A method and an apparatus for a device for a wireless communication system

Abstract

A method, for example a computer-implemented method, for a device for a wireless communication system, the method comprising: determining, at least temporarily using at least one procedure based on at least one of artificial intelligence, AI, or machine learning, ML, first information characterizing a resource allocation for positioning of at least one target, providing at least the first information to at least one further device.

Description

[0001] R.414609

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[0003] A Method and an Apparatus for a Device for a Wireless Communication System

[0004] Technical Field

[0005] The disclosure relates to a method for a device for a wireless communication system.

[0006] The disclosure further relates to an apparatus for a device for a wireless communication system.

[0007] Summary

[0008] Some examples relate to a method, for example a computer-implemented method, for a device for a wireless communication system, the method comprising: determining, at least temporarily using at least one procedure based on at least one of artificial intelligence, Al, or machine learning, ML, first information characterizing a resource allocation for positioning of at least one target, providing at least the first information to at least one further device. In some examples, this may enable to efficiently allocate and / or update resources for positioning and / or to efficiently provide information on the allocated resources to at least one further device, which, in some examples, may perform positioning, e.g., based on the provided information e.g., using at least some of the allocated resources.

[0009] In some examples, the wireless communications system may, e.g., be a wireless, for example cellular, communications system, which is for example based on and / or adheres at least partially to at least one third generation partnership project, 3GPP, radio standard such as 4G (fourth generation), 5G (fifth generation), or 6G (sixth generation), or to another other radio access technology. R.414609

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[0011] In some examples, the device for the wireless communication system may, e.g., be a network device, e.g., base station, e.g., gNB or a device of a network management system.

[0012] In some examples, the device for the wireless communication system may, e.g., be a terminal device, e.g., user equipment, e.g., UE.

[0013] In some examples, signals of the wireless communication system may be used for the positioning, such as, e.g., reference signals, e.g., positioning reference signals and / or sounding reference signals, and the like.

[0014] In some examples, other signals than those of the wireless communication system may be used for the positioning, e.g., specific radar signals, such as, e.g., frequency modulated continuous wave (FMCW) signals, and the like.

[0015] In some examples, the method comprises: receiving a request for the first information, for example from the at least one further device, and, optionally, transmitting a response to the at least one further device, the response at least comprising the first information. In some examples, this enables to efficiently distribute resource allocation information to further devices, e.g., upon request of the further devices.

[0016] In some examples, the at least one procedure comprises at least one of the following aspects: a) a reinforcement learning, RL, technique, or b) a supervised learning technique, or c) an unsupervised learning technique. In some examples, this enables to provide further degrees of freedom for designing Al- and / or ML- based techniques for determining the first information.

[0017] In some examples, the determining comprises at least temporarily using at least one procedure that is not based on at least one of artificial intelligence, Al, or machine learning, ML, e.g., a legacy procedure, wherein for example the legacy procedure comprises at least one of: a) a rule-based technique, or b) a prioritybased technique. Thus, in some examples, even further degrees of freedom regarding the specific procedure(s) for determining the first information are provided. R.414609

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[0019] In some examples, the method comprises: determining updated first information, preferably based on the first information, providing the updated first information to the at least one further device, wherein for example the providing comprises using a resource allocation update message, wherein for example the determining is based on at least one of a) the procedure based on at least one of artificial intelligence, Al, or machine learning, ML, or b) a or the legacy procedure. This may enable to adapt the first information, e.g., to changed operational conditions such as, e.g., a load of the wireless communication system.

[0020] In some examples, the method comprises at least one of: a) receiving second information, for example from the at least one further device, for example as or within a resource release notification message, the second information indicating at least one of: a1) resources released (e.g., not used any more) by the at least one further device (e.g., for the positioning), or a2) a, for example current, state of network utilization, or a3) change, e.g., with respect to network utilization, e.g., since a or the, e.g., last, resource allocation, or b) receiving third information, for example from the at least one further device, for example as or within a performance feedback report message, the third information indicating at least one of: b1) performance metrics, or b2) feedback information for modifying, e.g., refining, the at least one procedure based on at least one of Al or ML. In some examples, this may enable a device performing aspects of the disclosure to adapt its operation, e.g., regarding resource allocation, e.g., to the received information.

[0021] In some examples, the method comprises: performing an adaptive resource allocation for the positioning of the at least one target, preferably for obtaining an adapted resource allocation, wherein for example the adaptive resource allocation is based on at least one of: a) signal analysis of at least one signal associated with, e.g., of the, wireless communication system, or b) a mobility pattern of at least one mobile device, e.g., for the wireless communication system, e.g. a user equipment, or c) at least one bandwidth associated with at least one user and / or task associated with, e.g., of the wireless communication system, and, optionally, using an adapted resource allocation, e.g., by providing the adapted resource allocation, e.g., to the at least one further device. In some examples, this enables to perform a particularly precise adaptation of the resource allocation. R.414609

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[0023] In some examples, the method comprises at least one of: a) using at least one feedback loop, or b) using at least one continuous learning technique, e.g., for at least one of A) the determining of the first information, or B) the determining of the updated first information. In some examples, this may enable to ensure effectiveness of resource allocation and / or a continuous improvement regarding resource allocation.

[0024] In some examples, the method comprises at least one of: a) using at least one message container, e.g., for at least one of: a1) the providing of the first information, or a2) the transmitting of the response to the at least one further device, or a3) the receiving of the second information, or a4) the receiving of the third information. In some examples, this may enable to facilitate efficient communication and resource management. As an example, in some variants, message containers that adhere to and / or are based on at least one 3GPP standard may be used for aspects of information exchange according to the disclosure.

[0025] Some examples relate to a method, for example a computer-implemented method, for a device for a wireless communication system, e.g., for the at least one further device, the method comprising: receiving first information characterizing a resource allocation for positioning of at least one target, e.g., from a device, e.g., from the device according to the disclosure as, e.g., mentioned above, wherein for example the first information is determined by the device at least temporarily using at least one procedure based on at least one of artificial intelligence, Al, or machine learning, ML, and, optionally, using the received first information, e.g., for positioning.

