Method of generating a dynamic and / or irregular voxelated environment model for signal processing in wireless communication and / or environmental perception, and apparatus implementing the method and / or applying its output

Abstract

A method of processing voxelated environment models (VEM) of a region of interest (ROI) for use thereof in wireless communication signal processing and / or environment perception or mapping comprises obtaining one or more first and second representations of VEMs of the ROI determined from or based on one or more first and second sets of transmit symbols transmitted by one or more transmitters into or across the ROI and received at one or more receivers at one or more corresponding first and second time instants. The first and second representations of the VEMs of the ROI are compared, considering the respective first and second time instants, for extracting or deriving ancillary voxel information (AVI), and a predicted VEM of the ROI is generated based representations of the VEMs of the ROI and the AVI. The predicted VEM of the ROI is output for further use.

Description

[0001] METHOD OF GENERATING A DYNAMIC AND / OR IRREGULAR VOXELATED ENVIRONMENT MODEL FOR SIGNAL PROCESSING IN WIRELESS COMMUNICATION AND / OR ENVIRONMENTAL PERCEPTION, AND APPARATUS IMPLEMENTING THE METHOD AND / OR APPLYING ITS OUTPUT

[0002] FIELD OF THE INVENTION

[0003] The invention relates to the fields of wireless communication and environmental perception, in particular to wireless communication that generates a spatial representation of an environment and / or employs information associated with the spatial representation of the environment for optimising operating parameters in transmitters and / or receivers. Generating a spatial representation of an environment is also referred to as environmental perception, and generating such representation of an environment using wireless communication signals is also referred to as joint communication and sensing (JCAS).

[0004] Throughout this specification the term environmental perception, environment mapping or environment sensing may be used for the various expressions widely used for capturing information about an environment for creating a three- dimensional representation thereof.

[0005] BACKGROUND

[0006] Wireless communication may be affected by objects located between a transmitter and a receiver, which objects may, inter alia, block or attenuate signals, and / or may cause reflexions that result in multi-path reception and / or cause a phase shift at a receiver. Advanced wireless receivers typically try to estimate a channel matrix whose elements represent the properties of the wireless communication channel in terms of attenuation or gain, phase shift and the like for the frequency range used for communicating, which estimated channel matrix is then used for equalising a received signal prior to symbol detection. Having a proper knowledge about objects located between a transmitter and a receiver, i.e. , about an environment, may facilitate estimating the channel matrix and / or may improve the accuracy of the estimated channel matrix, which may reduce the computational complexity required for the estimation and / or the symbol detection. In addition to reducing the electrical energy required for the computation, reducing the computational complexity can reduce the time that passes between receiving a signal at an antenna and the availability of the transmitted symbols at an output of the receiver.

[0007] Knowledge of an environment between a transmitter and a receiver may also be used for other purposes, inter alia for generating a map of the environment, which may be used for various purposes in mobile apparatus, including, inter alia, finding a path through the environment, collision avoidance, and the like.

[0008] While various techniques for generating a representation of an environment are known, e.g., using cameras, RADAR, LIDAR, or combinations thereof, many of these known techniques require additional apparatus and do not necessarily provide information or a format that benefits wireless communication.

[0009] Spatial representations of environments are widely used in the field of robotic vision, localizing and mapping, as well as in computer graphics and medical imaging. One known technique involves discretely approximating a true environment by a voxelated occupancy grid. Typically, voxels are regular cubes arranged in a 3D space covering the environment, which are assigned information about the space they occupy. The resulting discreteness of the voxelated space is well suited for 3D modelling where, depending on the size of the unit voxel, objects in the region of interest may be represented by rough estimates or more accurate shapes.

[0010] An exemplary environment is shown in figure 1 a). The total region of interest (ROI) is defined as a cuboidal space of dimensions Lxx Lyx Lz, each denoting the lengths of the x, y, z-axes in meters, respectively. The entire ROI is subdivided into a grid consisting of Nv = • Ny• Nzvoxels, where Nx= — Ly, Ny= — Ly , and Nz= — Ly denote the number of voxels per x, y, z -axes, respectively, and Lv is the edge length of a voxel cube in meters, corresponding to the image resolution. If represented as a tensor of three dimensions (Nxx Nyx Nz), the voxelated occupancy grid directly represents a discretized model of the ROI as shown in Fig. 1 b) and c), where the size of the voxels corresponds to the image resolution. The elements of the three-dimensional tensor indicate the occupancy of the voxels, and thus, whether that portion of the space is empty or filled with a given material. In addition to the 3D geometric information as provided by the classic voxelated occupancy grid, the electromagnetic scattering behaviour of the true environment may also be incorporated to tailor the modelling method to be utilized in a wireless communication scenario, along with any further data or information as required by a respective application or use case. The voxelated occupancy grid may also be referred to herein as voxelated environment model.

[0011] More recently, Joint Communication and Sensing (JCAS) has been developed, which is a technique in wireless communications with the objective of retrieving information about the environment from signal scattering which is present in the effective channel state information (CSI), e.g., due to objects in the environment, blockage, user activity, etc., while simultaneously achieving data communication. Most known JCAS methods exploit radar technology to infer information about the environment. This is also known as joint radar and communication (JRC).

[0012] Various methods are known in JRC, including alternating or sharing spectrum between radar and communication signals, using standard radar signals to embed information, extracting radar parameters from standard communication signals, or even designing new waveforms suited for both tasks. The known techniques are highly based on conventional radar signal processing, e.g., ambiguity function estimation, and dependent on the radar frequency-delay properties and prone to similar challenges.

[0013] Recent developments in communication technology have identified that modelling the space between a transmitter and a receiver by a voxelated environments is beneficial for JCAS, sometimes also referred to as integrated sensing and communication, or ISAC.

[0014] To further the benefits of environmental representations for communication purposes the electromagnetic scattering behaviour of objects in the true environment may be added to the 3D geometric information as provided by the classic voxelated occupancy grid, for tailoring the communication channel modelling method that is utilized in a wireless communication scenario. For considering the scattering behaviour, each voxel is assigned a voxel occupancy coefficient xike{0, 1} with k e{l, Nv }, where t = 0 indicates that the &-th voxel is empty, i.e., the corresponding environment is free-space, and xik= 1 indicates that the k -th voxel is occupied by a scatterer object, e.g., the table, chair or the object on the wall, as illustrated in Fig. 1 . Note that the binary voxel occupancy coefficients may be extended to complex voxel scattering coefficients, i.e., \ik = / 3k-e~^ke (C, to also capture the effect incurred to the reflected electromagnetic waves by the occupied voxels. The constants / k and x depend not only on the material itself, but also on the frequency and the angle of incidence of propagating signals, and capture the effect of the material occupying a given voxel onto the electromagnetic wave reflected or refracted by it. In other words, the values of the voxel scattering coefficients are expected to be highly dependent on the electromagnetic characteristics of the scatterer object and the impinging wave, which may be empirically measured and modelled as a function of the properties such as frequency and material.

