System and method for generating a corrective action for an obstacle affecting a communication link

Abstract

Methods and systems improve the path loss of a communication link between a UE and a network node of a communication system. Channel state information derived from the communication link is monitored for one or more parameters. A change is detected for at least one of the one or more parameters, where the change reflects an increase of the path loss of the communication link. Based on the change, an obstacle is identified. At least one corrective action message is then generated which includes one or more instructions to address the obstacle.

Description

P110655 SYSTEM AND METHOD FOR GENERATING A CORRECTIVE ACTION FOR AN OBSTACLE AFFECTING A COMMUNICATION LINK TECHNICAL FIELD

[0001] The present disclosure generally relates to systems and methods for generating a corrective action for an obstacle affecting at least one communication link in a wireless communication system. A corresponding computer program and computer program product are also disclosed. BACKGROUND

[0002] 5G service technology (5G) leverages reciprocity assisted transmission to beamform the transmission channel in the downlink (DL) between a node (e.g., gNB) and a user equipment (UE) in both frequency range 1 (FR1) and frequency range 2 (FR2). The UE sounding reference signal (SRS) is used to calculate the beamform. The goal of beamforming is to direct the DL power from the node in an efficient way to the UE.

[0003] Low power UEs, such as internet of things (IoT) devices, are an emerging category of UEs that are incorporating 5G. As these low power UEs are inherently power limited, efforts have been made to optimize these UEs for low power consumption. For example, Cat-M and NB-IOT have made 5G IoT devices ultra lean from a power consumption perspective. However, these technologies cannot improve physical hindrances to the transmission channel itself. A low power UE behind an obstacle — in a non-line of sight (NLOS) position with itself and the node it is connected to — will use more energy to communicate than low power UEs in line of sight (LOS) positions.

[0004] Additionally, in use cases where keeping a high quality of service (QoS) is important (e.g., when 5G service is used for fixed wireless access (FWA), a factory setting with numerous fixed and mobile UEs), it is important to understand the sudden worsening of a connection. In these use cases, as well as with low power UEs utilizing FR2, the sudden appearance of an obstacle leads to high path loss for the transmission channel between the low power UE and the node, which can negatively impact QoS.

[0005] Accordingly, there is a need to address these issues. It is believed that no one prior to the inventors has made or used an invention as described herein.P110655 SUMMARY

[0006] An object of the disclosed embodiments is to optimize the communication in a communication system.

[0007] A first embodiment of the disclosed technology includes a system for improving the path loss of a communication link between a UE and a network node of a communication system, the system comprising processing circuitry and a memory. The memory contains instructions executable by the processing circuitry whereby the system is operative to monitor channel state information derived from the communication link, the channel state information comprising one or more parameters, detect a change of at least one of the one or more parameters, wherein the change reflects an increase of the path loss of the communication link, identify, based on the change, an obstacle, and generate at least one corrective action message comprising one or more instructions to address the obstacle.

[0008] A second embodiment is the system of the first embodiment where the channel state information comprises at least one of an Angle of Arrival, AoA, parameter and a power headroom, PH, parameter.

[0009] A third embodiment is the system of the first embodiment that further comprises after identifying the obstacle but before generating the at least one corrective action message, generating a profile for the obstacle, where the profile comprises information about the obstacle, and where the at least one corrective action message is generated based on the profile.

[0010] A fourth embodiment is the system of the third embodiment where the information is one or more of a classification of the obstacle, the shape of the obstacle, and the spatial position of the obstacle.

[0011] A fifth embodiment is the system of the first embodiment further operative to send the at least one corrective active message to another system for processing.

[0012] A sixth embodiment is the system of the fifth embodiment where the at least one corrective action message is sent using a network API.

[0013] A seventh embodiment is the system of the fifth embodiment where the one or more instructions comprise one or more of a notification message indicating the obstacle is affecting the communication link, a change to the scheduling of the UE, and a map indicating the location of the blockage.P110655

[0014] An eight embodiment is the system of the seventh embodiment where the notification message, when the obstacle is an autonomous system, is directed to the autonomous system.

[0015] A ninth embodiment is the system of the seventh embodiment where the notification message includes a solution to improve the path loss.

[0016] A tenth embodiment is the system of the seventh embodiment where the notification message, when the obstacle is a user, instructs the user to move.

[0017] An eleventh embodiment is the system of the first embodiment further operative to identify the UE as stationary.

[0018] A twelfth embodiment is the system of the eleventh embodiment where the UE is identified as stationary based on at least one of the channel state information, a UE type identifier, and a UE subscription.

[0019] A thirteenth embodiment is the system of the first embodiment where the UE is a fixed wireless access-type UE.

[0020] A fourteenth embodiment is the system of the first embodiment where the obstacle is identified using a machine learning model.

[0021] A fifteenth embodiment is the system of the third embodiment where the profile is generated, at least in part, using a machine learning model.

[0022] A sixteenth embodiment is the system of the fourteenth and fifteenth embodiments where the machine learning model is a neural network.

[0023] A seventeenth embodiment is the system of the first embodiment where the communication link is one of a plurality of communication links in the communication system, the channel state information is further derived from the remaining communication links of the plurality of communication links, and the change may further be detected with at least one of the one or more parameters from the remaining communication links.

[0024] A eighteenth embodiment is a computer implemented method for improving the path loss of a communication link between a UE and a network node of a communication system. The method comprises monitoring channel state information derived from the communication link, the channel state information comprising one or more parameters, detecting a change of at least one of the one or more parameters, wherein the change reflects an increase of the path loss of the communication link, identifying, based on the change, an obstacle, and generating at least one corrective action message comprising one or more instructions to address the obstacle.P110655

[0025] A nineteenth embodiment is the method of the eighteenth embodiment where the channel state information comprises an Angle of Arrival, AoA, parameter and a power headroom, PH, parameter.

[0026] A twentieth embodiment is the method of the eighteenth embodiment where after identifying the obstacle but before generating the at least one corrective action message, generating a profile for the obstacle, where the profile comprises information about the obstacle, and where the at least one corrective action message is generated based on the profile.

[0027] A twenty-first embodiment is the method of the twentieth embodiment where the information is one or more of a classification of the obstacle, the shape of the obstacle, and the spatial position of the obstacle.

[0028] A twenty-second embodiment is the method of the eighteenth embodiment further comprising sending the at least one corrective active message to another system for processing.

[0029] A twenty-third embodiment is the method of the twenty-second embodiment where the at least one corrective action message is sent using a network API.

[0030] A twenty-fourth embodiment is the method of twenty-second embodiment where the one or more instructions comprise one or more of a notification message indicating the obstacle is affecting the communication link, a change to the scheduling of the UE, and a map indicating the location of the blockage.

[0031] A twenty-fifth embodiment is the method of the twenty-fourth embodiment where the notification message, when the obstacle is an autonomous system, is directed to the autonomous system.

[0032] A twenty-sixth embodiment is the method of the twenty-fourth embodiment where the notification message includes a solution to improve the path loss.

[0033] A twenty-seventh embodiment is the method of the twenty-fourth embodiment where the notification message, when the obstacle is a user, instructs the user to move.

[0034] A twenty-eighth embodiment is the method of the eighteenth embodiment, further comprising identifying the UE as stationary.

[0035] A twenty-ninth embodiment is the method of the twenty-eighth embodiment where the UE is identified as stationary based on at least one of the channel state information, a UE type identifier, and a UE subscription.

[0036] A thirtieth embodiment is the method of eighteenth embodiment where the UE is a fixed wireless access-type UE.

[0037] A thirty-first embodiment is the method of eighteenth embodiment where the obstacle is identified using a machine learning model.P110655

[0038] A thirty-second embodiment is the method of twentieth embodiment where the profile is generated, at least in part, using a machine learning model.

[0039] A thirty-third embodiment is the method of the thirty-first embodiment where the machine learning model is a neural network.

[0040] A thirty-fourth embodiment is the method of the eighteenth embodiment where the communication link is one of a plurality of communication links in the communication system, the channel state information is further derived from the remaining communication links of the plurality of communication links, and the change may further be detected with at least one of the one or more parameters from the remaining communication links.

[0041] A thirty-fifth embodiment is a computer program comprising instructions which, when executed on at least one processor, cause the at least one processor to carry out the method according to any one of the eighteenth embodiment to the thirty-fourth embodiment.

[0042] A thirty-sixth embodiment is a computer program product, comprising instructions which, when executed on at least one processor, cause the at least one processor to carry out the method according to any one of the eighteenth embodiment to the thirty-fourth embodiment.

[0043] This summary is provided to introduce a selection of concepts in a simplified form that are further described below in the detailed description. This summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used as an indication of the scope of the claimed subject matter. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] For a more complete understanding of the present disclosure, reference is now made to the following descriptions taken in conjunction with the accompanying drawings, in which:

[0045] Fig.1 shows a flowchart illustrating one method for improving the path loss of a communication link;

[0046] Fig.2 shows a flowchart illustrating one method for determining whether a UE is a fixed wireless access (FWA) according to the present disclosure;

[0047] Fig.3 shows a flowchart illustrating one method for detecting an obstacle according to the present disclosure;P110655

[0048] Fig.4 shows a flowchart illustrating one method for detecting the properties of, or classifying, an obstacle according to the present disclosure;

[0049] Fig. 5A and 5B illustrate how the communication link between a UE and a node changes upon the appearance of an obstacle;

[0050] Fig.6 shows a flowchart illustrating a use case for the method of Fig.1;

[0051] Fig.7 illustrates a second use case for the method of Fig.1;

[0052] Fig.8 shows a flow chart illustrating training and inference pipelines for machine learning in accordance with some embodiments under the present disclosure;

[0053] Fig.9 shows an embodiment of a neural network under the present disclosure;

[0054] Fig. 10 shows a schematic of a communication system embodiment according to the present disclosure;

[0055] Fig. 11 shows a schematic of a user equipment, or computing device, for use in various embodiments under the present disclosure;

[0056] Fig.12 shows a schematic of network node embodiment according to the present disclosure; and

[0057] Fig.13 shows a schematic of a virtualization environment according to the present disclosure. DETAILED DESCRIPTION

[0058] Before describing various embodiments of the present disclosure in detail, it is to be understood that this disclosure is not limited to the parameters of the particularly exemplified systems, methods, apparatus, products, processes, and / or kits, which may, of course, vary. Thus, while certain embodiments of the present disclosure will be described in detail, with reference to specific configurations, parameters, components, elements, etc., the descriptions are illustrative and are not to be construed as limiting the scope of the claimed embodiments. Further, embodiments disclosed herein may be computer implemented, but are not limited to computer implementation. In addition, the terminology used herein is for the purpose of describing the embodiments and is not necessarily intended to limit the scope of the claimed embodiments.