[0026] In some examples, the method comprises: transmitting a request for the first information, for example to the device, and, optionally, receiving a response from the device, the response at least comprising the first information. In some examples, this enables the device to proactively obtain the first information and / or updated first information, e.g., from the further device.

[0027] In some examples, the method comprises: transmitting second information, for example to the device, for example as or within a resource release notification R.414609

[0028] - 5 - message, the second information indicating at least one of: a1) resources released by the device, or a2) a, for example current, state of network utilization, or a3) change, e.g., with respect to network utilization, e.g., since a or the, e.g., last, resource allocation.

[0029] In some examples, the method comprises: transmitting third information, for example to the device, for example as or within a performance feedback report message, the third information indicating at least one of: b1) performance metrics, or b2) feedback information for modifying, e.g., refining, the at least one procedure based on at least one of Al or ML.

[0030] Some examples relate to an apparatus for performing the method(s) according to the disclosure. In some examples, the apparatus is configured to perform the method(s) according to the disclosure.

[0031] Some examples relate to a computer program comprising instructions which, when the program is executed by a computer, cause the computer to perform the method according to the disclosure.

[0032] Some examples relate to a computer-readable storage medium comprising instructions which, when executed by a computer and / or the apparatus, cause the computer and / or the apparatus to carry out the method(s) according to the disclosure.

[0033] Some examples relate to a data carrier signal carrying and / or characterizing the computer program according to the disclosure.

[0034] Some examples relate to a device for a wireless communication system, comprising at least one apparatus according to the disclosure. In some examples, as mentioned above, the device may, e.g., be a network device, e.g., base station or a device of a network management system.

[0035] In some examples, the device for the wireless communication system may, e.g., be a terminal device, e.g., user equipment, e.g., UE. R.414609

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[0037] Some examples relate to a use of the method according to the disclosure and / or of the apparatus according to the disclosure and / or of the computer program according to the disclosure and / or of the computer-readable storage medium according to the disclosure and / or of the data carrier signal according to the disclosure and / or of the device according to the disclosure for at least one of: a) resource allocation for positioning, or b) dynamic management of resources for positioning, or c) providing a framework for, for example continuously, monitoring network conditions and / or user demands, or d) adjusting resource allocation for positioning, or e) optimizing positioning accuracy, or f) optimizing service quality.

[0038] Brief Description of Some Example Figures

[0039] Some example embodiments will now be described with reference to the accompanying drawings, in which:

[0040] Fig. 1 schematically depicts a simplified flow-chart,

[0041] Fig. 2 schematically depicts a simplified block diagram,

[0042] Fig. 3 schematically depicts a simplified flow-chart,

[0043] Fig. 4 schematically depicts a simplified flow-chart,

[0044] Fig. 5 schematically depicts a simplified flow-chart,

[0045] Fig. 6 schematically depicts a simplified flow-chart,

[0046] Fig. 7 schematically depicts a simplified flow-chart,

[0047] Fig. 8 schematically depicts a simplified flow-chart,

[0048] Fig. 9 schematically depicts a simplified flow-chart,

[0049] Fig. 10 schematically depicts a simplified signaling diagram,

[0050] Fig. 11 schematically depicts a simplified block diagram, R.414609

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[0052] Fig. 12 schematically depicts aspects of use.

[0053] Some examples, see, for example Fig. 1, 2, relate to a method, for example a computer-implemented method, for a device 10 for a wireless communication system 1000 or network, respectively, the method comprising: determining 102 (Fig. 1), at least temporarily using 102a at least one procedure PROC-AI-ML (Fig. 2) based on at least one of artificial intelligence, Al, or machine learning, ML, first information 1-1 characterizing a resource allocation RES-ALLOC-POS for positioning 20-POS of at least one target 20, providing 104 at least the first information 1-1 to at least one further device 30. In some examples, this may enable to efficiently allocate and / or update resources for positioning and / or to efficiently provide information on the allocated resources to at least one further device 30, which, in some examples, may perform positioning, e.g., based on the provided information e.g., using at least some of the allocated resources.

[0054] In some examples, the positioning may, e.g., comprise determining a position 20- POS of at least one target 20. In some examples, the at least one target 20 may, e.g., be an object (or a person) in the environment ENV of the device 30.

[0055] In some examples, Fig. 2, the wireless communications system 1000 may, e.g., be a wireless, for example cellular, communications system, which is for example based on and / or adheres at least partially to at least one third generation partnership project, 3GPP, radio standard such as 4G (fourth generation), 5G (fifth generation), or 6G (sixth generation), or to another other radio access technology.

[0056] In some examples, Fig. 2, the device 10 for the wireless communication system 1000 may, e.g., be a network device, e.g., base station, e.g., gNB or a device of a network management system (NMS).

[0057] In some examples, Fig. 2, the device 10 for the wireless communication system 1000 may, e.g., be a terminal device, e.g., user equipment, e.g., UE.

[0058] In some examples, Fig. 2, signals of the wireless communication system 1000 may be used for the positioning (e.g., by the device 30), such as, e.g., reference R.414609

[0059] - 8 - signals, e.g., positioning reference signals and / or sounding reference signals, and the like.

[0060] In some examples, Fig. 2, other signals than those of the wireless communication system 1000 may be used for the positioning, e.g., specific radar signals, such as, e.g., frequency modulated continuous wave (FMCW) signals, and the like.

[0061] In some examples, the first information 1-1 characterize the resource allocation RES-ALLOC-POS for the positioning 20-POS of at least one target 20. In other words, the first information 1-1 may denote one or more resources, e.g., of the wireless communication system 1000, which may be used, e.g., by the device 30 (and / or by at least one further device (not shown)), for performing the positioning, e.g., for determining the position 20-POS of the at least one target 20.

[0062] In some examples, Fig. 1, the method comprises: receiving 100 a request REQ for the first information 1-1 , for example from the at least one further device 30, and, optionally, transmitting 104a a response RESP to the at least one further device 30, the response RESP at least comprising the first information 1-1. In some examples, this enables to efficiently distribute resource allocation information to further devices 30, e.g., upon request of the further devices 30.

[0063] In some examples, Fig. 2, the at least one procedure PROC-AI-ML comprises at least one of the following aspects: a) a reinforcement learning, RL, technique RL- TECH, or b) a supervised learning technique SVL-TECH, or c) an unsupervised learning technique USVL-TECH. In some examples, this enables to provide further degrees of freedom for designing Al- and / or ML-based techniques for determining the first information.

