EP4690556A1

EP4690556A1 - Characterization of a physical object

Info

Publication number: EP4690556A1
Application number: EP23931911.4A
Authority: EP
Inventors: Prayag Gowgi SOMANAHALLI KRISHNA MURTHY; Vijaya Yajnanarayana
Original assignee: Telefonaktiebolaget LM Ericsson AB
Current assignee: Telefonaktiebolaget LM Ericsson AB
Priority date: 2023-04-05
Filing date: 2023-04-05
Publication date: 2026-02-11
Also published as: WO2024209480A1

Abstract

A method performed by a network node (110, 111, 112, 101) for enabling characterization of a physical object (131) by a wireless communications network (100) comprising at least two spatially separated network nodes (110, 111, 112) is provided. The method comprise obtaining, from each of the at least two spatially separated network nodes (110, 111, 112), information indicating the signal energy reflected by the physical object (131) and captured, by the respective at least two spatially separated network nodes (110, 111, 112), on a set of different frequencies in different operating bands of pilot reference signals (Pband.1, Pband,2, Pband.3) transmitted by the respective at least two spatially separated network nodes (110, 111, 112). The method further comprise estimating at least one characteristic parameter value (9) for the physical object (131) based on the obtained information. A network node is also provided, as well as, computer programs and carriers.