[0059] As discussed herein, when discussing the communication link between a UE and a node, along with the properties original to, or shared with, the communication link, the UE, and the node, the terms communication link, UE, or node may be used interchangeably when referring to any of the properties with the understanding that the properties stem from theirP110655 respective source as required. For example, while the angle of arrival (AoA) is often attributed to a UE, it may also be referred to herein as a communication link’s AoA. This shall also apply to like or related terms, such as channels, beam pattern, and beam channel, or when making reference to an obstacle.

[0060] Certain aspects of the disclosure and their embodiments may provide solutions to the aforementioned challenges. A node, such as a base station, supporting reciprocity assisted transmission may possess a large quantity of channel state information (CSI). CSI is typically acquired during the estimation process of the SRS from a UE such as a terminal device and is used primarily for beamforming. However, CSI can be also be leveraged to further characterize and extract additional information related to the connection between the node (e.g., base station) and the UE (e.g., terminal device, IoT device). For example, CSI may comprise information related to an angle of arrival (AoA) and power headroom (PH). This CSI information may be used to: 1. Detect whether a UE is stationary, including before or after detecting a physical obstacle 2. Detect whether a UE’s radio is blocked by a physical obstacle 3. Attempt to identify the type of physical obstacle causing a blockage to a UE’s radio 4. Provide notice to a UE’s user or the UE itself of a blockage to its radio 5. Propose a solution to improve connectivity 6. Monitor a plurality of UEs to detect physical clusters of obstacles

[0061] By way of a non-limiting example, it can be observed in scenarios where a UE is a fixed wireless access (FWA)-type UE that the AoA and PH attributed to the UE’s connection to a node (e.g., gNB) is constant or very similar over time because the UE is not moving. This fact may be used to detect sudden blockages / obstacles between the connection. Continuing in this scenario, if an obstacle appears between the node and the UE, the channel will move from line of sight (LOS) to non-line of sight (NLOS). The AoA will change because the beam power from the UE to the node can no longer be received in LOS. Reflections need to be used in order to direct the power from the node to the UE. Power headroom indicates the difference between the current power and the maximum allowed power for the UE. With the obstacle suddenly appearing while the channel is in LOS, the power will be reduced because the obstacle is forcing the use of reflection for the radio path to maintain the communication link, and reflection beams are inherently less powerful than direct LOS beams.

[0062] The uplink (UL) sounding reference signal (SRS) can provide the pilot signal from the UE to the node in order to obtain CSI and make calculations such as channel estimationsP110655 and beam weights. Thereafter, AoA can be derived from beam directions and the power headroom report (PHR) can provide the PH. A similar example can be observed with line of sight (LOS) uses.

[0063] When an obstacle has been identified, the obstacle, its user, or another device or user with oversight of the obstacle, UE, or node may be notified that the obstacle is causing a blockage. The notification may be communicated via a network application programming interface (API) and may include: a message to move out of the way, instructions to change the UE scheduling, or any other solution suggestion or way to notify the obstacle, its user, or other device or user with oversight of the obstacle, UE, or node of the need to resolve the blockage.

[0064] The network API may also deliver a map highlighting where the majority of blockages are present as the position of all UEs is known, or can be derived, by the node and the information related to respective UE connections can be aggregated. Such an ability affords service providers meaningful information on how to improve the capacity of a network at a network level and better manage its connectivity. The map may be, in some embodiments, a heatmap.

[0065] Certain embodiments may provide one or more of the following technical advantages. Broaden the use of the reciprocity assisted transmission framework to improve the communication quality of UEs, to include low power consumption UEs such as IoT devices. Improve network-level energy efficiency, especially with low power consumption UEs such as IoT devices. Enable fully remote monitoring and control of low power consumption UEs such as IoT devices through the use of a network API. Improve overall signal quality by avoiding certain blockages.

[0066] Some of the embodiments contemplated herein will now be described more fully with reference to the accompanying drawings. Embodiments are provided by way of example to convey the scope of the subject matter to those skilled in the art.

[0067] Fig.1 shows a flowchart illustrating one method 100 for improving the path loss of a communication link according to an embodiment. The communication link may be for a UE and node pair, such as an IoT device and a gNB, or any other wireless communication link. Initially, the channel state information (CSI) is monitored 102 for a communication link. The monitoring may include one or more CSI parameters, both observed and calculated / derived, such as the angle of arrival (AoA) and power headroom (PH). In some embodiments, an attempt is made to identify the UE as stationary. This attempt may be made before method 100 commences, or at any point after the monitoring has commenced, including after detecting / identifying an obstacle. In embodiments where the UE is identified as stationaryP110655 before monitoring commences, the reliability of physical obstacle detection during monitoring is improved because it is understood that the path loss of a stationary UE should remain relatively constant. The UE may be identified as stationary based on one or more of the CSI, a UE type identifier (e.g., a fixed wireless access, a window sensor), a UE subscription, or any other type of preconfigured information.

[0068] While monitoring the CSI, a change may be detected 104. When the CSI has also been used to identify the UE as stationary, the change detection occurs after the stationary identification. The change may be to one or more of the monitored CSI parameters and may also represent changes due to an increase in the path loss for a communication link. Additionally, the change may be a single occurrence detected while monitoring or may be based on multiple occurrences detected over time. Multiple occurrences can reinforce that the cause of the change is permanent (e.g., an obstacle is now present). In some embodiments, when the change to the monitored CSI parameters is detected, an additional check (not shown) may be made to confirm that the UE is stationary if not already known. This additional check can reaffirm that the detected change is due to an obstacle as the CSI parameters for stationary UEs tend to remain relatively constant. The detected change may then be used to identify 106 an obstacle as the source of the communication link degradation. One example of how to identify an obstacle is with a method such as method 400 disclosed herein, which may use a machine learning model, such as a neural network as described with reference to Fig.10 herein. A corrective action message may then be generated 108 comprising one or more instructions and / or solutions to address the obstacle. By addressing the obstacle, an attempt is made using the one or more instructions and / or solutions to improve the path loss degradation occurring due to the obstacle. Examples of the one or more instructions include a notification message indicating the obstacle is affecting the communication link, a change to the scheduling of the UE, a map indicating the location of the blockage, and a notification message instructing a user to move if the user is the source of the blockage.

[0069] In some embodiments, a profile for the obstacle may be generated (not shown) before generating 108 the corrective action message. The profile may include information about the obstacle, such as a classification of the obstacle, its shape, and its spatial position. Additionally, the profile, or parts thereof, may be used when generating 108 the corrective action message. The profile may also be generated, in some embodiments, with a machine learning model such as the neural network described with reference to Fig.10.

[0070] In yet other embodiments, the method 100 may send (not shown) the corrective action message to another system, such as an autonomous system, for processing. AnP110655 autonomous system may be moving robot, forklift, car, or any other system capable of independent movement. A network API may, in some embodiments, be used to send the corrective action message.

[0071] Embodiments may also include a plurality of communication links. In such an embodiment, CSI can be derived from multiple communication links, with a change being capable of being detected amongst any CSI parameter from the communication links. Multiple changes may also be detected, which may subsequently be aggregated for further analysis when identifying 106 an obstacle.

[0072] Fig.2 shows a flowchart illustrating one method 200 for determining whether a UE is a fixed wireless access (FWA) type UE according to the present disclosure. Other ways include from the: UE type identifier, UE subscription, or other preconfigured information. Understanding whether a UE is static, as in the case with a FWA, aids in detecting sudden changes in the channel, or communication link, between the UE and the node.

[0073] Initially, the PH for a UE of a communication link is detected and stored 202. The PH may be acquired from NR L1 properties. The AoA is also detected and stored 204. For the AoA, it may be detected when it is calculated based on the UL SRS. A PH change, measured as the difference between the stored PH and a new PH detection, is then compared 206 to a threshold. If the PH change is not within the threshold, the UE may be marked 208 as a non- FWA UE. If the PH change is within the threshold, an AoA change, measured as the difference between the stored AoA and a new AoA detection, is then compared 210 to a second threshold. If the AoA change is not within the second threshold, the UE may be marked 208 as a non- FWA UE. If the AoA change is within the second threshold, the UE may be marked 212 as a FWA UE.

[0074] Fig. 3 shows a flowchart illustrating one method 300 for detecting an obstacle according to the present disclosure when the UE is a static UE type, such as a FWA UE. In addition to utilizing the PH and AoA based on the UL SRS, the time of when the obstacle appears is monitored along with its occurrence count and duration.

[0075] Initially, method 300 begins to monitor 302 for obstacles amongst UEs with established communication links. A PH level for a UE may be detected 304 from a PHR and identified as^^^^^^^^^^^^^^^^^^^^. The AoA may then be detected 306 based on the signal to interference plus noise ratio (SINR) level per beam and identified as ^^^^^^^^^^^^^^^^^^^^^^. A determination 308, which may follow the method 200 disclosed herein, is then made onP110655 whether the UE is static. If the UE is not found to be static, no UE obstacle has been detected and method 300 returns to detecting 304 the PH for UEs.