[0064] In some examples, reinforcement learning (RL) may be used, e.g., for implementing the at least one procedure PROC-AI-ML, e.g., for resource allocation, e.g., for determining resources that may be used by the at least one device 30, e.g., for the positioning. In some examples, one or more RL algorithms may be provided, which, can, e.g., learn one or more optimal resource allocation strategies, e.g., by interacting with a network environment, e.g., of the wireless communication system or network 1000. In some examples, one or more RL algorithm may use feedback, e.g., from previous actions (e.g., earlier resource R.414609

[0065] - 9 - allocations, e.g., for positioning), e.g., to improve future decisions, thus, e.g., optimizing resource usage, e.g., for the positioning, e.g., over time.

[0066] In some examples, an example implementation of an RL technique RL-TECH may, e.g., provide at least one RL agent which is configured to observe a current state of the system or network 1000, respectively (e.g., as characterized by at least one of a) signal strength, or b) interference levels), and the RL agent may be configured to take one or more actions to maximize a cumulative reward.

[0067] In some examples, the one or more actions the RL agent may take may, e.g., comprise at least one of: a) allocating resources such as, e.g., bandwidth and / or other time and / or frequency resources, e.g., for the positioning, or b) adjusting one or more power levels, e.g., of signals that b1) may be used for the positioning, or b2) of signals other than those that may be used for the positioning.

[0068] In some examples, the cumulative reward of the RL technique RL-TECH may, e.g., be associated with, e.g., characterized by, at least one of: a) a, for example comparatively, high positioning accuracy, or b) a, for example comparatively, low latency.

[0069] In some examples, an example implementation of a supervised learning technique SVL-TECH may, e.g., be used to predict resource needs, e.g., based on historical data. In some examples, an algorithm of the supervised learning technique SVL-TECH may be trained on labeled datasets that include past network conditions, e.g., of the wireless communication system 1000, and resource allocation decisions.

[0070] In some examples, for an example implementation of a supervised learning technique SVL-TECH, a model may be trained, e.g., to predict an optimal resource allocation, e.g., given current network conditions. In some examples, the predictions may then be used, e.g., to dynamically determine and / or adjust, e.g., update, resource allocations, e.g., in real-time.

[0071] In some examples, an example implementation of an unsupervised learning technique USVL-TECH may, e.g., be used to identify patterns and / or clusters in R.414609

[0072] - 10 - data associated with the wireless communication system 1000, e.g., without using labeled examples. In some examples, one or more algorithms of the unsupervised learning technique USVL-TECH may, e.g., be used to discover underlying structures in data associated with the wireless communication system 1000 that may, e.g., influence resource needs.

[0073] In some examples, for an example implementation of an unsupervised learning technique USVL-TECH, one or more clustering algorithms may be provided that are configured to group similar network states of the wireless communication system 1000 together, thus, e.g., allowing the system 1000 or device 10, respectively, to apply similar resource allocation strategies to similar states, improving efficiency and consistency.

[0074] In some examples, Fig. 1 , the determining 102 comprises at least temporarily using 102b at least one procedure PROC-LEG that is not based on at least one of artificial intelligence, Al, or machine learning, ML, e.g., a legacy procedure, wherein for example the legacy procedure PROC-LEG comprises at least one of: a) a rule-based technique RB-TECH, or b) a priority-based technique PRIO- TECH. Thus, in some examples, even further degrees of freedom regarding the specific procedure(s) for determining the first information 1-1 are provided.

[0075] In other words, the principle of the disclosure is not limited to AI / ML-based procedures. In some examples, the principle of the disclosure does not preclude a usage of, e.g., more traditional, methods, such as rule based or priority based legacy procedures PROC-LEG.

[0076] As an example for an implementation of legacy procedures PROC-LEG, a set of rules may be developed, e.g., to ensure an appropriate resource allocation. A further example is when the accuracy requirements are comparatively high, an allocated bandwidth may be increased. As a further example, when there is competition for the resources, and all requesters 30 may, e.g., have a same priority, the matter may be solved on a first-come first-serve basis. In some examples, the set of rules may then, e.g., be used for a constrained optimization problem, which can, in some examples, be solved by ML and / or Al methods. R.414609

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[0078] In some examples, Fig. 3, the method comprises: determining 110 updated first information 1-1-UPD, providing 112 the updated first information 1-1-UPD to the at least one further device 30 (Fig. 2), wherein for example the providing 112 comprises using 112a a resource allocation update message RAU-MSG, wherein for example the determining 110 is based on at least one of a) the procedure PROC-AI-ML based on at least one of artificial intelligence, Al, or machine learning, ML, or b) a or the legacy procedure PROC-LEG. This may enable to adapt the first information 1-1 , e.g., to changed operational conditions such as, e.g., a load of the wireless communication system 1000. At the same time, in some examples, at least one of the procedures PROC-AI-ML, PROC-LEG may be used.

[0079] In some examples, Fig. 4, the method comprises at least one of: a) receiving 120 second information I-2, for example from the at least one further device 30 (Fig. 2), for example as or within a resource release notification message RRN-MSG, the second information I-2 indicating at least one of: a1) resources released (e.g., not used any more) by the at least one further device 30 (e.g., for the positioning), or a2) a, for example current, state of network utilization, or a3) change, e.g., with respect to network utilization, e.g., since a or the, e.g., last, resource allocation, or b) receiving 122 third information I-3, for example from the at least one further device, for example as or within a performance feedback report message PFR-MESSAGE, the third information indicating at least one of: b1) performance metrics, or b2) feedback information for modifying, e.g., refining, the at least one procedure based on at least one of Al or ML. In some examples, this may enable a or the device 10 performing aspects of the disclosure to adapt its operation, e.g., regarding resource allocation, e.g., to the received information I-2, I-3.