Description

CHARACTERIZATION OF A PHYSICAL OBJECT TECHNICAL FIELD Embodiments herein relate to characterization of a physical object. In particular, embodiments herein relate to a network node and method therein for enabling characterization of a physical object by a wireless communications network. Further, the embodiments herein also relate to a computer program and a carrier. BACKGROUND In today’s wireless communications networks a number of different technologies are used, such as 5G/6G, New Radio (NR), Long Term Evolution (LTE), LTE-Advanced, Wideband Code Division Multiple Access (WCDMA), Global System for Mobile communications/Enhanced Data rate for GSM Evolution (GSM/EDGE), Worldwide Interoperability for Microwave Access (WiMax), or Ultra Mobile Broadband (UMB), just to mention a few possible technologies for wireless communication. A wireless communications network commonly comprises radio base stations providing radio coverage over at least one respective geographical area forming a cell. This is commonly referred to as a Radio Access Network (RAN). The RAN is in turn connected to the central network in the wireless communications network via a so-called backhaul network. Wireless devices, User Equipments (UEs), mobile stations, and/or wireless terminals, are served in the cells by the respective radio base station and are communicating with respective radio base station in the RAN over an air/radio interface. Commonly, the wireless devices transmit data over the air/radio interface to the radio base stations in uplink, UL, transmissions and the radio base stations transmit data over the air/radio interface to the wireless devices in downlink, DL, transmissions. When attempting to characterize a physical object automatically by any type of system, estimating some form of parameters of the physical object is fundamental. For example, detecting a physical object’s physical parameters, such as, size, shape, and materials, is often needed to determine the physical object’s object type (e.g., human, vehicle, etc.). The physical object’s object type may be very useful in many different cases, such as, for example, in traffic monitoring or in manufacturing systems. In traffic monitoring, the identification of objects, such as, cars, trucks, pedestrians, the density of vehicles, etc., may enable improved transportation management systems. Similarly, for manufacturing systems, the identification of objects, such as, a person, a robot, etc., may enable improvements in the safety and security of the manufacturing systems on a factory floor. One way of estimating parameters of a physical object may be to used vision/ image- based methods. Examples of such vision/ image-based methods are described in "Rapid object detection using a boosted cascade of simple features", P. Viola et al (Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2001, 2001, pp. I-I, doi: 10.1109/CVPR.2001.990517) or in "ImageNet: A large-scale hierarchical image database", J. Deng et al (2009 IEEE Conference on Computer Vision and Pattern Recognition, 2009, pp. 248-255, doi: 10.1109/CVPR.2009.5206848). In these documents, classical image processing approaches of edge-detection and image segmentation are applied on captured images to obtain object parameters estimations. However, there are critical issues with these types of vision/image-based estimators of object parameters; specifically with respect to security. The images captured often contains detailed description of the scene, together with the object whose parameters it is of interest to estimate. For example, if it is of interest to guide a traffic signal by recognizing the road- crossing pedestrians, then these types of vision/image-based object parameters estimations require that the captured image have the real persons in them. How these images that are handled could easily be the subject of a potential breach of privacy for the individual if not handled correctly. Hence, there may be many situations and use-cases where these vision- based object parameters estimations do not work due to inherent security and privacy issues. An additional drawback is that vision/image-based object parameters estimations also require dedicated imaging infrastructure and installation of video/image cameras, which may be costly. Furthermore, vision/image-based object parameters estimations may not be optimal for estimating certain object parameters, such as, for example, if the object parameter of interest is the material composition of the physical object. SUMMARY It is an object of the present disclosure to mitigate, alleviate or eliminate one or more of the above-identified deficiencies and disadvantages in the prior art to improve automatic characterization of objects, such as, for example, by improving systematic characterizations of physical objects. According to a first aspect of embodiments herein, the object is achieved by a method performed by a network node for enabling characterization of a physical object by a wireless communications network comprising at least two spatially separated network nodes. The method comprises obtaining, from each of the at least two spatially separated network nodes, information indicating the signal energy reflected by the physical object and captured, by the respective at least two spatially separated network nodes, on a set of different frequencies in different operating bands of pilot reference signals transmitted by the respective at least two spatially separated network nodes. The method further comprises estimating at least one characteristic parameter value for the physical object based on the obtained information. According to a second aspect of embodiments herein, the object is achieved by a network node for enabling characterization of a physical object by a wireless communications network comprising at least two spatially separated network nodes. The network node is configured to obtain, from each of the at least two spatially separated network nodes, information indicating the signal energy reflected by the physical object and captured, by the respective at least two spatially separated network nodes, on a set of different frequencies in different operating bands of pilot reference signals transmitted by the respective at least two spatially separated network nodes. The network node is also configured to estimate at least one characteristic parameter value for the physical object based on the obtained information. According to a third aspect of the embodiments herein, a computer program is also provided configured to perform the method described above. Further, according to a fourth aspect of the embodiments herein, carriers are also provided configured to carry the computer program configured for performing the method described above. By purposefully illuminating a distinct region of a physical object from a set of spatially separated network nodes using existing signalling spanning several different frequency operating bands and obtain the resulting reflected signal energy from the physical object, each of the spatially separated network nodes will be able to co-operatively provide several different geometric perspectives of the physical object that subsequently may be used to estimate characteristic parameter values for the physical object. Based on these estimated characteristic parameter values for the physical object, a characterization of the object type of the physical object (such as e.g., cars, trucks, pedestrians, etc.) may be made. Hence, a characterization of an object type of a physical object that uses radio-based sensing within an existing wireless communications network’s infrastructure and ensures privacy and security is obtained. Hence, systematic characterizations of physical objects are improved. BRIEF DESCRIPTION OF THE DRAWINGS Features and advantages of the embodiments will become readily apparent to those skilled in the art by the following detailed description of exemplary embodiments thereof with reference to the accompanying drawings, wherein: Figs.1-4 are schematic block diagrams of a wireless communications network according to some embodiments, Fig.5 is a flowchart depicting embodiments of a method, Fig.6 illustrates an example of scattered energy variation with wavelength, Fig.7 illustrates an example of an LC circuit, Fig.8 illustrates an example of resource element utilization in the wireless communications network according to some embodiments, and Fig.9 is a block diagram depicting embodiments of a network node. DETAILED DESCRIPTION The figures are schematic and simplified for clarity, and they merely show details which are essential to the understanding of the embodiments presented herein, while other details have been left out. Throughout, the same reference numerals are used for identical or corresponding parts or steps. Fig.1 depicts a wireless communications network 100 in which embodiments herein may operate. In some embodiments, the wireless communications network 100 may be a radio communications network, such as, 6G, NR or NR+ telecommunications network. However, the wireless communications network 100 may also employ technology of any one of 3/4/5G, LTE, LTE-Advanced, WCDMA, GSM/EDGE, WiMax, UMB, GSM, or any other similar network or system. The wireless communications network 100 may also employ technology transmitting on millimetre-waves (mmW), such as, an Ultra Dense Network, UDN. In some embodiments, the wireless communications network 100 may also employ transmission supporting WiFi transmissions, e.g., the wireless communications standard IEEE 802.11ad or similar. The wireless communications network 100 comprises spatially separated network nodes 110, 111, 112, 113, 114, 115. Each of the spatially separated network nodes 110, 111, 112, 113, 114, 115 may be configured to serve wireless devices in at least one cell or coverage area 115. Also, each of the spatially separated network nodes 110, 111, 112, 113, 114, 115 may correspond to any type of network node or radio network node capable of communicating with wireless devices in the wireless communications network 100, such as, a base station (BS), a radio base station, gNB, eNB, eNodeB, a Home NodeB, a Home eNodeB, a femto Base Station (BS), or a pico BS in the wireless communications network 100. Further examples of the spatially separated network nodes 110, 111, 112, 113, 114, 115 are repeaters, multi-standard radio (MSR) radio nodes such as MSR BSs, network controllers, radio network controllers (RNCs), base station controllers (BSCs), relays, donor node controlling relays, base transceiver stations (BTSs), access points (APs), transmission points, transmission nodes, Remote Radio Units (RRUs), Remote Radio Heads (RRHs) or nodes in distributed antenna system (DAS). Furthermore, the wireless communications network 100 may comprise a central network node(s) 101 arranged to communicate with each of the spatially separated network