[0076] If the UE is found to be static,calculated and compared 310 to an AoA threshold parameter, where ^^^^^^^^^^^^^^^^^^is a previously stored AoA value associated with the UE. The AoA threshold parameter may be a user defined system parameter that can be tuned according to the deployment scenario. If− ^^^^^^^^^^^^^^^^^^) is less than the AoA threshold parameter, no UEobstacle has been detected and method 300 returns to detecting 304 the PH for UEs. If^^^^^^(^^^^^^^^^^^^^^^^^^^^^^ − ^^^^^^^^^^^^^^^^^^) is greater than the AoA threshold parameter,method 300 progresses to analyze^^^^^^^^^^^^^^^^^^^^.

[0077] First, ^^^^^^^^^^^^ value is calculated 312, where−^^^^^^^^^^^^^^^^. Then, the ^^^^^^(^^^^^^^^^^^^) is calculated and compared to a PH threshold parameter. The PH threshold parameter may be a user defined system parameter that can be tuned according to the deployment scenario. If ^^^^^^(^^^^^^^^^^^^) is less than the PH threshold value,^^^^^^^^^^^^is insignificant, no UE obstacle has been potentially detected or removed, and method 300 returns to detecting 304 the PH for UEs.^^^^^^^^^^^^is also compared to the PH threshold parameter. If^^^^^^^^^^^^is greater than the PH threshold parameter and a positive value, the channel condition of the communication link has improved and an obstacle, if previously detected, may have been detected as being removed 314. When detecting an obstacle for removal, the time and obstacle properties that were detected may be stored. If^^^^^^^^^^^^is less than the PH threshold parameter and a negative value, the channel condition of the communication link has worsened and an obstacle may have been detected 316. When possibly detecting 316 an obstacle, the time and obstacle properties at detection may be stored.

[0078] In order to confirm an obstacle detected as being removed 314 or that a new obstacle may be detected 316, either of which being referred to as a potential obstacle action, a waiting period may be used. In this embodiment, the method 300 compares 318 the time elapsed since the potential obstacle action against a notification duration^^.^^may be a user defined system parameter that can be tuned according to the deployment scenario. If the elapsedP110655 time does not exceed^^, not enough time has passed since the potential obstacle action occurred for the potential obstacle action to be considered reliable, and the method 300 returns to detecting 304 the PH for UEs. In this scenario, the obstacle properties for the potentially removed obstacle may persist so that the obstacle continues to be considered through subsequent iterations of the method 300, each iteration extending the elapsed time since the potential obstacle action until the elapsed time exceeds^^. Stated differently, even though a new obstacle may have been detected or an existing obstacle may have been removed, the potential obstacle action is not considered reliable until the elapsed time from the potential obstacle action exceeds ^^. If the elapsed time exceeds ^^, sufficient time has passed since the potential obstacle action occurred to consider the potential obstacle action reliable, and an obstacle update message is sent 320. The obstacle update message may include information about the direction of the blocked UE based on the AoA as well as obstacle history time patterns, such as: Obstacle type A detected. Also seen from: 11:34 – 11:44 10:34 –10:44 A network API may be leveraged to send the obstacle update message to the desired destination (e.g., a user on the network the UE is connected to). After sending the obstacle update message, the method 300 may return to detecting 304 the PH for UEs. In some embodiments, the obstacle update message and the corrective action message of method 100 may be one in the same, or the corrective action message may comprise the obstacle update message.

[0079] Fig. 4 shows a flowchart illustrating one method 400 for detecting the properties of, or classifying, an obstacle according to the present disclosure. Initially, an obstacle can be detected 402 based on properties such as its affect on a UE’s beam pattern, AoA, and PH as well as time factors (e.g., when and the duration of the obstacle’s presence). The properties can then be collected 404 to identify an obstacle and know if multiple obstacles may be present. With the collected properties, classification may then be performed 406 on the obstacles(s). In some embodiments, classification may be performed using prediction algorithms and / or AI / ML that has been trained to identify patterns for different types of obstacles. A non-limiting set of example obstacle types that may be used for classification include: human, moving (autonomous) robot, and dry wall.P110655

[0080] Method 400 continues, in this embodiment, with a series of actions based on the classification performed 406 using the non-limiting set of example obstacle types. If the obstacle is found to be a human 408a, using the classification and / or the obstacle properties, the obstacle is tagged human 410a. Tagging allows for an obstacle to be monitored with the option of having additional actions performed. For example, if the obstacle is tagged a human, an additional action may be setting off an alarm if no human is supposed to be in the area where the obstacle was detected. If the obstacle is found to be a moving (autonomous) robot 408b, using the classification and / or the obstacle properties, the obstacle is tagged moving (autonomous) robot 410c. If the obstacle is found to be dry wall 408c, using the classification and / or the obstacle properties, the obstacle is tagged dry wall 410c.

[0081] Figs. 5A and 5B illustrate how the communication link 506 between a UE 502, here a stationary UE such as a window sensor IoT device, and a node 504, here a 5G network node, changes upon the appearance of an obstacle 508 according to the present disclosure. Initially, and as shown in Fig.5A, the UE 502 is connected to the node 504 with communication link 506 in a line of sight position. Fig.5B introduces obstacle 508, which forces the original communication link 506’ to be blocked to some degree. In this example, the obstacle 508 is a newly installed movable office wall. Having increased the path loss (reduced connectivity), obstacle 508 causes communication link 506 to be redirected, or reflected, off of wall 510 to provide the best connection to node 504. In Fig.5B, communication link 506 is now in a non- line of sight position.

[0082] To further demonstrate the example illustrated by Figs.5A and 5B, UE 502 is small and intended to be used for a considerable amount of time before changing its power source (e.g., battery). During normal operation, UE 502 may, at regular intervals, be transmitting information regarding the status of the window via communication link 506 to the node 504. In addition to an increase in path loss, the presence of obstacle 508 greatly increases the power consumption of the UE 502 as UE 502 tries to improve its suddenly worsened communication link 506’. This increase in power consumption drains the power source at a much faster rate, forcing a replacement much earlier than intended. An obstacle detection service that may operate according to method 100 disclosed herein will continuously monitor the AoA and PH of the UE 502. The service may detect a change in the AoA as well as a reduction in the PH, and then attempt to indicate what kind of object is blocking UE 502 using channel properties as discussed with reference to Fig. 4. The service may then inform the service owner or a subscriber about the obstacle 508 and the position of the UE 502 using, for example, a network API.P110655

[0083] Fig. 6 shows a flowchart illustrating a use case 600 of a fixed wireless access (FWA) UE suddenly losing line of sight (LOS) connectivity, according to the method 100 of Fig. 1. The method 100 may be embodied in an obstacle detection service operating in the network. Initially, a FWA UE is connected 602 to a communication system and commences to be monitored. At some point in time while the FWA UE is being monitored, an obstacle appears 604 in the direction of the node (e.g., gNB) the FWA UE is connected to. In other words, an obstacle is now blocking the communication link between the FWA UE and the node to some degree. The obstacle appearance results in the FWA UE no longer being in LOS, but rather now in NLOS. Shortly thereafter, the obstacle is detected 606 with a detection algorithm (utilizing the methods disclosed herein) to identify the object. A user is then notified 608, using the network API, to move the obstacle to another location.

[0084] Fig. 7 illustrates a second use case for the method of Fig. 1, where in a factory environment, an automated connected forklift 708 suddenly blocks the communication link, the combination of 706a and 706b, between surveillance camera 702 and node 704. The method 100 may be embodied in an obstacle detection service operating in the network. Surveillance camera 702 is located in a large manufacturing plant and is used to stream video to a server with AI / ML detection algorithms to detect faulty production specimens. The plant contains a large number of automated and connected devices (e.g., truck robots). An automated and connected device, such as forklift 708, stops at a location that blocks the LOS connectivity for the communication link established between the surveillance camera 702 and node 704. This can be seen in Fig.7 with communication link 706a ceasing at forklift 708 and communication link 706b showing the portion of the communication link that can no longer be established or is a very weak connection. As a result, the QoS of the transmission from the surveillance camera 702 is significantly reduced. In short order, the forklift 708 is detected using the observed changes to the AoA and PH. The obstacle detection service may then predict that the obstacle is metal and large, and further predicts that the blockage is likely caused by another UE, automated connected forklift 708, using UL positioning and AoA beams. A notification regarding the obstacle is then generated and sent using the network API. The network API may be connected to a factory management system for the factory, in which case the forklift 708 may be instructed, via forklift communication link 710, to move. The obstacle detection service may then indicate if / when the obstacle, forklift 708, has moved. Additional EmbodimentsP110655

[0085] Fig.8 shows a flow chart illustrating training and inference pipelines for machine learning in accordance with some embodiments under the present disclosure.

[0086] The methods of Figs.1 – 4 can be performed in part using ML-based methods such as those described herein, for e.g., classifying a detected obstacle and more. In certain embodiments a ML / AI engine may be included somewhere in the communication system 2100, such as in host 2116 or in other components for training or implementing a ML / AI model. The architecture of an ML model (e.g., structure, number of layers, nodes per layer, activation function etc.) may need to be tailored for each particular use case. For example, properties to vary can include the types of obstacles to classify and their characteristics, as discussed below. These may all need to be considered when designing the ML model’s architecture.

[0087] Building an AI / ML model includes several development steps where the actual training of the ML model is just one step in a training pipeline. An important part in AI / ML development is the AI / ML model lifecycle management. One embodiment of a model lifecycle management procedure 2700 is illustrated in Fig. 8. The model lifecycle management comprises two pipelines: a training pipeline 2705 and an inference pipeline 2750.