[0080] In some examples, Fig. 5, the method comprises: performing 130 an adaptive resource allocation for the positioning of the at least one target 20 (Fig. 2), wherein for example the adaptive resource allocation is based on at least one of: a) signal analysis of at least one signal associated with, e.g., of the, wireless communication system 1000, or b) a mobility pattern of at least one mobile device, e.g., for the wireless communication system 1000, e.g. a user equipment, or c) at least one bandwidth associated with at least one user and / or task associated with, e.g., of the wireless communication system 1000, and, R.414609

[0081] - 12 - optionally, using 132 an adapted resource allocation, e.g., by providing 132a the adapted resource allocation, e.g., to the at least one further device 30, e.g., in the form of a resource allocation update message RAU-MSG. In some examples, this enables to perform a particularly precise adaptation of the resource allocation, which, e.g., ensures that the at least one further device 30 may perform the positioning based on, e.g., updated first information 1-1-UPD.

[0082] In some examples, one or more of the following aspects may be used for an adaptive resource allocation, see, e.g., block 130 of Fig. 5. Examples of parameters that can be adapted, e.g., to obtain a target resource allocation, may, e.g., comprise at least one of: a) signal analysis: an Al algorithm (e.g., associated with the procedure PROC-AI-ML, Fig. 2) may analyze a signal strength and / or interference, e.g., to determine an optimal allocation of bandwidth and / or processing power, or b) user mobility patterns: mobility patterns, e.g., of users, e.g., user equipment (and / or of other mobile devices for the wireless communications system 1000) may be used, e.g., to predict future resource needs and / or to adjust resource allocations accordingly, or c) bandwidth management: In some examples, a bandwidth allocation may be dynamically adjusted, e.g., to ensure optimal performance, e.g., balancing needs of different users I devices and tasks.

[0083] In some examples, Fig. 6, the method comprises at least one of: a) using 140 at least one feedback loop FL, or b) using 142 at least one continuous learning technique CL-TECH, e.g., for at least one of A) the determining 102 (Fig. 1) of the first information, or B) the determining 110 (Fig. 3) of the updated first information 1-1-UPD. In some examples, this may enable to ensure effectiveness of resource allocation and / or a continuous improvement regarding resource allocation.

[0084] As an example, in some configurations, e.g., for providing an adaptive resource management framework and / or for ensuring that the adaptive resource management framework remains effective and improves over time, one or more feedback loops FL, e.g., robust feedback loops, and / or continuous learning mechanisms, e.g., using the at least one continuous learning technique CL- TECH, may be provided. R.414609

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[0086] In some examples, the one or more feedback loops FL may, e.g., provide performance data, e.g., real-time performance data, that may, e.g., be fed back, e.g., into at least one Al and / or ML algorithm (e.g., of the procedure PROC-AI- ML, Fig. 2), thus, e.g., enabling to provide a continuous refinement and / or optimization, e.g., of resource allocation strategies, e.g., for the positioning.

[0087] In some examples, one or more of the following example aspects or components may be used, e.g., by the device 10 (Fig. 2), e.g., for implementing the at least one feedback loop FL:

[0088] Performance Monitoring: Continuous Monitoring: In some examples, one or more key performance indicators (KPIs), such as, e.g., positioning accuracy, latency, resource usage, network load, may be, e.g., continuously, monitored. Data Collection: In some examples, data, e.g., performance data, is collected from various elements of the wireless communication system 1000, e.g., from network elements, including, for example a gNB and / or a Network Management System (NMS), and / or from terminal devices, e.g., UE.

[0089] Data Analysis: In some examples, a real-time analysis may be performed, wherein, for example, one or more Al and / or ML algorithms analyze collected data, e.g., performance data, e.g., in real-time, e.g., to identify at least one of a) trends, or b) anomalies, or c) areas for improvement. In some examples, a historical analysis, e.g., analysis of historical data, e.g., performance data, may be performed, e.g., to gain insights on, e.g., understand, long-term patterns, and, for example, to inform future resource management decisions on this basis.

[0090] Feedback Integration: In some examples, adjustments, e.g., immediate adjustments to the resource allocation, e.g., for the positioning, may be made, e.g., based on the real-time analysis. In some examples, this may enable to address current performance issues. In some examples, aspects of a long-term refinement, e.g., for the resource allocation, e.g., for the positioning, may be considered. As an example, one or more insights, e.g., from the historical analysis, may be used, e.g., to refine and / or improve at least one Al and / or ML model, e.g., enabling to improve one or more resource allocation strategies over time. R.414609

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[0092] Model Update Mechanisms: In some examples, one or more models, e.g., Al and / or ML models, may be updated, e.g., regularly updated, e.g., with new data, e.g., to ensure they remain accurate and effective, e.g., in changing network conditions, e.g., with respect to resource allocation, e.g., for the positioning. In some examples, one or more automated learning mechanisms may be used, e.g., to continuously incorporate new data and improve model performance, e.g., without manual intervention.

[0093] In the following, further examples for implementing the at least one feedback loop FL in the sense of the disclosure are provided.

[0094] Data Collection: One or more devices or components of the wireless communication system 1000 (e.g., UE, gNB, and NMS), may, e.g., continuously, monitor performance metrics such as signal strength, positioning accuracy, latency, and resource usage. This data may be collected, e.g., by the one or more devices or components, and may, e.g., be sent to a, for example central, performance monitoring system PMS, Fig. 2. In some examples, at least some aspects of the performance monitoring system PMS may, e.g., be implemented by the device 10 and / or by an apparatus 100 for the device 10.

[0095] In some examples related, e.g., to real-time analysis, the performance monitoring system PMS may use one or more Al and / or ML algorithms, e.g., as associated with or characterized by the procedure PROC-AI-ML, e.g., to analyze collected data, e.g., in real-time. In some examples, e.g., if an anomaly is detected (e.g., a drop in positioning accuracy), a cause for the anomaly may be identified, e.g., by the performance monitoring system PMS, and one or more adjustments may be determined which enable to remove the anomaly, e.g., improve the positioning accuracy.

[0096] In some examples related, e.g., to adjustments, e.g., immediate adjustments, e.g., of resource allocation, e.g., for the positioning, the device 10 (e.g., operating as an NMS device) may send a message, e.g., a Resource Allocation Update (RAU) message RAU-MSG, e.g., to the at least one further device 30 (e.g., UE or gNB), e.g., for adjusting the resource allocation, e.g., for the positioning, e.g., by the at least one further device 30, e.g., based on the abovementioned real-time analysis. In some examples, the adjustments may, e.g., include at least one of: a) R.414609

[0097] - 15 - reallocating bandwidth, or b) changing power levels, or c) modifying processing priorities.