nodes 110, 111, 112, 113, 114, 115 in the wireless communications network 100. The central network node(s) 101 may be centrally located within the wireless communications network 100, such e.g., in a core network node, or connected thereto as one or more cloud or online- processing server(s) or similar. Fig.1 also depicts a physical object 131 located in the vicinity of one or more of the spatially separated network nodes 110, 111, 112, 113, 114, 115. In reference to the embodiments described hereinafter, the methods and apparatuses described by the embodiments below may be implemented in and performed by one or several of the one or more of the spatially separated network nodes 110, 111, 112, 113, 114, 115 and central network node(s) 101. Figs.2-4 illustrates a scenario in the wireless communications network 100, which is described further below in relation to embodiments described herein with reference to Fig.5. As part of the developing of the embodiments described herein, it has been realized that some of the drawbacks with vision/image-based object parameters estimations may be overcome by instead using wireless network radio signal processing methods. Previously, for example, wireless network radio signal processing methods have been used for identification of material composition of physical objects, such as, for example, described in "Study of Reflection-Loss-Based Material Identification from Common Building Surfaces", Y. Geng et al (2021 Joint European Conference on Networks and Communications & 6G Summit (EuCNC/6G Summit), 2021, pp. 526-531, doi: 10.1109/EuCNC/6GSummit51104.2021.9482524). In the methods suggested therein, a single transmitter beams a radio signal towards the physical object at various incident angles and then observe the variation in the received signal power to infer a material composition of the physical object (e.g., concrete, glass, etc.). Nowhere does the method described by Y. Geng et al suggest any further object parameter estimations which may enable characterization of the physical object. In another example, co-operative wireless network radio signal processing methods involving multiple base stations have also been used for macro level characterization of surroundings of a wireless device, such as, e.g., determining if the wireless device is located in an urban canyon or dense forest canopy, or similar. Neither in this case is any method suggested that infers multiple object parameter estimations in order to enable a characterization of a physical object. Further, it should be noted that other radio signal processing methods especially dedicated to object detection and parameter estimation exist, such as, conventional radar deployments. In these dedicated systems, a radar signature at an operating frequency is normally determined based on the strength of the reflected signal at a particular carrier frequency. With this in mind and in order to address some of the drawbacks with vision/image- based object parameters estimations, a wireless network radio signal processing method is suggested according to the embodiments described herein. By purposefully illuminating a distinct region of the physical object from a set of spatially separated network nodes using existing signalling spanning several different frequency operating bands and obtain the resulting reflected signal energy from the physical object, each of the spatially separated network nodes will be able to co-operatively provide several different geometric perspectives of the physical object that subsequently may be used to estimate characteristic parameters for the physical object. For example, each spatially separated network node may obtain its own geometric perspective by parametrizing a Radar Cross Section, RCS, over the sweep of frequencies used in the different operating bands. Collectively, a functional mapping may then be obtained between the hyperspace of the parameterized RCSs from the multiple spatially separated network nodes and specific object parameters, such as, size, shape, material. Based on the estimated specific object parameters, a characterization of the object type of the physical object (such as, e.g., cars, trucks, pedestrians, etc.) may be made. Hence, an advantage of the embodiments herein is that characterization of an object type of a physical object that uses radio-based sensing within an existing wireless communications network’s infrastructure while ensuring privacy and security. Thus, systematic characterizations of physical objects is improved. Examples of embodiments of a method performed by a network node 110, 111, 112, 101 for enabling characterization of a physical object 131 by a wireless communications network 100 comprising at least two spatially separated network nodes 110, 111, 112, will now be described with reference to the flowchart depicted in Fig. 5. Fig. 5 is an illustrated example of actions or operations which may be taken by one or more network nodes, such as, any of the network nodes 110, 111, 112, or central network node 101, in the wireless communications network 100. The method may comprise the following actions. Action 501 Optionally, the network node 110, 111, 112, 101 obtains, from spatially separated network nodes 110-115 in the wireless communications network 100, information indicating a signal energy, reflected by the physical object 131 and captured by each respective spatially separated network node 110-115, on a respectively determined frequency for pilot reference signals Pf,1-6 transmitted by each of the spatially separated network nodes 110-115, respectively. This means, for example, that each of the spatially separated network nodes 110- 115 in the wireless communications network 100 may transmit a pilot reference signal towards the physical object 131 and monitor its captured signal energy of the pilot reference signal that reflected of the physical object 131 back towards it. Hence, the network node 110, 111, 112, 101 may be informed about the received signal energy levels of each of the spatially separated network nodes 110-115 in the wireless communications network 100. This is illustrated in the scenario shown in Fig. 2. It should be noted that, according to some embodiments herein, the information indicating the signal energy reflected by the physical object 131 is the Reference Signal Received Power, RSRP, of the reflected pilot reference signals. Action 502 After obtaining the information in Action 501, the network node 110, 111, 112, 101 may determine one of the spatially separated network nodes 110-115 as a primary network node based on which one of the spatially separated network nodes 110-115 that received the largest amount of signal energy reflected by the physical object 131. This means, for example, that the network node 110, 111, 112, 101 may compare the received signal energy levels, e.g., RSRPs, of each of the spatially separated network nodes 110-115 in the wireless communications network 100 with a determined signal energy threshold ( ^^). Consequently, the network node 110, 111, 112, 101 may, for example, select any of the spatially separated network nodes 110-115 which has an RSRP that is higher than the determined signal energy threshold to be considered as a primary network node. Preferably, the spatially separated network node 110-115 having the highest received signal energy level, e.g., highest RSRP. In the scenario shown in Fig.3, the network node 110 is selected as the primary network node, since it has the highest received signal energy level among the spatially separated network nodes 110-115 in the wireless communications network 100. Action 503 Optionally, after determination in Action 502, the network node 110, 111, 112, 101 may obtain, from spatially separated network nodes 110-115 in the wireless communications network 100, information indicating the signal energy, reflected by the physical object 131 and captured by each respective spatially separated network node 110-115, on a determined frequency for a pilot reference signal Pp,1 transmitted by the determined primary network node 110. This means, for example, that the network node 110 may, after being selected as the primary network node, determine the Direction-of-Arrival, DoA, of its reflected pilot reference signal, i.e. the direction towards the physical object 131. The network node 110 may then transmit its pilot reference signal Pp,1 in the determined direction towards the physical object 131, as shown in the scenario of Fig.3. It follows that some of the spatially separated network nodes 110-115 in the wireless communications network 100 may receive or capture some of the signal energy of the primary network node’s pilot reference signal Pp,1 that reflects of the physical object 131. This is also illustrated in the scenario shown in Fig.3 in which the spatially separated network nodes 111, 112, 115 capture reflected signal energy from the primary network node’s pilot reference signal Pp,1. If the captured signal energy is above a determined signal energy threshold, ^^, the spatially separated network nodes 111, 112, 115 may report this information to the network node 110, 111, 112, 101. For example, in the scenario shown in Fig. 3, the spatially separated network nodes 111, 112 have captured reflected signal energy from the primary network node’s pilot reference signal P^p,1 that is above the determined signal energy threshold ^^ (indicated by the solid arrows), while the spatially separated network node 115 has captured reflected signal energy from the primary network node’s pilot reference signal Pp,1 that is below the determined signal energy threshold ^^ (indicated by the dashed arrow). Also, in this scenario, none of the other spatially separated network nodes 113-114 captures any reflected signal energy from the primary network node’s pilot reference signal Pp,1 reflected of the physical object 131. Hence, the network node 110, 111, 112, 101 may be informed about the received signal energy levels of the spatially separated network nodes 111, 112 originating from the primary network node’s pilot reference signal Pp,1 that reflected of the physical object 131. Optionally, all spatially separated network nodes 111, 112, 115 having captured reflected signal energy from the primary network node’s pilot reference signal Pp,1 may report this information to the network node 110, 111, 112, 101. Thus, the network node 110, 111, 112, 101 may perform the comparisons with determined signal energy threshold ^^. Action 504 After obtaining the information in Action 504, the network node 110, 111, 112, 101 may determine at least two spatially separated network nodes 110, 111, 112 as a subset of the spatially separated network nodes 110-115 for which the obtained information indicates that the respectively received signal energy reflected by the physical object 131 is above a determined threshold value. This means, for example, that the network