[0088] At 2710 in the training pipeline 2705, data ingestion 2710 occurs, which includes gathering raw (training) data from a data storage. After data ingestion 2710, there may also be a step that controls the validity of the gathered data. At 2715 data pre-processing occurs, which can include feature engineering applied to the gathered data. This may involve, e.g., data normalization or data formatting or transformation required for the input data to the AI / ML model. After the ML model’s architecture is fixed, it should be trained on one or more datasets. At 2720 model training is performed in which the AI / ML model is trained with the raw training data. To achieve good performance during live operation in a system (the so-called inference phase), the training datasets should be representative of actual data the ML model will encounter during live operation. The training process often involves numerically tuning the ML model’s trainable parameters (e.g., the weights and biases of the underlying neural network (NN)) to minimize a loss function on the training datasets. The loss function may be, for example, based on a maximum / minimum spend on tools or supplies, a maximum / minimum speed of performing a task, or other output. The purpose of the loss function is to meaningfully quantify the reconstruction error for the particular use case at hand. At 2725 model evaluation can be performed where the performance is benchmarked to some baseline. Model training 2720 and evaluation 2725 can be iterated until an acceptable level of performance is achieved. At 2730 model registration occurs, in which the AI / ML model is registered with any corresponding data on how the AI / ML model was developed, and e.g., AI / ML modelP110655 evaluation data. At 2735 model deployment occurs, wherein the trained / re-trained AI / ML model is implemented in the inference pipeline 2750.

[0089] Data ingestion 2755 in the inference pipeline 2750 refers to gathering raw (inference) data from a data source. Data pre-processing 2760 can be essentially identical / similar to the data pre-processing 2715 of the training pipeline 2705. At 2765, the operational model received from the training pipeline 2705 is used to process new data received during operation of e.g., the classification of a detected obstacle. At 2770 data and model monitoring is performed. Here the inference data is analyzed to determine whether the inference data are from a distribution that aligns with the training data, as well as monitoring model outputs for detecting any performance, or operational, variance or drifts. The variance or drift is used at 2745 (drift detection) to update the AI / ML model registration.

[0090] The training process is typically based on some variant of a gradient descent algorithm, which, at its core, can comprise three components: a feedforward step, a back propagation step, and a parameter optimization step.

[0091] In some implementations, a function F(⋅) may be generated by a ML process, such as, for example, supervised learning, reinforcement learning, and / or unsupervised learning. It should further be understood that supervised learning may be done in various ways, such as, for example, using random forests, support vector machines, neural networks, and the like. By way of non-limiting example, any of the following types of neural networks that may be utilized, including, deep neural networks (DNNs), convolutional neural networks (CNNs), and recurrent neural networks (RNNs), or any other known or future neural network that satisfies the needs of the system. In an implementation using supervised learning the neural networks may be easily integrated into hardware described in communication system 2100 of Fig. 10, such as host 2116 (e.g., in the form of simple vector-matrix multiplications).

[0092] Referring now to Fig. 9, an example NN 2900 (e.g., DNN) is shown. In some implementations, and as shown, the neural network 2900 may include two hidden layers represented by dashed boxes 2901 and 2902. In one implementation, the inputs 2903 may be fed into the NN 2900. Next, the inputs 2903 may go through a set of hidden layers (e.g., 2901 and / or 2902). Once the inputs 2903 pass though the hidden layers 2901 and / or 2902, they may be output (e.g., as an output layer) as e.g., a classification of a human, moving robot, dry wall, the type of material the obstacle may be made of, the size of the obstacle, etc. Possible inputs can include e.g.: changes to a UE’s beam pattern, AoA, PH, as well as time factors (e.g., when and the duration of the obstacle’s presence) and obstacle factors such as its shape, or other variables.P110655

[0093] As should be understood by one of ordinary skill in the art, in order for the NN 2900 to output a proper analysis, it should be trained properly (e.g., with a collection of samples) to accurately extract the likelihood values. If not trained properly, overfitting (e.g., when the NN memorizes the structure of the preambles but is unable to generalize to unseen preamble characteristics) or underfitting (e.g., when the NN is unable to learn a proper function even on the data that it was trained on) may happen. Thus, implementations may exist that prevent overfitting or underfitting, involving a set of well-engineered features that must be extracted from the preamble characteristics.

[0094] Fig. 10 shows an example of a communication system 2100 in accordance with some embodiments.

[0095] In the example, the communication system 2100 includes a telecommunication network 2102 that includes an access network 2104, such as a radio access network (RAN), and a core network 2106, which includes one or more core network nodes 2108. The access network 2104 includes one or more access network nodes, such as network nodes 2110a and 2110b (one or more of which may be generally referred to as network nodes 2110), or any other similar 3rdGeneration Partnership Project (3GPP) access nodes or non-3GPP access points. Moreover, as will be appreciated by those of skill in the art, a network node is not necessarily limited to an implementation in which a radio portion and a baseband portion are supplied and integrated by a single vendor. Thus, it will be understood that network nodes include disaggregated implementations or portions thereof. For example, in some embodiments, the telecommunication network 2102 includes one or more Open-RAN (ORAN) network nodes. An ORAN network node is a node in the telecommunication network 2102 that supports an ORAN specification (e.g., a specification published by the O-RAN Alliance, or any similar organization) and may operate alone or together with other nodes to implement one or more functionalities of any node in the telecommunication network 2102, including one or more network nodes 2110 and / or core network nodes 2108.

[0096] Examples of an ORAN network node include an open radio unit (O-RU), an open distributed unit (O-DU), an open central unit (O-CU), including an O-CU control plane (O- CU-CP) or an O-CU user plane (O-CU-UP), a RAN intelligent controller (near-real time or non-real time) hosting software or software plug-ins, such as a near-real time control application (e.g., xApp) or a non-real time control application (e.g., rApp), or any combination thereof (the adjective “open” designating support of an ORAN specification). The network node may support a specification by, for example, supporting an interface defined by the ORAN specification, such as an A1, F1, W1, E1, E2, X2, Xn interface, an open fronthaul userP110655 plane interface, or an open fronthaul management plane interface. Moreover, an ORAN access node may be a logical node in a physical node. Furthermore, an ORAN network node may be implemented in a virtualization environment (described further below) in which one or more network functions are virtualized. For example, the virtualization environment may include an O-Cloud computing platform orchestrated by a Service Management and Orchestration Framework via an O-2 interface defined by the O-RAN Alliance or comparable technologies. The network nodes 2110 facilitate direct or indirect connection of user equipment (UE), such as by connecting UEs 2112a, 2112b, 2112c, and 2112d (one or more of which may be generally referred to as UEs 2112) to the core network 2106 over one or more wireless connections.

[0097] Example wireless communications over a wireless connection include transmitting and / or receiving wireless signals using electromagnetic waves, radio waves, infrared waves, and / or other types of signals suitable for conveying information without the use of wires, cables, or other material conductors. Moreover, in different embodiments, the communication system 2100 may include any number of wired or wireless networks, network nodes, UEs, and / or any other components or systems that may facilitate or participate in the communication of data and / or signals whether via wired or wireless connections. The communication system 2100 may include and / or interface with any type of communication, telecommunication, data, cellular, radio network, and / or other similar type of system.

[0098] The UEs 2112 may be any of a wide variety of communication devices, including wireless devices arranged, configured, and / or operable to communicate wirelessly with the network nodes 2110 and other communication devices. Similarly, the network nodes 2110 are arranged, capable, configured, and / or operable to communicate directly or indirectly with the UEs 2112 and / or with other network nodes or equipment in the telecommunication network 2102 to enable and / or provide network access, such as wireless network access, and / or to perform other functions, such as administration in the telecommunication network 2102.

[0099] In the depicted example, the core network 2106 connects the network nodes 2110 to one or more host computing systems, such as host 2116. These connections may be direct or indirect via one or more intermediary networks or devices. In other examples, network nodes may be directly coupled to hosts. The core network 2106 includes one more core network nodes (e.g., core network node 2108) that are structured with hardware and software components. Features of these components may be substantially similar to those described with respect to the UEs, network nodes, and / or hosts, such that the descriptions thereof are generally applicable to the corresponding components of the core network node 2108. Example core network nodes include functions of one or more of a Mobile Switching Center (MSC), MobilityP110655 Management Entity (MME), Home Subscriber Server (HSS), Access and Mobility Management Function (AMF), Session Management Function (SMF), Authentication Server Function (AUSF), Subscription Identifier De-concealing function (SIDF), Unified Data Management (UDM), Security Edge Protection Proxy (SEPP), Network Exposure Function (NEF), and / or a User Plane Function (UPF).

[0100] The host 2116 may be under the ownership or control of a service provider other than an operator or provider of the access network 2104 and / or the telecommunication network 2102. The host 2116 may host a variety of applications to provide one or more service. Examples of such applications include live and pre-recorded audio / video content, data collection services such as retrieving and compiling data on various ambient conditions detected by a plurality of UEs, analytics functionality, social media, functions for controlling or otherwise interacting with remote devices, functions for an alarm and surveillance center, AI / ML related functions or any other such function performed by a server.

[0101] As a whole, the communication system 2100 of Fig. 10 enables connectivity between the UEs, network nodes, and hosts. In that sense, the communication system may be configured to operate according to predefined rules or procedures, such as specific standards that include, but are not limited to: Global System for Mobile Communications (GSM); Universal Mobile Telecommunications System (UMTS); Long Term Evolution (LTE), and / or other suitable 2G, 3G, 4G, 5G standards, or any applicable future generation standard (e.g., 6G); wireless local area network (WLAN) standards, such as the Institute of Electrical and Electronics Engineers (IEEE) 802.11 standards (WiFi); and / or any other appropriate wireless communication standard, such as the Worldwide Interoperability for Microwave Access (WiMax), Bluetooth, Z-Wave, Near Field Communication (NFC) ZigBee, LiFi, and / or any low-power wide-area network (LPWAN) standards such as LoRa and Sigfox.

[0102] In some examples, the telecommunication network 2102 is a cellular network that implements 3GPP standardized features. Accordingly, the telecommunications network 2102 may support network slicing to provide different logical networks to different devices that are connected to the telecommunication network 2102. For example, the telecommunications network 2102 may provide Ultra Reliable Low Latency Communication (URLLC) services to some UEs, while providing Enhanced Mobile Broadband (eMBB) services to other UEs, and / or Massive Machine Type Communication (mMTC) / Massive IoT services to yet further UEs.