[0015] For example, in “Joint Multi-User Communication and Sensing Exploiting Both Signal and Environment Sparsity,” IEEE Journal of Selected Topics in Signal Processing, vol. 15, no. 6, pp. 1409-1422, Nov. 2021 , X. Tong, Z. Zhang, J. Wang, C. Huang and M. Debbah consider a regular voxelated 3D space with some scatterer objects accommodating a single access point (AP), a single reconfigurable intelligent surface (RIS), and multiple single-antenna user equipment (UEs). The multiple UEs are communicating to the AP via sparse code multiple access (SCMA) over multiple frequency subcarriers and over multiple transmission instances, via line-of-sight (LOS) paths and non-line-of-sight (NLOS) paths from the UEs, to the scatters, to the RIS, then finally to the AP.

[0016] Figure 2 shows a schematic representation of the 3D space considered in the known system and method, including the RIS. Here, the signal reflected off the RIS towards the AP is shown in a dash-dotted line, to highlight its specific origin. The figure shows direct or LOS signal paths, and indirect or NLOS signal paths. Figure 3 shows a general concept of LOS paths and NLOS paths in a voxelated space, where all paths are available and the reflection angles are within a range that actually reflects impinging electromagnetic waves. In the figure the LOS path is the direct path between the user equipment UE and the access point AP, while the two occupied voxels in the ROI reflect signals emitted by the UE towards the AP. The dashed lines represent the NLOS UE-to-voxel path, and the dotted lines represent the NLOS voxel-to-AP path.

[0017] In addition to the estimation accuracy flexibility that voxelated grids provide, one of the biggest advantages that the grid-based models provide is the simplicity of the data representation, especially as opposed to 3D vertex-based or point cloudbased methods, which makes this even more favourable from a machine-learning point of view.

[0018] Like most, if not all, other known methods of JCAS in voxelated environments, the prior art method discussed further above relies on the idealised assumptions that all paths between all transmit and receive antennas are fully available, i.e. , no paths are blocked, that the channel gains for UEs-to-AP LOS paths are known, that the channel gains for the UEs-to-voxels, voxels-to-RIS, RIS-to-AP NLOS paths are known, that the RIS reflection coefficients are known, that the voxelated environment model is binary, i.e., only discrete occupancy values 0 or 1 are possible, that all scattered paths have realistic reflection angles, that an SCMA communication scheme is used, and that only a single AP is present. However, some paths may not be available for the wireless signals, while others are.

[0019] Figure 4 shows a general concept of infeasible paths between two UEs and an AP, i.e., paths that are not available. The path between UE1 and the AP has a reflexion angle that is too shallow to actually result in a signal propagating from the occupied voxel to the AP. The path between UE2 and the AP, which would be the most direct path, is blocked by the occupied voxel. Note that any feasible path is not shown in the figure.

[0020] Also, the energy loss at reflection scattering that depends on the material and surface structure of the scatterer object, the frequency-dependent scattering behaviour, as well as the relative distances between voxels and devices and the resulting effect on the channel gains are not considered in the known methods.

[0021] Finally, the known methods, while probably providing sufficiently accurate voxelated representations of environments, consider static environments, i.e., the positions of occupied voxels do not change, and the time required for obtaining a more or less complete voxelated representation of an environment is not considered relevant, if considered at all.

[0022] The idealised assumptions found in the prior art methods pose severe limitations to usefully applying the known methods to actual 3D real-world environments and applications, giving rise to the need for an improved method of generating an voxelated environment model for signal processing in wireless communication, methods of transmitting or receiving using said improved voxelated environment model, and apparatus implementing the methods.

[0023] SUMMARY OF THE INVENTION

[0024] This need is addressed by the methods of claims 1 , 7 and 9, the apparatus of claims 6 and 10, and the computer program product of claim 11. A corresponding computer-readable storage medium is presented in claim 12. Embodiments and developments are provided in the respective dependent claims.

[0025] In accordance with a first aspect of the present invention a method of processing a voxelated environment model of a region of interest (ROI) for use thereof in wireless communication signal processing and / or environment perception or mapping is presented.

[0026] The method comprises obtaining one or more first representations of voxelated environment models (VEM) of the ROI determined from or based on one or more first sets of transmit symbols transmitted by one or more transmitters into or across the ROI and received at one or more receivers at one or more corresponding first time instants. The method further comprises obtaining one or more second representations of VEMs of the RO I determined from or based on one or more second sets of transmit symbols wirelessly transmitted by one or more transmitters into or across the ROI and received at one or more receivers at one or more corresponding second time instants that are different from the respective first time instants. Different first and second time instants may be separated by one or more transmit intervals, e.g., as defined in a wireless communication standard or the like. It is assumed that the wireless communication standard permits unambiguously identifying different transmit intervals, e.g., through time stamps, fixed transmission intervals or the like. For the sake of clarity it is assumed that second time instants occur after first time instants. It is readily apparent that an inversion of the first and second time instants merely require straightforward inversion of corresponding inputs to respective method steps, and that such invention is also within the scope of the invention.

[0027] Obtaining the first and second representations of VEMs of the ROI may comprise receiving corresponding first and / or second sets of transmit symbols and deriving the respective representations of VEMs of the ROI therefrom. Deriving may comprise iterative or non-iterative methods, including the process disclosed in the German patent application no. DE 10 2022 212 615 A1 , the entire content of which is hereby incorporated by reference.

[0028] The transmit symbols may be received at first and second time instants, respectively, at one or more antennas connected to an apparatus executing the method. In this context the expression ‘connected to’ may represent a direct electrical connection or any other connection that provides the received signal or a suitable representation thereof to the apparatus executing the method.