node 110, 111, 112, 101 may determine that the spatially separated network nodes 111, 112, which have reported information that they have captured reflected signal energy from the primary network node’s pilot reference signal Pp,1, are to be a part of a co-operative cluster of spatially separated network nodes 110, 111, 112 that will take part in the characterization of the physical object 131 in the wireless communications network 100. Optionally, the network node 110, 111, 112, 101 may determine the co-operative cluster of spatially separated network nodes 110, 111, 112 based on its own comparisons with determined signal energy threshold ^^ based on the reported information from spatially separated network nodes 111, 112, 115. The co- operative cluster of spatially separated network nodes 110, 111, 112 may then be coordinated by the primary network node, i.e. the network node 110, for the characterization of the physical object 131 in the wireless communications network 100. Furthermore, it should also be noted that the network node 110, 111, 112, 101 may, according to some embodiments, determine the subset of the spatially separated network nodes 110-115 as the spatially separated network nodes that are closest to the physical object 131 or that are spatially well- separated to give increased degrees of freedom. Action 505 The network node 110, 111, 112, 101 obtains, from each of the at least two spatially separated network nodes 110, 111, 112, information indicating the signal energy reflected by the physical object 131 and captured, by the respective at least two spatially separated network nodes 110, 111, 112, on a set of different frequencies in different operating bands of pilot reference signals Pband,1, Pband,2, Pband,3 transmitted by the respective at least two spatially separated network nodes 110, 111, 112. This means, for example, that the network node 110, 111, 112, 101 may obtain information from each of the spatially separated network nodes in the co-operative cluster of spatially separated network nodes 110, 111, 112 in order to enable the characterization of the physical object 131 in the wireless communications network 100. This is illustrated in the scenario shown in Fig. 4, wherein each of the spatially separated network nodes in the co-operative cluster of spatially separated network nodes 110, 111, 112 transmits separate pilot reference signals Pband,1, Pband,2, Pband,3 on a set of different frequencies in different operating bands of the spatially separated network nodes 110, 111, 112 towards the physical object 131 and captures their corresponding reflected signal energy. Hence, information indicating this captured reflected signal energy by each of the spatially separated network nodes 110, 111, 112, respectively, may be collected in the network node 110, 111, 112, 101. Here, it should be noted that, for a mono-static conventional radar, the power ^^ _^^ of the back-scattered or reflected signal from a target may be given by Eq.1: (Eq.1) wherein ^^ _^^ is the transmit power, ^^ _^^ and ^^ _^^ is the antenna gain of the transmitter and the receiver, respectively, R is the distance between the radar and the target, and σ is the Radar Cross Section, RCS, of the target. The RCS of the target may be described as a function based on the shape of the target, the size of the target, the frequency of operation of the radar, and the composition of materials of the target. When the target is illuminated with electromagnetic radio waves from the radar, normalized power of the back-scattered or reflected signal from the target will vary with according to the wavelength of the used electromagnetic radio waves of the radar. This is illustrated in Fig.6 which shows an example of scattered energy variation depending on the wavelength of the illuminating signal. According to the embodiments described herein, this variation of RCS at different wavelengths may be used for the characterization of the physical object 131 in the wireless communications network 100. In other words, by having each of spatially separated network nodes 110, 111, 112 transmit their separate pilot reference signals Pband,1, Pband,2, Pband,3 on a set of different frequencies in different operating bands of the spatially separated network nodes 110, 111, 112 in the wireless communications network 100 towards the physical object 131, the respectively captured back-scattered or reflected signal energy and their variation in the different wavelength regions, such as, e.g., the Rayleigh, Mie and Optical regions, may carry information about the shape, the size and composition of materials of the physical object 131. Thus, according to some embodiments, the different operating bands of the pilot reference signals Pband,1, Pband,2, Pband,3 comprise a low-frequency spectrum band, a mid-frequency spectrum band, and high-frequency spectrum band. Here, the low-frequency spectrum band may be selected to capture the Rayleigh region, a mid-frequency spectrum band may be selected to capture the Mie region, and high-frequency spectrum band low may be selected to capture the Optical region. In some embodiments, the information indicating the signal energy reflected by the physical object 131 may be model parameters α, β, γ of a model defining a physical object 131. In this case, the network node 110, 111, 112, 101 may determine the model parameters α, β, γ based on the variation of the signal energy over the set of different frequencies within the different operating bands of the pilot reference signals Pband,1, Pband,2, Pband,3 as captured by the at least two spatially separated network nodes 110, 111, 112. This means, for example, that the network node 110, 111, 112, 101 may utilize the shape (such as slope) of RCS frequency variation of the physical object 131 in the different wavelength regions, such as, e.g., the Rayleigh, Mie and Optical regions, for parameterization of the physical object 131. Hereinafter, this parameterized RCS versus frequency may be denoted by the model parameters ^^, ^^, ^^ indicating the parameterized RCS version for each of three exemplified wavelength regions. According to some embodiments, the parameterized RCS version for each of three exemplified wavelength regions may be described in terms of amplitude, modes, valleys, etc., of the RCS versus frequency curve exemplified in Fig.6. For example, the set of frequencies in the low-frequency spectrum band (e.g., the Rayleigh region) may generate a RCS versus frequency model parameter ^^ that, for example, may be approximated using a exponent of an exponential function together with the peak power within the low-frequency spectrum band (e.g., < 1 Hz in Fig.6). Contrary, the set of frequencies in the mid-frequency spectrum band (e.g., the Mie region) may generate a RCS versus frequency model parameter ^^ that, for example, may be approximated in terms of damped oscillations of a second order LC circuit for the mid-frequency spectrum band (e.g., 1 Hz to about 10 Hz in Fig.6). Further, the set of frequencies in the high-frequency spectrum band (e.g., the Optical region) may generate a RCS versus frequency model parameter ^^ that reflects a steady state and may thus be estimated as such for the high-frequency spectrum band (e.g., higher than 10 Hz in Fig. 6). Hence, the RCS versus frequency model parameters ^^, ^^, ^^ may be derived from the subset of measurements of the captured back-scattered or reflected signal energy in each of their different wavelength regions. As an illustrative example, the RCS versus frequency model parameters ^^, ^^, ^^ may be modelled as a Resistor-Inductor-Capacitor, RLC, circuit as shown in Fig.7. Initially, while switch S¹ is closed and S² open, the capacitor C charges to its maximum capacity. Then, when the switch S1 is opened and switch S2 is closed, the capacitor C discharges through the RL circuit part resulting in damped oscillations of voltage as described by the following second order differential equation according to Eq.2: _^ ² _^^ ^^ ^ ^^ 1 _{^ ^^} ² + ^^ ^ ^^ + ^^ ^^ ^^ = 0 (Eq.2) wherein q is the charge. This equation may be parameterized according to Eq.3: wherein ^^( ^̅^ ), ^^( ^̅^ ), ^^( ^̅^ ) are model parameters. In the low-frequency spectrum band (e.g., the Rayleigh region), the model parameters may here be set to ^^( ^̅^) = 0, ^^( ^̅^) ≠ 0, ^^( ^̅^) ≠ 0. This model parameter setting would result in a first order Ordinary Differential Equation, ODE, that is exponentially increasing, and thus suitable to approximate the low-frequency spectrum band. For the mid-frequency spectrum band (e.g., the Mie region), the model parameters may instead be set to ^^( ^̅^) ≠ 0, ^^( ^̅^) ≠ 0, ^^( ^̅^) ≠ 0. This model parameter setting would result in a second order ODE, whose solution involves periodically varying functions. Lastly, for the high-frequency spectrum band (e.g., the Optical region), the model parameters may be set to ^^( ^̅^) = 0, ^^( ^̅^) ≠ 0, ^^( ^̅^) = 0. This model parameter setting would result in a constant function in accordance with a steady state. According to some embodiments, a first machine learning model may be trained to determine the model parameters α, β, γ based on the variation of the signal energy over the set of different frequencies within the different operating bands of the pilot reference signals Pband,1, Pband,2, Pband,3 as captured by the at least two spatially separated network nodes 110, 111, 112. This means, for example, that the network node 110, 111, 112, 101 may be configured to utilize an Machine Learning, ML, model that has been trained to extract the RCS versus frequency model parameters ^^, ^^, ^^ from the captured back-scattered or reflected signal energy in each of their different wavelength regions by spatially separated network nodes 110-115 in a wireless communications network 100. For example, the network node 110, 111, 112, 101 may provide the information about the signal energy over the set of different frequencies within the different operating bands as input to e.g., a neural network of the trained ML model respectively for each of the spatially separated network nodes 110, 111, 112. In this case, as each of the spatially separated network nodes 110, 111, 112 illuminates different surfaces of the physical object 131, the network node 110, 111, 112, 101 may, for example, average the RCS versus frequency model parameters ^^, ^^, ^^ from all different neural networks of the ML model. Alternatively, each of the spatially separated network nodes 110, 111, 112 may comprise its own trained ML model to extract the RCS versus frequency model parameters ^^, ^^, ^^ from its captured back-scattered or reflected signal energy in each of the different wavelength regions. In this case, each of the spatially separated network nodes 110, 111, 112 may report its determined RCS versus frequency model parameters ^^, ^^, ^^ to the network node 110, 111, 112, 101. The trained neural