[0098] In some examples related, e.g., to historical analysis, the device 10 (e.g., operating as an NMS device), may perform a, for example comprehensive, analysis of historical performance data, which, e.g., may enable to identify longterm trends and / or patterns that may be beneficial for planning and / or modifying future resource management strategies.

[0099] In some examples related, e.g., to model refinement, insights from the historical analysis may, e.g., be used to refine at least one Al and / or ML model. In some examples, the one or more refined Al and / or ML models may, e.g., be deployed in the system, e.g., within the device 10, thus, e.g., enhancing the accuracy and effectiveness of future resource allocations, e.g., for the positioning.

[0100] In some examples related, e.g., to continuous learning, the device 10 may continuously learn from new data, e.g., incorporating it into the Al and / or ML models, e.g., to improve performance related to resource allocation, e.g., for the positioning, e.g., over time. In some examples, one or more automated learning mechanisms may be used, e.g., to ensure that the Al and / or ML models remain up-to-date and effective, e.g., without requiring manual updates.

[0101] In some examples, Fig. 7, the method comprises at least one of: a) using 150 at least one message container MC, e.g., for at least one of: a1) the providing 104 of the first information 1-1 , or a2) the transmitting 104a of the response RESP to the at least one further device 30, or a3) the receiving 120 of the second information I-2, or a4) the receiving 122 of the third information I-3. In some examples, this may enable to facilitate efficient communication and resource management. As an example, in some variants, message containers that adhere to and / or are based on at least one 3GPP standard may be used for aspects of information exchange according to the disclosure.

[0102] The optional block 152 of Fig. 7 symbolizes an optional information exchange, e.g., within the wireless communication system 1000, e.g., between the devices 10, 30, using the at least one message container MC. R.414609

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[0104] In some examples, e.g., to facilitate efficient communication and resource management, message containers may be used, e.g., for exchanging information 1-1 , 1-1-UPD, I-2, I-3 and / or other information or data. In some examples, message containers that adhere to or are based on 3GPP standards, e.g., specialized message containers as defined by 3GPP standards, may be used. In some examples, the use of such message containers may enable to ensure minimal signaling overhead, e.g., while providing information, e.g., all necessary information, e.g., for dynamic resource allocation.

[0105] In some examples related to a message container structure, a container header of a message container may, e.g., comprise at least one standard 3GPP protocol header, e.g., with one or more extensions, e.g., for resource management information. In some examples, at least one information element for a "Message Type", e.g., identifying a type of a message (e.g., Resource Allocation Request (RAR), Resource Allocation Update (RAU), and the like), may be provided, e.g., in the container header. In some examples, at least one information element for a transaction identifier, e.g., "Transaction ID", may be provided, e.g., for uniquely identifying a specific transaction, e.g., enabling a traceability and / or a correlation of messages.

[0106] In some examples related to resource management information, at least one of the following elements, e.g., information elements, may be provided, e.g., for the message container structure: A) at least one information element for network state information, e.g., comprising at least one of: A1) Signal Strength: Current signal strength measurements, or A2) Interference Levels: Detected interference levels in the network, or A3) Bandwidth Availability: Current available bandwidth for allocation, B) at least one information element for user context information, e.g., comprising at least one of: B1) user mobility patterns: Data on user movements and predicted mobility, or B2) service requirements: QoS requirements and priorities for different services, or B3) resource allocation decision: e.g., characterizing allocated resources, e.g., details of an allocated bandwidth, and / or power levels, and / or other resources. In some examples, the resource allocation decision may also comprise information on an allocation rationale, e.g., an explanation of the decision, e.g., based on the AI / ML algorithm's analysis. R.414609

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[0108] Further details and aspects of an example message flow according to the disclosure are provided further below with reference to the signaling diagram of Fig. 10.

[0109] Some examples, Fig. 8, relate to a method, for example a computer-implemented method, for a device 30 (Fig. 2) for a wireless communication system 1000, e.g., for the at least one further device 30, the method comprising: receiving 182 (Fig,. 8) first information 1-1 characterizing a resource allocation RES-ALLOC-POS for positioning of at least one target 20, e.g., from a device 10, e.g., from the device 10 according to the disclosure as, e.g., mentioned above, wherein for example the first information 1-1 is determined by the device 10 at least temporarily using at least one procedure PROC-AI-ML based on at least one of artificial intelligence, Al, or machine learning, ML, and, optionally, using 184 (Fig. 8) the received first information 1-1 , e.g., for positioning 184a.

[0110] In some examples, Fig. 8, the method comprises: transmitting 180 a request REQ for the first information 1-1 , for example to the device 10, and, optionally, receiving 182a a response RESP from the device 10, the response RESP at least comprising the first information 1-1. In some examples, this enables the device 30 to proactively obtain the first information 1-1 and / or updated first information 1-1-UPD, e.g., from the further device 10.

[0111] In some examples, Fig. 9, the method comprises: transmitting 186 second information I-2, for example to the device 10, for example as or within a resource release notification message RRN-MSG (also see block 120 of Fig. 4), the second information I-2 indicating at least one of: a1) resources released by the device 30, or a2) a, for example current, state of network utilization, or a3) change, e.g., with respect to network utilization, e.g., since a or the, e.g., last, resource allocation.

[0112] In some examples, Fig. 9, the method comprises: transmitting 188 third information I-3, for example to the device 10, for example as or within a performance feedback report message PFR-MSG (also see block 122 of Fig. 4), the third information I-3 indicating at least one of: b1) performance metrics, or b2) feedback information for modifying, e.g., refining, the at least one procedure based on at least one of Al or ML. R.414609

[0113] - 18 -

[0114] Fig. 10 schematically depicts a simplified signaling diagram according to some examples. Element E1 symbolizes a network management system, NMS, e.g. a device 10 (Fig. 2) for an NMS. Element E2 symbolizes a UE or gNB, e.g., at least similar to device 30 of Fig. 2. In the following, an example information flow, e.g., message flow, according to some examples is explained.