[0103] In some examples, the UEs 2112 are configured to transmit and / or receive information without direct human interaction. For instance, a UE may be designed to transmit information to the access network 2104 on a predetermined schedule, when triggered by anP110655 internal or external event, or in response to requests from the access network 2104. Additionally, a UE may be configured for operating in single- or multi-RAT or multi-standard mode. For example, a UE may operate with any one or combination of Wi-Fi, NR (New Radio) and LTE, i.e. being configured for multi-radio dual connectivity (MR-DC), such as E-UTRAN (Evolved-UMTS Terrestrial Radio Access Network) New Radio – Dual Connectivity (EN- DC).

[0104] In the example, the hub 2114 communicates with the access network 2104 to facilitate indirect communication between one or more UEs (e.g., UE 2112c and / or 2112d) and network nodes (e.g., network node 2110b). In some examples, the hub 2114 may be a controller, router, content source and analytics, or any of the other communication devices described herein regarding UEs. For example, the hub 2114 may be a broadband router enabling access to the core network 2106 for the UEs. As another example, the hub 2114 may be a controller that sends commands or instructions to one or more actuators in the UEs. Commands or instructions may be received from the UEs, network nodes 2110, or by executable code, script, process, or other instructions in the hub 2114. As another example, the hub 2114 may be a data collector that acts as temporary storage for UE data and, in some embodiments, may perform analysis or other processing of the data. As another example, the hub 2114 may be a content source. For example, for a UE that is a VR device, display, loudspeaker, or other media delivery device, the hub 2114 may retrieve VR assets, video, audio, or other media or data related to sensory information via a network node, which the hub 2114 then provides to the UE either directly, after performing local processing, and / or after adding additional local content. In still another example, the hub 2114 acts as a proxy server or orchestrator for the UEs, in particular if one or more of the UEs are low energy IoT devices.

[0105] The hub 2114 may have a constant / persistent or intermittent connection to the network node 2110b. The hub 2114 may also allow for a different communication scheme and / or schedule between the hub 2114 and UEs (e.g., UE 2112c and / or 2112d), and between the hub 2114 and the core network 2106. In other examples, the hub 2114 is connected to the core network 2106 and / or one or more UEs via a wired connection. Moreover, the hub 2114 may be configured to connect to an M2M service provider over the access network 2104 and / or to another UE over a direct connection. In some scenarios, UEs may establish a wireless connection with the network nodes 2110 while still connected via the hub 2114 via a wired or wireless connection. In some embodiments, the hub 2114 may be a dedicated hub – that is, a hub whose primary function is to route communications to / from the UEs from / to the network node 2110b. In other embodiments, the hub 2114 may be a non-dedicated hub – that is, a deviceP110655 which is capable of operating to route communications between the UEs and network node 2110b, but which is additionally capable of operating as a communication start and / or end point for certain data channels.

[0106] Fig. 11 shows a UE 2200 in accordance with some embodiments. The UE 2200 presents additional details of some embodiments of the UE 2112 of Fig.10. As used herein, a UE refers to a device capable, configured, arranged and / or operable to communicate wirelessly with network nodes and / or other UEs. Examples of a UE include, but are not limited to, a smart phone, mobile phone, cell phone, voice over IP (VoIP) phone, wireless local loop phone, desktop computer, personal digital assistant (PDA), wireless cameras, gaming console or device, music storage / playback device, wearable terminal device, wireless endpoint, mobile station, tablet, laptop, laptop-embedded equipment (LEE), laptop-mounted equipment (LME), an Augmented Reality (AR) or Virtual Reality (VR) device, wireless customer-premise equipment (CPE), vehicle, vehicle-mounted or vehicle embedded / integrated wireless device, etc. Other examples include any UE identified by the 3rd Generation Partnership Project (3GPP), including a narrow band internet of things (NB-IoT) UE, a machine type communication (MTC) UE, and / or an enhanced MTC (eMTC) UE.

[0107] A UE may support device-to-device (D2D) communication, for example by implementing a 3GPP standard for sidelink communication, Dedicated Short-Range Communication (DSRC), vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I), or vehicle- to-everything (V2X). In other examples, a UE may not necessarily have a user in the sense of a human user who owns and / or operates the relevant device. Instead, a UE may represent a device that is intended for sale to, or operation by, a human user but which may not, or which may not initially, be associated with a specific human user (e.g., a smart sprinkler controller). Alternatively, a UE may represent a device that is not intended for sale to, or operation by, an end user but which may be associated with or operated for the benefit of a user (e.g., a smart power meter).

[0108] The UE 2200 includes processing circuitry 2202 that is operatively coupled via a bus 2204 to an input / output interface 2206, a power source 2208, a computer program product 2215 in the form of a memory 2210, a communication interface 2212, and / or any other component, or any combination thereof. Certain UEs may utilize all or a subset of the components shown in Fig.11. The level of integration between the components may vary from one UE to another UE. Further, certain UEs may contain multiple instances of a component, such as multiple processors, memories, transceivers, transmitters, receivers, etc.P110655

[0109] The processing circuitry 2202 is configured to process instructions and data and may be configured to implement any sequential state machine operative to execute instructions stored as machine-readable computer programs in the memory 2210. The processing circuitry 2202 may be implemented as one or more hardware-implemented state machines (e.g., in discrete logic, field-programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), etc.); programmable logic together with appropriate firmware; one or more stored computer programs, general-purpose processors, such as a microprocessor or digital signal processor (DSP), together with appropriate software; or any combination of the above. For example, the processing circuitry 2202 may include multiple central processing units (CPUs).

[0110] In the example, the input / output interface 2206 may be configured to provide an interface or interfaces to an input device, output device, or one or more input and / or output devices. Examples of an output device include a speaker, a sound card, a video card, a display, a monitor, a printer, an actuator, an emitter, a smartcard, another output device, or any combination thereof. An input device may allow a user to capture information into the UE 2200. Examples of an input device include a touch-sensitive or presence-sensitive display, a camera (e.g., a digital camera, a digital video camera, a web camera, etc.), a microphone, a sensor, a mouse, a trackball, a directional pad, a trackpad, a scroll wheel, a smartcard, and the like. The presence-sensitive display may include a capacitive or resistive touch sensor to sense input from a user. A sensor may be, for instance, an accelerometer, a gyroscope, a tilt sensor, a force sensor, a magnetometer, an optical sensor, a proximity sensor, a biometric sensor, etc., or any combination thereof. An output device may use the same type of interface port as an input device. For example, a Universal Serial Bus (USB) port may be used to provide an input device and an output device.

[0111] In some embodiments, the power source 2208 is structured as a battery or battery pack. Other types of power sources, such as an external power source (e.g., an electricity outlet), photovoltaic device, or power cell, may be used. The power source 2208 may further include power circuitry for delivering power from the power source 2208 itself, and / or an external power source, to the various parts of the UE 2200 via input circuitry or an interface such as an electrical power cable. Delivering power may be, for example, for charging of the power source 2208. Power circuitry may perform any formatting, converting, or other modification to the power from the power source 2208 to make the power suitable for the respective components of the UE 2200 to which power is supplied.

[0112] The memory 2210 may be or be configured to include memory such as random access memory (RAM), read-only memory (ROM), programmable read-only memoryP110655 (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), magnetic disks, optical disks, hard disks, removable cartridges, flash drives, and so forth. In one example, the memory 2210 includes one or more computer programs 2214, such as an operating system, web browser application, a widget, gadget engine, or other application, and corresponding data 2216. The memory 2210 may store, for use by the UE 2200, any of a variety of various operating systems or combinations of operating systems.

[0113] The memory 2210 may be configured to include a number of physical drive units, such as redundant array of independent disks (RAID), flash memory, USB flash drive, external hard disk drive, thumb drive, pen drive, key drive, high-density digital versatile disc (HD- DVD) optical disc drive, internal hard disk drive, Blu-Ray optical disc drive, holographic digital data storage (HDDS) optical disc drive, external mini-dual in-line memory module (DIMM), synchronous dynamic random access memory (SDRAM), external micro-DIMM SDRAM, smartcard memory such as tamper resistant module in the form of a universal integrated circuit card (UICC) including one or more subscriber identity modules (SIMs), such as a USIM and / or ISIM, other memory, or any combination thereof. The UICC may for example be an embedded UICC (eUICC), integrated UICC (iUICC) or a removable UICC commonly known as ‘SIM card.’ The memory 2210 may allow the UE 2200 to access instructions, application programs and the like, stored on transitory or non-transitory memory media, to off-load data, or to upload data. An article of manufacture, such as one utilizing a communication system may be tangibly embodied as or in the memory 2210, which may be or comprise a device-readable storage medium.

[0114] The computer program product 2215 comprises one or more computer programs 2214 which comprise computer program code loadable into the processing circuitry 2202, wherein the one or more computer programs 2214 comprise code adapted to cause the UE 2200 to perform the steps of the method described herein, when the computer program code is executed by the processing circuitry 2202. In other words, the one or more computer programs 2214 may be a software hosted by the UE 2200.

[0115] The processing circuitry 2202 may be configured to communicate with an access network or other network using the communication interface 2212. The communication interface 2212 may comprise one or more communication subsystems and may include or be communicatively coupled to an antenna 2222. The communication interface 2212 may include one or more transceivers used to communicate, such as by communicating with one or more remote transceivers of another device capable of wireless communication (e.g., another UE orP110655 a network node in an access network). Each transceiver may include a transmitter 2218 and / or a receiver 2220 appropriate to provide network communications (e.g., optical, electrical, frequency allocations, and so forth). Moreover, the transmitter 2218 and receiver 2220 may be coupled to one or more antennas (e.g., antenna 2222) and may share circuit components, software or firmware, or alternatively be implemented separately.

[0116] In the illustrated embodiment, communication functions of the communication interface 2212 may include cellular communication, Wi-Fi communication, LPWAN communication, data communication, voice communication, multimedia communication, short-range communications such as Bluetooth, near-field communication, location-based communication such as the use of the global positioning system (GPS) to determine a location, another like communication function, or any combination thereof. Communications may be implemented in according to one or more communication protocols and / or standards, such as IEEE 802.11, Code Division Multiplexing Access (CDMA), Wideband Code Division Multiple Access (WCDMA), GSM, LTE, New Radio (NR), UMTS, WiMax, Ethernet, transmission control protocol / internet protocol (TCP / IP), synchronous optical networking (SONET), Asynchronous Transfer Mode (ATM), QUIC, Hypertext Transfer Protocol (HTTP), and so forth.