networks of the ML model may, for example, use the ground truth as the desired output in supervised manner. It should also be noted that in order to estimate the RCS versus frequency model parameters ^^, ^^, ^^ in the above mentioned manner in a wireless communications network 100, there is no need for any new type of signalling. For example, regular downlink reference signals, such as, e.g., Demodulation Reference Signals (DMRS), Positional Reference Signals (PRS), etc., may be used by the spatially separated network nodes 110, 111, 112 to illuminate the physical object 131 over the set of different frequencies within the different operating bands. Furthermore, since in order for a wireless device or UE in the wireless communications network 100 to interpret a reference signal, the reference signal should be associated with some type of RRC signalling. Hence, in some embodiments, the pilot reference signals are transmitted on transmission resources separate from any transmission resources used by reference signals associated with Radio Resource Control, RRC, signalling in the wireless communications network 100. This means, for example, that the pilot reference signals transmitted by the spatially separated network nodes 110, 111, 112 to illuminate the physical object 131 over the set of different frequencies within the different operating bands will not be able to decode these “dummy” reference signals. However, while the reflected signal energy from the physical object 131 may create interference in the radio environment, this reflected signal energy is similar to the reflections from any other object in the radio environment and the interference floor should be considered negligible. Furthermore, according to some embodiments, the network node 110, 111, 112, 101 may control the at least two spatially separated network nodes 110, 111, 112 to respectively transmit pilot reference signals Pband,1, Pband,2, Pband,3 towards the physical object 131. This means, for example, that the network node 110, 111, 112, 101 may coordinate the transmissions from the at least two spatially separated network nodes 110, 111, 112 in the wireless communications network 100. Alternatively, if e.g., the network node 110, 111, 112, 101 is not the primary network node, then the network node 110, 111, 112, 101 may indicate to the primary network node, e.g., the network node 110, how the network node 110 should coordinate the transmissions from the at least one spatially separated network nodes 110, 111, 112 in the wireless communications network 100. In this case, in some embodiments, the network node 110, 111, 112, 101 may determine a non-overlapping timing schedule for the transmission of the pilot reference signals Pband,1, Pband,2, Pband,3 towards the physical object 131 by the at least two spatially separated network nodes 110, 111, 112. Subsequently, the network node 110, 111, 112, 101 may also coordinate the transmission of the pilot reference signals Pband,1, Pband,2, Pband,3 towards the physical object 131 by the at least two spatially separated network nodes 110, 111, 112 according to the determined non-overlapping timing schedule. This means, for example, that the network node 110, 111, 112, 101 may set a transmission pattern for the at least two spatially separated network nodes 110, 111, 112 that eliminates, or at least reduces, potential inference between each of the individual transmissions from each of the at least two spatially separated network nodes 110, 111, 112. Fig. 8 shows one example of a non-overlapping Resource Element, RE, utilization for the transmissions of the pilot reference signals Pband,1, Pband,2, Pband,3 towards the physical object 131 by the at least two spatially separated network nodes 110, 111, 112 in the wireless communications network 100. Here, the dashed areas indicated different REs that may be used for the transmissions of the pilot reference signals Pband,1, Pband,2, Pband,3 towards the physical object 131. For example, when the network node 110 transmits its pilot reference signal P^band,1 towards the physical object 131, the remaining network nodes 111, 112 will remain silent and not transmit their pilot reference signals Pband,2, Pband,3 towards the physical object 131, thus assisting in an interference free illumination and capture of the back-scattered or reflected signal energy. Here, for example, the primary network node, e.g., network node 110, may schedule the transmissions at least two spatially separated network nodes 110, 111, 112 such that the remaining network nodes 111, 112 are muted while the network node 110 transmits its pilot reference signal Pband,1 towards the physical object 131 as shown in the scenario of Fig.4. Action 507 After the detection in Action 506, the network node 110, 111, 112, 101 estimates at least one characteristic parameter value θ for the physical object 131 based on the obtained information. This means, for example, that the network node 110, 111, 112, 101 may use information from the at least two spatially separated network nodes 110, 111, 112, i.e. information indicating the respectively captured back-scattered or reflected signal energy and their variation in the different wavelength regions for each of the at least two spatially separated network nodes 110, 111, 112, for estimating at least one characteristic parameter value θ for the physical object 131. For example, the RCS versus frequency model parameters ^^ _^^ , ^^ _^^ , ^^ _^^ extracted from the captured back-scattered or reflected signal energy in the different wavelength regions by each of the at least two spatially separated network nodes 110, 111, 112 (denoted by the subscript i) in the wireless communications network 100 as shown in the scenario of Fig.4, provides their own geometric perspective of the physical object 131 and naturally depends on the exposed section of the physical object 131 facing respective spatially separated network node 110, 111, 112. Thus, as each of the at least two spatially separated network nodes 110, 111, 112, for example, transfers its RCS versus frequency model parameters ^^ _^^ , ^^ _^^ , ^^ _^^ to the network node 110, 111, 112, 101, the network node 110, 111, 112, 101 may comprise a functional mapping that maps the collected RCS versus frequency model parameters ^^ _^^ , ^^ _^^ , ^^ _^^ from each of the at least two spatially separated network nodes 110, 111, 112 to one or more characteristic parameter values θ for the physical object 131. This functional mapping F between the collected RCS versus frequency model parameters ^^ _^^ , ^^ _^^ , ^^ _^^ and the characteristic parameter values θ for the physical object 131 may be described according to Eq.4: ^^: {( ^^₁, ^^₁ ^^₁) … ( ^^ _^^, ^^ _^^ ^^ _^^)} → ^^ (Eq.4) wherein N is the number of the at least two spatially separated network nodes 110, 111, 112, and θ is the at least one characteristic parameter value for the physical object 131. Hence, in some embodiments, the network node 110, 111, 112, 101 may further estimate the at least one characteristic parameter value θ for the physical object 131 based on the model parameters α, β, γ. Further, according to some embodiments, a second machine learning model may be trained to estimate the at least one characteristic parameter value θ for the physical object 131 based on the determined model parameters α, β, γ.This means, for example, that the functional mapping between the collected RCS versus frequency model parameters ^^ _^^ , ^^ _^^ , ^^ _^^ and the object parameters θ may implemented by a trained ML model in the network node 110, 111, 112, 101. The ML model may, for example, be trained using a supervised learning and the collected RCS versus frequency model parameters _^^ , ^^ _^^ , ^^ _^^ as features. Action 508 After the estimation in Action 507, the network node 110, 111, 112, 101 characterize the physical object 131 based on the estimated at least one characteristic parameter value θ. This means, for example, that the network node 110, 111, 112, 101 may determine the object type of the physical object 131, e.g., characterizing the physical object 131 as a human, a robot, a car, a truck, a concrete structure, glass structure, etc. Other object type characteristics may also be determined, such as, shape, size, materials, etc. To perform the method actions in a network node 110, 111, 112, 101 for enabling characterization of a physical object 131 by a wireless communications network 100 comprising at least two spatially separated network nodes 110, 111, 112, the network node 110, 111, 112, 101 may comprise the following arrangement depicted in Fig.9. Fig.9 shows a schematic block diagram of embodiments of a network node 110, 111, 112, 101. The embodiments of the network node 110, 111, 112, 101 described herein may be considered as independent embodiments or may be considered in any combination with each other to describe non-limiting examples of the example embodiments described herein. The network node 110, 111, 112, 101 may comprise processing circuitry or processor 910 and a memory 920. The processing circuitry 910 may also comprise a receiving module 911 and a transmitting module 912. The receiving module 911 and the transmitting module 912 may also be configured to communicate and perform transmissions over the wireless communications network 100. Optionally, in case network node 110, 111, 112, 101 is base station node 110, 111, 112, the receiving module 911 and the transmitting module 912 may comprise Radio Frequency, RF, processing circuitry capable of transmitting a radio signal via a radio interface (not shown) within the wireless communications network 100. The receiving module 911 and the transmitting module 912 may also form part of a single transceiver. It should also be noted that some or all of the functionality described in the embodiments above as being performed by the network node 110, 111, 112, 101 may be provided by the processing circuitry 910 executing instructions stored on a computer-readable medium, such as, e.g., the memory 920 shown in Fig. 9. Alternative embodiments of the network node 110, 111, 112, 101 may comprise additional components, such as, for example, an obtaining module 913, a estimating module 914, a determining module 915, a controlling module 916, and a characterising module 917, each responsible for providing its respective functionality necessary to support the embodiments described herein. The network node 110, 111, 112, 101 or processing circuitry 910 is configured to, or may comprise the obtaining module 913 configured to, obtain, from each of the at least two spatially separated network nodes 110, 111, 112, information indicating the signal energy reflected by the physical object 131 and captured, by the respective at least two spatially separated network nodes 110, 111, 112, on a set of different frequencies in different operating bands of pilot reference signals Pband,1, Pband,2, Pband,3 