[0115] Element E3 of Fig. 10 symbolizes a Resource Allocation Request (RAR) message according to some examples, e.g., at least similar to the request REQ (Fig. 1 , 2). In some examples, the UE or gNB (see, for example device of Fig. 2) may send the RAR message E3 to the Network Management System (NMS) E1 (also see, for example, device 10 of Fig. 2). In some examples, the RAR message E3 may include at least one of: a) current signal strength measurements, or b) detected interference levels, or c) an available bandwidth, or d) user mobility patterns, or e) service requirements, or f) QoS (quality of service) priorities. In some examples, the RAR message E3 may be used to request, by the device E2, a resource allocation, e.g., from the NMS E1.

[0116] Element E4 of Fig. 10 symbolizes a Resource Allocation Decision (RAD) message according to some examples, e.g., at least similar to the response RESP (Fig. 1 , 2). In some examples, e.g., upon receiving the request E3, the NMS E1 may process at least a part of the information comprised in the request E3, e.g., at least temporarily using Al and / or ML-based algorithms, also see, for example, the block PROC-AI-ML of Fig. 2. In some examples, a decision-making process employing the Al and / or ML-based algorithms, also see, for example, the block PROC-AI-ML of Fig. 2, may involve at least one of the following elements: a) analyzing the current network state and user context, or b) predicting future resource needs, e.g., based on mobility patterns and service requirements, or c) allocating the optimal amount of bandwidth and / or processing power. In some examples, the NMS E1 may send a Resource Allocation Decision message E4 back to the device E2, e.g., detailing at least one of: a) the allocated resources (bandwidth, power levels, etc.), or b) the rationale behind the allocation decision, e.g., including how the AI / ML-algorithm arrived at this decision.

[0117] Element E5 of Fig. 10 symbolizes a Resource Allocation Update (RAU) message according to some examples, e.g., at least similar to the updated first information 1-1-UPD (see, for example, block 110 of Fig. 3). As an example, if network R.414609

[0118] - 19 - conditions change, the NMS E1 may send the Resource Allocation Update message E5 to the device E2. In some examples, the RAU message E5 may comprise at least one of: a) updated signal strength measurements and / or interference levels, or b) adjusted bandwidth allocations based on the new network state, or c) updated resource allocation rationale, reflecting the latest analysis by the AI / ML- algorithm(s).

[0119] Element E6 of Fig. 10 symbolizes a Resource Release Notification (RRN) message according to some examples. In some examples, e.g., when (previously allocated) resources are no longer needed or can be optimized further, the device E2 may send the Resource Release Notification message E6 to the NMS E1. In some examples, the RRN message E6 may comprise at least one of: a) information on the resources being released, or b) a current state of network utilization and / or any changes since the last allocation. In some examples, the NMS may process information from the RRN message E6 and may, e.g., update an overall resource management plan, e.g., freeing up resources for other tasks.

[0120] Element E7 of Fig. 10 symbolizes a Performance Feedback Report (PFR) message according to some examples. In some examples, e.g., throughout a resource allocation process, continuous feedback may be provided, e.g., to at least one Al and / or ML- algorithm, e.g., using one or more PFR messages E7. In some examples, thus, a feedback loop may be provided using the one or more PFR messages E7, wherein the feedback loop may, e.g., involve at least one of: a) monitoring performance metrics (e.g., accuracy and / or latency and / or resource usage), or b) sending a PFR message E7 from the device E2 to the NMS E1, e.g., with detailed performance metrics, or c) feeding performance data back into one or more Al and / or ML- models, e.g., to refine and / or improve resource allocation strategies, or d) ensuring that adaptations are made, e.g., reacting to changes in network conditions and / or user demands, thus, e.g., enabling to optimize a performance over time.

[0121] As already explained above, in some examples, at least some aspects of the messages E3, E4, E5, E6, E6 may, at least temporarily, be provided, e.g., implemented, exchanging the information 1-1, REQ, RESP, I-2, I-3 explained above. R.414609

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[0123] Some examples, Fig. 11 , relate to an apparatus 300 for performing the method according to the disclosure. In some examples, the apparatus 300 is configured to perform the method according to the disclosure.

[0124] In some examples, Fig. 11, the apparatus 300 is configured to perform the method according to any of the claims 1 to 9, wherein, for example, the apparatus 300 may, e.g., be provided for the device 10 (Fig. 2). In some, but not necessarily all, examples, the apparatus 300 may be integrated in the device 10.

[0125] In some examples, Fig. 11, the apparatus 300 is configured to perform the method according to any of the claims 10 to 13, wherein, for example, the apparatus may, e.g., be provided for the device 30 (Fig. 2). In some, but not necessarily all, examples, the apparatus 300 may be integrated in the device 30.

[0126] In some examples, Fig. 11, the apparatus 300 is configured to perform the method according to any of the claims 1 to 13, wherein, for example, the apparatus 300 may, e.g., be provided for the device 10 (Fig. 2), and / or for the device 30.

[0127] In some examples, Fig. 11, the apparatus 300 comprises at least one calculating unit, e.g. processor, 302 and at least one memory unit 304 associated with (i.e. , usable by) the at least one calculating unit 302 for at least temporarily storing a computer program PRG and / or data DAT, wherein the computer program PRG is e.g. configured to at least temporarily control an operation of the apparatus 300 and / or the device 10, 30, e.g. an execution of a method according to the disclosure.

[0128] In some examples, Fig. 11 , the at least one calculating unit 302 comprises at least one core (see the dashed rectangle) for executing the computer program PRG or at least parts thereof, e.g. for executing the method according to the disclosure or at least one or more steps thereof.

[0129] According to some examples, the at least one calculating unit 302 may comprise at least one of the following elements: a microprocessor, a microcontroller, a digital signal processor (DSP), a programmable logic element (e.g., FPGA, field programmable gate array), an ASIC (application specific integrated circuit), R.414609

[0130] - 21 - hardware circuitry, a tensor processor, a graphics processing unit (GPU). According to further preferred embodiments, any combination of two or more of these elements is also possible.

[0131] According to some examples, the memory unit 304 comprises at least one of the following elements: a volatile memory 304a, e.g., a random-access memory (RAM), a non-volatile memory 304b, e.g., a Flash-EEPROM.