[0029] The transmit symbols transmitted by the one or more transmitters may be received at a single receiver, or at several receivers. Each of the one or more receivers may determine a VEM of the ROI from all respective received transmit symbols, and the resulting one or more VEMs of the ROI determined in each of the one or more receivers, or processed combinations thereof, e.g., merged representations or the like, may be used in further steps of the method. The first and / or second representations of VEMs of the ROI may comprise at least an occupancy value for each voxel, indicating if the voxel is void or occupied. Void voxels may be considered not containing objects or material having a significant influence on the propagation of radio waves impinging on the voxel. Within Earth’s atmosphere, void voxels may comprise the gaseous elements making up the atmosphere, and also aerosols or very small solid particles suspended in the atmosphere that, while imparting some attenuation, do not have a significant influence on the propagation of radio waves. Significant influence on the propagation of radio waves may refer to strong attenuation normally not found in open ranges, blocking or reflexion of radio waves, and the like. The occupancy value may further comprise or be complemented by a value indicating the probability that the occupancy value is true or correct. In embodiments of the method the occupancy value may yet further comprise or be complemented by values indicating a critical angle beyond which signals are no longer reflected but blocked, a value indicating the signal attenuation and / or phase shift upon reflexion and its dependency on the angle of incidence, frequency-dependent attenuation, reflexion and / or blocking, and the like.

[0030] In a further step the method comprises comparing the one or more first representations of the VEMs of the ROI and the one or more second representations of the VEMs of the ROI, considering the respective first and second time instants, for extracting or deriving ancillary voxel information (AVI).

[0031] In the context of the present description the expression ‘comparison’ may comprise, next to plain value-to-value comparing, applying advanced analytic methods, such as machine learning (ML) or artificial intelligence (Al) tools for extracting or deriving AVI. While not limited thereto such advanced analytic methods may include spatial target tracking methods for identifying voxels associated with stationary and moving objects, respectively.

[0032] The method yet further comprises generating a predicted VEM of the ROI, e.g., for a present or a future time instant, based on the one or more second representations of the voxelated environment models of the region of interest and the ancillary voxel information extracted / derived in the comparing step. The predicted VEM of the ROI comprises predicted occupancy values for each voxel. Predicted occupancy values may be complemented by a respective value indicating the probability that the predicted occupancy value is true or correct. Finally, the predicted VEM of the ROI is output, e.g., to a process of detecting transmit symbols, to a process of determining an environment model of the region of interest based on received transmit symbols, and / or to a sensor fusion process that determines an environment model of the region of interest.

[0033] When output to a process of detecting transmit symbols in a receiver the predicted voxelated environment may improve, inter alia, the associated channel estimation and signal equalisation.

[0034] Similarly, when the predicted voxelated environment is output to a process of determining an environment model of the region of interest based on received transmit symbols the accuracy of the determined environment model may be improved and / or the unavoidable inaccuracy associated with predictions may be reduced.

[0035] Likewise, when the predicted voxelated environment is output to a sensor fusion process that determines an environment model of the region of interest, a corresponding environment model may benefit from the additional input.

[0036] It is obvious that the predicted VEM may serve as a first representation of a VEM for the next execution of the method and / or as a prior or priming input to respective processes that determine the one or more second representations of VEMs that are input to the method.

[0037] As mentioned above, the extracted or derived AVI may, in one or more embodiments, include information about a change of a location or spatial position of one or more occupied voxels between the first and the second representation of the VEM of the ROI, e.g., positional, directional and / or velocity information. The change of a location of one or more voxels may be determined only for voxels having a probability of occupation and / or a value indicating the probability that the predicted occupancy value is true or correct exceeding respective predetermined thresholds. The change of a location or of a spatial position may be determined relative to one or more antennas whose signal is processed in the apparatus executing the method.

[0038] One or more voxels, preferably adjacent voxels, may be clustered into one or more clusters, each cluster preferably comprising coherent voxels, and the change of the location may be determined for the entire respective cluster. Clustering may be done for voxels having similar probabilities of occupation and / or a value indicating the probability that the predicted occupancy value is true or correct exceeding a predetermined threshold. Such clustering may reduce the computational effort.

[0039] Information about the change of a location of one or more occupied voxels may be determined, e.g., by applying the methods discussed by J. Pdschmann, T. Pfeifer and P. Protzel in "Factor Graph based 3D Multi-Object Tracking in Point Clouds," 2020 IEEE / RSJ International Conference on Intelligent Robots and Systems (IROS), Las Vegas, NV, USA, 2020, pp. 10343-10350, or by Jia-qiang Li et al. in "Target tracking algorithm based on adaptive strong tracking particle filter." IET Science, Measurement & Technology 10.7, 2016, pp. 704-710.

[0040] While the information about a change of a location of one or more occupied voxels between the first and the second representation of the VEM of the RO I may be expressed in any other suitable form, in one or more embodiments a motion prediction vector is determined for one or more voxels for which the AVI indicates a change of location between the first and the second representation of the VEM of the ROI. As mentioned above, determining the motion prediction vector may also consider occupation probabilities of voxels included in the AVI determined in the comparing step.

[0041] It is also conceivable to store past changes in the locations of occupied voxels and the associated AVI, and use such past information in the determination of the prediction vector. Storing the past location changes can be considered a tracking history, and may permit for more accurate prediction, notably of non-linear trajectories. Such motion prediction may be determined exclusively for voxels or clusters that have a respective occupation probability exceeding a first predetermined threshold in at least the second representation of the VEM of the ROI, which may reduce the computational effort without exceedingly compromising the usefulness and reliability of the prediction.

[0042] Voxels having an occupation probability exceeding a second predetermined threshold that is lower than the first predetermined threshold, but non-zero, and that are located adjacent to a voxel having an occupation probability exceeding the first predetermined threshold in at least the second representation of the VEM of the ROI may be assigned the same motion prediction vector as the voxel they are adjacent to. This may significantly reduce the computational effort while still producing a useful predicted output.

[0043] Due to expected added uncertainty in predicted locations of voxels in the VEM of the ROI, the occupation probability of at least voxels lying ahead and behind a predicted voxel along a corresponding prediction vector may be corrected. The correction may be additive, subtractive, by applying a factor, by averaging and / or by weighting. The amount of correction may be dependent of the respective voxel’s occupation probability in the one or more second representations of the VEM of the ROI, and may yield a higher occupation probability for predicted voxels lying ahead of the voxel the one or more second representations of the VEM of the ROI in the direction of the prediction vector and may yield a lower occupation probability for those predicted voxels lying behind the voxel the one or more second representations of the VEM of the ROI. The same correction may be applied to voxels adjacent to a predicted voxel that are assigned the motion prediction vector of the predicted voxel. In particular, correcting the occupation probability of those adjacent voxels towards higher values may account for inaccuracies of the prediction vector, or for unpredictable changes of a direction of an object represented by a voxel cluster, notably when the voxel cluster is associated with a non-stationary object.

