is selected when the digital twin predicts that coherence time and / or coherence bandwidth is relatively long / large.

[0149] Further, reciprocity-based beamforming is dependent on UL SNR of the reference signals. Usually, the SNR of the reference signals is worse in UL compared to DL, hence for cell edge users with large pathloss a code book-based beamforming is beneficial. Therefore, beamforming method could be controlled using the digital twin’s pathloss predictions.

[0150] In this embodiment, the radio channel characteristics comprises at least radio channel coherence time, wherein controlling transmission characteristics comprises controlling beamforming behavior based on the radio channel coherence bandwidth and / or radio channel coherence time.

[0151] Additionally, or alternatively the method described in relation to Fig. 4, wherein predicting radio channel characteristics further comprises: sending a request to network node comprising a digital twin, the request comprising the spatial properties of a user equipment, UE (231), receiving a response, the response comprising radio channel characteristics of the one or more radio channels.

[0152] Fig. 11 shows details of a network node 250 according to one or more embodiments of the present disclosure.

[0153] The network node nodes 250 may be in the form of a selection of any of network node, a desktop computer, server, laptop, mobile device, a smartphone, a tablet computer, a smart watch etc. The network node 250 may comprise processing circuitry 1112. The network node 250 may optionally comprise a communications interface 1104 for wired and / or wireless communication. Further, the network node 250 may further comprise at least one optional antenna (not shown in figure). The antenna may be coupled to a transceiver of the communications interface 1104 and is configured to transmit and / or emit and / or receive wireless signals, e.g., in a wireless communication system. In one example, the processing circuitry 1112 may be any of a selection of processor and / or a central processing unit and / or processor modules and / or multiple processors configured to cooperate with each-other. Further, the network node 250 may further comprise a memory 1115. The memory 1115 may contain instructions executable by the processing circuitry 1112, that when executed causes the processing circuitry 1112 to perform any of the methods and / or method steps described herein.

[0154] The communications interface 1104, e.g., the wireless transceiver and / or a wired / wireless communications network adapter is configured to send and / or receive data values or parameters as a signal. In an embodiment, the communications interface 1104 communicates directly between nodes or via a communications network.

[0155] In one or more embodiments the network node 250 may further comprise an input device or interface 1117, configured to receive input or indications from a user and output / send a userinput signal indicative of the user input or indications to the processing circuitry 1112. In other words, the input device / interface 1117 receives input or indications from the user and translates this into data that the processing circuitry 1112 can interpret.

[0156] In one or more embodiments the network node 250 may further comprise a display 1118 configured to receive a display signal indicative of rendered objects, such as text or graphical user input objects, from the processing circuitry 1112 and to display the received signal as objects, such as text or graphical user input objects.

[0157] In one embodiment the display 1118 is integrated with the user input device / interface 1117 and is configured to receive a display signal indicative of rendered objects, such as text or graphical user input objects, from the processing circuitry 1112 and to display the received signal as objects, such as text or graphical user input objects, and / or configured to receive input or indications from a user and send a user-input signal indicative of the user input or indications to the processing circuitry 1112.

[0158] In one or more embodiments the network node 250 may further comprise one or more sensors (not shown).

[0159] In embodiments, the processing circuitry 1112 is communicatively coupled to the memory 1115 and / or the communications interface 1104 and / or the input device 1117 and / or the display 1118.

[0160] In embodiments, the communications interface and / or transceiver 1104 communicates using wired and / or wireless communication techniques. In embodiments, the one or more memory 1115 may comprise a selection of a hard RAM, disk drive, a floppy disk drive, a magnetic tape drive, an optical disk drive, a CD or DVD drive (R or RW), or other removable or fixed media drive.

[0161] In a further embodiment, the network node 250 may further comprise and / or be coupled to one or more additional sensors (not shown) configured to receive and / or obtain and / or measure physical properties pertaining to the network node or the environment of the network node and send one or more sensor signals indicative of the physical properties to the processing circuitry 1112.

[0162] It is to be understood that a network node comprises any suitable combination of hardware and / or software needed to perform the tasks, features, functions, and methods disclosed herein. Moreover, while the components of the network node are depicted as single boxes located within a larger box, or nested within multiple boxes, in practice, a network node may comprise multiple different physical components that make up a single illustrated component (e.g., memory 1115 may comprise multiple separate hard drives as well as multiple RAM modules).

[0163] Similarly, the network node 250 may be composed of multiple physically separate components, which may each have their own respective components.