[0132] In some examples, the computer program PRG is at least temporarily stored in the non-volatile memory 304b. Data DAT (e.g. associated with the first information 1-1 and / or the second information I-2 and / or the third information I-3 and the like), which may, e.g. be used for performing the method according to the disclosure, may at least temporarily be stored in the RAM 304a.

[0133] In some examples, an optional computer-readable storage medium SM comprising instructions, e.g. in the form of a or the computer program PRG, may be provided. As an example, the storage medium SM may comprise or represent a digital storage medium such as a semiconductor memory device (e.g., solid state drive, SSD) and / or a magnetic storage medium such as a disk or harddisk drive (HDD) and / or an optical storage medium such as a compact disc (CD) or DVD (digital versatile disc) or the like.

[0134] In some examples, Fig. 11, the configuration 300 may comprise an optional data interface 306, e.g. for bidirectional data exchange with at least one further device. As an example, by means of the data interface 306, a data carrier signal DCS may be received, e.g. from the external device, for example via a wired or a wireless data transmission medium, e.g. over a (virtual) private computer network and / or a public computer network such as e.g. the Internet.

[0135] Some examples, Fig. 2, relate to a device 10, 30 for a wireless communication system 1000 or network, respectively, comprising at least one apparatus 300 according to the disclosure. In some examples, as mentioned above, the device 10, 30 may, e.g., be a network device, e.g., base station or a device of a network management system or the like. In some examples, the device 10, 30 for the wireless communication system 1000 may, e.g., be a terminal device, e.g., user equipment, e.g., UE. R.414609

[0136] - 22 -

[0137] Some examples, Fig. 12, relate to a use 400 of the method according to the disclosure and / or of the apparatus 300 according to the disclosure and / or of the computer program PRG according to the disclosure and / or of the computer- readable storage medium SM according to the disclosure and / or of the data carrier signal DCS according to the disclosure and / or of the device 10, 30 according to the disclosure for at least one of: a) resource allocation 401 for positioning, or b) dynamic management 402 of resources for positioning, or c) providing 403 a framework for, for example continuously, monitoring network conditions and / or user demands, or d) adjusting 404 resource allocation for positioning, or e) optimizing 405 positioning accuracy, or f) optimizing 406 service quality.

[0138] In some examples, the principle of the disclosure may enable to address challenges of conventional approaches, e.g., systems, for positioning, e.g., the challenges, e.g., being related to resource management, particularly in dynamic and resource-constrained environments. As some conventional resource allocation methods do not fully leverage the capabilities of AI / ML, at least some of these challenges of the conventional approaches may, e.g., be addressed using the principle of the disclosure, which, inter alia, provides the Al- and / or ML- based procedure PROC-AI-ML for determining the first information 1-1 , which characterizes the resource allocation RES-ALLOC-POS for the positioning 20- POS of the at least one target 20. In some examples, this also enables to dynamically adjust resource allocation, e.g., based on real-time network conditions and / or based on user demands.

[0139] In some examples, using the principle of the disclosure enables to provide an adaptive resource management framework that utilizes one or more Al and / or ML algorithms, e.g., to dynamically allocate network resources for positioning, e.g., in the context of the wireless communication system 1000 (Fig. 2). In some examples, the approach according to the disclosure enables to enhance an efficiency and effectiveness of resource utilization, thus, e.g., enabling to attain optimal performance in various network conditions.

[0140] In some examples, using the principle of the disclosure enables to create an adaptive resource management framework for AI / ML-based positioning systems, R.414609

[0141] - 23 - e.g., enabling to a) assess real-time network conditions and user demands, and / or b) dynamically allocate network resources based on these assessments, and / or c) optimize the balance between resource usage and positioning accuracy.

[0142] In some examples, the principle of the disclosure enables to continuously monitor network conditions and user demands, adjusting resource allocation, e.g., to optimize positioning accuracy and service quality. In some examples, a framework for resource allocation for positioning may be provided, the framework, e.g., running in the network (e.g., on at least one network device 10), e.g., interacting with one or more devices 30 for positioning, such as, e.g., one or more UE 30.

[0143] In some examples, the principle of the disclosure enables to analyze various factors of the wireless communication system 1000 (Fig. 2), including signal strength, interference levels, bandwidth availability, and user mobility patterns. In some examples, e.g., based on this analysis, the principle of the disclosure enables to dynamically allocate resources, such as bandwidth and processing power, e.g., to different positioning tasks (and / or different devices 30).

[0144] In some examples, the principle of the disclosure enables to include mechanisms for continuous performance monitoring and feedback, thus, e.g., ensuring that resource management strategies may be continuously refined and improved, e.g., based on real-time data.

[0145] As a result, at least some examples or aspects according to the disclosure may at least temporarily address at least some of the following aspects and / or may at least temporarily attain at least one of the following advantages over at least some conventional approaches:

[0146] Improved Efficiency: By dynamically allocating resources based on real-time conditions, in some examples, the principle of the disclosure may enable to optimize resource usage, reducing wastage and improving overall efficiency, R.414609

[0147] - 24 - enhanced Accuracy: In some examples, adaptive resource management may help to ensure that sufficient resources are allocated to critical tasks, thus, e.g., maintaining high positioning accuracy even in dynamic environments.

Claims

R.414609- 25 -Claims1. A method, for example a computer-implemented method, for a device (10) for a wireless communication system (1000), the method comprising:- determining (102), at least temporarily using (102a) at least one procedure (PROC-AI-ML) based on at least one of artificial intelligence, Al, or machine learning, ML, first information (1-1) characterizing a resource allocation (RES-ALLOC-POS) for positioning (20-POS) of at least one target (20),- providing (104) at least the first information (1-1) to at least one further device (30).

2. The method according to claim 1 , comprising:- receiving (100) a request (REQ) for the first information (1-1), for example from the at least one further device (30), and,- optionally, transmitting (104a) a response (RESP) to the at least one further device (30), the response (RESP) at least comprising the first information (1-1).