[0044] The identification of voxels or voxel clusters associated with moving or stationary objects may be improved by providing information about changes of the location of the one or more receivers, or the antennas thereof, that provided the one or more first and / or second representations of the VEMs of the ROI. The information may be obtained from sensors of a mobile platform the antennas are mounted on, e.g., a vehicle or a handheld apparatus. The sensors may comprise a speedometer, acceleration sensors, a geolocation system or the like. Accordingly, in one or more embodiments of the method such information is received and provided to the comparing step and / or the generating step as additional input. It is readily apparent that the changes of the locations of the one or more receivers, or the antennas thereof, should be correlated to the time at which the respective first and second representations are determined.

[0045] In one or more embodiments the method further comprises classifying voxels in the predicted VEM of the ROI based on their respective occupancy probability, and merging adjacent voxels of at least one class into a corresponding coherent merged voxel. The classification may comprise at least two classes, a single threshold value of the occupation probability serving as the classification criterion in this case. Voxels having an occupation probability below the threshold value may be merged, while voxels having occupation probabilities above the threshold may be exempt from merging. Generally, the classification may merge voxels having similar occupation probabilities into the respective same class. The merging of the voxels may comprise a machine learning-based clustering algorithm. The coherent merged voxels may have an irregular shape in the predicted VEM of the ROI or appear as a rectangular cuboid.

[0046] Each respective coherent merged voxel may be assigned a single occupancy probability. The single occupancy probability may be determined by averaging the individual occupancy probabilities of the merged voxels, by determining a median value from the individual occupancy probabilities of the merged voxels, by determining a root mean square (RMS) value from the individual occupancy probabilities of the merged voxels, or the like.

[0047] Merging voxels may be executed for each predicted VEM of the ROI determined from the sets of one or more first representations and one or more second representations of VEMs of the ROI, such that changes in the composition of the merged voxels in subsequent predicted VEMs are possible, accounting for changes in the position of non-stationary objects in the ROI. Merging voxels may reduce the computational effort for later use of the VEM of the ROI and may also reduce the storage requirement. For example, if a merged voxel is marked as not occupied it may be considered fully open for radio waves, and a channel estimation that uses the VEM of the ROI as input may apply a coarser metric for signals known to traverse this merged voxel. Likewise, merged voxels marked as not occupied or having a very low occupancy probability imply a strong assumption of empty space, which may be used in vehicle path generation or route planning.

[0048] As already indicated further above, the predicted VEM of the ROI can be used in a variety of applications, including in apparatus running processes of detecting symbols in wireless communication, apparatus running processes of environmental perception and apparatus configured for providing a representation of an environment based on sensor fusion. Hence, in accordance with a second aspect of the invention, an apparatus is presented comprising at least one first interface configured for receiving or retrieving one or more first and second representations of VEMs of an ROI or configured for receiving wireless signals transmitted by one or more transmitters into or across the ROI, for determining therefrom said one or more first and second representations of the VEMs of the ROI, further comprising at least one second interface configured for outputting a predicted VEM of the ROI, one or more microprocessors, and associated volatile and non-volatile memory. The at least one first and second interfaces may at least partially share the same hardware components or may comprise separate hardware components. In the former case, the interfaces may be logically separated. However, it is also conceivable that the first and second interfaces of the apparatus are one and the same and operate bi-directionally. The aforementioned elements of the apparatus are communicatively coupled via one or more data lines or data buses. The nonvolatile memory comprises computer program instructions which, when executed by at least one of the one or more microprocessors, configure the apparatus to execute one or more embodiments of the method in accordance with the first aspect of the invention. When the at least one second interface of the apparatus is configured for receiving or retrieving one or more first and second representations of VEMs of an ROI determined from one or more first and second sets of transmit symbols transmitted by one or more transmitters into or across the region of interest and received at one or more receivers at one or more corresponding first and second time instants, the interface may simply be a digital communication interface, and the data is received in accordance with any suitable digital communication protocol, standardised or proprietary. In this case, the determination of the VEMs of the ROI has previously been performed by other processes and possibly in other apparatus, which are communicatively coupled to the presently described apparatus. Receiving or retrieving the one or more first representations may comprise accessing a storage device storing such information. In this case the predicted VEM of the ROI provided by executing embodiments of the method in accordance with the first aspect of the invention presented hereinbefore may be provided to the other apparatus for use as prior input in determining the one or more VEMs of the ROI, if the respective processes accept or require such prior input.

[0049] When the at least one first interface of the apparatus is configured for receiving wireless signals transmitted by one or more transmitters into or across the ROI the non-volatile memory comprises computer program instructions which, when executed by at least one of the one or more processors, further configure the apparatus to determine corresponding first and second VEMs of the ROI prior to executing one or more embodiments of the method in accordance with the first aspect of the invention as presented hereinbefore. Such determining may, for example, comprise methods as described in German patent publication no. DE 10 2022 212 615 A1 , the entire content of which is hereby incorporated by reference. In this case the predicted VEM of the ROI provided by executing embodiments of the method in accordance with the first aspect of the invention presented hereinbefore may be used as first representation of the VEM of the ROI in a subsequent execution of the method.

[0050] In accordance with a third aspect of the invention a first method of operating a wireless communication apparatus is presented. The apparatus comprises a third interface configured for receiving VEMs of an ROI predicted by executing embodiments of the method in accordance with the first aspect of the invention and a fourth interface configured for receiving wireless communication signals. The method comprises receiving, at the third interface, a predicted VEM of an ROI for a current or future point in time, at which a reception of wireless communication signals is scheduled or expected. The predicted VEM of the ROI may be received from an apparatus in accordance with the second aspect of the invention. Alternatively, the predicted VEM of the ROI may be determined by a further process executed in the present apparatus, e.g., by a process implementing one or more embodiments of the method in accordance with the first aspect of the invention. The method further comprises determining, based on the received VEM of the ROI, one or more signal paths along which the wireless communication signals are received or will be received, and predicting channel coefficients for the determined signal paths. Predicting channel coefficients may comprise iterative or non-iterative methods, including the process disclosed in patent publication no. DE 10 2023 202 288 A1 , the entire content of which is hereby incorporated by reference.