[0164] The communications interface 1104 may also include multiple sets of various illustrated components for different wireless technologies, such as, for example, Global System for Mobile Communications GSM, Wideband Code-Division Multiple Access, WCDMA, Long- Term Evolution, LTE, New Radio, NR, Wireless Fidelity, Wi-Fi, or Bluetooth wireless technologies. These wireless technologies may be integrated into the same or different chip or set of chips and other components within the network node 250.

[0165] Processing circuitry 1112 is configured to perform any determining, calculating, or similar operations (e.g., certain obtaining operations) described herein as being provided by a network 250. These operations performed by processing circuitry 1112 may include processing information obtained by processing circuitry 1112 by, for example, converting the obtained information into other information, comparing the obtained information, or converted information to information stored in the network node, and / or performing one or more operations based on the obtained information or converted information, and as a result of said processing making a determination.

[0166] Processing circuitry 1112 may comprise a combination of one or more of a microprocessor, controller, microcontroller, central processing unit, digital signal processor, application-specific integrated circuit, field programmable gate array, or any other suitable computing device, resource, or combination of hardware, software and / or encoded logic operable to provide, either alone or in conjunction with other network node 250 components, such as device readable medium, computer 250 functionality. For example, processing circuitry 1112 may execute instructions stored in device readable medium 1115 or in memory within processing circuitry 1112. Such functionality may include providing any of the various wireless features, functions, or benefits discussed herein. In some embodiments, processing circuitry 1112 may include a system on a chip.

[0167] In some embodiments, processing circuitry 1112 may include one or more of radio frequency, RF, transceiver circuitry and baseband processing circuitry. In some embodiments, RF transceiver circuitry and baseband processing circuitry 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 and baseband processing circuitry may be on the same chip or set of chips, boards, or units.

[0168] In certain embodiments, some or all the functionality described herein as being provided by a network node 250 may be performed by the processing circuitry 1112 executing instructions stored on device readable medium 1115 or memory within processing circuitry 1112. In alternative embodiments, some or all the functionalities may be provided by processing circuitry 1112 without executing instructions stored on a separate or discrete device readable medium, such as in a hard-wired manner. In any of those embodiments, whether executing instructions stored on a device readable storage medium or not, processing circuitry 1112 can be configured to perform the described functionality. The benefits provided by such functionality are not limited to processing circuitry 1112 alone or to other components of network node NI-NM, UEI-UEN but are enjoyed by network node 250 and / or by end users.

[0169] Device readable medium or memory 1115 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 computerexecutable memory devices that store information, data, and / or instructions that may be used by processing circuitry 1112. Device readable medium 1115 may store any suitable instructions, data, or information, including a computer program, software, an application including one or more of logic, rules, code, tables, etc. and / or other instructions capable of being executed by processing circuitry 1112 and, utilized by network node 250. Device readable medium may be used to store any calculations made by processing circuitry 1112 and / or any data received via interface 1104. In some embodiments, processing circuitry 1112 and device readable medium 1115 may be considered to be integrated.

[0170] The communications interface 1104 is used in the wired or wireless communication of signaling and / or data between network node 250 and other nodes. Interface 1104 may comprise port(s) / terminal(s) to send and receive data, for example to and from network node 250 over a wired connection. Interface 1104 also includes radio front end circuitry that may be coupled to, or in certain embodiments a part of, an antenna. Radio front end circuitry may comprise filters and amplifiers. Radio front end circuitry may be connected to the antenna and / or processing circuitry 1112.

[0171] Examples of a network node 250 include, but are not limited to an gNB, a gateway, a smart phone, a mobile phone, a cell phone, a voice over IP (VoIP) phone, a wireless local loop phone, a tablet computer, a desktop computer, a personal digital assistant (PDA), a wireless cameras, a gaming console or device, a music storage device, a playback appliance, a wearable terminal device, a wireless endpoint, a mobile station, a tablet, a laptop, a laptop-embedded equipment (LEE), a laptop-mounted equipment (LME), a smart device, a wireless customer-premise equipment (CPE), a vehicle-mounted wireless terminal device, a drone etc.

[0172] The communication interface 1104 may encompass wired and / or wireless networks such as a local-area network (LAN), a wide-area network (WAN), a computer network, a wireless network, a telecommunications network, another like network or any combination thereof. The communication interface may be configured to include a receiver and a transmitter interface used to communicate with one or more other devices over a communication network according to one or more communication protocols, such as Ethernet, TCP / IP, SONET, ATM, optical, electrical, and the like). The transmitter and receiver interface may share circuit components, software, or firmware, or alternatively may be implemented separately.