[0117] Regardless of the type of sensor, a UE may provide an output of data captured by its sensors, through its communication interface 2212, via a wireless connection to a network node. Data captured by sensors of a UE can be communicated through a wireless connection to a network node via another UE. The output may be periodic (e.g., once every 15 minutes if it reports the sensed temperature), random (e.g., to even out the load from reporting from several sensors), in response to a triggering event (e.g., when moisture is detected an alert is sent), in response to a request (e.g., a user initiated request), or a continuous stream (e.g., a live video feed of a patient).

[0118] As another example, a UE comprises an actuator, a motor, or a switch, related to a communication interface configured to receive wireless input from a network node via a wireless connection. In response to the received wireless input the states of the actuator, the motor, or the switch may change. For example, the UE may comprise a motor that adjusts the control surfaces or rotors of a drone in flight according to the received input or to a robotic arm performing a medical procedure according to the received input.

[0119] A UE, when in the form of an Internet of Things (IoT) device, may be a device for use in one or more application domains, these domains comprising, but not limited to, city wearable technology, extended industrial application and healthcare. Non-limiting examplesP110655 of such an IoT device are a device which is or which is embedded in: a connected refrigerator or freezer, a TV, a connected lighting device, an electricity meter, a robot vacuum cleaner, a voice controlled smart speaker, a home security camera, a motion detector, a thermostat, a smoke detector, a door / window sensor, a flood / moisture sensor, an electrical door lock, a connected doorbell, an air conditioning system like a heat pump, an autonomous vehicle, a surveillance system, a weather monitoring device, a vehicle parking monitoring device, an electric vehicle charging station, a smart watch, a fitness tracker, a wearable for tactile augmentation or sensory enhancement, a water sprinkler, an animal- or item-tracking device, a sensor for monitoring a plant or animal, an industrial robot, an Unmanned Aerial Vehicle (UAV), and any kind of medical device, like a heart rate monitor or a remote controlled surgical robot. A UE in the form of an IoT device comprises circuitry and / or software in dependence of the intended application of the IoT device in addition to other components as described in relation to the UE 2200 shown in Fig.11.

[0120] As yet another specific example, in an IoT scenario, a UE may represent a machine or other device that performs monitoring and / or measurements, and transmits the results of such monitoring and / or measurements to another UE and / or a network node. The UE may in this case be an M2M device, which may in a 3GPP context be referred to as an MTC device. As one particular example, the UE may implement the 3GPP NB-IoT standard. In other scenarios, a UE may represent a vehicle, such as a car, a bus, a truck, a ship and an airplane, or other equipment that is capable of monitoring and / or reporting on its operational status or other functions associated with its operation.

[0121] The UE 2200 may also be used to analyze and / or optimize the functionalities described with respect to object classification, for example, or to perform AI / ML-related tasks and analyses as described herein.

[0122] In practice, any number of UEs may be used together with respect to a single use case. For example, a first UE might be or be integrated in a drone and provide the drone’s speed information (obtained through a speed sensor) to a second UE that is a remote controller operating the drone. When the user makes changes from the remote controller, the first UE may adjust the throttle on the drone (e.g. by controlling an actuator) to increase or decrease the drone’s speed. The first and / or the second UE can also include more than one of the functionalities described above. For example, a UE might comprise the sensor and the actuator, and handle communication of data for both the speed sensor and the actuators.

[0123] Fig. 12 shows a network node 2300 in accordance with some embodiments. As used herein, network node refers to equipment capable, configured, arranged and / or operableP110655 to communicate directly or indirectly with a UE and / or with other network nodes or equipment, in a telecommunication network. Examples of network nodes include, but are not limited to, access points (APs) (e.g., radio access points), base stations (BSs) (e.g., radio base stations, Node Bs, evolved Node Bs (eNBs) and NR NodeBs (gNBs)), O-RAN nodes or components of an O-RAN node (e.g., O-RU, O-DU, O-CU).

[0124] Base stations may be categorized based on the amount of coverage they provide (or, stated differently, their transmit power level) and so, depending on the provided amount of coverage, may be referred to as femto base stations, pico base stations, micro base stations, or macro base stations. A base station may be a relay node or a relay donor node controlling a relay. A network node may also include one or more (or all) parts of a distributed radio base station such as centralized digital units, distributed units (e.g., in an O-RAN access node) and / or remote radio units (RRUs), sometimes referred to as Remote Radio Heads (RRHs). Such remote radio units may or may not be integrated with an antenna as an antenna integrated radio. Parts of a distributed radio base station may also be referred to as nodes in a distributed antenna system (DAS).

[0125] Other examples of network nodes include multiple transmission point (multi-TRP) 5G access nodes, multi-standard radio (MSR) equipment such as MSR BSs, network controllers such as radio network controllers (RNCs) or base station controllers (BSCs), base transceiver stations (BTSs), transmission points, transmission nodes, multi-cell / multicast coordination entities (MCEs), Operation and Maintenance (O&M) nodes, Operations Support System (OSS) nodes, Self-Organizing Network (SON) nodes, positioning nodes (e.g., Evolved Serving Mobile Location Centers (E-SMLCs)), and / or Minimization of Drive Tests (MDTs).

[0126] The network node 2300 includes a processing circuitry 2302, a memory 2304, a communication interface 2306, and a power source 2308. The network node 2300 may be composed of multiple physically separate components (e.g., a NodeB component and a RNC component, or a BTS component and a BSC component, etc.), which may each have their own respective components. In certain scenarios in which the network node 2300 comprises multiple separate components (e.g., BTS and BSC components), one or more of the separate components may be shared among several network nodes. For example, a single RNC may control multiple NodeBs. In such a scenario, each unique NodeB and RNC pair, may in some instances be considered a single separate network node. In some embodiments, the network node 2300 may be configured to support multiple radio access technologies (RATs). In such embodiments, some components may be duplicated (e.g., separate memory 2304 for different RATs) and some components may be reused (e.g., a same antenna 2310 may be shared byP110655 different RATs). The network node 2300 may also include multiple sets of the various illustrated components for different wireless technologies integrated into network node 2300, for example GSM, WCDMA, LTE, NR, WiFi, Zigbee, Z-wave, LoRaWAN, Radio Frequency Identification (RFID) or Bluetooth wireless technologies. These wireless technologies may be integrated into the same or different chip or set of chips and other components within network node 2300.

[0127] The processing circuitry 2302 may comprise a combination of one or more of a microprocessor, controller, microcontroller, central processing unit, digital signal processor, application-specific integrated circuit, field programmable gate array, or any other suitable computing device, resource, or combination of hardware, software and / or encoded logic operable to provide, either alone or in conjunction with other network node 2300 components, such as the memory 2304, to provide network node 2300 functionality.

[0128] In some embodiments, the processing circuitry 2302 includes a system on a chip (SOC). In some embodiments, the processing circuitry 2302 includes one or more of radio frequency (RF) transceiver circuitry 2312 and baseband processing circuitry 2314. In some embodiments, the radio frequency (RF) transceiver circuitry 2312 and the baseband processing circuitry 2314 may be on separate chips (or sets of chips), boards, or units, such as radio units and digital units. In alternative embodiments, part or all of RF transceiver circuitry 2312 and baseband processing circuitry 2314 may be on the same chip or set of chips, boards, or units.

[0129] The memory 2304 may comprise any form of volatile or non-volatile computer- readable memory including, without limitation, persistent storage, solid-state memory, remotely mounted memory, magnetic media, optical media, random access memory (RAM), read-only memory (ROM), mass storage media (for example, a hard disk), removable storage media (for example, a flash drive, a Compact Disk (CD) or a Digital Video Disk (DVD)), and / or any other volatile or non-volatile, non-transitory device-readable and / or computer- executable memory devices that store information, data, and / or instructions that may be used by the processing circuitry 2302. The memory 2304 may store any suitable instructions, data, or information, including a computer program, software, an application including one or more of logic, rules, code, tables, and / or other instructions capable of being executed by the processing circuitry 2302 and utilized by the network node 2300. The memory 2304 may be used to store any calculations made by the processing circuitry 2302 and / or any data received via the communication interface 2306. In some embodiments, the processing circuitry 2302 and memory 2304 is integrated.P110655

[0130] The communication interface 2306 is used in wired or wireless communication of signaling and / or data between a network node, access network, and / or UE. As illustrated, the communication interface 2306 comprises port(s) / terminal(s) 2316 to send and receive data, for example to and from a network over a wired connection. The communication interface 2306 also includes radio front-end circuitry 2318 that may be coupled to, or in certain embodiments a part of, the antenna 2310. Radio front-end circuitry 2318 comprises filters 2320 and amplifiers 2322. The radio front-end circuitry 2318 may be connected to an antenna 2310 and processing circuitry 2302. The radio front-end circuitry may be configured to condition signals communicated between antenna 2310 and processing circuitry 2302. The radio front-end circuitry 2318 may receive digital data that is to be sent out to other network nodes or UEs via a wireless connection. The radio front-end circuitry 2318 may convert the digital data into a radio signal having the appropriate channel and bandwidth parameters using a combination of filters 2320 and / or amplifiers 2322. The radio signal may then be transmitted via the antenna 2310. Similarly, when receiving data, the antenna 2310 may collect radio signals which are then converted into digital data by the radio front-end circuitry 2318. The digital data may be passed to the processing circuitry 2302. In other embodiments, the communication interface may comprise different components and / or different combinations of components.