[0051] The method yet further comprises receiving wireless communication signals at the fourth interface and providing the predicted channel coefficients for the respective determined signal paths as further input to a process of estimating the channel coefficients from the received wireless communication signals. Alternatively or in addition the predicted channel coefficients for the respective determined signal paths may be provided to a process of determining and / or reconstructing transmit symbols from the received wireless communication signals. Finally, the estimated channel coefficients and / or the determined and / or reconstructed transmit symbols are provided at an output.

[0052] In one or more embodiments of the method in accordance with the third aspect of the invention the method further comprises determining, based on the received VEM of the ROI and / or the AVI associated therewith, a velocity vector of a transmitter whose signals are received, relative to the antennas at which the signals are received. Information represented by the velocity vector may be provided to a process of estimating the channel coefficients from the received wireless communication signals and / or to a process of determining and / or reconstructing transmit symbols from the received wireless communication signals. In the latter case information represented by the velocity vector may be used, e.g., for anticipating and compensating Doppler shift or spread in the received signals. Such compensation may include appropriately setting parameters of signal detection processes in a receiver in accordance with an anticipated Doppler shift or spread.

[0053] In accordance with a fourth aspect of the invention a second method of operating a wireless communication apparatus is presented. The apparatus comprises a third interface configured for receiving VEMs of an ROI predicted by executing embodiments of the method in accordance with the first aspect of the invention, a fifth interface configured for receiving transmit symbols to be transmitted, and a sixth interface configured for transmitting wireless communication signals. The method comprises receiving, at the third interface, a predicted VEM of an ROI for a current or future point in time, at which a transmission of wireless communication signals is scheduled. Like in the method in accordance with the third aspect of the invention the predicted VEM of the ROI may be received from an apparatus in accordance with the second aspect of the invention. Alternatively, the predicted VEM of the ROI may be determined by a further process executed in the present apparatus, e.g., by a process implementing one or more embodiments of the method in accordance with the first aspect of the invention, and using prior received wireless signals as input. The method further comprises determining, based on the received VEM of the ROI, one or more signal paths along which the wireless communication signals are transmitted or will be transmitted, and predicting channel coefficients for the determined signal paths. Like in the method according to the third aspect of the invention, predicting channel coefficients may comprise iterative or non-iterative methods, including the process disclosed in patent publication no. DE 10 2023 202 288 A1 . The method yet further comprises receiving transmit symbols to be transmitted and providing the predicted channel coefficients for the respective determined signal paths as further input to a process of pre-equalising the transmit signal representing the transmit symbols after coding and / or modulating prior to transmitting. Finally, the pre-equalised transmit signal is transmitted. It is obvious that the methods in accordance with the third and fourth aspects may be executed in the same apparatus, e.g., when the apparatus is configured for bidirectional wireless communication. In this case the fourth and fifth interfaces may share hardware components, e.g., one or more antennas, radio frequency (RF) amplifiers, modulators, oscillators, etc. Likewise, the third interfaces may be implemented as a single shared interface.

[0054] The channel estimation employed in the methods in accordance with the third and fourth aspects may apply methods including those disclosed in patent publication no. DE 10 2023 202 288 A1 and / or patent applications or patents claiming the priority of German patent application no. 10 2023 106 237.9, the entire content of which is hereby incorporated by reference.

[0055] In accordance with a fifth aspect of the invention a wireless communication apparatus is presented. The wireless communication apparatus comprises a third interface configured for receiving VEMs of an ROI, e.g., a predicted VEM of an ROI for a current or future point in time, at which a reception of wireless communication signals is scheduled or expected. A fourth interface for receiving wireless communication signals and / or a fifth interface are likewise provided with the apparatus. The fifth interface is configured for receiving transmit symbols to be transmitted via a sixth interface of the apparatus that is configured for transmitting wireless communication signals. The wireless communication apparatus further comprises one or more microprocessors and associated volatile and non-volatile memory. The various components and elements of the wireless communication apparatus are connected via one or more data and / or signal lines or buses. The non-volatile memory comprises computer program instructions which, when executed by at least one of the one or more microprocessors, configure the wireless communication apparatus to execute the method in accordance with the third aspect of the invention and / or the method in accordance with the fourth aspect of the invention.

[0056] The third interface of the wireless communication apparatus may provide a communicative connection to an external source or may be internal to the apparatus. In the latter case the apparatus may be further configured for executing embodiments of the method in accordance with the first aspect of the present invention.

[0057] The fourth and fifth interfaces may comprise and share hardware components, e.g., one or more antennas, radio frequency (RF) amplifiers, mixers, modulators, oscillators, circuitry for processing radio frequency signals, etc., and may be configured for receiving and / or transmitting, respectively, in accordance with a wireless communication protocol or standard

[0058] The method presented hereinbefore may be represented by computer program instructions of a computer program product. Accordingly, in accordance with a third aspect of the invention, a computer program product comprises computer program instructions, which, when executed by a processor of an apparatus in accordance with the second aspect of the invention, cause the apparatus to execute one or more embodiments of a method in accordance with the first aspect of the present invention.

[0059] The computer program instructions may be retrievably stored or transmitted on a computer-readable medium or data carrier. The medium or the data carrier may by physically embodied, e.g., in the form of a hard disk, solid state disk, flash memory device or the like. However, the medium or the data carrier may also comprise a modulated electro-magnetic, electrical, or optical signal that is received by the computer by means of a corresponding receiver, and that is transferred to and stored in a memory of the computer.

[0060] The methods presented herein are applicable to any scenario where a voxelated grid model is to be used to model the environment, namely the scenarios that may be considered in the joint communication and sensing settings. In particular, the present invention can be used in indoor and outdoor scenarios with stationary APs, communicating and detecting an environment by mobile vehicles communicating to roadside units (RSUs) while achieving vehicular / pedestrian detection, by multiple vehicles cooperatively sensing an environment and road conditions without RSUs, by multiple connected UEs (Bluetooth, Wi-Fi, loT, etc.) for passively sensing an environment (i.e. , without the use of sensing specific signals), and the like. The use-cases and benefits of the methods of communication and environment sensing functionalities presented herein involve and can be achieved within, respectively, the same wireless interface.

[0061] The present invention provides an improved modelling accuracy of the VEM by dynamically updating the voxelated grid occupancy probabilities, incorporating uncertainty based on determined or predicted target motion, and other target information, e.g., on available paths and the respective properties thereof, signal scattering at voxels in the predicted VEM, or on target velocity, which may be used in channel equalisation and symbol recovery. Further, the present invention provides an improved computational efficiency through dynamically adjusting and refining the grid sizing based on probabilistic updates. The computational complexity for the successive environment sensing and the amount of memory required for executing the method is significantly reduced by reducing the resolution, i.e., the voxel size, for areas of lower interest over the resolution for other areas.