[0173] In embodiments, the radio access node (211-213), may comprise all of the features described in relation to Fig. 11 , or a subset of the features described in relation to Fig. 11.

[0174] In one embodiment, a radio access node 211-213, is provided. The radio access node 211- 213 comprises: a processor, and a memory, said memory containing instructions executable by said processor, whereby said radio access node 211-213 is operative to perform the method steps described herein.

[0175] In one embodiment, A system 200 is provided, the system comprising: a network node 250 configured to generate and provide a digital twin for a geographical area, a radio access node 211-213, the radio access node being configured to: obtain spatial properties of a user equipment, UE, the UE and the radio access node being located within the geographical area; obtain predicted radio channel characteristics of one or more radio channels between the UE and the radio access node based on the obtained spatial properties using the digital twin, the UE and the radio access node being located within the geographical area, the digital twin being configured to model one or more physical objects located within the geographical area and emulate radio propagation of the one or more radio channels influenced by the modelled one or more physical objects to predict the radio channel characteristics; control transmission characteristics between the UE 231 and the radio access node 211-213 using the predicted radio channel characteristics.

[0176] In one embodiment, the spatial properties of the user equipment are received in a first message from the UE and / or is estimated using received reference signals.

[0177] In one embodiment, wherein the predicted radio channel characteristics is obtained by the radio access node 211-213 by receiving a second message from the network node 250.

[0178] In one embodiment, the transmission characteristics is controlled by sending a third message from the radio access node 211-213 to the network node 250.

[0179] Finally, it should be understood that the invention is not limited to the embodiments described above, but also relates to and incorporates all embodiments within the scope of the appended independent claims.

Claims

CLAIMS1. A method (400), the method comprising: obtaining (410) spatial properties of a user equipment, UE (231 ), the UE (231 ) being located within a geographical area (240); predicting (420) radio channel characteristics of one or more radio channels between the UE (231 ) and one or more radio access nodes (211-213) being located within the geographical area (240) using a digital twin, the digital twin being configured to model one or more physical objects located within the geographical area (240) and to emulate radio propagation of the one or more radio channels influenced by the modelled one or more physical objects to predict the radio channel characteristics; controlling (430) transmission characteristics between the UE (231 ) and the one or more radio access nodes (211-213) using the predicted radio channel characteristics.

2. The method according to claim 1 , wherein the radio channel characteristics comprises at least radio channel coherence bandwidth and / or radio channel coherence time, wherein controlling transmission characteristics comprises increasing reference signal density based on the radio channel coherence bandwidth and / or radio channel coherence time.

3. The method according to any of the preceding claims, wherein the radio channel characteristics comprises at least radio channel coherence bandwidth and / or radio channel coherence time, wherein controlling transmission characteristics comprises decreasing reference signal density based on the radio channel coherence bandwidth and / or radio channel coherence time.

4. The method according to any of the preceding claims, wherein the radio channel characteristics comprises at least radio channel coherence bandwidth and / or radio channel coherence time, wherein controlling transmission characteristics comprises controlling transmission power and / or beamforming for an upcoming transmission based on the radio channel coherence bandwidth and / or radio channel coherence time.

5. The method according to any of the preceding claims, wherein the radio channel characteristics comprises at least radio channel coherence time, wherein controlling transmission characteristics comprises controlling reference symbol measurement behavior of the UE (231 ) based on the radio channel coherence time.

6. The method according to any of the preceding claims, wherein the radio channel characteristics comprises at least radio channel coherence bandwidth and / or radio channel coherence time, wherein controlling transmission characteristics comprises controlling scheduling behavior of the UE (231 ) based on the radio channel coherence bandwidth and / or radio channel coherence time.

7. The method according to any of the preceding claims, wherein the radio channel characteristics comprises at least radio channel coherence bandwidth and / or radio channel coherence time, wherein controlling transmission characteristics comprises identifying suitable Multi-user multiple-input multiple-output technology, MU-MIMO, pairs (931 , 932) based on the radio channel coherence bandwidth and / or radio channel coherence time.

8. The method according to any of the preceding claims, wherein the radio channel characteristics comprises at least radio channel coherence time, wherein controlling transmission characteristics comprises controlling UE reporting behavior based on the radio channel coherence time.

9. The method according to any of the preceding claims, wherein the radio channel characteristics comprises at least radio channel coherence bandwidth and / or radio channel coherence time, wherein controlling transmission characteristics comprises controlling beamforming behavior based on the radio channel coherence bandwidth and / or radio channel coherence time.