3. The method according to any of the preceding claims, wherein the at least one procedure (PROC-AI-ML) comprises at least one of the following aspects: a) a reinforcement learning, RL, technique (RL-TECH), or b) a supervised learning technique (SVL-TECH), or c) an unsupervised learning technique (USVL-TECH).

4. The method according to any of the preceding claims, wherein the determining (102) comprises at least temporarily using (102b) at least one procedure (PROC-LEG) that is not based on at least one of artificial intelligence, Al, or machine learning, ML, e.g., a legacy procedure (PROC-LEG), wherein for example the legacy procedure (PROC-LEG) comprises at least one of: a) a rule-based technique (RB-TECH), or b) a priority-based technique (PRIO-TECH).R.414609- 26 -5. The method according to any of the preceding claims, comprising:- determining (110) updated (1-1-UPD) first information (1-1),- providing (112, 112a) the updated first information (1-1-UPD) to the at least one further device (30),- wherein for example the providing (112) comprises using (112a) a resource allocation update message (RAU-MSG),- wherein for example the determining (110) is based on at least one of a) the procedure (PROC-AI-ML) based on at least one of artificial intelligence, Al, or machine learning, ML, or b) a or the legacy procedure (PROC-LEG).

6. The method according to any of the preceding claims, comprising at least one of: a) receiving (120) second information (I-2), for example from the at least one further device (30), for example as or within a resource release notification message (RRN-MSG), the second information (I-2) indicating at least one of: a1) resources released by the at least one further device (30), or a2) a, for example current, state of network utilization, or a3) change, e.g., with respect to network utilization, e.g., since a or the, e.g., last, resource allocation (RES-ALLOC-POS), or b) receiving (122) third information (I-3), for example from the at least one further device (30), for example as or within a performance feedback report message (PFR-MSG), the third information (I-3) indicating at least one of: b1) performance metrics, or b2) feedback information for modifying, e.g., refining, the at least one procedure (PROC-AI-ML) based on at least one of Al or ML.

7. The method according to any of the preceding claims, comprising:- performing (130) an adaptive resource allocation (RES-ALLOC-ADAP) for the positioning (20-POS) of the at least one target (20), wherein for example the adaptive resource allocation (RES-ALLOC-ADAP) is based on at least one of: a) signal analysis of at least one signal associated with, e.g., of the, wireless communication system (1000), or b) a mobility pattern of at least one mobile device (20), e.g., for the wireless communication system (1000), e.g. a user equipment, orR.414609- 27 - c) at least one bandwidth associated with at least one user and / or task associated with, e.g., of the wireless communication system (1000), and,- optionally, using (132) an adapted resource allocation, e.g., by providing (132a) the adapted resource allocation, e.g., to the at least one further device (30).

8. The method according to any of the preceding claims, comprising at least one of: a) using (140) at least one feedback loop (FL), or b) using (142) at least one continuous learning technique (CL-TECH), e.g., for at least one ofA) the determining (102) of the first information (1-1), orB) the determining (110) of the updated first information (1-1).

9. The method according to any of the preceding claims, comprising at least one of: a) using (150) at least one message container (MC), e.g., for at least one of: a1) the providing (104), or a2) the transmitting (104a), or a3) the receiving (120), or a4) the receiving (122).

10. A method, for example a computer-implemented method, for a device (30) for a wireless communication system (1000), the method comprising:- receiving (182) first information (1-1) characterizing a resource allocation (RES-ALLOC-POS) for positioning (20-POS) of at least one target (20), e.g., from a device (10), wherein for example the first information (1-1) is determined (102) by the device (10) at least temporarily using (102a) at least one procedure (PROC-AI-ML) based on at least one of artificial intelligence, Al, or machine learning, ML, and,- optionally, using (184) the received first information (1-1), e.g., for positioning (184a).

11. The method according to claim 10, comprising:- transmitting (180) a request (REQ) for the first information (1-1), for example to the device (10), and,R.414609- 28 -- optionally, receiving (182a) a response (RESP) from the device (10), the response (RESP) at least comprising the first information (1-1).

12. The method according to any of the claims 10 to 11 , comprising:- transmitting (186) second information (I-2), for example to the device (10), for example as or within a resource release notification message (RRN- MSG), the second information (I-2) indicating at least one of: a1) resources released by the device (30), or a2) a, for example current, state of network utilization, or a3) change, e.g., with respect to network utilization, e.g., since a or the, e.g., last, resource allocation (RES-ALLOC-POS).

13. The method according to any of the claims 10 to 11 , comprising:- transmitting (188) third information (I-3), for example to the device (10), for example as or within a performance feedback report message (PFR- MSG), the third information (I-3) indicating at least one of: b1) performance metrics, or b2) feedback information for modifying, e.g., refining, the at least one procedure (PROC-AI-ML) based on at least one of Al or ML.

14. An apparatus (300) for performing the method(s) according to at least one of the preceding claims, wherein for example the apparatus (300) is configured to perform the method(s) according to at least one of the preceding claims.

15. A computer program (PRG) comprising instructions which, when the program (PRG) is executed by a computer (102) and / or the apparatus (300) of claim 14, cause the computer (102) and / or the apparatus (300) to perform the method(s) according to at least one of the claims 1 to 13.

16. A computer-readable storage medium (SM) comprising instructions (PRG) which, when executed by a computer (102) and / or the apparatus (300) of claim 14, cause the computer (102) and / or the apparatus (300) to carry out the method(s) according to at least one of the claims 1 to 13.

17. A data carrier signal (DCS) carrying and / or characterizing the computer program (PRG) of claim 15.R.414609- 29 -18. A device (10; 30) for a wireless communication system (1000), comprising at least one apparatus (300) according to claim 14.

19. A use (400) of the method according to any of the claims 1 to 13 and / or of the apparatus (300) according to claim 14 and / or of the computer program (PRG) according to claim 15 and / or of the computer-readable storage medium (SM) according to claim 16 and / or of the data carrier signal (DCS) according to claim 17 and / or of the device (10) according to claim 18 for at least one of: a) resource allocation (401) for positioning, or b) dynamic management (402) of resources for positioning, or c) providing (403) a framework for, for example continuously, monitoring network conditions and / or user demands, or d) adjusting (404) resource allocation for positioning, or e) optimizing (405) positioning accuracy, or f) optimizing (406) service quality.

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