[0062] In addition, when the refined VEMs provided by the present invention are used as prior input to iteration steps for determining VEMs from subsequently received signals, the improved accuracy of the refined VEMs, e.g., in terms of considers path blockage, scattering, non-ideal geometry-based behaviour, unrealistic reflection angles, energy loss at scattering, frequency-dependent behaviour, and channel path losses based on geometry, can help speeding up the convergence of such process.

[0063] BRIEF DESCRIPTION OF THE DRAWING

[0064] The figures in the attached drawing are used for detailing aspects of the present invention. In the figures

[0065] Fig. 1 shows an exemplary environment with objects in a region of interest, and voxelated representations thereof,

[0066] Fig. 2 shows a representation of the general concept of LOS and NLOS paths in a voxelated space, Fig. 3 shows a schematic representation of the 3D space considered in a known system and method,

[0067] Fig. 4 shows a schematic representation of unavailable and unrealistic communication paths in the voxelated space,

[0068] Fig. 5 shows a schematic and simplified representation of the inputs to the method in accordance with the first aspect of the invention and an output in accordance with a basic aspect thereof,

[0069] Fig. 6 shows a schematic and simplified representation of the inputs and an output in accordance with a more advanced aspect of the method discussed with reference to figure 5,

[0070] Fig. 7 shows a simplified overview of the proposed method in accordance with the first aspect of the invention in its context,

[0071] Fig. 8 shows an exemplary flow diagram of the method in accordance with the first aspect of the invention,

[0072] Fig. 9 shows an exemplary flow diagram of the method in accordance with the third aspect of the invention,

[0073] Fig. 10 shows an exemplary flow diagram of the method in accordance with the fourth aspect of the invention,

[0074] Fig. 11 shows an exemplary and schematic block diagram of an apparatus in accordance with the second aspect of the invention, and

[0075] Fig. 12 shows an exemplary and schematic block diagram of a wireless communication apparatus in accordance with the fifth aspect of the invention.

[0076] DETAILED DESCRIPTION OF EMBODIMENTS

[0077] Figures 1 to 4 have been described further above and will not be discussed again.

[0078] Figure 5 shows a schematic and simplified representation of the inputs to the method 100 in accordance with the first aspect of the invention and an output in accordance with a basic aspect thereof. The simplification lies in using a 2-dimensional ROI rather than a 3-dimensional one. However, the concept is the same irrespective of the dimensionality. Figure 5 a) shows a VEM of an ROI that had been established prior to the current estimated VEM of the ROI shown in figure 5 b). In the terminology of the present method, figure 5 a) shows a first representation of a VEM of the RO I, and figure 5 b) shows a second representation of a VEM of the ROI. In the example it is assumed that two voxels in the prior VEM of the ROI are occupied and that the probability of occupation is 1 . Note, however, that it is also possible that the occupation probability may be lower than 1 , and that adjacent voxels may accordingly be likewise occupied, albeit with an even lower occupation probability. The current estimated VEM of the ROI shows some uncertainty in the occupation of the voxels, with two voxels having a rather high occupation probability, while the respective adjacent voxels have a lower occupation probability. The uncertainty may, e.g., result from a movement of objects that are represented by the occupied voxels, since the current estimate is taken at a later point in time than the prior established VEM of the ROI and there is no accurate knowledge of the motion vector. The prior and current estimation may be obtained by applying any of the known methods, including the method presented in DE 10 2022 212 615 A1 , as long as it permits generating a representation of a voxelated environment. Note that it is irrelevant if the prior and current estimation of the VEM of the ROI are generated by the same apparatus and / or methods, as long as the VEMs can be transformed into a common grid scale representation, e.g., by scaling, voxel-binning or other suitable methods. In accordance with the present method an updated or predicted VEM of the ROI is determined. This updated or predicted VEM of the ROI may be targeted to be valid for a point in time that is later than that at which the current VEM of the ROI had been determined. This may, for example, be beneficial when the determination of the VEM of the ROI is not carried out continuously for any reason, and a VEM of the ROI is not available for the targeted point in time. Such situation may occur in high mobility scenarios, when the location of an object is likely to have changed, and a wireless receiver that uses VEMs of the ROI for tuning parameters of a channel estimation and equalisation process relies on the best-possible knowledge of the ROI’s properties. The updated or predicted output of the method is shown in Figure 5 c). Here, a motion of objects represented by occupied voxels has been determined from the prior and current VEMs of the ROI and used for predicting or updating the VEM of the ROI for a current or future point in time. As mentioned further above, the motion may be determined using object tracking and generally known motion prediction methods, including machine learning (ML) based and artificial intelligence (Al) based methods. As prediction goes, the uncertainties of the voxels cannot simply be moved along in accordance with a motion vector, but must be adjusted based on the current estimated VEM of the ROI and the motion vector. In the figure, the occupancy probability of a voxel is indicated by the pattern it is filled with, where darker filling indicates a higher occupancy probability.

[0079] Figure 6 shows a schematic representation of schematic and simplified representation of the inputs and an output in accordance with a more advanced aspect of the method discussed with reference to figure 5. The input shown in figures 6 a) and 6 b) is assumed to be the same as the one discussed in figures 5 a) and 5 b). The relevant part of the output, i.e. , voxels that have an occupancy probability higher than a predetermined threshold, is likewise the same as shown in figure 5 c). However, as a further optimisation, adjacent voxels having a sufficiently low occupancy probability are grouped. In the figure, the assumed empty voxels in the two top most rows and the voxels in the four rightmost columns in the two bottommost rows are grouped into voxels having larger dimensions. Note that the voxels are grouped into two by four-sized voxels at the top of the VEM and into two by two-sized voxels at the bottom of the VEM, expressed as multiples of the regular voxel size.

[0080] Figure 7 shows a simplified overview of the proposed method 100 in accordance with the first aspect of the invention in its context. Input signal 11 receives one or more first representations of the VEM of the ROI, and input signal I2 receives one or more second representations of the VEM of the ROI. The one or more second representations of the VEM of the ROI received at input I2 may be determined through JCAS methods, e.g., as disclosed in German patent publication no. DE 10 2022 212 615 A1 . Other methods outputting a VEM of an ROI may likewise be used. The signal received at input 11 may be provided to the method or methods providing the one or more second representations of the VEM of the ROI as additional input, indicated by the dashed line. The method 100 further receives one or more uncertainty thresholds at input I4, which may be used for merging voxels. The predicted VEM of the ROI is then output at output 01 .