[0131] In certain alternative embodiments, the network node 2300 does not include separate radio front-end circuitry 2318, instead, the processing circuitry 2302 includes radio front-end circuitry and is connected to the antenna 2310. Similarly, in some embodiments, all or some of the RF transceiver circuitry 2312 is part of the communication interface 2306. In still other embodiments, the communication interface 2306 includes one or more ports or terminals 2316, the radio front-end circuitry 2318, and the RF transceiver circuitry 2312, as part of a radio unit (not shown), and the communication interface 2306 communicates with the baseband processing circuitry 2314, which is part of a digital unit (not shown).

[0132] The antenna 2310 may include one or more antennas, or antenna arrays, configured to send and / or receive wireless signals. The antenna 2310 may be coupled to the radio front- end circuitry 2318 and may be any type of antenna capable of transmitting and receiving data and / or signals wirelessly. In certain embodiments, the antenna 2310 is separate from the network node 2300 and connectable to the network node 2300 through an interface or port.

[0133] The antenna 2310, communication interface 2306, and / or the processing circuitry 2302 may be configured to perform any receiving operations and / or certain obtaining operations described herein as being performed by the network node. Any information, data and / or signals may be received from a UE, another network node and / or any other networkP110655 equipment. Similarly, the antenna 2310, the communication interface 2306, and / or the processing circuitry 2302 may be configured to perform any transmitting operations described herein as being performed by the network node. Any information, data and / or signals may be transmitted to a UE, another network node and / or any other network equipment.

[0134] The power source 2308 provides power to the various components of network node 2300 in a form suitable for the respective components (e.g., at a voltage and current level needed for each respective component). The power source 2308 may further comprise, or be coupled to, power management circuitry to supply the components of the network node 2300 with power for performing the functionality described herein. For example, the network node 2300 may be connectable to an external power source (e.g., the power grid, an electricity outlet) via an input circuitry or interface such as an electrical cable, whereby the external power source supplies power to power circuitry of the power source 2308. As a further example, the power source 2308 may comprise a source of power in the form of a battery or battery pack which is connected to, or integrated in, power circuitry. The battery may provide backup power should the external power source fail.

[0135] Embodiments of the network node 2300 may include additional components beyond those shown in Fig. 12 for providing certain aspects of the network node’s functionality, including any of the functionality described herein and / or any functionality necessary to support the subject matter described herein. For example, the network node 2300 may include user interface equipment to allow input of information into the network node 2300 and to allow output of information from the network node 2300. This may allow a user to perform diagnostic, maintenance, repair, and other administrative functions for the network node 2300. In some embodiments providing a core network node, such as core network node 2108 of Fig. 10, some components, such as the radio front-end circuitry 2318 and the RF transceiver circuitry 2312 may be omitted.

[0136] Fig.13 is a block diagram illustrating a virtualization environment 2400 in which functions implemented by some embodiments may be virtualized. In the present context, virtualizing means creating virtual versions of apparatuses or devices which may include virtualizing hardware platforms, storage devices and networking resources. As used herein, virtualization can be applied to any device described herein, or components thereof, and relates to an implementation in which at least a portion of the functionality is implemented as one or more virtual components. Some or all of the functions described herein may be implemented as virtual components executed by one or more virtual machines (VMs) implemented in one or more virtual environments 2400 hosted by one or more of hardware nodes, such as aP110655 hardware computing device that operates as a network node, UE, core network node, or host. Further, in embodiments in which the virtual node does not require radio connectivity (e.g., a core network node or host), then the node may be entirely virtualized. In some embodiments, the virtualization environment 2400 includes components defined by the O-RAN Alliance, such as an O-Cloud environment orchestrated by a Service Management and Orchestration Framework via an O-2 interface. Virtualization may facilitate distributed implementations of a network node, UE, core network node, or host.

[0137] Applications 2402 (which may alternatively be called software instances, virtual appliances, network functions, virtual nodes, virtual network functions, etc.) are run in the virtualization environment Q400 to implement some of the features, functions, and / or benefits of some of the embodiments disclosed herein.

[0138] Hardware 2404 includes processing circuitry, memory that stores software and / or instructions executable by hardware processing circuitry, and / or other hardware devices as described herein, such as a network interface, input / output interface, and so forth. Software may be executed by the processing circuitry to instantiate one or more virtualization layers 2406 (also referred to as hypervisors or virtual machine monitors (VMMs)), provide VMs 2408a and 2408b (one or more of which may be generally referred to as VMs 2408), and / or perform any of the functions, features and / or benefits described in relation with some embodiments described herein. The virtualization layer 2406 may present a virtual operating platform that appears like networking hardware to the VMs 2408.

[0139] The VMs 2408 comprise virtual processing, virtual memory, virtual networking or interface and virtual storage, and may be run by a corresponding virtualization layer 2406. Different embodiments of the instance of a virtual appliance 2402 may be implemented on one or more of VMs 2408, and the implementations may be made in different ways. Virtualization of the hardware is in some contexts referred to as network function virtualization (NFV). NFV may be used to consolidate many network equipment types onto industry standard high volume server hardware, physical switches, and physical storage, which can be located in data centers, and customer premise equipment.

[0140] In the context of NFV, a VM 2408 may be a software implementation of a physical machine that runs programs as if they were executing on a physical, non-virtualized machine. Each of the VMs 2408, and that part of hardware 2404 that executes that VM, be it hardware dedicated to that VM and / or hardware shared by that VM with others of the VMs, forms separate virtual network elements. Still in the context of NFV, a virtual network function isP110655 responsible for handling specific network functions that run in one or more VMs 2408 on top of the hardware 2404 and corresponds to the application 2402.

[0141] Hardware 2404 may be implemented in a standalone network node with generic or specific components. Hardware 2404 may implement some functions via virtualization. Alternatively, hardware 2404 may be part of a larger cluster of hardware (e.g. such as in a data center or CPE) where many hardware nodes work together and are managed via management and orchestration 2410, which, among others, oversees lifecycle management of applications 2402. In some embodiments, hardware 2404 is coupled to one or more radio units that each include one or more transmitters and one or more receivers that may be coupled to one or more antennas. Radio units may communicate directly with other hardware nodes via one or more appropriate network interfaces and may be used in combination with the virtual components to provide a virtual node with radio capabilities, such as a radio access node or a base station. In some embodiments, some signaling can be provided with the use of a control system 2412 which may alternatively be used for communication between hardware nodes and radio units.

[0142] Although the computing devices described herein (e.g., UEs, network nodes) may include the illustrated combination of hardware components, other embodiments may comprise computing devices with different combinations of components. It is to be understood that these computing devices may comprise any suitable combination of hardware and / or software needed to perform the tasks, features, functions and methods disclosed herein. Determining, calculating, obtaining or similar operations described herein may be performed by processing circuitry, which may process information by, for example, converting the obtained information into other information, comparing the obtained information or converted information to information stored in the network node, and / or performing one or more operations based on the obtained information or converted information, and as a result of said processing making a determination. Moreover, while components are depicted as single boxes located within a larger box, or nested within multiple boxes, in practice, computing devices may comprise multiple different physical components that make up a single illustrated component, and functionality may be partitioned between separate components. For example, a communication interface may be configured to include any of the components described herein, and / or the functionality of the components may be partitioned between the processing circuitry and the communication interface. In another example, non-computationally intensive functions of any of such components may be implemented in software or firmware and computationally intensive functions may be implemented in hardware.P110655

[0143] In certain embodiments, some or all of the functionality described herein may be provided by processing circuitry executing instructions stored on in memory, which in certain embodiments may be a computer program product in the form of a non-transitory computer- readable storage medium. In alternative embodiments, some or all of the functionality may be provided by the processing circuitry without executing instructions stored on a separate or discrete device-readable storage medium, such as in a hard-wired manner. In any of those particular embodiments, whether executing instructions stored on a non-transitory computer- readable storage medium or not, the processing circuitry can be configured to perform the described functionality. The benefits provided by such functionality are not limited to the processing circuitry alone or to other components of the computing device, but are enjoyed by the computing device as a whole, and / or by end users and a wireless network generally. Abbreviations and Defined Terms

[0144] To assist in understanding the scope and content of this written description and the appended claims, a select few terms are defined directly below. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the present disclosure pertains.

[0145] The terms “approximately,” “about,” and “substantially,” as used herein, represent an amount or condition close to the specific stated amount or condition that still performs a desired function or achieves a desired result. For example, the terms “approximately,” “about,” and “substantially” may refer to an amount or condition that deviates by less than 10%, or by less than 5%, or by less than 1%, or by less than 0.1%, or by less than 0.01% from a specifically stated amount or condition.

[0146] Various aspects of the present disclosure, including devices, systems, and methods may be illustrated with reference to one or more embodiments or implementations, which are exemplary in nature. As used herein, the term “exemplary” means “serving as an example, instance, or illustration,” and should not necessarily be construed as preferred or advantageous over other embodiments disclosed herein. In addition, reference to an “implementation” of the present disclosure or embodiments includes a specific reference to one or more embodiments thereof, and vice versa, and is intended to provide illustrative examples without limiting the scope of the present disclosure, which is indicated by the appended claims rather than by the present description.

[0147] As used in the specification, a word appearing in the singular encompasses its plural counterpart, and a word appearing in the plural encompasses its singular counterpart,P110655 unless implicitly or explicitly understood or stated otherwise. Thus, it will be noted that, as used in this specification and the appended claims, the singular forms “a,” “an” and “the” include plural referents unless the context clearly dictates otherwise. For example, reference to a singular referent (e.g., “a widget”) includes one, two, or more referents unless implicitly or explicitly understood or stated otherwise. Similarly, reference to a plurality of referents should be interpreted as comprising a single referent and / or a plurality of referents unless the content and / or context clearly dictate otherwise. For example, reference to referents in the plural form (e.g., “widgets”) does not necessarily require a plurality of such referents. Instead, it will be appreciated that independent of the inferred number of referents, one or more referents are contemplated herein unless stated otherwise.