transmitted by the respective at least two spatially separated network nodes 110, 111, 112. Also, the network node 110, 111, 112, 101 or processing circuitry 910 is configured to, or may comprise the estimating module 914 configured to, estimate at least one characteristic parameter value θ for the physical object 131 based on the obtained information. In some embodiments, the information indicating the signal energy reflected by the physical object 131 are model parameters α, β, γ of a model defining a physical object 131. In this case, the network node 110, 111, 112, 101 or processing circuitry 910 may be configured to, or may comprise the determining module 915 configured to, determine the model parameters α, β, γ based on the variation of the signal energy over the set of different frequencies within the different operating bands of the pilot reference signals Pband,1, Pband,2, Pband,3 as captured by the at least two spatially separated network nodes 110, 111, 112. Also, in some embodiments, the network node 110, 111, 112, 101, processing circuitry 910 or the determining module 915 may comprise a first machine learning model that is trained to determine the model parameters α, β, γ based on the variation of the signal energy over the set of different frequencies within the different operating bands of the pilot reference signals Pband,1, Pband,2, Pband,3 as captured by the at least two spatially separated network nodes 110, 111, 112. According to some embodiments, the network node 110, 111, 112, 101 or processing circuitry 910 may be configured to, or may comprise the estimating module 914 configured to, estimate the at least one characteristic parameter value θ for the physical object 131 based on the model parameters α, β, γ. Also, in some embodiments, the network node 110, 111, 112, 101, processing circuitry 910 or the determining module 915 may comprise a second machine learning model is trained to estimate the at least one characteristic parameter value θ for the physical object 131 based on the determined model parameters α, β, γ. In some embodiments, the different operating bands of the pilot reference signals Pband,1, Pband,2, Pband,3 comprise a low-frequency spectrum band, a mid-frequency spectrum band, and high-frequency spectrum band. According to some embodiments, the network node 110, 111, 112, 101 or processing circuitry 910 may be configured to, or may comprise the controlling module 916 configured to, control the at least two spatially separated network nodes 110, 111, 112 to respectively transmit the pilot reference signals Pband,1, Pband,2, Pband,3 towards the physical object 131. In this case, according to some embodiments, the network node 110, 111, 112, 101 or processing circuitry 910 may be configured to, or may comprise the determining module 915 configured to, determine a non-overlapping timing schedule for the transmission of the pilot reference signals Pband,1, Pband,2, Pband,3 towards the physical object 131 by the at least two spatially separated network nodes 110, 111, 112, and coordinate the transmission of the pilot reference signals Pband,1, Pband,2, Pband,3 towards the physical object 131 by the at least two spatially separated network nodes 110, 111, 112 according to the determined non-overlapping timing schedule. Furthermore, in some embodiments, the network node 110, 111, 112, 101 or processing circuitry 910 may be configured to, or may comprise the obtaining module 913 configured to, obtain, from spatially separated network nodes 110-115 in the wireless communications network 100, information indicating a signal energy, reflected by the physical object 131 and captured by each respective spatially separated network node 110- 115, on a respectively determined frequency for pilot reference signals P^f,1-6 transmitted by each respective spatially separated network node 110-115. In this case, according to some embodiments, the network node 110, 111, 112, 101 or processing circuitry 910 may be configured to, or may comprise the determining module 915 configured to, determine one of the spatially separated network nodes 110-115 as a primary network node based on which one of the spatially separated network nodes 110-115 that received the largest amount of signal energy reflected by the physical object 131. Also, in some embodiments, the network node 110, 111, 112, 101 or processing circuitry 910 may be configured to, or may comprise the obtaining module 913 configured to, obtain, from spatially separated network nodes 110-115 in the wireless communications network 100, information indicating the signal energy, reflected by the physical object 131 and captured by each respective spatially separated network node 110-115, on a determined frequency for a pilot reference signal Pp,1 transmitted by the determined primary network node 110. Here, in this case, according to some embodiments, the network node 110, 111, 112, 101 or processing circuitry 910 may be configured to, or may comprise the determining module 915 configured to, determine the at least two spatially separated network nodes 110, 111, 112 as a subset of the spatially separated network nodes 110-115 for which the obtained information indicates that the respectively received signal energy reflected by the physical object 131 is above a determined threshold value. According to some embodiments, the information indicating the signal energy reflected by the physical object 131 is the Reference Signal Received Power, RSRP, of the reflected pilot reference signals. Further, in some embodiments, the network node 110, 111, 112, 101 or processing circuitry 910 may be configured to, or may comprise the transmitting module 912 configured to, transmit the pilot reference signals on transmission resources separate from any transmission resources used by reference signals associated with Radio Resource Control, RRC, signalling in the wireless communications network 100. Further, the network node 110, 111, 112, 101 or processing circuitry 910 may be configured to, or may comprise the characterizing module 917 configured to, characterize the physical object 131 based on the estimated at least one characteristic parameter value θ. Furthermore, the embodiments for enabling characterization of a physical object 131 by a wireless communications network 100 comprising at least two spatially separated network nodes 110, 111, 112 described above may be implemented through one or more processors, such as, the processing circuitry 910 in the network node 110, 111, 112, 101 depicted in Fig. 9, together with computer program code for performing the functions and actions of the embodiments herein. The program code mentioned above may also be provided as a computer program product, for instance in the form of a data carrier carrying computer program code or code means for performing the embodiments herein when being loaded into the processing circuitry 910 in the network node 110, 111, 112, 101. The computer program code may e.g., be provided as pure program code in the network node 110, 111, 112, 101 or on a server and downloaded to the network node 110, 111, 112, 101. Thus, it should be noted that the modules of the network node 110, 111, 112, 101 may in some embodiments be implemented as computer programs stored in memory, e.g., in the memory modules 920 in Fig.9, for execution by processors or processing modules, e.g., the processing circuitry 910 of Fig.9. Those skilled in the art will also appreciate that the processing circuitry 910 and the memory 920 described above may refer to a combination of analog and digital circuits, and/or one or more processors configured with software and/or firmware, e.g., stored in a memory, that when executed by the one or more processors such as the processing circuitry 920 perform as described above. One or more of these processors, as well as the other digital hardware, may be included in a single application-specific integrated circuit (ASIC), or several processors and various digital hardware may be distributed among several separate components, whether individually packaged or assembled into a system-on-a-chip (SoC). The description of the example embodiments provided herein have been presented for purposes of illustration. The description is not intended to be exhaustive or to limit example embodiments to the precise form disclosed, and modifications and variations are possible in light of the above teachings or may be acquired from practice of various alternatives to the provided embodiments. The examples discussed herein were chosen and described in order to explain the principles and the nature of various example embodiments and its practical application to enable one skilled in the art to utilize the example embodiments in various manners and with various modifications as are suited to the particular use contemplated. The features of the embodiments described herein may be combined in all possible combinations of methods, apparatus, modules, systems, and computer program products. It should be appreciated that the example embodiments presented herein may be practiced in any combination with each other. It should be noted that the word “comprising” does not necessarily exclude the presence of other elements or steps than those listed and the words “a” or “an” preceding an element do not exclude the presence of a plurality of such elements. It should further be noted that any reference signs do not limit the scope of the claims, that the example embodiments may be implemented at least in part by means of both hardware and software, and that several “means”, “units” or “devices” may be represented by the same item of hardware. It should also be noted that the various example embodiments described herein are described in the general context of method steps or processes, which may be implemented in one aspect by a computer program product, embodied in a computer-readable medium, including computer-executable instructions, such as program code, executed by computers in networked environments. A computer-readable medium may include removable and non- removable storage devices including, but not limited to, Read Only Memory (ROM), Random Access Memory (RAM), compact discs (CDs), digital versatile discs (DVD), etc. Generally, program modules may include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. Computer-executable instructions, associated data structures, and program modules represent examples of program code for executing steps of the methods disclosed herein. The particular sequence of such executable instructions or associated data structures represents examples of corresponding acts for implementing the functions described in such steps or processes. The embodiments herein are not limited to the above described preferred embodiments. Various alternatives, modifications and equivalents may be used. Therefore, the above embodiments should not be construed as limiting.

