For improved messaging reliability in 5G and 6G, mobile users and their base stations can adjust their transmission power according to the current location of the mobile user. Each entity can maintain a map of known attenuation values, including “dead zones”, and can adjust their transmission power and/or reception gain to compensate. Instead of constantly exchanging location-update messages, the users can indicate their speed and direction, and the base station (or other users) can extrapolate the location versus time to determine a future location, and thereby determine the attenuation factor at the new position. In addition, the base station can use a map to follow the mobile user device's progress, and can thereby update the attenuation factor in real-time. If the mobile user makes a change, it can inform the base station at that time, or during initial access. Result: improved reliability, lower energy consumption, improved traffic safety.

In one embodiment, a method includes storing, by a tracking server, a hash value for each of one or more tracking devices associated with the tracking server. The method includes receiving, at the tracking server from a user device, a hash value associated with a tracking device, the hash value computed based on a static value uniquely associated with the tracking device and a dynamic value maintained by the tracking device. The method includes identifying, by the tracking server, the tracking device based on a comparison between one of the stored hash values and the received hash value. The method includes updating, by the tracking server, one or more records associated with the identified tracking device based on the receiving the hash value.

Systems, devices, methods, and program products for enhancing structure walkthroughs are disclosed. In various embodiments, a method includes: monitoring a current location of an interested party (IP) device utilizing data collected by the IP device during a walkthrough of a structure having a structure representative (SR); detecting, based on the current location of the IP device, when the IP device is brought into proximity of a first SR-marked location included in a plurality of structure SR-marked locations; and in response to detecting that the IP device has been brought into proximity of the first SR-marked location, (i) identifying a first SR-created message corresponding to the first SR-marked location and contained in a location-referenced message set; and (ii) generating or causing generation of an SR message notification on the IP device, the SR message notification presenting or offering to present the first SR-created message to the interested party.

A framework for dynamic network resource allocation and energy saving based on the real-time environment, radio network information, and machine learning (ML) can be utilized via a radio access network (RAN) intelligent controller (RIC). Real-time and predicted network utilization can facilitate resource and energy savings by leveraging the RIC platform. For example, a network information base (NIB) in the RIC platform can collects RAN and user equipment (UE) resource related information in real time and provides the abstraction of the access network in the real time. ML can predict real-time information about the UEs at time t based on data analytics and real time radio resource needs. The RIC can then instruct the network to reduce or increase resources.

In one embodiment, a service receives a device registration request sent by an endpoint device, wherein the endpoint device executes an onboarding agent that causes the endpoint device to send the device registration request via a cellular connection to a private access point name (APN) associated with the service. The service verifies that a network address of the endpoint device from which the device registration request was sent is associated with an integrated circuit card identifier (ICCID) or international mobile equipment identity (IMEI) indicated by the device registration request. The service identifies a tenant identifier associated with the ICCID or IMEI. The service sends, based on the tenant identifier, a device registration response to the endpoint device via the private APN.

In one embodiment, a service receives a device registration request sent by an endpoint device, wherein the endpoint device executes an onboarding agent that causes the endpoint device to send the device registration request via a cellular connection to a private access point name (APN) associated with the service. The service verifies that a network address of the endpoint device from which the device registration request was sent is associated with an integrated circuit card identifier (ICCID) or international mobile equipment identity (IMEI) indicated by the device registration request. The service identifies a tenant identifier associated with the ICCID or IMEI. The service sends, based on the tenant identifier, a device registration response to the endpoint device via the private APN.

Method of determining a position of a plurality of mobile network devices relative to a plurality of reference network devices, wherein the plurality of mobile network devices and the plurality of reference network devices communicate with one another over a wireless channel and access the wireless channel according to an access policy comprising a sequence of time frames, wherein each time frame of the sequence comprises a plurality of portions reserved for communicating messages relating to different time-of-flight (ToF) computation methods, each of the different ToF computation methods allowing for determining a position of at least one of the plurality of mobile network devices. A positioning system implements the above method.

Method of determining a position of a plurality of mobile network devices relative to a plurality of reference network devices, wherein the plurality of mobile network devices and the plurality of reference network devices communicate with one another over a wireless channel and access the wireless channel according to an access policy comprising a sequence of time frames, wherein each time frame of the sequence comprises a plurality of portions reserved for communicating messages relating to different time-of-flight (ToF) computation methods, each of the different ToF computation methods allowing for determining a position of at least one of the plurality of mobile network devices. A positioning system implements the above method.

Geolocating Minimization of Drive Test (MDT) measurement reports (MRs) with missing satellite navigation system coordinates is disclosed. In some embodiments, a computing node receives a plurality of complete MRs corresponding to a plurality of user equipments (UEs), wherein each complete MR comprises satellite navigation system coordinates identifying a geographic location of the corresponding UE. The computing node then trains a machine learning (ML) model for estimating UE geographic locations based on the plurality of complete MRs, wherein the ML model maps radio frequency (RF) signatures of complete MRs to corresponding UE geographic locations. In some embodiments, a radio access node obtains the ML model from the computing node, and receives an incomplete MR corresponding to a UE. Upon determining that the second MR lacks satellite navigation system coordinates, the radio access node predicts the geographic location of the UE based on measurements in the incomplete MR and the ML model.

Geolocating Minimization of Drive Test (MDT) measurement reports (MRs) with missing satellite navigation system coordinates is disclosed. In some embodiments, a computing node receives a plurality of complete MRs corresponding to a plurality of user equipments (UEs), wherein each complete MR comprises satellite navigation system coordinates identifying a geographic location of the corresponding UE. The computing node then trains a machine learning (ML) model for estimating UE geographic locations based on the plurality of complete MRs, wherein the ML model maps radio frequency (RF) signatures of complete MRs to corresponding UE geographic locations. In some embodiments, a radio access node obtains the ML model from the computing node, and receives an incomplete MR corresponding to a UE. Upon determining that the second MR lacks satellite navigation system coordinates, the radio access node predicts the geographic location of the UE based on measurements in the incomplete MR and the ML model.