[0148] References in the specification to "one embodiment," "an embodiment," "an example embodiment," and the like indicate that the embodiment described may include a particular feature, structure, or characteristic, but it is not necessary that every embodiment includes the particular feature, structure, or characteristic. Moreover, such phrases are not necessarily referring to the same embodiment. Further, when a particular feature, structure, or characteristic is described in connection with an embodiment, it is submitted that it is within the knowledge of one skilled in the art to affect such feature, structure, or characteristic in connection with other embodiments whether or not explicitly described.

[0149] It shall be understood that although the terms "first" and "second" etc. may be used herein to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another. For example, a first element could be termed a second element, and similarly, a second element could be termed a first element, without departing from the scope of example embodiments. As used herein, the term "and / or" includes any and all combinations of one or more of the associated listed terms.

[0150] It will be further understood that the terms "comprises", "comprising", "has", "having", "includes" and / or "including", when used herein, specify the presence of stated features, elements, and / or components etc., but do not preclude the presence or addition of one or more other features, elements, components and / or combinations thereof. Conclusion

[0151] The present disclosure includes any novel feature or combination of features disclosed herein either explicitly or any generalization thereof. Various modifications and adaptations to the foregoing exemplary embodiments of this disclosure may become apparent to those skilled in the relevant arts in view of the foregoing description, when read inP110655 conjunction with the accompanying drawings. However, any and all modifications will still fall within the scope of the non-limiting and exemplary embodiments of this disclosure.

[0152] It is understood that for any given component or embodiment described herein, any of the possible candidates or alternatives listed for that component may generally be used individually or in combination with one another, unless implicitly or explicitly understood or stated otherwise. Additionally, it will be understood that any list of such candidates or alternatives is merely illustrative, not limiting, unless implicitly or explicitly understood or stated otherwise.

[0153] In addition, unless otherwise indicated, numbers expressing quantities, constituents, distances, or other measurements used in the specification and claims are to be understood as being modified by the term “about,” as that term is defined herein. Accordingly, unless indicated to the contrary, the numerical parameters set forth in the specification and attached claims are approximations that may vary depending upon the desired properties sought to be obtained by the subject matter presented herein. At the very least, and not as an attempt to limit the application of the doctrine of equivalents to the scope of the claims, each numerical parameter should at least be construed in light of the number of reported significant digits and by applying ordinary rounding techniques. Notwithstanding that the numerical ranges and parameters setting forth the broad scope of the subject matter presented herein are approximations, the numerical values set forth in the specific examples are reported as precisely as possible. Any numerical values, however, inherently contain certain errors necessarily resulting from the standard deviation found in their respective testing measurements.

[0154] Any headings and subheadings used herein are for organizational purposes only and are not meant to be used to limit the scope of the description or the claims. The terms and expressions which have been employed herein are used as terms of description and not of limitation, and there is no intention in the use of such terms and expressions of excluding any equivalents of the features shown and described or portions thereof, but it is recognized that various modifications are possible within the scope of the present disclosure. Thus, it should be understood that although the present disclosure has been specifically disclosed in part by certain embodiments, and optional features, modification and variation of the concepts herein disclosed may be resorted to by those skilled in the art, and such modifications and variations are considered to be within the scope of this present description.

[0155] It will also be appreciated that systems, devices, products, kits, methods, and / or processes, according to certain embodiments of the present disclosure may include,P110655 incorporate, or otherwise comprise properties or features (e.g., components, members, elements, parts, and / or portions) described in other embodiments disclosed and / or described herein. Accordingly, the various features of certain embodiments can be compatible with, combined with, included in, and / or incorporated into other embodiments of the present disclosure. Thus, disclosure of certain features relative to a specific embodiment of the present disclosure should not be construed as limiting application or inclusion of said features to the specific embodiment. Rather, it will be appreciated that other embodiments can also include said features, members, elements, parts, and / or portions without necessarily departing from the scope of the present disclosure.

[0156] Moreover, unless a feature is described as requiring another feature in combination therewith, any feature herein may be combined with any other feature of a same or different embodiment disclosed herein. Furthermore, various well-known aspects of illustrative systems, methods, apparatus, and the like are not described herein in particular detail in order to avoid obscuring aspects of the example embodiments. Such aspects are, however, also contemplated herein.

[0157] It will be apparent to one of ordinary skill in the art that methods, devices, device elements, materials, procedures, and techniques other than those specifically described herein can be applied to the practice of the described embodiments as broadly disclosed herein without resort to undue experimentation. All art-known functional equivalents of methods, devices, device elements, materials, procedures, and techniques specifically described herein are intended to be encompassed by this present disclosure.

[0158] When a group of materials, compositions, components, or compounds is disclosed herein, it is understood that all individual members of those groups and all subgroups thereof are disclosed separately. When a Markush group or other grouping is used herein, all individual members of the group and all combinations and sub-combinations possible of the group are intended to be individually included in the disclosure.

[0159] The above-described embodiments are examples only. Alterations, modifications, and variations may be effected to the particular embodiments by those of skill in the art without departing from the scope of the description, which is defined solely by the appended claims.

Claims

P110655 CLAIMS What is claimed is:

1. A system (2114, 2116, 2200) for improving the path loss of a communication link (506) between a UE (502) and a network node (504) of a communication system (2100), the system comprising processing circuitry (2202, 2302, 2402) and a memory (2210, 2304, 2412), the memory containing instructions executable by the processing circuitry whereby the system is operative to: monitor (102) channel state information derived from the communication link, the channel state information comprising one or more parameters; detect (104) a change of at least one of the one or more parameters, wherein the change reflects an increase of the path loss of the communication link; identify (106), based on the change, an obstacle (508); and generate (108) at least one corrective action message comprising one or more instructions to address the obstacle.

2. The system of claim 1, wherein the channel state information comprises at least one of an Angle of Arrival, AoA, parameter and a power headroom, PH, parameter.

3. The system of claim 1, further comprising after identifying the obstacle but before generating the at least one corrective action message: generating a profile for the obstacle, wherein the profile comprises information about the obstacle, and wherein the at least one corrective action message is generated based on the profile.

4. The system of claim 3, wherein the information is one or more of a classification of the obstacle, the shape of the obstacle, and the spatial position of the obstacle.

5. The system of claim 1, further operative to send (320, 608) the at least one corrective active message to another system for processing.

6. The system of claim 5, wherein the at least one corrective action message is sent using a network API.

7. The system of claim 5, wherein the one or more instructions comprise one or more of:P110655 a notification message indicating the obstacle is affecting the communication link, a change to the scheduling of the UE, and a map indicating the location of the blockage.

8. The system of claim 7, wherein the notification message, when the obstacle is an autonomous system (708), is directed to the autonomous system.

9. The system of claim 7, wherein the notification message includes a solution to improve the path loss.

10. The system of claim 7, wherein the notification message, when the obstacle is a user, instructs the user to move.

11. The system of claim 1, further operative to identify the UE as stationary.

12. The system of claim 11, wherein the UE is identified as stationary based on at least one of the channel state information, a UE type identifier, and a UE subscription.

13. The system of claim 1, wherein the UE is a fixed wireless access-type UE.

14. The system of claim 1, wherein the obstacle is identified using a machine learning model.

15. The system of claim 3, wherein the profile is generated, at least in part, using a machine learning model.

16. The system of claims 14 and 15, wherein the machine learning model is a neural network (2900).

17. The system of claim 1: wherein the communication link is one of a plurality of communication links in the communication system, wherein the channel state information is further derived from the remaining communication links of the plurality of communication links, and wherein the change may further be detected with at least one of the one or more parameters from the remaining communication links.P110655 18. A computer implemented method (100) for improving the path loss of a communication link (506) between a UE (502) and a network node (504) of a communication system (2100), the method comprising: monitoring (102) channel state information derived from the communication link, the channel state information comprising one or more parameters; detecting (104) a change of at least one of the one or more parameters, wherein the change reflects an increase of the path loss of the communication link; identifying (106), based on the change, an obstacle (508); and generating (108) at least one corrective action message comprising one or more instructions to address the obstacle.

19. The method of claim 18, wherein the channel state information comprises an Angle of Arrival, AoA, parameter and a power headroom, PH, parameter.

20. The method of claim 18, wherein after identifying the obstacle but before generating the at least one corrective action message, generating a profile for the obstacle, wherein the profile comprises information about the obstacle, and wherein the at least one corrective action message is generated based on the profile.

21. The method of claim 20, wherein the information is one or more of a classification of the obstacle, the shape of the obstacle, and the spatial position of the obstacle.

22. The method of claim 18, further comprising sending (320, 608) the at least one corrective active message to another system for processing.

23. The method of claim 22, wherein the at least one corrective action message is sent using a network API.

24. The method of claim 22, wherein the one or more instructions comprise one or more of: a notification message indicating the obstacle is affecting the communication link, a change to the scheduling of the UE, and a map indicating the location of the blockage.

25. The method of claim 24, wherein the notification message, when the obstacle is an autonomous system (708), is directed to the autonomous system.P110655 26. The method of claim 24, wherein the notification message includes a solution to improve the path loss.

27. The method of claim 24, wherein the notification message, when the obstacle is a user, instructs the user to move.

28. The method of claim 18, further comprising identifying the UE as stationary.

29. The method of claim 28, wherein the UE is identified as stationary based on at least one of the channel state information, a UE type identifier, and a UE subscription.

30. The method of claim 18, wherein the UE is a fixed wireless access-type UE.

31. The method of claim 18, wherein the obstacle is identified using a machine learning model.

32. The method of claim 20, wherein the profile is generated, at least in part, using a machine learning model.

33. The method of claim 31, wherein the machine learning model is a neural network (2900).

34. The method of claim 18: wherein the communication link is one of a plurality of communication links in the communication system, wherein the channel state information is further derived from the remaining communication links of the plurality of communication links, and wherein the change may further be detected with at least one of the one or more parameters from the remaining communication links.

35. A computer program (2214) comprising instructions which, when executed on at least one processor (2202), cause the at least one processor to carry out the method according to any one of claims 18 to 34.

36. A computer program product (2215), comprising instructions which, when executed on at least one processor (2202), cause the at least one processor to carry out the method according to any one of claims 18 to 34.

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