10. The method according to any of the preceding claims, wherein predicting radio channel characteristics further comprises: sending a request to network node comprising a digital twin, the request comprising the spatial properties of a user equipment, UE (231 ),receiving a response, the response comprising radio channel characteristics of the one or more radio channels.11 . A radio access node (211 -213), the radio access node comprising: a processor, and a memory, said memory containing instructions executable by said processor, whereby said channel emulator is operative to: obtain spatial properties of a user equipment, UE (231 ), the UE (231 ) being located within a geographical area (240); predict radio channel characteristics of one or more radio channels between the UE (231 ) and one or more radio access nodes (211 -213) being located within the geographical area (240) using a digital twin, the digital twin being configured to model one or more physical objects located within the geographical area (240) and to emulate radio propagation of the one or more radio channels influenced by the modelled one or more physical objects to predict the radio channel characteristics; control transmission characteristics between the UE (231 ) and the one or more radio access nodes (211 -213) using the predicted radio channel characteristics.

12. The node according to claim 11 , wherein the radio channel characteristics comprises at least radio channel coherence bandwidth and / or radio channel coherence time, wherein control transmission characteristics comprises to increase reference signal density based on the radio channel coherence bandwidth and / or radio channel coherence time.

13. The node according to claims 11 -12, wherein the radio channel characteristics comprises at least radio channel coherence bandwidth and / or radio channel coherence time, wherein control transmission characteristics comprise decrease reference signal density based on the radio channel coherence bandwidth and / or radio channel coherence time.

14. The node according to claims 11 -13, wherein the radio channel characteristics comprises at least radio channel coherence bandwidth and / or radio channelcoherence time, wherein control transmission characteristics comprises control transmission power and / or beamforming for an upcoming transmission based on the radio channel coherence bandwidth and / or radio channel coherence time.

15. The node according to claims 11 -14, wherein the radio channel characteristics comprises at least radio channel coherence time, wherein control transmission characteristics comprises to control reference symbol measurement behavior of the UE (231 ) based on the radio channel coherence time.

16. The node according to claims 11 -15, wherein the radio channel characteristics comprises at least radio channel coherence bandwidth and / or radio channel coherence time, wherein control transmission characteristics comprises to control scheduling behavior of the UE (231 ) based on the radio channel coherence bandwidth and / or radio channel coherence time.

17. The node according to claims 11 -16, wherein the radio channel characteristics comprises at least radio channel coherence bandwidth and / or radio channel coherence time, wherein control transmission characteristics comprises to identify suitable Multi-user multiple-input multiple-output technology, MU-MIMO, pairs (931 , 932) based on the radio channel coherence bandwidth and / or radio channel coherence time.

18. The node according to claims 11 -17, wherein the radio channel characteristics comprises at least radio channel coherence time, wherein control transmission characteristics comprises to control UE reporting behavior based on the radio channel coherence time.

19. The node according to claims 11 -18, wherein the radio channel characteristics comprises at least radio channel coherence bandwidth and / or radio channel coherence time, wherein control transmission characteristics comprises to control beamforming behavior based on the radio channel coherence bandwidth and / or radio channel coherence time.

20. The node according to claims 11 -19, wherein the node is configured to:send a request to network node (250) comprising a digital twin, the request comprising the spatial properties of a user equipment, UE (231 ), receiving a response, the response comprising radio channel characteristics of the one or more radio channels.

21. A system (200), the system comprising: a network node (250) configured to generate and provide a digital twin for a geographical area, a radio access node (211-213), the radio access node being configured to: obtain spatial properties of a user equipment, UE, the UE and the radio access node being located within the geographical area; obtain predicted radio channel characteristics of one or more radio channels between the UE and the radio access node based on the obtained spatial properties using the digital twin, the UE and the radio access node being located within the geographical area, the digital twin configured to model one or more physical objects located within the geographical area and emulate radio propagation of the one or more radio channels influenced by the modelled one or more physical objects to predict the radio channel characteristics; control transmission characteristics between the UE (231 ) and the radio access node (211-213) using the predicted radio channel characteristics.

22. The system according to claim 21 , wherein the spatial properties of the user equipment are received in a first message from the UE and / or is estimated using received reference signals.

23. The system according to any of claims 21-22, wherein the predicted radio channel characteristics is obtained by the radio access node (211-213) by receiving a second message from the network node (250).

24. The system according to any of claims 21-23, wherein the transmission characteristics is controlled by sending a third message from the radio access node (211-213) to the network node (250).

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