[0081] Figure 8 shows an exemplary flow diagram of the method 100 in accordance with the first aspect of the invention. In step 110 one or more first representations of voxelated environment models of the region of interest determined from or based on one or more first sets of transmit symbols transmitted by one or more transmitters into or across the region of interest and received at one or more receivers at one or more corresponding first time instants are obtained. In step 120 one or more second representations of voxelated environment models of the region of interest determined from or based on one or more second sets of transmit symbols transmitted by one or more transmitters into or across the region of interest and received at one or more receivers at one or more corresponding second time instants different from the respective first time instants are obtained. The one or more first representations of the voxelated environment models of the region of interest and the one or more second representations of the voxelated environment models of the region of interest are compared, in step 130, for extracting or deriving ancillary voxel information, under consideration of the respective first and second time instants. In step 140 a predicted voxelated environment model of the region of interest is generated based on the one or more second representations of the voxelated environment models of the region of interest and the ancillary voxel information extracted / derived in comparing step 130. The predicted voxelated environment model of the region of interest is then output, in step 150, to a process of detecting transmit symbols, to a process of determining an environment model of the region of interest based on received transmit symbols, and / or to a sensor fusion process that determines an environment model of the region of interest.

[0082] Figure 9 shows an exemplary flow diagram of the method 300 in accordance with the third aspect of the invention. The method relates to operating a wireless communication apparatus 500 comprising a third interface 510 configured for receiving VEMs of an ROI predicted by a method in accordance with the first aspect of the invention, and further comprising a fourth interface 520 for receiving wireless communication signals. In step 310 of the method a predicted VEM of a ROI for a current or future point in time, at which a reception of wireless communication signals is scheduled or expected, is received. In step 320 one or more signal paths along which the wireless communication signals are received or will be received are determined based on the received predicted VEM of the ROI. The received and / or determined information is then used, in step 330, for predicting channel coefficients for the respective determined signal paths. In step 340 wireless communication signals are received at the fourth interface 520 of the apparatus 500. The predicted channel coefficients for the respective determined signal paths are provided to a step 350 of estimating the channel coefficients from the received wireless communication signals as further input. In addition or alternatively, the predicted channel coefficients for the respective determined signal paths are provided to a step 360 of determining and / or reconstructing transmit symbols from the received wireless communication signals. In step 370 the estimated channel coefficients and / or the determined and / or reconstructed transmit symbols are output.

[0083] Figure 10 shows an exemplary flow diagram of the method 400 in accordance with the fourth aspect of the invention. The method relates to operating a wireless communication apparatus 500 comprising a third interface 510 configured for receiving VEMs of an ROI predicted by a method in accordance with the first aspect of the invention, and further comprising a fifth interface 530 configured for receiving transmit symbols to be transmitted, and a sixth interface 540 configured for transmitting wireless communication signals. In steps 410 of the method a predicted VEM of a ROI for a current or future point in time, at which a transmission of wireless communication signals is scheduled. In step 420 one or more signal paths along which the wireless communication signals are transmitted or will be transmitted are determined based on the received predicted VEM of the ROI. The determined information is then used, in step 430, for predicting channel coefficients for the respective determined signal paths. In step 440 transmit symbols to be transmitted are received at the fifth interface 540. The transmit signal representing the transmit symbols after coding and / or modulating is pre-equalised in step 450 prior to transmitting the pre-equalised transmit signal via the sixth interface 540 in step 460.

[0084] Figure 11 shows an exemplary and schematic block diagram of an apparatus 200 in accordance with the second aspect of the invention. The apparatus 200 comprises at least one first interface 210 configured for receiving or retrieving one or more first and second representations of VEMs of an ROI, or configured for receiving wireless signals transmitted by one or more transmitters into or across the ROI for determining therefrom said one or more first and second representations of the VEMs of the ROI. The apparatus 200 further comprises at least one second interface 220 configured for outputting a predicted VEM of the ROI, one or more microprocessors 230, and associated volatile 240 and nonvolatile memory 250. Some or all of the aforementioned components and elements are communicatively connected via one or more data lines or buses 260. The nonvolatile memory 250 comprises computer program instructions which, when executed by at least one of the one or more microprocessors 230, configure the apparatus 200 to execute the method 100 in accordance with the first aspect of the invention.

[0085] Figure 12 shows an exemplary and schematic block diagram of a wireless communication apparatus 500 in accordance with the fifth aspect of the invention. The apparatus 500 comprises a third interface 510 configured for receiving VEMs of an ROI. The apparatus further comprises a fourth interface 520 for receiving wireless communication signals and / or a fifth interface 530 configured for receiving transmit symbols to be transmitted and a sixth interface 540 configured for transmitting wireless communication signals. The apparatus yet further comprises one or more microprocessors 550 and associated volatile 560 and non-volatile memory 570. Some or all of the aforementioned components and elements are communicatively connected via one or more data lines or buses 580. The nonvolatile memory 570 comprises computer program instructions which, when executed by at least one of the one or more microprocessors 550, configure the wireless communication apparatus 500 to execute the method 300 in accordance with the third aspect of the invention and / or configure the apparatus 500 to execute the method 400 in accordance with the fourth aspect of the invention. LIST OF REFERENCE NUMERALS (PART OF THE DESCRIPTION)

[0086] 100 method of processing VEMs 360 determ ine / reconstruct

[0087] 110 obtain first representations transmit symbols

[0088] 120 obtain second 370 output estimated channel representations coefficients and / or

[0089] 130 compare first and second determ ined / reconstructed representations transmit symbols

[0090] 140 generate predicted VEM 400 method

[0091] 150 output predicted VEM 410 receive predicted VEM

[0092] 200 wireless communication 420 determine signal path(s) apparatus 430 predict channel coefficients

[0093] 210 first interface 440 receive transmit symbols

[0094] 220 second interface 450 pre-equalise transmit signal

[0095] 230 microprocessor(s) 460 transmit pre-equalised

[0096] 240 volatile memory transmit signal

[0097] 250 non-volatile memory 500 wireless apparatus

[0098] 260 data line / bus 510 third interface

[0099] 300 method 520 fourth interface

[0100] 310 receive predicted VEM 530 fifth interface

[0101] 320 determine signal path(s) 540 sixth interface

[0102] 330 predict channel coefficients 550 microprocessor(s)