Claims

CLAIMS: 1. A method performed by a network node (110, 111, 112, 101) for enabling characterization of a physical object (131) by a wireless communications network (100) comprising at least two spatially separated network nodes (110, 111, 112), the method comprising: obtaining (505), from each of the at least two spatially separated network nodes (110, 111, 112), information indicating the signal energy reflected by the physical object (131) and captured, by the respective at least two spatially separated network nodes (110, 111, 112), on a set of different frequencies in different operating bands of pilot reference signals (Pband,1, Pband,2, Pband,3) transmitted by the respective at least two spatially separated network nodes (110, 111, 112); and estimating (506) at least one characteristic parameter value (θ) for the physical object (131) based on the obtained information. 2. The method according to claim 1, wherein the information indicating the signal energy reflected by the physical object (131) are model parameters (α, β, γ) of a model defining a physical object (131), wherein the model parameters (α, β, γ) are determined based on the variation of the signal energy over the set of different frequencies within the different operating bands of the pilot reference signals (Pband,1, Pband,2, Pband,3) as captured by the at least two spatially separated network nodes (110, 111, 112). 3. The method according to claim 2, wherein a first machine learning model is trained to determine the model parameters (α, β, γ) based on the variation of the signal energy over the set of different frequencies within the different operating bands of the pilot reference signals (Pband,1, Pband,

2, Pband,

3) as captured by the at least two spatially separated network nodes (110, 111, 112).

4. The method according to claim 2, wherein the estimating (507) comprises estimating the at least one characteristic parameter value (θ) for the physical object (131) based on the model parameters (α, β, γ).

5. The method according to claim 4, wherein a second machine learning model is trained to estimate the at least one characteristic parameter value (θ) for the physical object (131) based on the determined model parameters (α, β, γ).

6. The method according to any of claims 1-5, wherein the different operating bands of the pilot reference signals (Pband,1, Pband,2, Pband,3) comprise a low-frequency spectrum band, a mid-frequency spectrum band, and high-frequency spectrum band.

7. The method according to any of claims 1-6, further comprising controlling the at least two spatially separated network nodes (110, 111, 112) to respectively transmit the pilot reference signals (Pband,1, Pband,2, Pband,3) towards the physical object (131).

8. The method according to claim 7, further comprising determining a non-overlapping timing schedule for the transmission of the pilot reference signals (Pband,1, Pband,2, Pband,3) towards the physical object (131) by the at least two spatially separated network nodes (110, 111, 112), and coordinating the transmission of the pilot reference signals (Pband,1, Pband,2, Pband,3) towards the physical object (131) by the at least two spatially separated network nodes (110, 111, 112) according to the determined non-overlapping timing schedule.

9. The method according to any of claims 1-8, further comprising obtaining (501), from spatially separated network nodes (110-115) in the wireless communications network (100), information indicating a signal energy, reflected by the physical object (131) and captured by each respective spatially separated network node (110-115), on a respectively determined frequency for pilot reference signals (Pf,1-6) transmitted by each of the spatially separated network nodes (110-115), respectively; and determining (502) one of the spatially separated network nodes (110-115) as a primary network node based on which one of the spatially separated network nodes (110-115) that received the largest amount of signal energy reflected by the physical object (131).