[0103] 340 receive wireless 560 volatile memory communication signals 570 non-volatile memory

[0104] 350 estimate channel 580 data line / bus coefficients from received wireless communication signals

Claims

CLAIMS1. A method (100) of processing voxelated environment models of a region of interest for use thereof in wireless communication signal processing and / or environment perception or mapping comprising:- obtaining (110) one or more first representations of voxelated environment models of the region of interest (ROI) determined from or based on one or more first sets of transmit symbols transmitted by one or more transmitters into or across the region of interest and received at one or more receivers at one or more corresponding first time instants,- obtaining (120) one or more second representations of voxelated environment models of the region of interest determined from or based on one or more second sets of transmit symbols transmitted by one or more transmitters into or across the region of interest and received at one or more receivers at one or more corresponding second time instants different from the respective first time instants,- comparing (130) the one or more first representations of the voxelated environment models of the region of interest and the one or more second representations of the voxelated environment models of the region of interest, considering the respective first and second time instants, for extracting or deriving ancillary voxel information (AVI),- generating (140) a predicted voxelated environment model of the region of interest based on the one or more second representations of the voxelated environment models of the region of interest and the ancillary voxel information extracted or derived in the comparing step (130),- outputting (150) the predicted voxelated environment model (VEM) of the region of interest, e.g., to a process of detecting transmit symbols, to a process of determining an environment model of the region of interest based on received transmit symbols, and / or to a sensor fusion process that determines an environment model of the region of interest.

2. The method (100) of claim 1 , wherein the extracted or derived AVI includes information about a change of a location of one or more occupied voxels between the first and the second representation of the VEM of the ROI.

3. The method (100) of claim 1 or 2, further comprising determining a motion prediction vector for one or more voxels for which the AVI indicates a change of location between the first and the second representation of the VEM of the ROI.

4. The method (100) of one or more of claims 1 to 3, further comprising receiving information about changes of the location of the one or more receivers, or the antennas thereof, that provided the one or more first and / or second representations of the VEMs of the ROI, and providing the received information to the comparing step (130) and / or the generating step (140) as additional input.

5. The method of one or more of claims 1 to 4, further comprising classifying voxels in the predicted VEM of the ROI based on their respective occupancy probability, and merging adjacent voxels of at least one class into a corresponding coherent merged voxel.

6. An apparatus (200) comprising at least one first interface (210) configured for receiving or retrieving one or more first and second representations of VEMs of an ROI or configured for receiving wireless signals transmitted by one or more transmitters into or across the ROI, for determining therefrom said one or more first and second representations of the VEMs of the ROI, the apparatus further comprising at least one second interface (220) configured for outputting a predicted VEM of the ROI, one or more microprocessors (230), and associated volatile (240) and non-volatile memory (250), wherein the non-volatile memory (250) comprises computer program instructions which, when executed by at least one of the one or more microprocessors (230), configure the apparatus (200) to execute the method (100) of one or more of claims 1 to 5.

7. A method (300) of operating a wireless communication apparatus (500) comprising a third interface (510) configured for receiving VEMs of an ROI predicted in accordance with the method of one or more of claims 1 to 5and a fourth interface (520) for receiving wireless communication signals, the method (300) comprising:- receiving (310), at the third interface (510), a predicted VEM of an ROI for a current or future point in time, at which a reception of wireless communication signals is scheduled or expected,- determining (320), based on the received predicted VEM of the ROI, one or more signal paths along which the wireless communication signals are received or will be received,- predicting (330) channel coefficients for the respective determined signal paths,- receiving (340), at the fourth interface (520), wireless communication signals,- providing the predicted channel coefficients for the respective determined signal paths as further input to a process of estimating (350) the channel coefficients from the received wireless communication signals and / or to a process (360) of determining and / or reconstructing transmit symbols from the received wireless communication signals, and- outputting (370) the estimated channel coefficients and / or the determined and / or reconstructed transmit symbols.

8. The method (300) of claim 7, further comprising determining, based on the received VEM of the ROI and / or the AVI associated therewith, a velocity vector of a transmitter whose signals are received, relative to the antennas at which the signals are received, and providing information represented by the velocity vector to a process of estimating (360) the channel coefficients from the received wireless communication signals and / or to a process of determining and / or reconstructing transmit symbols from the received wireless communication signals.

9. A method (400) of operating a wireless communication apparatus (500) comprising a third interface (510) configured for receiving VEMs of an ROI predicted in accordance with the method of one or more of claims 1 to 5, a fifth interface (530) configured for receiving transmit symbols to be transmitted, and a sixth interface (540) configured for transmitting wirelesscommunication signals, the method (400) comprising:- receiving (410), at the third interface (510), a predicted VEM of an ROI for a current or future point in time, at which a transmission of wireless communication signals is scheduled,- determining (420), based on the received VEM of the ROI, one or more signal paths along which the wireless communication signals are transmitted or will be transmitted,- predicting (430) channel coefficients for the respective determined signal paths,- receiving (440), at the fifth interface (540), transmit symbols to be transmitted,- pre-equalising (450) the transmit signal representing the transmit symbols after coding and / or modulating prior to transmitting (460), and- transmitting (460) the pre-equalised transmit signal via the sixth interface (540).

10. A wireless communication apparatus (500) comprising a third interface (510) configured for receiving VEMs of an ROI, further comprising a fourth interface (520) for receiving wireless communication signals and / or comprising a fifth interface (530) configured for receiving transmit symbols to be transmitted and a sixth interface (540) configured for transmitting wireless communication signals, further comprising one or more microprocessors (550) and associated volatile (560) and non-volatile memory (570), wherein the non-volatile memory (570) comprises computer program instructions which, when executed by at least one of the one or more microprocessors (550), configure the wireless communication apparatus (500) to execute the method (300) of one of claims 7 or 8 and / or to execute the method (400) of claim 9.

11. A computer program product comprising computer program instructions, which, when executed by a processor (230) of an apparatus (200) in accordance with claim 6, configure the apparatus (200) to execute the method of one or more of claims 1 to 5 or, when executed by a processor (550) of an apparatus (500) in accordance with claim 10, configure theapparatus (500) to execute the method of one of claims 7 or 8 and / or to execute the method of claim 9.

12. Computer readable medium or data carrier retrievably transmitting or storing the computer program product of claim 11 .

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