10. The method according to any of claims 9, further comprising obtaining (503), from spatially separated network nodes (110-115) in the wireless communications network (100), information indicating the signal energy, reflected by the physical object (131) and captured by each respective spatially separated network node (110-115), on a determined frequency for a pilot reference signal (Pp,1) transmitted by the determined primary network node (110); and determining (504) the at least two spatially separated network nodes (110, 111, 112) as a subset of the spatially separated network nodes (110-115) for which the obtained information indicates that the respectively received signal energy reflected by the physical object (131) is above a determined threshold value.

11. The method according to any of claims 1-10, wherein the information indicating the signal energy reflected by the physical object (131) is the Reference Signal Received Power, RSRP, of the reflected pilot reference signals.

12. The method according to any of claims 1-11, wherein the pilot reference signals are transmitted on transmission resources separate from any transmission resources used by reference signals associated with Radio Resource Control, RRC, signalling in the wireless communications network (100).

13. The method according to any of claims 1-12, further comprising characterizing (507) the physical object (131) based on the estimated at least one characteristic parameter value (θ).

14. A network node (110, 111, 112, 101) for enabling characterization of a physical object (131) by a wireless communications network (100) comprising at least two spatially separated network nodes (110, 111, 112), the network node (110, 111, 112, 101) being configured to obtain from each of the at least two spatially separated network nodes (110, 111, 112), information indicating the signal energy reflected by the physical object (131) and captured, by the respective at least two spatially separated network nodes (110, 111, 112), on a set of different frequencies in different operating bands of pilot reference signals (Pband,1, Pband,2, Pband,3) transmitted by the respective at least two spatially separated network nodes (110, 111, 112), and estimate at least one characteristic parameter value (θ) for the physical object (131) based on the obtained information.

15. The network node (110, 111, 112, 101) according to claim 14, wherein the information indicating the signal energy reflected by the physical object (131) are model parameters (α, β, γ) of a model defining a physical object (131), and the network node (110, 111, 112, 101) is configure to determine the model parameters (α, β, γ) based on the variation of the signal energy over the set of different frequencies within the different operating bands of the pilot reference signals (Pband,1, Pband,2, Pband,3) as captured by the at least two spatially separated network nodes (110, 111, 112).

16. The network node (110, 111, 112, 101) according to claim 15, wherein a first machine learning model is trained to determine the model parameters (α, β, γ) based on the variation of the signal energy over the set of different frequencies within the different operating bands of the pilot reference signals (P^band,1, P^band,2, P^band,3) as captured by the at least two spatially separated network nodes (110, 111, 112).

17. The network node (110, 111, 112, 101) according to claim 15, wherein the estimating (507) comprises estimating the at least one characteristic parameter value (θ) for the physical object (131) based on the model parameters (α, β, γ).

18. The network node (110, 111, 112, 101) according to claim 17, wherein a second machine learning model is trained to estimate the at least one characteristic parameter value (θ) for the physical object (131) based on the determined model parameters (α, β, γ).

19. The network node (110, 111, 112, 101) according to any of claims 14-18, wherein the different operating bands of the pilot reference signals (P^band,1, P^band,2, P^band,3) comprise a low-frequency spectrum band, a mid-frequency spectrum band, and high-frequency spectrum band.

20. The network node (110, 111, 112, 101) according to any of claims 14-19, further configured to control the at least two spatially separated network nodes (110, 111, 112) to respectively transmit the pilot reference signals (Pband,1, Pband,2, Pband,3) towards the physical object (131).

21. The network node (110, 111, 112, 101) according to claim 20, further configured to determine a non-overlapping timing schedule for the transmission of the pilot reference signals (Pband,1, Pband,2, Pband,3) towards the physical object (131) by the at least two spatially separated network nodes (110, 111, 112), and coordinate the transmission of the pilot reference signals (Pband,1, Pband,2, Pband,3) towards the physical object (131) by the at least two spatially separated network nodes (110, 111, 112) according to the determined non-overlapping timing schedule.

22. The network node (110, 111, 112, 101) according to any of claims 14-21, further configured to obtain, from spatially separated network nodes (110-115) in the wireless communications network (100), information indicating a signal energy , reflected by the physical object (131) and captured by each respective spatially separated network node (110-115), on a respectively determined frequency for pilot reference signals (Pf,1- 6) transmitted by each respective spatially separated network node (110-115), and determine one of the spatially separated network nodes (110-115) as a primary network node based on which one of the spatially separated network nodes (110-115) that received the largest amount of signal energy reflected by the physical object (131).

23. The network node (110, 111, 112, 101) according to any of claims 9, further configured to obtain, from spatially separated network nodes (110-115) in the wireless communications network (100), information indicating the signal energy, reflected by the physical object (131) and captured by each respective spatially separated network node (110-115), on a determined frequency for a pilot reference signal (Pp,1) transmitted by the determined primary network node (110), and determine the at least two spatially separated network nodes (110, 111, 112) as a subset of the spatially separated network nodes (110-115) for which the obtained information indicates that the respectively received signal energy reflected by the physical object (131) is above a determined threshold value.

24. The network node (110, 111, 112, 101) according to any of claims 14-23, wherein the information indicating the signal energy reflected by the physical object (131) is the Reference Signal Received Power, RSRP, of the reflected pilot reference signals.

25. The network node (110, 111, 112, 101) according to any of claims 1-11, further configured to transmit the pilot reference signals on transmission resources separate from any transmission resources used by reference signals associated with Radio Resource Control, RRC, signalling in the wireless communications network (100).

26. The network node (110, 111, 112, 101) according to any of claims 1-12, further configured to characterize the physical object (131) based on the estimated at least one characteristic parameter value (θ).

27. The network node (110, 111, 112, 101) according to any of claims 14-26, comprising a processor circuitry (910) and a memory (920), wherein the memory (920) is containing instructions executable by the processor circuitry (910).

28. A network node (110, 111, 112, 101) for enabling characterization of a physical object (131) by a wireless communications network (100) comprising at least two spatially separated network nodes (110, 111, 112), the network node (110, 111, 112, 101) comprising a processor (910), wherein the processor (910) is configured to obtain, from each of the at least two spatially separated network nodes (110, 111, 112), information indicating the signal energy reflected by the physical object (131) and captured, by the respective at least two spatially separated network nodes (110, 111, 112), on a set of different frequencies in different operating bands of pilot reference signals (Pband,1, Pband,2, Pband,3) transmitted by the respective at least two spatially separated network nodes (110, 111, 112), and estimate at least one characteristic parameter value (θ) for the physical object (131) based on the obtained information.

29. The network node (110, 111, 112, 101) according to claim 28, wherein the processor (910) is further configured to perform the method according to any of claims 2-13.

30. A computer program, comprising instructions which, when executed on at least one processor (910), cause the at least one processor (910) to carry out the method according to any of claims 1-13.

31. A carrier containing the computer program according to claim 28, wherein the carrier is one of an electronic signal, optical signal, radio signal, or computer-readable storage medium.